Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS.
Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.
Simple radiocarbon calibration and chronological analysis.
    This package allows the calibration of radiocarbon ages and modern
    carbon fraction values using multiple calibration curves. It allows
    the calculation of highest density region intervals and credible
    intervals. The package also provides tools for visualising results and
    estimating statistical summaries.
Enables gene regulatory network (GRN) analysis on single cell clusters,
    using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>.
    Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' 
    implemented in 'snakemake'. Finally, incorporate results for visualization 
    and interpretation.
An interactive 'shiny' application for performing non-compartmental analysis (NCA) on
  pre-clinical and clinical pharmacokinetic data. The package builds on 'PKNCA' for core estimators and provides interactive visualizations, CDISC
  outputs ('ADNCA', 'PP', 'ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory
  analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core
  estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic
  Noncompartmental Analysis: The PKNCA R Package" <doi:10.1007/s10928-015-9432-2>.
Performs AnchorRegression proposed by Rothenhäusler et al. 2020. 
    The code is adapted from the original paper repository. (<https://github.com/rothenhaeusler/anchor-regression>)
    The code was developed independently from the authors of the paper.
Causal discovery in linear structural equation models (Schultheiss, and Bühlmann (2023) <doi:10.1093/biomet/asad008>) and vector autoregressive models (Schultheiss, Ulmer, and Bühlmann (2025) <doi:10.1515/jci-2024-0011>) with explicit error control for false discovery, at least asymptotically.
Construct language-aware lists. Make "and"-separated and
    "or"-separated lists that automatically conform to the user's language
    settings.
A decision support tool to strategically prioritise evidence gathering 
  in complex, hierarchical AND-OR decision trees. It is designed for situations 
  with incomplete or uncertain information where the goal is to reach a confident 
  conclusion as efficiently as possible (responding to the minimum number of 
  questions, and only spending resources on generating improved evidence when 
  it is of significant value to the final decision). The framework excels in 
  complex analyses with multiple potential successful pathways to a conclusion 
  ('OR' nodes). Key features include a dynamic influence index to guide users to 
  the most impactful question, a system for propagating answers and 
  semi-quantitative confidence scores (0-5) up the tree, and post-conclusion 
  guidance to identify the best actions to increase the final confidence. 
  These components are brought together in an interactive command-line workflow 
  that guides the analysis from start to finish.
Visualisation of multidimensional data through different Andrews curves: 
   Andrews, D. F. (1972) Plots of High-Dimensional Data. Biometrics, 28(1), 125-136. <doi:10.2307/2528964>.
Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.
Provides a comprehensive system for selecting
    variables and weighting data to match the specifications of the American
    National Election Studies. The package includes methods for identifying
    discrepant variables, raking data, and assessing the effects of the raking
    algorithm. It also allows automated re-raking if target variables fall
    outside identified bounds and allows greater user specification than other
    available raking algorithms. A variety of simple weighted statistics that
    were previously in this package (version .55 and earlier) have been moved to
    the package 'weights.'
Simulate an angler population, sample the simulated population with a user-specified survey times, and calculate metrics from a bus route-type creel survey.
Opens and imports log files from Angstrom Engineering Thermal Evaporator
    and extracts basic characteristics, such as base pressure, time of the evaporation. 
    It can visualize the deposition observables for review.
Helper functions for working with Regional Ocean Modeling System 'ROMS' output. See
    <https://www.myroms.org/> for more information about 'ROMS'.
Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.
Simple animated versions of basic R plots, using the 'animation'
    package. Includes animated versions of plot, barplot, persp, contour,
    filled.contour, hist, curve, points, lines, text, symbols, segments, and
    arrows.
All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences.
Implements a web-based graphics device for animated visualisations.
  Modelled on the 'base' syntax, it extends the 'base' graphics functions to
  support frame-by-frame animation and keyframes animation.
  The target use cases are real-time animated visualisations, including agent-based
  models, dynamical systems, and animated diagrams.
  The generated visualisations can be deployed as GIF images / MP4 videos, as
  'Shiny' apps (with interactivity) or as HTML documents through embedding into
  R Markdown documents.
Provides functions for animations in statistics, covering topics
    in probability theory, mathematical statistics, multivariate statistics,
    non-parametric statistics, sampling survey, linear models, time series,
    computational statistics, data mining and machine learning. These functions
    may be helpful in teaching statistics and data analysis. Also provided in this
    package are a series of functions to save animations to various formats, e.g.
    Flash, 'GIF', HTML pages, 'PDF' and videos. 'PDF' animations can be inserted
    into 'Sweave' / 'knitr' easily.
Create an interactive visualization to be used for communication purposes. Providing the function for preparing, plotting, and animating the data. Krisanat Anukarnsakulchularp (2023) <https://github.com/KrisanatA/animbook-journal>.
Provides low-level R bindings to the 'Anime.js' library
    (<https://animejs.com>), enabling the creation of browser-native SVG
    and HTML animations via the 'htmlwidgets' framework.
Functions are provided for defining animated,
 interactive data visualizations in R code, and rendering
 on a web page. The 2018 Journal of Computational and 
 Graphical Statistics paper,
 <doi:10.1080/10618600.2018.1513367>
 describes the concepts implemented.
Functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's 'MegaDector' model and then a second model trained on the classes of interest.
Animation of observed trajectories using spline-based interpolation (see for example, Buderman, F. E., Hooten, M. B., Ivan, J. S. and Shenk, T. M. (2016), <doi:10.1111/2041-210X.12465> "A functional model for characterizing long-distance movement behaviour". Methods Ecol Evol). Intended to be used exploratory data analysis, and perhaps for preparation of presentations.
Obtain network structures from animal GPS telemetry observations and 
    statistically analyse them to assess their adequacy for social network analysis. Methods include 
    pre-network data permutations, bootstrapping techniques to obtain confidence intervals 
    for global and node-level network metrics, and correlation and regression analysis of the local network metrics.
Animate Shiny and R Markdown content when it comes into view using 'animate-css' effects thanks to 'jQuery AniView'.
Comprehensive toolkit for reading and analyzing 'Anki'
        flashcard collection databases. Provides functions to access
        notes, cards, decks, note types, and review logs with a tidy
        interface. Features extensive analytics including retention
        rates, learning curves, forgetting curve fitting, and review 
        patterns. Supports 'FSRS' (Free Spaced Repetition Scheduler) 
        analysis with stability, difficulty, retrievability metrics, 
        parameter comparison, and workload predictions. Includes 
        visualization functions, comparative analysis, time-based 
        analytics, card quality assessment, sibling card analysis,
        interference detection, predictive features, session simulation,
        and an interactive Shiny dashboard. Academic/exam preparation
        tools for medical students and board exam preparation. Export
        capabilities include CSV, Org-mode, Markdown, SuperMemo, Mochi,
        Obsidian SR, and JSON formats with progress reports.
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
Training of neural networks for classification and regression tasks
    using mini-batch gradient descent. Special features include a function for 
    training autoencoders, which can be used to detect anomalies, and some 
    related plotting functions. Multiple activation functions are supported, 
    including tanh, relu, step and ramp. For the use of the step and ramp 
    activation functions in detecting anomalies using autoencoders, see 
    Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, 
    several loss functions are supported, including robust ones such as Huber 
    and pseudo-Huber loss, as well as L1 and L2 regularization. The possible 
    options for optimization algorithms are RMSprop, Adam and SGD with momentum.
    The package contains a vectorized C++ implementation that facilitates 
    fast training through mini-batch learning.
A 'reticulate' wrapper for the Python package 'anndata'.
    Provides a scalable way of keeping track of data and learned
    annotations.  Used to read from and write to the h5ad file format.
Implements persistent row and column annotations for R matrices. The annotations associated with rows and columns are preserved after subsetting, transposition, and various other matrix-specific operations. Intended use case is for storing and manipulating genomic datasets which typically consist of a matrix of measurements (like gene expression values) as well as annotations about rows (i.e. genomic locations) and annotations about columns (i.e. meta-data about collected samples). But 'annmatrix' objects are also expected to be useful in various other contexts.
We curated 147 of expression array, from 3 species(human,mouse,rat), 
              3 companies('Affymetrix','Illumina','Agilent'), 
              by aligning the 'Fasta' sequences of all probes of each platform to their corresponding reference genome, 
              and then annotate them to genes.
Provides non-invasive annotation of package load calls 
    such as \code{library()}, \code{p_load()}, and \code{require()} so that we can have an idea of what 
    the packages we are loading are meant for.
Extraction of subsequences into FASTA files from GenBank annotations where gene names may vary among accessions. Borstein & O'Meara (2018) <doi:10.7717/peerj.5179>.
Provides functions to create image annotations through polygon outlining. Annotator has the same function as 'graphics::locator()' but achieves its purpose through drawing, rather than multiple mouse clicks. It is based on the 'htmlwidgets' package and 'fabric.js' JavaScript library <https://fabricjs.com/>.
Provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test
    to compare clustering structures with applications in functional magnetic resonance imaging
    data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups
    simultaneously and also to identify features that contribute to the differential clustering.
Analyses of frequencies can be performed using an alternative test based 
    on the G statistic. The test has similar type-I error rates and power as 
    the chi-square test. However, it is based on a total statistic that can be 
    decomposed in an additive fashion into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call 
    this set of tools 'ANOFA' (Analysis of Frequency data) to highlight its 
    similarities with ANOVA. This framework also renders plots of frequencies 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. The ANOFA is a tool that assesses 
    the significance of effects instead of the significance of parameters; as such, 
    it is more intuitive to most researchers than alternative approaches based on 
    generalized linear models. 
    See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.
The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.
The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
    The main functions are time_decompose(), anomalize(), and time_recompose().
    When combined, it's quite simple to decompose time series, detect anomalies,
    and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). 
    Time series decomposition is used to remove trend and seasonal components via the time_decompose() function
    and methods include seasonal decomposition of time series by Loess ("stl") and 
    seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements
    two methods for anomaly detection of residuals including using an inner quartile range ("iqr")
    and generalized extreme studentized deviation ("gesd"). These methods are based on
    those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. 
    Refer to the associated functions for specific references for these methods.
Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.
Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.
Analysis of moderation (ANOMO) method conceptualizes
    the difference and equivalence tests as a moderation problem to 
    test the difference and equivalence of 
    two estimates (e.g., two means or two effects).
Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed  
    data. The 'ANOPA' package can analyze proportions obtained from up to 
    four factors. The factors can be within-subject or between-subject or a mix 
    of within- and between-subject. The main, omnibus analysis can be followed by
    additive decompositions into interaction effects, main effects, simple 
    effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For
    that reason, we call this set of tools 'ANOPA' (Analysis of Proportion
    using Anscombe transform) to highlight its similarities with ANOVA. 
    The 'ANOPA' framework also allows plots of proportions easy to obtain 
    along with confidence intervals. Finally, effect sizes and planning statistical 
    power are easily done under this framework. Only particularity, the 'ANOPA' computes  
    F statistics which have an infinite degree of freedom on the denominator. 
    See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.
An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at <https://tinyurl.com/ANOVAStatsTool>.
A-priori power simulations and power-calculations for within, between and mixed ANOVAs based on target (partial) eta-squared values. Supports complex designs with more than two factors and their interactions with a single function call.
An interactive document on  the topic of one-way and two-way analysis of variance using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/ANOVAShiny/>.
Epidemiological population dynamics models traditionally define 
    a pathogen's virulence as the increase in the per capita rate of mortality 
    of infected hosts due to infection. This package provides functions 
    allowing virulence to be estimated by maximum likelihood techniques. The 
    approach is based on the analysis of relative survival comparing survival 
    in matching cohorts of infected vs. uninfected hosts (Agnew 2019) 
    <doi:10.1101/530709>.
Anscombe's quartet are a set of four two-variable datasets that 
    have several common summary statistics but which have very different joint 
    distributions.  This becomes apparent when the data are plotted, which 
    illustrates the importance of using graphical displays in Statistics.  This
    package enables the creation of datasets that have identical marginal sample
    means and sample variances, sample correlation, least squares regression 
    coefficients and coefficient of determination.  The user supplies an initial 
    dataset, which is shifted, scaled and rotated in order to achieve target 
    summary statistics.  The general shape of the initial dataset is retained. 
    The target statistics can be supplied directly or calculated based on a 
    user-supplied dataset.  The 'datasauRus' package 
    <https://cran.r-project.org/package=datasauRus> provides further examples 
    of datasets that have markedly different scatter plots but share many 
    sample summary statistics.
Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive.
Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. 'AntAngioCOOL' exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, 'AntAngioCOOL' reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.
Edit an 'Antares' simulation before running it : create new areas, links, thermal
    clusters or binding constraints or edit existing ones. Update 'Antares' general & optimization settings.
    'Antares' is an open source power system generator, more information available here : <https://antares-simulator.org/>.
Process results generated by 'Antares', a powerful open source software developed by
    RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems (more information about
    'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions to create new columns like net load, load factors, upward and
    downward margins or to compute aggregated statistics like economic surpluses
    of consumers, producers and sectors.
Import, manipulate and explore results generated by 'Antares', a 
    powerful open source software developed by RTE (Réseau de Transport d’Électricité) to simulate and study electric power systems
    (more information about 'Antares' here : <https://antares-simulator.org/>).
Visualize results generated by Antares, a powerful open source software
    developed by RTE to simulate and study electric power systems
    (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>).
    This package provides functions that create interactive charts to help
    'Antares' users visually explore the results of their simulations.
Provides functions for the analysis of ant communities, aiming to
    standardize workflows in myrmecology. The package automates the assignment
    of species to functional guilds based on trophic strategies, feeding
    habits, and foraging behavior, using established classification frameworks
    (Silva et al., 2015 <doi:10.7476/9788574554419>; Silvestre et al., 2003
    <isbn:9588151236>; Delabie et al., 2000
    <https://www.researchgate.net/publication/44961742_Sampling_Ground-Dwelling_Ants_Case_Studies_from_the_World%27s_Rain_Forests>),
    and also includes a novel classification system implemented within the
    package, developed from ant species occurring in urban environments.
    It also includes routines to flag exotic species of Brazil (Vieira, 2025,
    unpublished master's thesis), identify endemic species (Silva et al., 2025
    <doi:10.37885/250920259>), and classify species rarity and rarity forms
    of the Atlantic Forest (Silva et al., 2024 <doi:10.1016/j.biocon.2024.110640>).
    The package reduces manual effort and improves reproducibility, supporting
    research and biodiversity management of Neotropical ant communities.
Fitting, simulation, and inference for antedependence models for
    longitudinal data, as described in Zimmerman and Nunez-Anton (2009,
    ISBN:9781420011074). Supports integer-valued antedependence (INAD) models
    for count data with thinning operators (binomial, Poisson, negative binomial)
    and flexible innovation distributions (Poisson, Bell, negative binomial),
    categorical antedependence models for discrete-state longitudinal outcomes,
    and Gaussian antedependence (AD) models for continuous data. Implements
    maximum likelihood estimation via time-separable optimization and block
    coordinate descent, with confidence intervals based on Louis' identity and
    profile likelihood.
Provides WHO Child Growth Standards (z-scores) with
             confidence intervals and standard errors around the
             prevalence estimates, taking into account complex sample designs.
             More information on the methods is
             available online:
             <https://www.who.int/tools/child-growth-standards>.
Provides WHO 2007 References for School-age Children and
             Adolescents (5 to 19 years) (z-scores) with
             confidence intervals and standard errors around the
             prevalence estimates, taking into account complex sample designs.
             More information on the methods is
             available online:
             <https://www.who.int/tools/growth-reference-data-for-5to19-years>.
Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations <doi:10.1002/ajpa.23336>.
Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed 		at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the 		statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) <doi:10.18637/jss.v077.i06>.
The generated wealth of immune repertoire sequencing data
    requires software to investigate and quantify inter- and
    intra-antibody repertoire evolution to uncover how B cells evolve
    during immune responses. Here, we present 'AntibodyForests', a software
    to investigate and quantify inter- and intra-antibody repertoire
    evolution.
Visualization of antibody titer scores is valuable for examination of vaccination 
    effects. 'AntibodyTiters' visualizes antibody titers of all or selected patients. This 
    package also produces empty excel files in a specified format, in which users can fill in 
    experimental data for visualization. Excel files with toy data can also be produced, so that 
    users can see how it is visualized before obtaining real data. The data should contain 
    titer scores at pre-vaccination, after-1st shot, after-2nd shot, and at least one 
    additional sampling points. Patients with missing values can be included. The first two 
    sampling points (pre-vaccination and after-1st shot) will be plotted discretely, whereas 
    those following will be plotted on a continuous time scale that starts from the day of 
    second shot. Half-life of titer can also be calculated for each pair of sampling points.
A collection of tools for antitrust practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of mergers under different competitive regimes.
Wraps the 'AntiWord' utility to extract text from Microsoft Word 
    documents. The utility only supports the old 'doc' format, not the new xml 
    based 'docx' format. Use the 'xml2' package to read the latter.
Implements the full suite of simulation, visualization, and
    analysis tools for exploring the mathematical isomorphisms between ant
    colony decision-making and three major paradigms of machine learning:
    random forests (Part I: variance reduction through decorrelation),
    boosting (Part II: bias reduction through adaptive recruitment), and
    neural networks (Part III: gradient-based generational learning).
    Accompanies the trilogy "Isomorphic Functionalities between Ant Colony
    and Ensemble Learning" (Fokoué, Babbitt, and Levental, 2026,
    <doi:10.48550/arXiv.2603.20328>, <doi:10.48550/arXiv.2604.00038>).
Data from the anxiety and confinement study from Alvarado-Aravena et al. (2022) <doi:10.3390/bs12100398>.
You can use this package to create custom pipeline badges in a standard 'svg' format.
    This is useful for a company to use internally, where it may not be possible
    to create badges through external providers.
    This project was inspired by the 'anybadge' library in python.
Supplies a set of functions to query air travel data for user-
    specified years and airports. Datasets include on-time flights, airlines,
    airports, planes, and weather.
Made to make your life simpler with packages, by
    installing and loading a list of packages, whether they are on CRAN,
    Bioconductor or github. For github, if you do not have the full path, with 
    the maintainer name in it (e.g. "achateigner/topReviGO"), it will be able to
    load it but not to install it.
Convert input in any one of character, integer, numeric, factor,
 or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of
 predefined formats, and relying on Boost facilities for date and time parsing.
Implementation of an iterative process that optimizes a function by
    alternately performing restricted optimization over parameter subsets.
    Instead of solving one joint optimization problem, alternating optimization
    breaks it into smaller sub-problems. This approach can make optimization
    feasible when joint optimization is too difficult.
To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.
Provides a set of functions to analyse
        overdispersed counts or proportions. Most of the methods are
        already available elsewhere but are scattered in different
        packages. The proposed functions should be considered as
        complements to more sophisticated methods such as generalized
        estimating equations (GEE) or generalized linear mixed effect
        models (GLMM).
Provides functions to analyse overdispersed counts or proportions. These functions should be considered as complements to more sophisticated methods such as generalized estimating equations (GEE) or generalized linear mixed effect models (GLMM). aods3 is an S3 re-implementation of the deprecated S4 package aod.
Another implementation of object-orientation in R. It provides
    syntactic sugar for the S4 class system and two alternative new
    implementations. One is an experimental version built around S4
    and the other one makes it more convenient to work with lists as objects.
Download data from the 'Access to Opportunities Project (AOP)'. The 
             'aopdata' package brings annual estimates of access to employment, 
             health, education and social assistance services by transport mode, 
             as well as data  on the spatial distribution of population, jobs, 
             health care, schools and social assistance facilities at a fine 
             spatial resolution for all cities included in the project. More 
             info on the 'AOP' website <https://www.ipea.gov.br/acessooportunidades/en/>.
A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.
It can sometimes be difficult to ascertain when some events (such as property crime)
    occur because the victim is not present when the crime happens. As a result, police databases often
    record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between
    these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. 
    Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. 
    For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, 
    and a start and end date/time, this package generates an aoristic data frame with aoristic weighted
    probability values for each hour of the week, for each observation. The coordinates are not 
    necessary for the program to calculate aoristic weights; however, they are part of this package 
    because a spatial component has been integral to aoristic analysis from the start. Dummy 
    coordinates can be introduced if the user only has temporal data. Outputs include an aoristic 
    data frame, as well as summary graphs and displays.   
        For more information see:
    Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, 
    Journal of Quantitative Criminology. 18 (1): 23-43.
    Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that 
    has been discontinued with his permission.
Fit, interpret, and compute predictions with oblique random
    forests. Includes support for partial dependence, variable importance,
    passing customized functions for variable importance and identification
    of linear combinations of features. Methods for the oblique random 
    survival forest are described in Jaeger et al., (2023)
    <DOI:10.1080/10618600.2023.2231048>.
Trigger animation effects on scroll on any HTML element 
    of 'shiny' and 'rmarkdown', such as any text or plot, thanks to 
    the 'AOS' Animate On Scroll jQuery library.
Functions for processing and analyzing survey data from the All of 
    Us Social Determinants of Health (AOUSDOH) program, including tools for 
    calculating health and well-being scores, recoding variables, and simplifying 
    survey data analysis. For more details see - Koleck TA, Dreisbach C, Zhang C, 
    Grayson S, Lor M, Deng Z, Conway A, Higgins PDR, Bakken S (2024) 
    <doi:10.1093/jamia/ocae214>.
Provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.
It covers various approaches to analysis of variance, provides an assumption testing section in order to provide a decision diagram that allows selecting the most appropriate technique. It provides the classical analysis of variance, the nonparametric equivalent of Kruskal Wallis, and the Bayesian approach. These results are shown in an interactive shiny panel, which allows modifying the arguments of the tests, contains interactive graphics and presents automatic conclusions depending on the tests in order to contribute to the interpretation of these analyzes. 'AovBay' uses 'Stan' and 'FactorBayes' for Bayesian analysis and 'Highcharts' for interactive charts.
Formatter functions in the 'apa' package take the return value of a
    statistical test function, e.g. a call to chisq.test() and return a string
    formatted according to the guidelines of the APA (American Psychological
    Association).
Create American Psychological Association Style, Seventh
    Edition documents. Format numbers and text consistent with APA style.
    Create tables that comply with APA style by extending flextable
    functions.
R interface for 'Apache Sedona' based on 'sparklyr'
    (<https://sedona.apache.org>).
Provides capabilities to process Apache HTTPD Log files.The main functionalities are to extract data from access and error log files to data frames.
Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.
A common task faced by researchers is the creation of APA style
    (i.e., American Psychological Association style) tables from statistical
    output. In R a large number of function calls are often needed to obtain all of
    the desired information for a single APA style table. As well, the process of
    manually creating APA style tables in a word processor is prone to transcription
    errors. This package creates Word files (.doc files) containing APA style tables
    for several types of analyses. Using this package minimizes transcription errors
    and reduces the number commands needed by the user.
Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported.
Facilitates writing computationally reproducible student theses in PDF format that conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package currently provides two R Markdown templates for homework and theses at the Psychology Department of the University of Cologne. The package builds on the package 'papaja' but is tailored to the requirements of student theses and omits features for simplicity.
Provides nine computational algorithms for dimensionality
    reduction via Principal Component Analysis (PCA), built using an
    object-oriented (S3) architecture. The package includes classical and
    modern methods: Singular Value Decomposition (SVD) based on Eckart and
    Young (1936) <doi:10.1007/BF02288367>, Power Iteration based on Hotelling
    (1933) <doi:10.1037/h0071325>, QR Algorithm based on Francis (1961)
    <doi:10.1093/comjnl/4.3.265>, Jacobi Algorithm based on Jacobi (1846)
    <doi:10.1515/crll.1846.30.51>, Arnoldi Iteration based on Arnoldi (1951)
    <doi:10.1090/qam/42792>, 'NIPALS' based on Wold (1975) <doi:10.1017/S0021900200047604>,
    Alternating Least Squares (ALS) based on Kolda and Bader (2009)
    <doi:10.1137/07070111X>, Probabilistic PCA (PPCA) with EM Algorithm based
    on Tipping and Bishop (1999) <doi:10.1111/1467-9868.00196>, and
    Generalized Hebbian Algorithm (GHA) based on Sanger (1989)
    <doi:10.1016/0893-6080(89)90044-0>.
The process of resolving and updating taxon names is necessary when
    working with biodiversity data. 'APCalign' uses the Australian Plant Census
    (APC) and the Australian Plant Name Index (APNI) to align and update plant
    taxon names to current, accepted standards. 'APCalign' also supplies
    information about the establishment status (i.e. native or introduced) of
    plant taxa across different states/territories.
Analysis of  data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.
The adapted pair correlation function transfers the concept of the
  pair correlation function from point patterns to patterns of objects of
  finite size and irregular shape (e.g. lakes within a country).  The pair
  correlation function describes the spatial distribution of objects, e.g.
  random, aggregated or regularly spaced. This is a reimplementation of the
  method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050>
  using the library 'GEOS' <doi:10.5281/zenodo.11396894>.
It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.
Provides novel nonparametric tests, 'APCSSA' and 'APCSSM', for interaction in two-way ANOVA designs with balanced replications using all possible comparisons. These statistics extend previous methods, allow greater flexibility, and demonstrate higher power in detecting interactions for non-normal data. The package includes optimized functions for computing these test statistics, generating interaction plots, and simulating their null distributions. The companion package 'APCinteractionData' is available on 'GitHub' <https://github.com/tranbaokhue/APCinteractionData>. Methods are described and compared empirically in Tran, Wagaman, Nguyen, Jacobson, and Hartlaub (2024) <doi:10.48550/arXiv.2410.04700>.
Implements Affinity Propagation clustering introduced by Frey and
	Dueck (2007) <DOI:10.1126/science.1136800>. The algorithms are largely
        analogous to the 'Matlab' code published by Frey and Dueck.
        The package further provides leveraged affinity propagation and an
        algorithm for exemplar-based agglomerative clustering that can also be
        used to join clusters obtained from affinity propagation. Various
        plotting functions are available for analyzing clustering results.
In fields such as ecology, microbiology, and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis (PCoA) is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide 'aPCoA' as an easy-to-use tool to improve data visualization in this context, enabling enhanced presentation of the effects of interest. Details are described in Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq (2020) Bioinformatics, Volume 36, Issue 13, 4099-4101.
Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments.
    The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis.
    Sophisticated functions are available both for descriptive and regression model-based analyses.
    For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization
    techniques building on the concept of Lexis diagrams.
    Model-based analyses build on the separation of the temporal dimensions based on generalized additive models,
    where a tensor product interaction surface (usually between age and period) is utilized
    to represent the third dimension (usually cohort) on its diagonal.
    Such tensor product surfaces can also be estimated while accounting for
    further covariates in the regression model.
    See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.
An implementation of the additive polynomial (AP) design matrix. It
    constructs and appends an AP design matrix to a data frame for use with
    longitudinal data subject to seasonality.
Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
The goal is to print an "aperçu", a short view of a vector, a
    matrix, a data.frame, a list or an array. By default, it prints the first 5
    elements of each dimension. By default, the number of columns is equal to
    the number of lines. If you want to control the selection of the elements,
    you can pass a list, with each element being a vector giving the selection
    for each dimension.
Toolkit for the analysis of multiple gene data (Jombart et al. 2017) <doi:10.1111/1755-0998.12567>. 
    'apex' implements the new S4 classes 'multidna', 'multiphyDat' and associated methods to handle aligned DNA sequences from multiple genes.
Provides an 'htmlwidgets' interface to 'apexcharts.js'. 
  'Apexcharts' is a modern JavaScript charting library to build interactive charts and visualizations with simple API.
  'Apexcharts' examples and documentation are available here: <https://apexcharts.com/>.
Implements a multiple testing approach to the
    choice of a threshold gamma on the p-values using the
    Average Power Function (APF) and Bayes False Discovery
    Rate (FDR) robust estimation. Function apf_fdr() 
    estimates both quantities from either raw data or
    p-values. Function apf_plot() produces smooth graphs 
    and tables of the relevant results. Details of the methods
    can be found in Quatto P, Margaritella N, et al. (2019) 
    <doi:10.1177/0962280219844288>.
Designed for the development and application of
    hidden Markov models and profile HMMs for biological sequence analysis. 
    Contains functions for multiple and pairwise sequence alignment, 
    model construction and parameter optimization, file import/export,
    implementation of the forward, backward and Viterbi algorithms for 
    conditional sequence probabilities, tree-based sequence weighting, 
    and sequence simulation. 
    Features a wide variety of potential applications including 
    database searching, gene-finding and annotation, phylogenetic 
    analysis and sequence classification.
    Based on the models and algorithms described in Durbin et 
    al (1998, ISBN: 9780521629713).
Implements a parsimonious evolutionary model to analyze and
  predict gene-functional annotations in phylogenetic trees as described in Vega
  Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on
  computational efficiency, 'aphylo' makes it possible to estimate pooled
  phylogenetic models, including thousands (hundreds) of annotations (trees) in
  the same run. The package also provides the tools for visualization of
  annotated phylogenies, calculation of posterior probabilities (prediction)
  and goodness-of-fit assessment featured in Vega Yon et al. (2021).
Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).
Parentage assignment package.
    Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion.
    The main functions of this package are the function APIS_2n(), APIS_3n() and launch_APIShiny(), which perform parentage assignment.
API for using 'episensr', Basic sensitivity analysis of the
    observed relative risks adjusting for unmeasured confounding and
    misclassification of the exposure/outcome, or both. See
    <https://cran.r-project.org/package=episensr>.
Set of tools for fitting the additive partial linear models with symmetric autoregressive errors of order p, or APLMS-AR(p). This setup enables the modeling of a time series response variable using linear and nonlinear structures of a set of explanatory variables, with nonparametric components approximated by natural cubic splines or P-splines. It also accounts for autoregressive error terms with distributions that have lighter or heavier tails than the normal distribution. The package includes various error distributions, such as normal, generalized normal, Student's t, generalized Student's t, power-exponential, and Cauchy distributions. Chou-Chen, S.W., Oliveira, R.A., Raicher, I., Gilberto A. Paula (2024) <doi:10.1007/s00362-024-01590-w>.
An unofficial companion to "Applied
    Logistic Regression" by D.W. Hosmer, S. Lemeshow and
    R.X. Sturdivant (3rd ed., 2013) containing the dataset used in the book.
Using this package you can interact with the 'Aplos NCA API'<https://docs.aplosanalytics.com/> using standard R functions. This will allow you to authenticate with your 'Aplos NCA' account, upload input datasets, initiate analyses, and download results.
For many times, we are not just aligning plots as what 'cowplot' and 'patchwork' did. Users would like to align associated information that requires axes to be exactly matched in subplots, e.g. hierarchical clustering with a heatmap. Inspired by the 'Method 2' in 'ggtree' (G Yu (2018) <doi:10.1093/molbev/msy194>), 'aplot' provides utilities to aligns associated subplots to a main plot at different sides (left, right, top and bottom) with axes exactly matched.
Many complex plots are actually composite plots, such as 'oncoplot', 'funkyheatmap', 'upsetplot', etc. We can produce subplots using 'ggplot2' and combine them to create composite plots using 'aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.
Some functions for drawing some special plots:
   The function 'bagplot' plots a bagplot,
   'faces' plots chernoff faces,
   'iconplot' plots a representation of a frequency table or a data matrix,
   'plothulls' plots hulls of a bivariate data set,
   'plotsummary' plots a graphical summary of a data set,
   'puticon' adds icons to a plot,
   'skyline.hist' combines several histograms of a one dimensional data set in one plot,
   'slider' functions supports some interactive graphics,
   'spin3R' helps an inspection of a 3-dim point cloud,
   'stem.leaf' plots a stem and leaf plot,
   'stem.leaf.backback' plots back-to-back versions of stem and leaf plot.
In panel data settings, specifies set of candidate models, fits them to data from pre-treatment validation periods, and selects model as average over candidate models, weighting each by posterior probability of being most robust given its differential average prediction errors in pre-treatment validation periods. Subsequent estimation and inference of causal effect's bounds accounts for both model and sampling uncertainty, and calculates the robustness changepoint value at which bounds go from excluding to including 0. The package also includes a range of diagnostic plots, such as those illustrating models' differential average prediction errors and the posterior distribution of which model is most robust.
Fit linear, logistic and Cox models regularized with L0, lasso
    (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and
    their adaptive forms, such as adaptive lasso / elastic-net and net
    adjusting for signs of linked coefficients. It solves the L0 penalty
    problem by simultaneously selecting regularization parameters and
    performing hard-thresholding or selecting the number of non-zeros. This
    augmented and penalized minimization method provides an approximation
    solution to the L0 penalty problem, but runs as fast as L1 regularization.
    The package uses a one-step coordinate descent algorithm and runs extremely
    fast by taking into account the sparsity structure of coefficients. It can
    handle very high dimensional data and has superior selection performance.
Automated methods to assemble population PK (pharmacokinetic) and
    PKPD (pharmacodynamic) datasets for analysis in 'NONMEM' (non-linear mixed effects
    modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions
    to build datasets from SDTM (study data tabulation module)
    <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset
    module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset
    formats. The package will combine population datasets, add covariates, and
    create documentation to support regulatory submission and internal communication.
Convert several png files into an animated png file.
  This package exports only a single function `apng'. Call the
  apng function with a vector of file names (which should be
  png files) to convert them to a single animated png file.
Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.
Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application 
    of choice models in R. Users are able to write their own 
    model functions or use a mix of already available ones. Random heterogeneity, 
    both continuous and discrete and at the level of individuals and 
    choices, can be incorporated for all models. There is support for both standalone 
    models and hybrid model structures.  Both classical 
    and Bayesian estimation is available, and multiple discrete 
    continuous models are covered in addition to discrete choice. 
    Multi-threading processing is supported for estimation and a large
    number of pre and post-estimation routines, including for computing posterior
    (individual-level) distributions are available. 
    For examples, a manual, and a support forum, visit 
    <https://www.ApolloChoiceModelling.com>. For more information on choice 
    models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, 
    S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview 
    of the field.
Produces several metrics to assess the prediction of ordinal 
    categories based on the estimated probability distribution for each unit of analysis
    produced by any model returning a matrix with these probabilities.
Programmatic interface to the NASA Application for Extracting and Exploring Analysis Ready Samples services (AppEEARS; <https://appeears.earthdatacloud.nasa.gov/>).
 The package provides easy access to analysis ready earth observation data in R.
Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed.
Collect  your data on digital marketing campaigns from Apple Search Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
A modeling package compiling applicability domain methods in
    R. It combines different methods to measure the amount of
    extrapolation new samples can have from the training set. See
    <doi:10.4018/IJQSPR.2016010102> for an overview of applicability
    domains.
A few functions and several data set for the Springer book 'Applied Predictive Modeling'.
Simple and transparent parsing of genotype/dosage data
    from an input Variant Call Format (VCF) file, matching of genotype
    coordinates to the component Single Nucleotide Polymorphisms (SNPs)
    of an existing polygenic score (PGS), and application of SNP weights
    to dosages for the calculation of a polygenic score for each individual
    in accordance with the additive weighted sum of dosages model. Methods
    are designed in reference to best practices described by
    Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.
Amyloid propensity prediction neural network (APPNN) is an amyloidogenicity propensity predictor based on a machine learning approach through recursive feature selection and feed-forward neural networks, taking advantage of newly published sequences with experimental, in vitro, evidence of amyloid formation.
Convert populations into integer number of seats for legislative 
    bodies. Implements apportionment methods used historically and currently in the
    United States for reapportionment after the Census, as described in 
    <https://www.census.gov/history/www/reference/apportionment/methods_of_apportionment.html>.
Implements a bias-aware framework for evidence synthesis in systematic reviews and health technology assessments, as described in Kabali (2025) <doi:10.1111/jep.70272>. The package models study-level effect estimates by explicitly accounting for multiple sources of bias through prior distributions and propagates uncertainty using posterior simulation. Evidence across studies is combined using posterior mixture distributions rather than a single pooled likelihood, enabling probabilistic inference on clinically or policy-relevant thresholds. The methods are designed to support transparent decision-making when study relevance and bias vary across the evidence base.
With 'appRiori' <doi:10.1177/25152459241293110>, users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.
Performs Bayesian prediction of complex computer codes when fast approximations are available.  It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.
Tools for constructing a matched design with multiple comparison groups.
 Further specifications of refined covariate balance restriction and exact match on 
 covariate can be imposed. Matches are approximately optimal in  the sense that the 
 cost of the solution is at most twice the optimal cost, Crama and Spieksma (1992) 
 <doi:10.1016/0377-2217(92)90078-N>, Karmakar, Small and Rosenbaum (2019)
 <doi:10.1080/10618600.2019.1584900>.
R and C++ functions to perform exact and 
  approximate optimal transport. All C++ methods can be linked 
  to other R packages via their header files.
Collect  your data on digital marketing campaigns from 'Appsflyer' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Functionality to add, delete, read and update table
    records from your 'AppSheet' apps, using the official API <https://api.appsheet.com/>.
An unofficial companion to the textbook "Applied Regression
    Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the
    accompanying datasets.
Assists the evaluation of whether and
    where to focus code optimization, using Amdahl's law and visual aids
    based on line profiling. Amdahl's profiler organizes profiling output
    files (including memory profiling) in a visually appealing way.
    It is meant to help to balance development
    vs. execution time by helping to identify the most promising sections
    of code to optimize and projecting potential gains. The package is
    an addition to R's standard profiling tools and is not a wrapper for them.
Implements the scenario analysis proposed by Antolin-Diaz, 
    Petrella and Rubio-Ramirez (2021) 
    "Structural scenario analysis with SVARs" <doi:10.1016/j.jmoneco.2020.06.001>.
Provides methods to analyse the stability of non-deterministic prediction models. 
    Prediction stability is quantified either as data-based prediction stability (phi) 
    or as model-based prediction stability (psi). The package implements measures for 
    categorical, ordinal, and metric predictions based on repeated model fitting 
    and corresponding predictions. Methods are based on Lange et al. (2025) <doi:10.1186/s12859-025-06097-1>.
The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON')
             and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to
	     'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation".
	     For more information about 'APSIM' see (<https://www.apsim.info/>) and for 'APSIM'
	     next generation (<https://apsimnextgeneration.netlify.app/>).
The transmission between two time-series prices is assessed. It contains several functions for linear and nonlinear threshold co-integration, and furthermore, symmetric and asymmetric error correction models.
It calculates the Air Pollution Tolerance Index (APTI) of plant species using biochemical parameters such as chlorophyll content, leaf extract pH, relative water content, and ascorbic acid content. It helps in identifying tolerant species for greenbelt development and pollution mitigation studies. It includes a 'shiny' app for interactive APTI calculation and visualisation. For method details see, Sahu et al. (2020).<DOI:10.1007/s42452-020-3120-6>.
Provides a quick method for visualizing non-aggregated line-list
    or aggregated census data stratified by age and one or two categorical
    variables (e.g. gender and health status) with any number of values. It
    returns a 'ggplot' object, allowing the user to further customize the
    output. This package is part of the 'R4Epis' project 
    <https://r4epis.netlify.app/>.
Developed for use by those tasked with the routine detection, characterisation and
            quantification of discrete changes in air quality time-series, such as identifying the impacts
            of air quality policy interventions. The main functions use signal isolation then
            break-point/segment (BP/S) methods based on 'strucchange' and 'segmented' methods to detect and
            quantify change events (Ropkins & Tate, 2021, <doi:10.1016/j.scitotenv.2020.142374>; Ropkins et
            al., 2026, <doi:10.21105/joss.08839>).
Display air quality model output and monitoring data using scatterplots, grids, and legends.
Functions are included for recalling AQL (Acceptable Quality Level or Acceptance Quality Level) 
 Based single, double, and multiple attribute sampling plans from the Military Standard (MIL-STD-105E) - American 
 National Standards Institute/American Society for Quality (ANSI/ASQ Z1.4) tables and for retrieving variable
 sampling plans from Military Standard (MIL-STD-414) - American National Standards Institute/American Society
 for Quality (ANSI/ASQ Z1.9) tables. The sources for these tables are listed in the URL: field. Also included
 are functions for computing the OC (Operating Characteristic) and ASN (Average Sample Number) coordinates
 for the attribute plans it recalls, and functions for computing the estimated proportion nonconforming and
 the maximum allowable proportion nonconforming for variable sampling plans. The MIL-STD AQL Sampling schemes
 were the most used and copied set of standards in the world. They are intended to be used for sampling a stream
 of lots, and were used in contract agreements between supplier and customer companies. When the US military
 dropped support of MIL-STD 105E and 414, The American National Standards Institute (ANSI) and the International
 Standards Organization (ISO) adopted the standard with few changes or no changes to the central tables. This
 package is useful because its computer implementation of these tables duplicates that available in other
 commercial software and subscription online calculators.
The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks <doi:10.1016/j.cageo.2012.10.020>. These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb <https://casoilresource.lawr.ucdavis.edu/soilweb-apps>. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.
A varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875).
Computes and integrates daily potential evapotranspiration (PET) 
    and a soil water balance model. It allows users to estimate and predict the
    wet season calendar, including onset, cessation, and duration, based
    on an agroclimatic approach for a specified period. This functionality
    helps in managing agricultural water resources more effectively. For
    detailed methodologies, users can refer to Allen et al. (1998,
    ISBN:92-5-104219-5); Allen (2005, ISBN:9780784408056); Doorenbos and
    Pruitt (1975, ISBN:9251002797); Guo et al. (2016)
    <doi:10.1016/j.envsoft.2015.12.019>; Hargreaves and Samani (1985)
    <doi:10.13031/2013.26773>; Priestley and Taylor (1972)
    <https://journals.ametsoc.org/view/journals/apme/18/7/1520-0450_1979_018_0898_tptema_2_0_co_2.xml>.
A collection of functions to compute frequently used metrics for 
             nutrition trials in aquaculture. Implementations include metrics 
             to calculate growth, feed conversion, nutrient use efficiency, 
             and feed digestibility. The package supports reproducible 
             workflows for summarising experimental results and reduces manual 
             calculation errors. For additional information see Machado e 
             Silva, Karthikeyan and Tellbüscher (2025) 
             <doi:10.13140/RG.2.2.27322.04808>.
Provides an automatic aggregation tool to manage point data privacy, 
    intended to be helpful for the production of official spatial data and for researchers.
    The package pursues the data accuracy at the smallest possible areas preventing 
    individual information disclosure. The methodology, based on hierarchical geographic 
    data structures performs aggregation and local suppression of point data to ensure privacy
    as described in Lagonigro, R., Oller, R., Martori J.C. (2017) <doi:10.2436/20.8080.02.55>.
    The data structures are created following the guidelines for grid datasets from the
    European Forum for Geography and Statistics.
Toolbox for the experimental aquatic chemist, focused on 
        acidification and CO2 air-water exchange. It contains all elements to
        model the pH, the related CO2 air-water exchange, and
        aquatic acid-base chemistry for an arbitrary marine,
        estuarine or freshwater system. It contains a suite of tools for 
        sensitivity analysis, visualisation, modelling of chemical batches, 
        and can be used to build dynamic models of aquatic systems. 
        As from version 1.0-4, it also contains functions to calculate 
        the buffer factors.
The functions proposed in this package allows to evaluate the process of measurement of the chemical components of water numerically or graphically. TSSS(), ICHS and datacheck() functions are useful to control the quality of measurements of chemical components of a sample of water. If one or more measurements include an error, the generated graph will indicate it with a position of the point that represents the sample outside the confidence interval. The function CI() allows to evaluate the possibility of contamination of a water sample after being obtained. Validation() is a function that allows to calculate the quality parameters of a technique for the measurement of a chemical component.
Estimate aquatic species life history using robust techniques.
    This package supports users undertaking two types of analysis: 1) Growth from
    length-at-age data, and 2) maturity analyses for length and/or age data.
    Maturity analyses are performed using generalised linear model approaches incorporating
    either a binomial or quasibinomial distribution.
    Growth modelling is performed using the multimodel approach presented by
    Smart et al. (2016) "Multimodel approaches in shark and ray growth studies:
    strengths, weaknesses and the future" <doi:10.1111/faf.12154>.
The Aquo Standard is the Dutch Standard for the exchange of 
    data in water management. With *aquodom* (short for aquo domaintables) 
    it is easy to exploit the API (<https://www.aquo.nl/index.php/Hoofdpagina>) to download domaintables 
    of the Aquo Standard and use them in R.
Simple functions to convert given Arabic numerals to Kansuji  
    numerical figures that represent numbers written in Chinese characters.
Allows users to stem Arabic texts for text analysis.
Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.
Computes low-dimensional point representations of high-dimensional numerical data according to the data visualization method Adaptable Radial Axes described in: Manuel Rubio-Sánchez, Alberto Sanchez, and Dirk J. Lehmann (2017) "Adaptable radial axes plots for improved multivariate data visualization" <doi:10.1111/cgf.13196>.
Processes noble gas mass spectrometer data to determine the isotopic composition of argon (comprised of Ar36, Ar37, Ar38, Ar39 and Ar40) released from neutron-irradiated potassium-bearing minerals. Then uses these compositions to calculate precise and accurate geochronological ages for multiple samples as well as the covariances between them. Error propagation is done in matrix form, which jointly treats all samples and all isotopes simultaneously at every step of the data reduction process. Includes methods for regression of the time-resolved mass spectrometer signals to t=0 ('time zero') for both single- and multi-collector instruments, blank correction, mass fractionation correction, detector intercalibration, decay corrections, interference corrections, interpolation of the irradiation parameter between neutron fluence monitors, and (weighted mean) age calculation. All operations are performed on the logs of the ratios between the different argon isotopes so as to properly treat them as 'compositional data', sensu Aitchison [1986, The Statistics of Compositional Data, Chapman and Hall].
Implements the Classification-based on
    Association Rules (CBA) algorithm for association rule classification.
    The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>,
    contains several convenience methods that allow to automatically
    set CBA parameters (minimum confidence, minimum support) and it also natively
    handles numeric attributes by integrating a pre-discretization step.
    The rule generation phase is handled by the 'arules' package. 
    To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 
    'qCBA' package is suggested.
It fits a univariate left, right, or interval censored linear regression model
    with autoregressive errors, considering the normal or the Student-t distribution for the 
    innovations. It provides estimates and standard errors of the parameters, predicts 
    future observations, and supports missing values on the dependent variable.
    References used for this package:
    Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with 
    autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics,
    45(4), 375-392 <doi:10.1002/cjs.11338>.
    Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence 
    diagnostics for censored regression models with autoregressive errors. Australian & New 
    Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>.
    Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored 
    autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 
    52(3), 804-828 <doi:10.1002/cjs.11804>.
Lightweight interface for converting addresses into
    geographic coordinates and coordinates into addresses using the
    'ArcGIS' REST API service
    <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.
    Address text can be converted to location candidates and locations can
    be converted into addresses. No API key is required.
Provides easy installation and loading of core ArcGIS
    location services packages 'arcgislayers', 'arcgisutils',
    'arcgisgeocode', and 'arcgisplaces'. Enabling developers to interact
    with spatial data and services from 'ArcGIS Online', 'ArcGIS
    Enterprise', and 'ArcGIS Platform'. Learn more about the 'arcgis'
    meta-package at <https://developers.arcgis.com/r-bridge/>.
A very fast and robust interface to ArcGIS 'Geocoding
    Services'. Provides capabilities for reverse geocoding, finding
    address candidates, character-by-character search autosuggestion, and
    batch geocoding. The public 'ArcGIS World Geocoder' is accessible for
    free use via 'arcgisgeocode' for all services except batch geocoding.
    'arcgisgeocode' also integrates with 'arcgisutils' to provide access
    to custom locators or private 'ArcGIS World Geocoder' hosted on
    'ArcGIS Enterprise'. Learn more in the 'Geocode service' API reference
    <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.
Enables users of 'ArcGIS Enterprise', 'ArcGIS Online', or
    'ArcGIS Platform' to read, write, publish, or manage vector and raster
    data via ArcGIS location services REST API endpoints
    <https://developers.arcgis.com/rest/>.
The ArcGIS 'Places service' is a ready-to-use location
    service that can search for businesses and geographic locations around
    the world. It allows you to find, locate, and discover detailed
    information about each place. Query for places near a point, within a
    bounding box, filter based on categories, or provide search text.
    'arcgisplaces' integrates with 'sf' for out of the box compatibility
    with other spatial libraries. Learn more in the 'Places service' API reference <https://developers.arcgis.com/rest/places/>.
Developer oriented utility functions designed to be used as
    the building blocks of R packages that work with ArcGIS Location
    Services. It provides functionality for authorization, Esri JSON
    construction and parsing, as well as other utilities pertaining to
    geometry and Esri type conversions. To support 'ArcGIS Pro' users,
    authorization can be done via 'arcgisbinding'. Installation
    instructions for 'arcgisbinding' can be found at
    <https://developers.arcgis.com/r-bridge/installation/>.
Statistical analysis of archaeological dates and groups of
    dates. This package allows to post-process Markov Chain Monte Carlo
    (MCMC) simulations from 'ChronoModel' <https://chronomodel.com/>,
    'Oxcal' <https://c14.arch.ox.ac.uk/oxcal.html> or 'BCal'
    <https://bcal.shef.ac.uk/>. It provides functions for the study of
    rhythms of the long term from the posterior distribution of a series
    of dates (tempo and activity plot). It also allows the estimation and
    visualization of time ranges from the posterior distribution of groups
    of dates (e.g. duration, transition and hiatus between successive
    phases) as described in Philippe and Vibet (2020)
    <doi:10.18637/jss.v093.c01>.
Provides the data sets used to build the 'ArchaeoPhases' vignettes.  The data sets were formerly distributed with 'ArchaeoPhases', however they exceed current CRAN policy for package size.
The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.
Methods to analyse spatial units in archaeology from the refitting relationships between fragments of objects scattered in these units (e.g. stratigraphic layers). Graphs are used to model archaeological observations. The package is mainly based on the 'igraph' package for graph analysis. Functions can: 1) create, manipulate, visualise, and simulate fragmentation graphs, 2) measure the cohesion and admixture of archaeological spatial units, and 3) characterise the topology of a specific set of refitting relationships. A series of published empirical datasets is included. Documentation about 'archeofrag' is provided by a vignette and by the accompanying scientific papers: Plutniak (2021, Journal of Archaeological Science, <doi:10.1016/j.jas.2021.105501>) and Plutniak (2022, Journal of Open Source Software, <doi:10.21105/joss.04335>). This package is complemented by the 'archeofrag.gui' R package, a companion GUI application available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.
A 'Shiny' application to access the functionalities and datasets of the 'archeofrag' package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and exports, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the 'openMOLE' model exploration software. A demonstration of the app is available at <https://analytics.huma-num.fr/Sebastien.Plutniak/archeofrag/>.
An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.
Performs archetypal analysis by using Principal Convex Hull Analysis 
    under a full control of all algorithmic parameters. 
    It contains a set of functions for determining the initial solution, the optimal 
    algorithmic parameters and the optimal number of archetypes.
    Post run tools are also available for the assessment of the derived solution. 
    Morup, M., Hansen, LK (2012) <doi:10.1016/j.neucom.2011.06.033>.
    Hochbaum, DS, Shmoys, DB (1985) <doi:10.1287/moor.10.2.180>.
    Eddy, WF (1977) <doi:10.1145/355759.355768>.
    Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) <doi:10.1145/235815.235821>.    
    Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076>.    
    Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), <doi:10.1093/qje/qjy013>. 
    Christopoulos, DT (2015) <doi:10.1016/j.jastp.2015.03.009> .  
    Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), <doi:10.3390/e21040394>.
A project template to support the data science workflow.
The main function archetypes implements a
    framework for archetypal analysis supporting arbitrary
    problem solving mechanisms for the different conceptual
    parts of the algorithm.
Analysis of complex plant root system architectures (RSA) using the output files created by Data Analysis of Root Tracings (DART), an open-access software dedicated to the study of plant root architecture and development across time series (Le Bot et al (2010) "DART: a software to analyse root system architecture and development from captured images", Plant and Soil, <DOI:10.1007/s11104-009-0005-2>), and RSA data encoded with the Root System Markup Language (RSML) (Lobet et al (2015) "Root System Markup Language: toward a unified root architecture description language", Plant Physiology, <DOI:10.1104/pp.114.253625>). More information can be found in Delory et al (2016) "archiDART: an R package for the automated computation of plant root architectural traits", Plant and Soil, <DOI:10.1007/s11104-015-2673-4>.
Provides a graphical method for joint visualisation of
    Variant Set Association Test (VSAT) results and individual variant
    association statistics. The Archipelago method assigns genomic
    coordinates to variant set statistics, allowing simultaneous display
    of variant-level and set-level signals in a unified plot. This supports
    interpretation of both collective and individual variant contributions
    in genetic association studies using variant aggregation approaches. For more see Lawless et al. (2026) <doi:10.1002/gepi.70025>.
Implements specialized K-Nearest Neighbor (KNN) logic to 
    address the unique challenges of spatial modeling in archipelagic 
    environments. Standard contiguity models often leave significant 
    portions of island nations (e.g., 20% of the Philippines) 
    mathematically isolated. This package provides tools to ensure 
    100% network connectivity, neutralizing spatial bias and 
    enabling robust econometric inference. Methodology follows 
    Anselin (1988, ISBN:9024737354) and LeSage and Pace (2009) <doi:10.1201/9781420064254>.
Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025).
Bindings to 'libarchive' <http://www.libarchive.org> the
    Multi-format archive and compression library. Offers R connections and
    direct extraction for many archive formats including 'tar', 'ZIP',
    '7-zip', 'RAR', 'CAB' and compression formats including 'gzip',
    'bzip2', 'compress', 'lzma' and 'xz'.
Scraping content from archived web pages stored in
    the 'Internet Archive' (<https://archive.org>) using a systematic
    workflow.  Get an overview of the mementos available from the
    respective homepage, retrieve the Urls and links of the page and
    finally scrape the content. The final output is stored in tibbles,
    which can be then easily used for further analysis.
Data exploration and modelling is a process in which a lot of data
    artifacts are produced. Artifacts like: subsets, data aggregates, plots,
    statistical models, different versions of data sets and different versions
    of results. The more projects we work with the more artifacts are produced
    and the harder it is to manage these artifacts. Archivist helps to store
    and manage artifacts created in R. Archivist allows you to store selected
    artifacts as a binary files together with their metadata and relations.
    Archivist allows to share artifacts with others, either through shared
    folder or github. Archivist allows to look for already created artifacts by
    using it's class, name, date of the creation or other properties. Makes it
    easy to restore such artifacts. Archivist allows to check if new artifact
    is the exact copy that was produced some time ago. That might be useful
    either for testing or caching.
Set of functions to analyse and estimate Artificial Counterfactual models from Carvalho, Masini and Medeiros (2016) <DOI:10.2139/ssrn.2823687>.
For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2022) <DOI:10.1111/rssc.12558>.
Implements cubic regularization methods (ARC) for local
    optimization problems common in statistics and applied research. Provides
    robust handling of ill-conditioned, nonconvex, and indefinite Hessian
    problems with automatic saddle point escape. Supports box constraints;
    linear equality constraints are planned for a future release.
Fast processing of ArcGIS FeatureCollection protocol buffers in R.
  It is designed to work seamlessly with 'httr2' and integrates with 'sf'.
Functions to efficiently query 'ArcGIS REST' APIs 
  <https://developers.arcgis.com/rest/>. 
  Both spatial and SQL queries can be used to retrieve data. 
  Simple Feature (sf) objects are utilized to perform spatial queries. 
  This package was neither produced nor is maintained by Esri.
An interface to the 'ArcGIS' 'arcpy' and 'arcgis' 'python' API
    <https://pro.arcgis.com/en/pro-app/latest/arcpy/get-started/arcgis-api-for-python.htm>.
    Provides various tools for installing and configuring a 'Conda' environment
    for accessing 'ArcGIS' geoprocessing functions. Helper functions for
    manipulating and converting 'ArcGIS' objects from R are also provided.
Performs requests to the 'Arctos' API to download data. Provides a
    set of builder classes for performing complex requests, as well as a set of
    simple functions for automating many common requests and workflows. More
    information about 'Arctos' can be found in 
    Cicero et al. (2024) <doi:10.1371/journal.pone.0296478> or on their website
    <https://arctosdb.org/>.
Fits a linear-binomial model using a modified Newton-type 
    algorithm for solving the maximum likelihood 
    estimation problem under linear box constraints. Similar methods are 
    described in Wagenpfeil, Schöpe and Bekhit (2025, ISBN:9783111341972) 
    "Estimation of adjusted relative risks in log-binomial regression 
    using the BSW algorithm". In: Mau, Mukhin, Wang and Xu (Eds.), 
    Biokybernetika. De Gruyter, Berlin, pp. 665–676.
Autoregressive-based decomposition of a time series based on the approach in West (1997). Particular cases include the extraction of trend and seasonal components.
A set of functions to access the 'ARDECO' (Annual Regional Database
    of the European Commission) data directly from the official ARDECO public
    repository through the exploitation of the 'ARDECO' APIs.
    The APIs are completely transparent to the user and the provided functions
    provide a direct access to the 'ARDECO' data.
    The 'ARDECO' database is a collection of variables related to demography,
    employment, labour market, domestic product, capital formation.
    Each variable can be exposed in one or more units of measure as well as
    refers to total values plus additional dimensions like economic sectors, gender,
    age classes. Data can be also aggregated at country level according
    to the tercet classes as defined by EUROSTAT.
    The description of the 'ARDECO' database can be found at the following URL
    <https://territorial.ec.europa.eu/ardeco>.
Creates complex autoregressive distributed lag (ARDL) models and 
    constructs the underlying unrestricted and restricted error correction 
    model (ECM) automatically, just by providing the order. It also performs
    the bounds-test for cointegration as described in Pesaran et al. (2001) 
    <doi:10.1002/jae.616> and provides the multipliers and the cointegrating
    equation. The validity and the accuracy of this package have been verified 
    by successfully replicating the results of Pesaran et al. (2001) in 
    Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.
Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.
A unified framework for Autoregressive Distributed Lag (ARDL) modeling 
    and cointegration analysis. Implements Panel ARDL with Pooled Mean Group (PMG), 
    Mean Group (MG), and Dynamic Fixed Effects (DFE) estimators following 
    Pesaran, Shin & Smith (1999) <doi:10.1002/jae.616>. 
    Provides bootstrap-based bounds testing per Pesaran, Shin & Smith (2001) 
    <doi:10.1002/jae.616>. Includes Quantile Nonlinear ARDL (QNARDL) combining 
    distributional and asymmetric effects based on Shin, Yu & Greenwood-Nimmo (2014) 
    <doi:10.1007/978-1-4899-8008-3_9>, and Fourier ARDL for modeling smooth 
    structural breaks following Enders & Lee (2012) <doi:10.1016/j.econlet.2012.05.019>. 
    Features include Augmented ARDL (AARDL) with deferred t and F tests, 
    Multiple-Threshold NARDL for complex asymmetries, Rolling/Recursive ARDL 
    for time-varying relationships, and Panel NARDL for nonlinear panel cointegration. 
    All methods include comprehensive diagnostics, publication-ready outputs, 
    and visualization tools.
Contains functions to help create an Analysis Results Dataset.
    The dataset follows industry recommended structure.  The dataset can be 
    created in multiple passes, using different data frames as input.  Analysis
    Results Datasets are used in the pharmaceutical and biotech industries
    to capture analysis in a common tabular data structure.
Calculate the area of triangles and polygons using the shoelace 
 formula. Area may be signed, taking into account path orientation, or unsigned, 
 ignoring path orientation. The shoelace formula is described at 
 <https://en.wikipedia.org/wiki/Shoelace_formula>.
Considering an (n x m) data matrix X, this package is based on the method proposed 
             by Gower, Groener, and Velden (2010) <doi:10.1198/jcgs.2010.07134>, and 
             utilize the  resulting matrices from the extended version of the NIPALS decomposition 
             to determine n triangles whose areas are used  to  visually  estimate the elements of
             a specific column of X. After a 90-degree rotation of the sample points, the triangles
             are drawn regarding the following points: 1.the origin of the axes; 2.the sample points;
             3. the vector endpoint representing some variable.
A pipeable, transparent implementation of areal weighted interpolation
    with support for interpolating multiple variables in a single function call.
    These tools provide a full-featured workflow for validation and estimation
    that fits into both modern data management (e.g. tidyverse) and spatial 
    data (e.g. sf) frameworks.
Formalizes spatial support at scale for ecological and geographical
    analysis. Given points and support polygons, classifies points as "core"
    (inside original support) or "halo" (inside scaled support but outside
    original), pruning all others. The default scale produces equal core and
    halo areas - a geometrically derived choice requiring no tuning. An optional
    mask enforces hard boundaries such as coastlines. Political borders are
    treated as soft boundaries with no ecological meaning.
Plot stacked areas and confidence bands as filled polygons, or add
  polygons to existing plots. A variety of input formats are supported,
  including vectors, matrices, data frames, formulas, etc.
Generates data for challenging machine learning models in 'Arena'
    <https://arena.drwhy.ai> - an interactive web application. You can start
    the server with XAI (Explainable Artificial Intelligence) plots to be
    generated on-demand or precalculate and auto-upload data file beside
    shareable 'Arena' URL.
A Python based pipeline for extraction of species occurrence data through the usage of large language models. Includes validation tools designed to handle model hallucinations for a scientific, rigorous use of LLM. Currently supports usage of GPT with more planned, including local and non-proprietary models. For more details on the methodology used please consult the references listed under each function, such as Kent, A. et al. (1995) <doi:10.1002/asi.5090060209>, van Rijsbergen, C.J. (1979, ISBN:978-0408709293, Levenshtein, V.I. (1966) <https://nymity.ch/sybilhunting/pdf/Levenshtein1966a.pdf> and Klaus Krippendorff (2011) <https://repository.upenn.edu/handle/20.500.14332/2089>.
Adversarial random forests (ARFs) recursively partition data into 
    fully factorized leaves, where features are jointly independent. The 
    procedure is iterative, with alternating rounds of generation and 
    discrimination. Data becomes increasingly realistic at each round, until 
    original and synthetic samples can no longer be reliably distinguished. 
    This is useful for several unsupervised learning tasks, such as density
    estimation and data synthesis. Methods for both are implemented in this
    package. ARFs naturally handle unstructured data with mixed continuous and 
    categorical covariates. They inherit many of the benefits of random forests, 
    including speed, flexibility, and solid performance with default parameters. 
    For details, see Watson et al. (2023) <https://proceedings.mlr.press/v206/watson23a.html>.
Simulates, fits, and predicts long-memory and anti-persistent
	time series, possibly mixed with ARMA, regression, transfer-function
	components.
	Exact methods (MLE, forecasting, simulation) are used.
	Bug reports should be done via GitHub (at
	<https://github.com/JQVeenstra/arfima>), where the development version
	of this package lives; it can be installed using devtools.
Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using 'cli'.
Provides functions to access data from public RESTful APIs including the 'ArgentinaDatos API', 
    'REST Countries API', and 'World Bank API' related to Argentina's exchange rates, inflation, 
    political figures, holidays, economic indicators, and general country-level statistics. 
    Additionally, the package includes curated datasets related to Argentina, covering topics 
    such as economic indicators, biodiversity, agriculture, human rights, genetic data, and 
    consumer prices. The package supports research and analysis focused on Argentina by 
    integrating open APIs with high-quality datasets from various domains. 
    For more details on the APIs, see: 
        'ArgentinaDatos API' <https://argentinadatos.com/>, 
        'REST Countries API' <https://restcountries.com/>, 
        and 'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>.
Provides functions to retrieve information from Web Feature Service (WFS) and Web Map Service (WMS) layers
    from various Argentine organizations and import them into R for further analysis.
    WFS and WMS are standardized protocols for serving georeferenced map data over the internet.
    For more information on these services, see <https://www.ogc.org/publications/standard/wfs/> and <https://www.ogc.org/publications/standard/wms/>.
Provides methods to construct frequentist confidence sets with valid marginal 
    coverage for identifying the population-level argmin or argmax based on IID data. 
    For instance, given an n by p loss matrix—where n is the sample size and p is the 
    number of models—the CS.argmin() method produces a discrete confidence set that contains 
    the model with the minimal (best) expected risk with desired probability. The argmin.HT() 
    method helps check if a specific model should be included in such a confidence set. The main
    implemented method is proposed by Tianyu Zhang, Hao Lee and Jing Lei (2024) 
    "Winners with confidence: Discrete argmin inference with an application to model selection".
Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) <doi:10.1073/pnas.1515373112>; Ning, S., Yang, S. and Kou, S.C. (2019) <doi:10.1038/s41598-019-41559-6>; Yang, S., Ning, S. and Kou, S.C. (2021) <doi:10.1038/s41598-021-83084-5>.
Supports the analysis of oceanographic data recorded by Argo autonomous drifting profiling floats. Functions are provided to (a) download and cache data files, (b) subset data in various ways, (c) handle quality-control flags and (d) plot the results according to oceanographic conventions. A shiny app is provided for easy exploration of datasets. The package is designed to work well with the 'oce' package, providing a wide range of processing capabilities that are particular to oceanographic analysis. See Kelley, Harbin, and Richards (2021) <doi:10.3389/fmars.2021.635922> for more on the scientific context and applications.
Create awesome 'Bootstrap 4' dashboards powered by 'Argon'.
R wrapper around the argon HTML library.
    More at <https://demos.creative-tim.com/argon-design-system/>.
Comprehensive set of tools for performing system identification of both linear and nonlinear dynamical systems directly from data. The Automatic Regression for Governing Equations (ARGOS) simplifies the complex task of constructing mathematical models of dynamical systems from observed input and output data, supporting various types of systems, including those described by ordinary differential equations. It employs optimal numerical derivatives for enhanced accuracy and employs formal variable selection techniques to help identify the most relevant variables, thereby enabling the development of predictive models for system behavior analysis.
Filters animal satellite tracking data obtained from the Argos system(<https://www.argos-system.org/>), following the algorithm described in Freitas et al (2008) <doi:10.1111/j.1748-7692.2007.00180.x>. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality.
A command line parser to
    be used with 'Rscript' to write "#!" shebang scripts that gracefully
    accept positional and optional arguments and automatically generate usage.
Cross-platform command-line argument parser written purely in R
    with no external dependencies. It is useful with the Rscript
    front-end and facilitates turning an R script into an executable script.
Random variate generation, density, CDF and quantile function 
  for the Argus distribution. Especially, it includes for random variate generation a
  flexible inversion method that is also fast in the varying parameter case.
  A Ratio-of-Uniforms method is provided as second alternative.
Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by
    Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with
    various probabilistic alternative prior models. It contains a function to consistently
    estimate higher order moments of the population covariance spectral distribution using
    the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>).
    In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately 
    with a given correlation matrix when the degrees of freedom are large.
It performs All-Resolutions Inference (ARI) on functional Magnetic Resonance Image (fMRI) data. As a main feature, 
 it estimates lower bounds for the proportion of active voxels in a set of clusters as, for example, given by a cluster-wise analysis. 
 The method is described in Rosenblatt, Finos, Weeda, Solari, Goeman (2018) <doi:10.1016/j.neuroimage.2018.07.060>.
Implements an efficient O(n) algorithm based on bucket-sorting for 
    fast computation of standard clustering comparison measures. Available measures
    include adjusted Rand index (ARI), normalized information distance (NID), 
    normalized mutual information (NMI), normalized variation information (NVI) and entropy, as described in Vinh et al (2009) 
    <doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2, 
    a modified version of ARI (MARI), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7> 
    and simple Chi-square distance since version 1.0.0.
A comprehensive suite of statistical and analytical tools for 
    agricultural research. Includes complete analysis of variance (ANOVA) 
    functions for all experimental designs: Completely Randomized Design (CRD), 
    Randomized Block Design (RBD), Pooled RBD, Split Plot with all variations, 
    Split-Split Plot, Strip Plot, Latin Square, Factorial, Augmented, and Alpha 
    Lattice, with proper error terms and comprehensive Standard Error (SE) and 
    Critical Difference (CD) calculations. Features multiple post-hoc tests: 
    Least Significant Difference (LSD), Duncan Multiple Range Test (DMRT), 
    Tukey Honestly Significant Difference (HSD), Student-Newman-Keuls (SNK), 
    Scheffe, Bonferroni, and Dunnett, along with assumption checking and 
    publication-ready output. Advanced methods include stability analysis using 
    Eberhart-Russell regression, Additive Main Effects and Multiplicative 
    Interaction (AMMI), Finlay-Wilkinson regression, Shukla stability variance, 
    Wricke ecovalence, Coefficient of Variation (CV), and Cultivar Superiority 
    Index as described in Eberhart and Russell (1966) 
    <doi:10.2135/cropsci1966.0011183X000600010011x>. Thermal indices include 
    Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units 
    (PTU), and Heat Use Efficiency (HUE). Crop growth analysis covers Crop 
    Growth Rate (CGR), Relative Growth Rate (RGR), Net Assimilation Rate (NAR), 
    and Leaf Area Index (LAI). Also provides harvest index, yield gap analysis, 
    economic efficiency indices (Benefit-Cost ratio), nutrient use efficiency 
    calculations, correlation matrix, Principal Component Analysis (PCA), path 
    analysis, and Structural Equation Modeling (SEM). Statistical methods follow 
    Gomez and Gomez (1984, ISBN:0471870927) and Panse and Sukhatme (1985, 
    ISBN:8170271169).
Describes a series first. After that does time series analysis using one hybrid model and two specially structured Machine Learning (ML) (Artificial Neural Network or ANN and Support Vector Regression or SVR) models. More information can be obtained from Paul and Garai (2022) <doi:10.1007/s41096-022-00128-3>.
Estimating and analyzing auto regressive integrated moving average
    (ARIMA) models. The primary function in this package is arima(), which fits 
    an ARIMA model to univariate time series data using a random restart
    algorithm. This approach frequently leads to models that have model 
    likelihood greater than or equal to that of the likelihood obtained by 
    fitting the same model using the arima() function from the 'stats' package. 
    This package enables proper optimization of model likelihoods, which is a 
    necessary condition for performing likelihood ratio tests. This package 
    relies heavily on the source code of the arima() function of the 'stats' 
    package. For more information, please see Jesse Wheeler and Edward L. 
    Ionides (2025) <doi:10.1371/journal.pone.0333993>.
Testing, Implementation, and Forecasting of the ARIMA-ANN hybrid model. The ARIMA-ANN hybrid model combines the distinct strengths of the Auto-Regressive Integrated Moving Average (ARIMA) model and the Artificial Neural Network (ANN) model for time series forecasting.For method details see Zhang, GP (2003) <doi:10.1016/S0925-2312(01)00702-0>.
Flat text files provide a robust, compressible, and portable
  way to store tables from databases.  This package provides convenient
  functions for exporting tables from relational database connections
  into compressed text files and streaming those text files back into
  a database without requiring the whole table to fit in working memory.
Tools for quantitative analysis related to archaeological and historical problems for irregularly spaced time indexed observations, toward evaluating linear dependence and homogeneity over time. Methods include effect sizes for measuring homogeneity, simulation from a truncated Poisson distribution for random right-censoring of count data, and least-squares spectral analysis by lowest frequency iteration for model fitting. Collins-Elliott (2026) <https://volweb.utk.edu/~scolli46/sce_aqysuppl2026.pdf>.
A dependency-free collection of simple functions for cleaning
    rectangular data. This package allows to detect, count and replace
    values or discard rows/columns using a predicate function. In
    addition, it provides tools to check conditions and return informative
    error messages.
Provides a Scheme-inspired Lisp dialect embedded in R, with
    macros, tail-call optimization, and seamless interoperability with R
    functions and data structures. (The name 'arl' is short for 'An R Lisp.')
    Implemented in pure R with no compiled code.
Implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.
Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.
Provides function declarations and inline function definitions
    that facilitate communication between R and the 'Armadillo' 'C++' library
    for linear algebra and scientific computing. This implementation is derived
    from Vargas Sepulveda and Schneider Malamud (2024)
    <doi:10.1016/j.softx.2025.102087>.
The real-life time series data are hardly pure linear or nonlinear. Merging a linear time series model like the autoregressive moving average (ARMA) model with a nonlinear neural network model such as the Long Short-Term Memory (LSTM) model can be used as a hybrid model for more accurate modeling purposes. Both the autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models can be implemented. Details can be found in Box et al. (2015, ISBN: 978-1-118-67502-1) and Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>.
Providing ways to estimate the value of European stock options 
    given historical stock price data. It includes functions for calculating option values 
    based on autoregressive–moving-average (ARMA) models and generates information about these models. 
    This package is made to be easy to understand and for financial analysis capabilities.
An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.
A cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system.  The only parameter that limits the number of chips that can be processed is the amount of available disk space.  The Aroma Framework has successfully been used in studies to process tens of thousands of arrays.  This package has actively been used since 2006.
DEPRECATED. Do not start building new projects based on this package. (The (in-house) APD file format was initially developed to store Affymetrix probe-level data, e.g. normalized CEL intensities.  Chip types can be added to APD file and similar to methods in the affxparser package, this package provides methods to read APDs organized by units (probesets).  In addition, the probe elements can be arranged optimally such that the elements are guaranteed to be read in order when, for instance, data is read unit by unit.  This speeds up the read substantially.  This package is supporting the Aroma framework and should not be used elsewhere.)
Methods for analyzing DNA copy-number data.  Specifically,
  this package implements the multi-source copy-number normalization (MSCN)
  method for normalizing copy-number data obtained on various platforms and
  technologies.  It also implements the TumorBoost method for normalizing
  paired tumor-normal SNP data.
Core methods and classes used by higher-level 'aroma.*' packages
        part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.
Tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).
Contains functions for retrieving, managing, and analyzing air quality and weather data from the Regione Lombardia open database (<https://www.dati.lombardia.it/>).
    Data are collected by ARPA Lombardia (Lombardia Environmental Protection Agency), Italy, through its ground monitoring network (<https://www.dati.lombardia.it/stories/s/auv9-c2sj>).
    See the website <https://www.arpalombardia.it/> for further information on ARPA Lombardia's activities and history.
    Data quality (e.g., missing values, extreme values, and graphical mapping) has been checked in collaboration with members of ARPA Lombardia's air quality control office.
    The package provides observations since 1989 (for weather) and 1968 (for air quality), and these data are updated daily by the regional agency.
    A full description of the package is available in the companion paper Maranzano \& Algieri (2024), "ARPALData: an R package for retrieving and analyzing air quality
    and weather data from ARPA Lombardia (Italy)", Environmental and Ecological Statistics, <doi:10.1007/s10651-024-00599-6>.
Tools for simulating data generated by direct observation
    recording. Behavior streams are simulated based on an alternating renewal
    process, given specified distributions of event durations and interim
    times. Different procedures for recording data can then be applied to the
    simulated behavior streams. Functions are provided for the following
    recording methods: continuous duration recording, event counting, momentary
    time sampling, partial interval recording, whole interval recording, and
    augmented interval recording.
Provides convenience functions for programming with
    'magrittr' pipes. Conditional pipes, a string prefixer and a function
    to pipe the given object into a specific argument given by character
    name are currently supported. It is named after the dadaist Hans Arp,
    a friend of Rene Magritte.
Functions for Arps decline-curve analysis on oil and gas data. Includes exponential, hyperbolic, harmonic, and hyperbolic-to-exponential models as well as the preceding with initial curtailment or a period of linear rate buildup. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits.
Fast generators and iterators for permutations, combinations,
    integer partitions and compositions. The arrangements are in
    lexicographical order and generated iteratively in a memory efficient manner. 
    It has been demonstrated that 'arrangements' outperforms most existing
    packages of similar kind. Benchmarks could be found at
    <https://randy3k.github.io/arrangements/articles/benchmark.html>.
High performance variant of apply() for a fixed set of functions.
  Considerable speedup of this implementation is a trade-off for universality: user defined
  functions cannot be used with this package. However, about 20 most currently employed
  functions are available for usage. They can be divided in three types:
  reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
  mapping function (like normalise(), cumsum() etc., giving a vector of the same length
  as the input vector) and finally, vector reducing function (like diff() which produces
  result vector of a length different from the length of input vector).
  Optional or mandatory additional arguments required by some functions
  (e.g. norm type for norm()) can be
  passed as named arguments in '...'.
Some convenient functions to work with arrays.
Argument parsing for R scripts, with support for long and short
    Unix-style options including option clustering, positional arguments
    including those of variable length, and multiple usage patterns which may
    take different subsets of options.
'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language
    development platform for in-memory data. It specifies a standardized
    language-independent columnar memory format for flat and hierarchical data,
    organized for efficient analytic operations on modern hardware. This
    package provides an interface to the 'Arrow C++' library.
The 'ggarrow' package is a 'ggplot2' extension that plots
    a variety of different arrow segments with many options to customize. 
    The 'arrowheadr' package makes it easy to create custom arrowheads 
    and fins within the parameters that 'ggarrow' functions expect. It 
    has preset arrowheads and a collection of functions to create and 
    transform data for customizing arrows.
Adaptive Rejection Sampling, Original version.
An Arsenal of 'R' functions for large-scale statistical summaries,
  which are streamlined to work within the latest reporting tools in 'R' and
  'RStudio' and which use formulas and versatile summary statistics for summary
  tables and models. The primary functions include tableby(), a Table-1-like
  summary of multiple variable types 'by' the levels of one or more categorical
  variables; paired(), a Table-1-like summary of multiple variable types paired across
  two time points; modelsum(), which performs simple model fits on one or more endpoints
  for many variables (univariate or adjusted for covariates);
  freqlist(), a powerful frequency table across many categorical variables;
  comparedf(), a function for comparing data.frames; and
  write2(), a function to output tables to a document.
Provides a unified and straightforward interface for
    performing a variety of meta-analysis methods directly from user data.
    Users can input a data frame, specify key parameters, and effortlessly
    execute and compare multiple common meta-analytic models. Designed for
    immediate usability, the package facilitates transparent, reproducible
    research without manual implementation of each analytical method.
    Ideal for researchers aiming for efficiency and reproducibility, it
    streamlines workflows from data preparation to results interpretation.
For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of 'bannerCommenter', for inserting neat comments, headers and dividers.
Reads and writes clinical-trial datasets losslessly across
    'SAS' XPORT (XPT), Clinical Data Interchange Standards Consortium
    (CDISC) Dataset-JSON, and 'Apache Parquet', applying a specification to
    produce submission-ready Study Data Tabulation Model (SDTM) and
    Analysis Data Model (ADaM) datasets. A single canonical metadata model
    carries labels, CDISC data types, lengths, 'SAS' display formats,
    controlled-terminology references, and sort keys identically across
    every format, so conversion between any two formats is lossless by
    construction. Pure 'R' and lightweight, with no external 'SAS' or
    'Java' runtime. Implements the published format specifications for
    CDISC Dataset-JSON
    (<https://cdisc-org.github.io/DataExchange-DatasetJson/doc/dataset-json1-1.html>)
    and 'SAS' XPORT
    (<https://www.loc.gov/preservation/digital/formats/fdd/fdd000466.shtml>).
The aligned rank transform for nonparametric
    factorial ANOVAs as described by Wobbrock,
    Findlater, Gergle, and Higgins (2011) <doi:10.1145/1978942.1978963>. 
    Also supports aligned rank transform contrasts as described
    by Elkin, Kay, Higgins, and Wobbrock (2021)
    <doi:10.1145/3472749.3474784>.
Create data that displays generative art when mapped into a 'ggplot2' plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using 'ggplot2' for generative art.
Provides algorithms for creating artworks in the 'ggplot2' language that incorporate some form of randomness.
Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.
Provides the infrastructure for representing, manipulating
    and analyzing transaction data and patterns (frequent itemsets and
    association rules).  Also provides C implementations of the
    association mining algorithms Apriori and Eclat.  Hahsler, Gruen and
    Hornik (2005) <doi:10.18637/jss.v014.i15>.
Provides the infrastructure for association rule-based classification including the algorithms 
  CBA, CMAR, CPAR, C4.5, FOIL, PART, PRM, RCAR, and RIPPER to build associative classifiers. 
  Hahsler et al (2019) <doi:10.32614/RJ-2019-048>.
NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets and NB-precise rules. Michael Hahsler (2006) <doi:10.1007/s10618-005-0026-2>.
Add-on for arules to handle and mine frequent sequences.
    Provides interfaces to the C++ implementation of cSPADE by  
    Mohammed J. Zaki.
Extends package 'arules' with various visualization
    techniques for association rules and itemsets. The package also
    includes several interactive visualizations for rule exploration.
    Michael Hahsler (2017) <doi:10.32614/RJ-2017-047>.
Parse Autonomous Recording Unit (ARU) data and for sub-sampling recordings. 
    Extract Metadata from your recordings, select a subset of recordings for 
    interpretation, and prepare files for processing on the 
    'WildTrax' <https://wildtrax.ca/> platform. Read and process metadata 
    from recordings collected using the SongMeter and BAR-LT types of ARUs.
Implements the 'Arvind' distribution and five novel stochastic
    regression models that replace the traditional Gaussian error assumption
    with 'Arvind'-distributed errors. The 'Arvind' distribution is a flexible
    single-parameter continuous distribution on the positive real line
    characterised by a polynomial numerator with Gaussian-type decay. The
    package provides complete distribution functions (darvind(), parvind(),
    qarvind(), rarvind()), maximum likelihood estimation via
    fit_arvind_mle(), and five model-fitting routines: Random Walk on
    Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via
    fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects
    Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via
    fit_hmm(). Additionally provides Monte Carlo forecasting with prediction
    intervals via forecast_arvind(), comprehensive goodness-of-fit
    diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and
    plot_arvind(), k-fold and rolling-window cross-validation via
    cv_arvind(), and unified model comparison via summary_arvind().
    For more details see Pandey, Singh, Tyagi, and Tyagi (2024)
    "Modelling climate, COVID-19, and reliability data: A new continuous
    lifetime model under different methods of estimation",
    Statistics and Applications, 22(2),
    <https://ssca.org.in/journal.html>.
An interface to the API for 'arXiv',
    a repository of electronic preprints for
    computer science, mathematics, physics, quantitative biology,
    quantitative finance, and statistics.
Data sets are referred to in the text "Applied Survival Analysis Using R"
 by Dirk F. Moore, Springer, 2016, ISBN: 978-3-319-31243-9, <DOI:10.1007/978-3-319-31245-3>.
Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.
Coerce R object to 'asciidoc', 'txt2tags',
    'restructuredText', 'org', 'textile' or 'pandoc' syntax.
    Package comes with a set of drivers for 'Sweave'.
Record 'asciicast' screen casts from R scripts. Convert them
    to animated SVG images, to be used in 'README' files, or blog posts.
    Includes 'asciinema-player' as an 'HTML' widget, and an 'asciicast'
    'knitr' engine, to embed 'ascii' screen casts in 'Rmarkdown'
    documents.
Create ASCII line graphs of a time series directly on
    your terminal in an easy way. There are some configurations you
    can add to make the plot the way you like. This project was
    inspired by the original 'asciichart' package by Igor Kroitor.
An ASCII ruler is for measuring text and is especially useful for sequence analysis. Included in this package are methods to create ASCII rulers and associated GenBank sequence blocks, multi-column text displays that make it easy for viewers to locate nucleotides by position.
Lets you open a fixed-width ASCII file (.txt or
    .dat) that has an accompanying setup file (.sps or .sas). These file
    combinations are sometimes referred to as .txt+.sps, .txt+.sas,
    .dat+.sps, or .dat+.sas. This will only run in a txt-sps or txt-sas
    pair in which the setup file contains instructions to open that text
    file. It will NOT open other text files, .sav, .sas, or .por data
    files. Fixed-width ASCII files with setup files are common in older
    (pre-2000) government data.
A spatiotemporal model that simulates the spread of Ascochyta
    blight in chickpea fields based on location-specific weather conditions.
    This model is adapted from a model developed by Diggle et al. (2002)
   <doi:10.1094/PHYTO.2002.92.10.1110> for simulating the spread of anthracnose
   in a lupin field.
Package runs simulations for adaptive seamless designs with and without early outcomes 
                           for treatment selection and subpopulation type designs.
A simple driver that reads binary data created by the ASD Inc.
    portable spectrometer instruments, such as the FieldSpec (for more information,
    see <http://www.asdi.com/products/fieldspec-spectroradiometers>). Spectral data
    can be extracted from the ASD files as raw (DN), white reference, radiance, or
    reflectance. Additionally, the metadata information contained in the ASD file
    header can also be accessed.
The Australian Statistical Geography Standard ('ASGS') is 
  a set of shapefiles by the Australian Bureau of Statistics. This package
  provides an interface to those shapefiles, as well as methods for converting
  coordinates to shapefiles.
David Scott's ASH routines ported from S-PLUS to R.
Understanding morphological variation is an important task in many applications. Recent studies in computational biology have focused on developing computational tools for the task of sub-image selection which aims at identifying structural features that best describe the variation between classes of shapes. A major part in assessing the utility of these approaches is to demonstrate their performance on both simulated and real datasets. However, when creating a model for shape statistics, real data can be difficult to access and the sample sizes for these data are often small due to them being expensive to collect. Meanwhile, the landscape of current shape simulation methods has been mostly limited to approaches that use black-box inference---making it difficult to systematically assess the power and calibration of sub-image models. In this R package, we introduce the alpha-shape sampler: a probabilistic framework for simulating realistic 2D and 3D shapes based on probability distributions which can be learned from real data or explicitly stated by the user. The 'ashapesampler' package supports two mechanisms for sampling shapes in two and three dimensions. The first, empirically sampling based on an existing data set, was highlighted in the original main text of the paper. The second, probabilistic sampling from a known distribution, is the computational implementation of the theory derived in that paper. Work based on Winn-Nunez et al. (2024) <doi:10.1101/2024.01.09.574919>.
The R package 'ashr' implements an Empirical Bayes
    approach for large-scale hypothesis testing and false discovery
    rate (FDR) estimation based on the methods proposed in
    M. Stephens, 2016, "False discovery rates: a new deal",
    <DOI:10.1093/biostatistics/kxw041>. These methods can be applied
    whenever two sets of summary statistics---estimated effects and
    standard errors---are available, just as 'qvalue' can be applied
    to previously computed p-values. Two main interfaces are
    provided: ash(), which is more user-friendly; and ash.workhorse(),
    which has more options and is geared toward advanced users. The
    ash() and ash.workhorse() also provides a flexible modeling
    interface that can accommodate a variety of likelihoods (e.g.,
    normal, Poisson) and mixture priors (e.g., uniform, normal).
Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).
Implements the framework of Tiwari and Majumdar (2025)
    <doi:10.48550/arXiv.2512.07154> for valuing arithmetic and geometric
    Asian options under transient and permanent market impact. Provides
    three pricing approaches: Kemna-Vorst frictionless benchmarks,
    exogenous diffusion pricing (closed-form for geometric, Monte Carlo
    for arithmetic), and endogenous Hamilton-Jacobi-Bellman valuation
    via a tree-based Bellman scheme producing indifference bid-ask prices.
'Asio' is a cross-platform C++ library for network and low-level
 I/O programming that provides developers with a consistent asynchronous model
 using a modern C++ approach. It is also included in Boost but requires linking
 when used with Boost. Standalone it can be used header-only (provided a recent
 compiler). 'Asio' is written and maintained by Christopher M. Kohlhoff, and
 released under the 'Boost Software License', Version 1.0.
A chat package connecting to API endpoints by 'OpenAI' 
  (<https://platform.openai.com/>) to answer questions (about R).
Cross-platform utilities for prompting the user for credentials or a 
    passphrase, for example to authenticate with a server or read a protected key.
    Includes native programs for MacOS and Windows, hence no 'tcltk' is required. 
    Password entry can be invoked in two different ways: directly from R via the 
    askpass() function, or indirectly as password-entry back-end for 'ssh-agent' 
    or 'git-credential' via the SSH_ASKPASS and GIT_ASKPASS environment variables.
    Thereby the user can be prompted for credentials or a passphrase if needed 
    when R calls out to git or ssh.
Provides an interface to the algorithm selection benchmark library
    at <https://www.coseal.net/aslib/> and the 'LLAMA' package
    (<https://cran.r-project.org/package=llama>) for building
    algorithm selection models; see Bischl et al. (2016)
    <doi:10.1016/j.artint.2016.04.003>.
Performs linear regression with respect to a data-driven convex loss function that is chosen to minimize the asymptotic covariance of the resulting M-estimator. The convex loss function is estimated in 5 steps: (1) form an initial OLS (ordinary least squares) or LAD (least absolute deviation) estimate of the regression coefficients; (2) use the resulting residuals to obtain a kernel estimator of the error density; (3) estimate the score function of the errors by differentiating the logarithm of the kernel density estimate; (4) compute the L2 projection of the estimated score function onto the set of decreasing functions; (5) take a negative antiderivative of the projected score function estimate. Newton's method (with Hessian modification) is then used to minimize the convex empirical risk function. Further details of the method are given in Feng et al. (2024) <doi:10.48550/arXiv.2403.16688>.
Provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo.
Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome.
A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).
Implements several tools that are used in animal social network analysis, as described in Whitehead (2007) Analyzing Animal Societies <University of Chicago Press> and Farine & Whitehead (2015) <doi: 10.1111/1365-2656.12418>. In particular, this package provides the tools to infer groups and generate networks from observation data, perform permutation tests on the data, calculate lagged association rates, and performed multiple regression analysis on social network data.
A collection of functions for computing centrographic
        statistics (e.g., standard distance, standard deviation
        ellipse, standard deviation box) for observations taken at
        point locations. Separate plotting functions have been
        developed for each measure. Users interested in writing results
        to ESRI shapefiles can do so by using results from 'aspace'
        functions as inputs to the convert.to.shapefile() and
        write.shapefile() functions in the 'shapefiles' library. We intend to
        provide 'terra' integration for geographic data in a future release.
        The 'aspace' package was originally conceived to aid in the analysis of
        spatial patterns of travel behaviour (see Buliung and Remmel 2008
        <doi:10.1007/s10109-008-0063-7>).
Contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.
Tools for the analysis and visualization of bilateral asymmetry in parasitic infections.
Perform one-dimensional spline regression with automatic knot selection.
      This package uses a penalized approach to select the most relevant knots.
      B-splines of any degree can be fitted. More details in 'Goepp et al. (2018)',
      "Spline Regression with Automatic Knot Selection", <arXiv:1808.01770>.
R codes for the (adaptive) Sum of Powered Score ('SPU' and 'aSPU')
    tests, inverse variance weighted Sum of Powered score ('SPUw' and 'aSPUw') tests
    and gene-based and some pathway based association tests (Pathway based Sum of
    Powered Score tests ('SPUpath'), adaptive 'SPUpath' ('aSPUpath') test, 'GEEaSPU'
    test for multiple traits - single 'SNP' (single nucleotide polymorphism)
    association in generalized estimation equations, 'MTaSPUs' test for multiple
    traits - single 'SNP' association with Genome Wide Association Studies ('GWAS')
    summary statistics, Gene-based Association Test that uses an extended 'Simes'
    procedure ('GATES'), Hybrid Set-based Test ('HYST') and extended version
    of 'GATES' test for pathway-based association testing ('GATES-Simes'). ).
    The tests can be used with genetic and other data sets with covariates. The
    response variable is binary or quantitative. Summary; (1) Single trait-'SNP' set
    association with individual-level data ('aSPU', 'aSPUw', 'aSPUr'), (2) Single trait-'SNP'
    set association with summary statistics ('aSPUs'), (3) Single trait-pathway
    association with individual-level data ('aSPUpath'), (4) Single trait-pathway
    association with summary statistics ('aSPUsPath'), (5) Multiple traits-single
    'SNP' association with individual-level data ('GEEaSPU'), (6) Multiple traits-
    single 'SNP' association with summary statistics ('MTaSPUs'), (7) Multiple traits-'SNP' set association with summary statistics('MTaSPUsSet'), (8) Multiple traits-pathway association with summary statistics('MTaSPUsSetPath').
Assists in automating the selection of terms to include in mixed models when  
  'asreml' is used to fit the models. Procedures are available for choosing models that 
  conform to the hierarchy or marginality principle, for fitting and choosing between 
  two-dimensional spatial models using correlation, natural cubic smoothing spline and 
  P-spline models. A history of the fitting of a sequence of models is kept in a data frame. 
  Also used to compute functions and contrasts of, to investigate differences between and 
  to plot predictions obtained using any model fitting function. The content  falls into 
  the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model 
  selection and description functions, (iv) Model diagnostics and simulation functions, 
  (v) Prediction production and presentation functions, (vi) Response transformation 
  functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions 
  (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a 
  computationally efficient algorithm for fitting a wide range of linear mixed models using 
  Residual Maximum Likelihood. It is a commercial package and a license for it can be 
  purchased from 'VSNi' <https://vsni.co.uk/> as 'asreml-R', who will supply a zip file 
  for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed 
  for functions that are methods for 'alldiffs'  and 'data.frame' objects. The package 
  'asremPlus' can also be installed from <http://chris.brien.name/rpackages/>.
Presents a series of molecular and genetic routines in the R
    environment with the aim of assisting in analytical pipelines before
    and after the use of 'asreml' or another library to perform analyses such
    as Genomic Selection or Genome-Wide Association Analyses. 
    Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) 
    <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.
Functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>).
Provides ready-to-use datasets from the Korean National Assembly
    (assemblies 20 through 22, 2016-2026) for teaching quantitative methods in
    political science. Includes legislator metadata, bill proposals, roll call
    votes, asset declarations, and policy seminar records. Designed as a Korean
    politics counterpart to packages like 'palmerpenguins', enabling students to
    practice regression, panel data analysis, text analysis, and network analysis
    with real legislative data. Roll call vote data and spatial voting models are
    described in Poole and Rosenthal (1985) <doi:10.2307/2111172>. Legislative
    data is sourced from the Korean National Assembly Open API.
Lightweight validation tool for checking function arguments and 
  validating data analysis scripts. This is an alternative to stopifnot() from 
  the 'base' package  and to assert_that() from the 'assertthat' package. It 
  provides more informative error messages and facilitates debugging.
Simple, flexible, assertions on data.frame or data.table objects with verbose output for vetting. While other assertion packages apply towards more general use-cases, assertable is tailored towards tabular data. It includes functions to check variable names and values, whether the dataset contains all combinations of a given set of unique identifiers, and whether it is a certain length. In addition, assertable includes utility functions to check the existence of target files and to efficiently import multiple tabular data files into one data.table.
Designed to help health economic modellers when building and reviewing models. 
    The visualisation functions allow users to more easily review the network of functions 
    in a project, and get lay summaries of them. The asserts included are intended to check for common errors,
    thereby freeing up time for modellers to focus on tests specific to the individual model in development or review.
    For more details see Smith and colleagues (2024)<doi:10.12688/wellcomeopenres.23180.1>.
Provides simple assertions with sensible defaults and customisable error messages. 
  It offers convenient assertion call wrappers and a general assert function that can handle any condition.
  Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the 'cli' package.
Provides functionality to assert conditions
    that have to be met so that errors in data used in
    analysis pipelines can fail quickly. Similar to
    'stopifnot()' but more powerful, friendly, and easier
    for use in pipelines.
An extension to stopifnot() that makes it easy to declare 
    the pre and post conditions that you code should satisfy, while also 
    producing friendly error messages so that your users know what's gone
    wrong.
Provides assessment tools for regression models with discrete and semicontinuous outcomes proposed in Yang (2021) <doi:10.1080/10618600.2021.1910042>, Yang (2024) <doi:10.1080/10618600.2024.2303336>, Yang (2024) <doi:10.1093/biomtc/ujae007>, and Yang (2026) <doi:10.1002/cjs.70046>. It calculates the double probability integral transform (DPIT) residuals. It also constructs QQ plots of residuals the ordered curve for assessing mean structures, quasi-empirical distribution function for overall assessment, and a formal goodness-of-fit test.
Calculates the optimal price of assets (such as
	airline flight seats, hotel room bookings) whose value
	becomes zero after a fixed ``expiry date''.  Assumes
	potential customers arrive (possibly in groups) according
	to a known inhomogeneous Poisson process.  Also assumes a
	known time-varying elasticity of demand (price sensitivity)
	function.  Uses elementary techniques based on ordinary
	differential equations.  Uses the package deSolve to effect
	the solution of these differential equations.
Use Monte-Carlo and K-fold cross-validation coupled with machine-
    learning classification algorithms to perform population assignment, with
    functionalities of evaluating discriminatory power of independent training
    samples, identifying informative loci, reducing data dimensionality for genomic
    data, integrating genetic and non-genetic data, and visualizing results.
Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.
Fit various smoothing spline models. Includes an ssr() function for smoothing 
    spline regression, an nnr() function for nonparametric nonlinear regression, an snr() 
    function for semiparametric nonlinear regression, an slm() function for semiparametric 
    linear mixed-effects models, and an snm() function for semiparametric nonlinear 
    mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.
Clinical trial design for subgroup selection in three-stage group
             sequential trial as described in Lai, Lavori and Liao (2014,
	     <doi:10.1016/j.cct.2014.09.001>). Includes facilities for design,
	     exploration and analysis of such trials. An implementation of
	     the initial DEFUSE-3 trial is also provided as a vignette.
An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a thorough discussion and demonstration of the algorithm, see Salahub and Oldford (2025) <doi:10.1002/sam.70042>.
A user-friendly 'shiny' application to explore statistical associations and visual patterns in multivariate datasets. The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) <doi:10.1016/j.softx.2025.102483>.
Implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method.
Functions to calculate the assortment of vertices in social networks.  This can be measured on both weighted and binary networks, with discrete or continuous vertex values.
A 'shiny' application to assess statistical assumptions and guide users toward appropriate tests. The app is designed for researchers with minimal statistical training and provides diagnostics, plots, and test recommendations for a wide range of analyses. Many statistical assumptions are implemented using the package 'rstatix' (Kassambara, 2019) <doi:10.32614/CRAN.package.rstatix> and 'performance' (Lüdecke et al., 2021) <doi:10.21105/joss.03139>.
Fits a model to adjust and consider additional variations in three dimensions of age groups, time, and space on residuals excluded from a prediction model that have residual such as: linear regression, mixed model and so on. Details are given in Foreman et al. (2015) <doi:10.1186/1478-7954-10-1>.
Aster models (Geyer, Wagenius, and Shaw, 2007,
    <doi:10.1093/biomet/asm030>; Shaw, Geyer, Wagenius, Hangelbroek, and
    Etterson, 2008, <doi:10.1086/588063>; Geyer, Ridley, Latta, Etterson,
    and Shaw, 2013, <doi:10.1214/13-AOAS653>) are exponential family
    regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, life table analysis,
    zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    There are also random effects versions of these models.
Aster models are exponential family regression models for life
    history analysis.  They are like generalized linear models except that
    elements of the response vector can have different families (e. g.,
    some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
    and can be dependent, the dependence indicated by a graphical structure.
    Discrete time survival analysis, zero-inflated Poisson regression, and
    generalized linear models that are exponential family (e. g., logistic
    regression and Poisson regression with log link) are special cases.
    Main use is for data in which there is survival over discrete time periods
    and there is additional data about what happens conditional on survival
    (e. g., number of offspring).  Uses the exponential family canonical
    parameterization (aster transform of usual parameterization).
    Unlike the aster package, this package does dependence groups (nodes of
    the graph need not be conditionally independent given their predecessor
    node), including multinomial and two-parameter normal as families.  Thus
    this package also generalizes mark-capture-recapture analysis.
Provides basic functionalities to calculate the position of
    satellites given a known state vector. The package includes implementations
    of the SGP4 and SDP4 simplified perturbation models to propagate orbital
    state vectors, as well as utilities to read TLE files and convert coordinates
    between different frames of reference. Several of the functionalities of the
    package (including the high-precision numerical orbit propagator) require
    the coefficients and data included in the 'asteRiskData' package, available
    in a 'drat' repository. To install this data package, run 
    'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'.
    Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.org/NORAD/documentation/spacetrk.pdf>.
    David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>.
    Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.
Parsing R code is key to build tools such as linters and stylers.
    This package provides a binding to the 'Rust' crate 'ast-grep' so that one
    can parse and explore R code.
Data sets and examples from National Health and Nutritional Examination Survey (NHANES).
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.
Miscellaneous astronomy functions, utilities, and data.
A comprehensive toolkit for astronomical and cosmological
    computations. Provides functions for angular coordinate conversions
    (degrees, hours-minutes-seconds, degrees-minutes-seconds, and radians),
    access to fundamental physical constants, queries to the Gaia Archive TAP
    (Table Access Protocol) service, cosmological distance calculations, and
    early-universe thermal
    physics including photon density and 'Saha' equation solutions.
Provides access to the 'Astronomy Engine' C library
    (<https://github.com/cosinekitty/astronomy>) by Don Cross. The library
    calculates positions of the Sun, Moon, and planets, and predicts
    astronomical events such as rise/set times, lunar phases, equinoxes,
    solstices, eclipses, and transits. It is based on the 'VSOP87' planetary
    model and is accurate to within approximately one arcminute. This package
    bundles the single-file C source so that other R packages can link against
    it via 'LinkingTo' without shipping their own copy.
Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, <DOI:10.1007/978-3-031-70584-7>, and Time Series: A Data Analysis Approach Using R (2nd ed). Chapman-Hall, 2026, <https://www.routledge.com/Time-Series-A-Data-Analysis-Approach-Using-R/Shumway-Stoffer/p/book/9781041031642>. Most scripts are designed to require minimal input to produce aesthetically pleasing output for ease of use in live demonstrations and course work.
Provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. 
    For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020).
The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.
A collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) <doi:10.1080/10485250310001624819> and Khan and Akbar.
Data on Asylum and Resettlement for the UK,
    provided by the Home Office <https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables>.
Response surface designs (RSDs) are widely used for Response Surface Methodology (RSM) based optimization studies, which aid in exploring the relationship between a group of explanatory variables and one or more response variable(s) (G.E.P. Box and K.B. Wilson (1951), "On the experimental attainment of optimum conditions" ; M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI: 10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts".). Second order rotatable designs are the most prominent and popular class of designs used for process and product optimization trials but it is suitable for situations when all the number of levels for each factor is the same. In many practical situations, RSDs with asymmetric levels (J.S. Mehta and M.N. Das (1968). "Asymmetric rotatable designs and orthogonal transformations" ; M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2020) <DOI: 10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)" .) are more suitable as these designs explore more regions in the design space.This package contains functions named Asords() ,CCD_coded(), CCD_original(), SORD_coded() and SORD_original() for generating asymmetric/symmetric RSDs along with the randomized layout. It also contains another function named Pred.var() for generating the variance of predicted response as well as the moment matrix based on a second order model.
Multidimensional scaling models and methods for the visualization and analysis of asymmetric proximity data. An asymmetric data matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example of an asymmetric matrix is a  student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides algorithms for three multidimensional scaling models, the slide-vector model, a scaling model with unique dimensions and the asymscal model. Furthermore, some other procedures, such as a heat map for skew-symmetric data, and the decomposition of asymmetry are also provided for the exploratory analysis of asymmetric tables.
Provides functions and examples for the weak and strong density asymmetry measures in the articles: "A measure of asymmetry", Patil, Patil and Bagkavos (2012) <doi:10.1007/s00362-011-0401-6> and "A measure of asymmetry based on a new necessary and sufficient condition for symmetry", Patil, Bagkavos and Wood (2014) <doi:10.1007/s13171-013-0034-z>. The measures provided here are useful for quantifying the asymmetry of the shape of a density of a random variable. The package facilitates implementation of the measures which are applicable in a variety of fields including e.g. probability theory, statistics and economics.
Leveraging Monte Carlo simulations, this package provides
    tools for diagnosing regression models. It implements a parametric
    bootstrap framework to compute statistics, generates diagnostic
    envelopes to assess goodness-of-fit, and evaluates type I error
    control for Wald tests. By simulating data under the assumption that
    the model is true, it helps to identify model mis-specifications and
    enhances the reliability of the model inferences.
One and two sample mean and variance
 tests (differences and ratios) are considered.
 The test statistics are all expressed in the same
 form as the Student t-test, which facilitates their
 presentation in the classroom. This contribution
 also fills the gap of a robust (to non-normality)
 alternative to the chi-square single variance test
 for large samples, since no such procedure is implemented
 in standard statistical software.
Estimate the lower and upper bound of asymptomatic cases in an 
    epidemic using the capture/recapture methods from Böhning et al. (2020) 
    <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020)
    <doi:10.1101/2020.07.14.20153445>. Note there is currently some discussion
    about the validity of the methods implemented in this package. You should
    read carefully the original articles, alongside this answer from Li et al.
    (2022) <doi:10.48550/arXiv.2209.11334> before using this package in your
    project.
Estimation of regression models for sparse asynchronous 
    longitudinal observations, where time-dependent response and covariates 
    are mismatched and observed intermittently within subjects. Kernel 
    weighted estimating equations are used for generalized linear models 
    with either time-invariant or time-dependent coefficients. Cao, H.,
    Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., 
    Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.
Provides a collection of psychometric methods to process item metadata
 and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic
 test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed
 by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing
 for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the 
 linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/>
 to enable a variety of approaches to select items.
Create Tables for Reporting Clinical Trials.
  Calculates descriptive statistics and hypothesis tests, 
  arranges the results in a table ready for reporting with LaTeX, HTML or Word.
The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>,
	Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the
	initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy
	implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend
	and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.
Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) <doi: 10.1007/s11004-007-9129-1>; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.
Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. 
    The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. 
    This robustness is achieved by only using limited "pooling" of information across observations. 
    For more details, see the paper by Lee and Weidner, "Bounding Treatment Effects by Pooling Limited Information across Observations," forthcoming at the Journal of Econometrics, <doi:10.48550/arXiv.2111.05243>.
Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, <doi:10.1177/0962280217743777>; 2019, <doi:10.1002/sim.8073>). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration.
A recent method proposed by Yi and Chen (2023) <doi:10.1177/09622802221146308> is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores.
Tools for reproducible, offline analysis of endurance-training data
    exported from 'Strava'. Provides data import, quality-control,
    cohort-reference, and visualization helpers for sports-science indicators
    including acute:chronic workload ratio, aerobic efficiency, cardiovascular
    decoupling, exposure, and personal-best profiles.
Computing and visualizing comparative
 asymptotic timings of different algorithms and code versions.
 Also includes functionality for comparing empirical timings with
 expected references such as linear or quadratic,
 <https://en.wikipedia.org/wiki/Asymptotic_computational_complexity>
 Also includes functionality for measuring asymptotic memory and other
 quantities.
Stanford 'ATLAS' (Advanced Temporal Search Engine) is a powerful tool that allows constructing
             cohorts of patients extremely quickly and efficiently. This package is designed to interface directly
             with an instance of 'ATLAS' search engine and facilitates API queries and data dumps. Prerequisite
             is a good knowledge of the temporal language to be able to efficiently construct a query.
             More information available at <https://shahlab.stanford.edu/start>.
Provides an interface in R to cell atlas approximations. See the vignette under "Getting started" for instructions. You can also explore the reference documentation for specific functions. Additional interfaces and resources are available at <https://atlasapprox.readthedocs.io>.
Simplify creating multiple, related 'leaflet' maps across tabs for a 'shiny' application. Users build lists of any polygons, points, and polylines needed for the project, use the map_server() function to assign built lists and other chosen aesthetics into each tab, and the package leverages modules to generate all map tabs.
Download air quality and meteorological information of Chile from  the National Air Quality System (S.I.N.C.A.)<https://sinca.mma.gob.cl/> dependent on the Ministry of the Environment and the Meteorological Directorate of Chile (D.M.C.)<https://www.meteochile.gob.cl/> dependent on the Directorate General of Civil Aeronautics.
Provides functions for implementing the Analysis-of-marginal-Tail-Means (ATM) method, a robust optimization method for discrete black-box optimization. Technical details can be found in Mak and Wu (2018+) <arXiv:1712.03589>. This work was supported by USARO grant W911NF-17-1-0007.
Implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.
Fetch Australian Taxation Office ('ATO') Taxation
    Statistics and related datasets via the 'data.gov.au' Comprehensive
    Knowledge Archive Network ('CKAN') API
    <https://data.gov.au/data/api/3/>. Provides tidy access to
    individual, company, superannuation, goods and services tax
    ('GST'), fringe benefits tax ('FBT'), Voluntary Tax Transparency
    Code ('VTTC'), Pay As You Go ('PAYG') withholding, charity,
    excise, and Corporate Tax Transparency data, plus Division 293,
    Petroleum Resource Rent Tax, Medicare Levy Surcharge, fuel tax
    credits, compliance, and Working Holiday Maker aggregates.
    Includes reproducibility helpers (snapshot pinning, SHA-256
    cache integrity, session manifest, optional 'Zenodo' deposit),
    classification crosswalks ('ANZSIC' 2006 to 2020, 'ANZSCO' 2013
    to 2021), panel harmonisation, reconciliation against Final
    Budget Outcome totals, and real-terms and per-capita helpers
    backed by bundled Australian Bureau of Statistics ('ABS')
    Consumer Price Index and Estimated Resident Population series.
    Bridges to the 'taxstats' 2 per cent microdata sample via
    column-schema mapping. Data is published by the Australian
    Taxation Office under Creative Commons Attribution 2.5 Australia
    or 3.0 Australia licences (dataset-dependent).
Provides tools to read/write/publish metadata based on the 'Atom' XML syndication format. This includes
 support of 'Dublin Core' XML implementation, and a client to API(s) implementing the 'AtomPub' - 'SWORD' API specification.
ATPOL is a rectangular grid system used for botanical studies in Poland. The ATPOL grid was developed in Institute of Botany, Jagiellonian University, Krakow, Poland in '70. Since then it is widely used to represent distribution of plants in Poland. 
    'atpolR' provides functions to translate geographic coordinates to the grid and vice versa. It also allows to create a choreograph map.
Provides tools for evaluating timely epidemic detection models within school absenteeism-based surveillance systems. Introduces the concept of alert time quality as an evaluation metric. Includes functions to simulate populations, epidemics, and alert metrics associated with epidemic spread using population census data. The methods are based on research published in Vanderkruk et al. (2023) <doi:10.1186/s12889-023-15747-z> and Ward et al. (2019) <doi:10.1186/s12889-019-7521-7>.
Plot party trees in left-right orientation instead of the
  classical top-down layout.
The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.
Wraps the 'AT' Protocol (Authenticated Transfer Protocol) behind 
    'Bluesky' <https://bsky.social>. Functions can be used for, among others, 
    retrieving posts and followers from the network or posting content.
Contains some tools for testing, analyzing time series data and
    fitting popular time series models such as ARIMA, Moving Average and Holt
    Winters, etc. Most functions also provide nice and clear outputs like SAS
    does, such as identify, estimate and forecast, which are the same statements
    in PROC ARIMA in SAS.
Manage dependencies during package development. This can
    retrieve all dependencies that are used in ".R" files in the "R/"
    directory, in ".Rmd" files in "vignettes/" directory and in 'roxygen2'
    documentation of functions. There is a function to update the
    "DESCRIPTION" file of your package with 'CRAN' packages or any other
    remote package.  All functions to retrieve dependencies of ".R"
    scripts and ".Rmd" or ".qmd" files can be used independently of a
    package development.
Tools for defensive programming, inspired by 'purrr' mappers and 
    based on 'rlang'.'attempt' extends and facilitates defensive programming by 
    providing a consistent grammar, and provides a set of easy to use functions 
    for common tests and conditions. 'attempt' only depends on 'rlang', and 
    focuses on speed, so it can be easily integrated in other functions and 
    used in data analysis.
Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>, Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".
Provides functions for estimating the attributable burden of disease due to risk factors. The posterior simulation is performed using arm::sim as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942> and the attributable burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>.
Various functions to compute the area under the curve of selected measures: The area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). Support for visualization and partial areas is included.
Interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio. Built-in devices include Windows MM, Mac OS X AudioUnits and PortAudio (the last one is very experimental).
Facilitates plotting audiometric data (mostly) by preparing the coordinate system according to standards, given e. g. in American Speech-Language-Hearing Association (2005), <doi:10.1044/policy.GL2005-00014>.
Creates pre- and post- intervention scattergrams based on audiometric data. These scattergrams are formatted for publication in Otology & Neurotology and other otolaryngology journals. For more details and instructions on how to use this package, please reference Pan and Oghalai (2026) <doi: 10.1097/MAO.0000000000004693>. For further history on the development of scattergrams for reporting audiometric results in clinical trials, please see Gurgel et al (2012) <doi:10.1177/0194599812458401>, Oghalai and Jackler (2016) <doi:10.1177/0194599816638314>.
Find an upper bound for the total amount of overstatement
    of assets in a set of accounts, or estimate the amount of sales tax
    owed on a collection of transactions (Meeden and Sargent, 2007,
    <doi:10.1080/03610920701386802>).
Provides an easy to use unified interface for creating validation plots for any model. 
  The 'auditor' helps to avoid repetitive work consisting of writing code needed to create residual plots. 
  This visualizations allow to asses and compare the goodness of fit, performance, and similarity of models.
Dynamic regression for time series using Extreme Gradient Boosting with hyper-parameter tuning via Bayesian Optimization or Random Search.
A collection of Japanese text processing tools for filling
    Japanese iteration marks, Japanese character type conversions,
    segmentation by phrase, and text normalization which is based on rules
    for the 'Sudachi' morphological analyzer and the 'NEologd' (Neologism
    dictionary for 'MeCab').  These features are specific to Japanese and
    are not implemented in 'ICU' (International Components for Unicode).
Implementation of the augmented
            Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) <doi:10.1080/01621459.2014.922777>
            for analyzing the data with mixed measurement error and misclassification. The main
            function provides a similar summary output as that of glm() function. Both parametric and
            empirical SIMEX are considered in the package.
Extract and process bird sightings records from
    eBird (<http://ebird.org>), an online tool for recording bird
    observations.  Public access to the full eBird database is via the
    eBird Basic Dataset (EBD; see <http://ebird.org/ebird/data/download>
    for access), a downloadable text file. This package is an interface to
    AWK for extracting data from the EBD based on taxonomic, spatial, or
    temporal filters, to produce a manageable file size that can be
    imported into R.
Computes the Area Under the Kendall (AUK) estimator for 
    multivariate independence. The AUK estimator is based on the 
    survival copula and quantifies the deviation from the null 
    hypothesis of independence. The methodology implemented in this 
    package is based on the work of 'Afendras', 'Markatou', and 'Papantonis' 
    (2025) <doi:10.1016/j.jmva.2025.105589>.
Efficient algorithms
 <https://jmlr.org/papers/v24/21-0751.html>
 for computing Area Under Minimum,
 directional derivatives, and
 line search optimization of a linear model,
 with objective defined as either
 max Area Under the Curve or
 min Area Under Minimum.
Uses locality sensitive hashing and creates a neighbourhood graph for a data set and calculates the adjusted rank index value for the same. It uses Gaussian random planes to decide the nature of a given point. Datar, Mayur, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni(2004) <doi:10.1145/997817.997857>.
Estimate the AUC using a variety of methods as follows: 
             (1) frequentist nonparametric methods based on the Mann-Whitney statistic or kernel methods. 
             (2) frequentist parametric methods using the likelihood ratio test based on higher-order 
             asymptotic results, the signed log-likelihood ratio test, the Wald test, 
             or the approximate ''t'' solution to the Behrens-Fisher problem. 
             (3) Bayesian parametric MCMC methods.
A scaffolding and deployment toolkit for building stateless web
    applications in R on top of the 'plumber2' web framework
    (<https://plumber2.posit.co/>). The UI is authored with 'bslib'
    and compiled to a static HTML asset at build time, while 'plumber2' serves
    the assets and exposes JSON API routes. Provides functions to scaffold app
    skeletons, run them locally, and generate Dockerfiles and images suitable
    for 'ShinyProxy' or plain Docker.
Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-land-observatory/ecosystem-surveillance-and-environmental-monitoring/>).
Provides access to biographical and political data about Australian 
    federal politicians who served between 1901 and 2021. This enhances how 
    reproducible research is that uses this data.
Performs approximate unconditional and permutation testing for
    2x2 contingency tables. Motivated by testing for disease association with rare
    genetic variants in case-control studies. When variants are extremely rare,
    these tests give better control of Type I error than standard tests.
Uses Auth0 API (see <https://auth0.com> for more 
    information) to use a simple authentication system. It provides 
    tools to log in and out a shiny application using social networks or a list
    of e-mails.
Manage author metadata and generate manuscript front matter.
    It produces title pages, acknowledgements, conflicts of interest, and
    contribution sections for large author lists, with helpers for validating
    and reading common spreadsheet formats.
Utilities to parse authors fields from DESCRIPTION files and
    general purpose functions to deduplicate names in database, beyond the
    specific case of R package authors.
PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.
Calculations of the most common metrics of automated advertisement and plotting of them with trend and forecast. Calculations and description of metrics is taken from different RTB platforms support documentation. Plotting and forecasting is based on packages 'forecast', described in Rob J Hyndman and George Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/> and Rob J Hyndman et al "Documentation for 'forecast'" (2003) <https://pkg.robjhyndman.com/forecast/>, and 'ggplot2', described in Hadley Wickham et al "Documentation for 'ggplot2'" (2015) <https://ggplot2.tidyverse.org/>, and Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen (2015) "ggplot2: Elegant Graphics for Data Analysis" <https://ggplot2-book.org/>.
Provides a systematic framework for neural network–based model selection and forecasting using single hidden layer feed-forward networks. It evaluates all possible combinations of predictor variables and hidden layer configurations, selecting the optimal model based on predictive accuracy criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Predictors are automatically standardized, and model performance is assessed using out-of-sample validation. The package is designed for empirical modelling and forecasting in economics, agriculture, trade, climate, and related applied research domains where nonlinear relationships and robust predictive performance are of primary interest.
A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
Wraps 'dplyr' verbs (mutate, summarise, filter) to automatically
    capture variable metadata (type, source columns, categories, and source
    code), producing a codebook and eligibility tracking table with zero
    manual documentation. Works with both 'sparklyr' (tbl_spark) and local
    data frames. Adds big-data optimizations (caching, assume-unique
    counting, checkpointing) and a standardized report module with an
    eligibility flowchart, editable codebook export (HTML, DOCX, XLSX), and
    cross-sectional or longitudinal variable inspection. The eligibility
    flowchart follows the CONSORT statement (Schulz, Altman and Moher (2010)
    <doi:10.1136/bmj.c332>) and the reporting of observational cohort studies
    follows the STROBE recommendations (von Elm and others (2007)
    <doi:10.1371/journal.pmed.0040296>).
Automatically calculates cognostic groups for plot objects
    and list column plot objects.  Results are returned in a nested data
    frame.
Contains functions to implement automated covariate selection using methods described in the
             high-dimensional propensity score (HDPS) algorithm by Schneeweiss et.al. Covariate adjustment in real-world-observational-data (RWD) is important for
             for estimating adjusted outcomes and this can be done by using methods such as, but not limited to, propensity score 
             matching, propensity score weighting and regression analysis. While these methods strive to statistically adjust for 
             confounding, the major challenge is in selecting the potential covariates that can bias the outcomes comparison estimates 
             in observational RWD (Real-World-Data). This is where the utility of automated covariate selection comes in. 
             The functions in this package help to implement the three major steps of automated covariate selection as described by
             Schneeweiss et. al elsewhere. These three functions, in order of the steps required to execute automated covariate 
             selection are, get_candidate_covariates(), get_recurrence_covariates() and get_prioritised_covariates(). 
             In addition to these functions, a sample real-world-data from publicly available de-identified medical claims data is 
             also available for running examples and also for further exploration. The original article where the algorithm is described 
             by Schneeweiss et.al. (2009) <doi:10.1097/EDE.0b013e3181a663cc> .
Automatic normalisation of a data frame to third normal form, with
  the intention of easing the process of data cleaning. (Usage to design your
  actual database for you is not advised.)
  Originally inspired by the 'AutoNormalize' library for 'Python' by 'Alteryx'
  (<https://github.com/alteryx/autonormalize>), with various changes and
  improvements. Automatic discovery of functional or approximate dependencies,
  normalisation based on those, and plotting of the resulting "database" via
  'Graphviz', with options to exclude some attributes at discovery time, or
  remove discovered dependencies at normalisation time.
An interface to the 'AutoDesk' Platform Services ('APS') 'API' including
    the Authentication 'API' for obtaining 'OAuth2' tokens with expiry tracking, Data
    Management 'API' for managing buckets and objects across the platform's cloud
    services, Design Automation 'API' for running automated tasks on design files in
    the cloud, Model Derivative 'API' for translating design files into 'SVF', 'SVF2',
    'OBJ', and 'STL' formats and extracting model data, Reality Capture 'API' for
    generating 3D models from photogrammetry image sets, and Viewer for rendering 2D
    and 3D models in 'Shiny' applications.
A stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.
Automated data quality auditing using unsupervised machine learning.
    Provides AI-driven anomaly detection for data quality assessment, primarily
    designed for Electronic Health Records (EHR) data, with benchmarking
    capabilities for validation and publication. Methods based on: Liu et al.
    (2008) <doi:10.1109/ICDM.2008.17>, Breunig et al. (2000)
    <doi:10.1145/342009.335388>.
Automatic fixed rank kriging for (irregularly located)
    spatial data using a class of basis functions with multi-resolution features
    and ordered in terms of their resolutions. The model parameters are estimated
    by maximum likelihood (ML) and the number of basis functions is determined
    by Akaike's information criterion (AIC). For spatial data with either one
    realization or independent replicates, the ML estimates and AIC are efficiently
    computed using their closed-form expressions when no missing value occurs. Details 
    regarding the basis function construction, parameter estimation, and AIC calculation  
    can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For
    data with missing values, the ML estimates are obtained using the expectation-
    maximization algorithm. Apart from the number of basis functions, there are
    no other tuning parameters, making the method fully automatic. Users can also
    include a stationary structure in the spatial covariance, which utilizes
    'LatticeKrig' package.
This wrapper package for 'mgcv' makes it easier to create high-performing Generalized Additive Models (GAMs). With its central function autogam(), by entering just a dataset and the name of the outcome column as inputs, 'AutoGAM' tries to automate the procedure of configuring a highly accurate GAM which performs at reasonably high speed, even for large datasets.
Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.
Visual exploration and presentation of networks should not be difficult.
   This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults.
   It includes 'ggplot2'-based plot methods for many popular network package classes.
   It also includes some novel layout algorithms, and options for straightforward, consistent themes.
A customisable set of tools for assessing and grading 
    R or R-markdown scripts from students. It allows for checking correctness 
    of code output, runtime statistics and static code analysis. The latter 
    feature is made possible by representing R expressions using a tree
    structure.
Analysis of task-related functional magnetic resonance imaging (fMRI) activity at the level of individual participants is commonly based on general linear modelling (GLM) that allows us to estimate to what extent the blood oxygenation level dependent (BOLD) signal can be explained by task response predictors specified in the GLM model. The predictors are constructed by convolving the hypothesised timecourse of neural activity with an assumed hemodynamic response function (HRF). To get valid and precise estimates of task response, it is important to construct a model of neural activity that best matches actual neuronal activity. The construction of models is most often driven by predefined assumptions on the components of brain activity and their duration based on the task design and specific aims of the study. However, our assumptions about the onset and duration of component processes might be wrong and can also differ across brain regions. This can result in inappropriate or suboptimal models, bad fitting of the model to the actual data and invalid estimations of brain activity. Here we present an approach in which theoretically driven models of task response are used to define constraints based on which the final model is derived computationally using the actual data. Specifically, we developed 'autohrf' — a package for the 'R' programming language that allows for data-driven estimation of HRF models. The package uses genetic algorithms to efficiently search for models that fit the underlying data well. The package uses automated parameter search to find the onset and duration of task predictors which result in the highest fitness of the resulting GLM based on the fMRI signal under predefined restrictions. We evaluate the usefulness of the 'autohrf' package on publicly available datasets of task-related fMRI activity. Our results suggest that by using 'autohrf' users can find better task related brain activity models in a quick and efficient manner.
Functions for displaying multiple images  or scatterplots with a color 
    scale, i.e., heat maps, possibly with projected coordinates.  The
    package relies on the base graphics system, so graphics are
    rendered rapidly.
A toolbox to read all R files inside a package and
    automatically generate @importFrom 'roxygen2' tags in the right place. 
    Includes a 'shiny' application to review the changes before applying them.
R Interface to 'AutoKeras' <https://autokeras.com/>.
    'AutoKeras' is an open source software library for Automated Machine
    Learning (AutoML). The ultimate goal of AutoML is to provide easily
    accessible deep learning tools to domain experts with limited data science
    or machine learning background. 'AutoKeras' provides functions to
    automatically search for architecture and hyperparameters of deep
    learning models.
Packages required for the search path may be located in the CRAN repository, the system library, or a local directory. We automate determining the disposition of each required package, retrieving it, and loading it as needed.
Parse R code in a given directory for R packages and attempt to install them from CRAN or GitHub. Optionally use a dependencies file for tighter control over which package versions to install.
An automatic interpolation is done by automatically estimating the variogram and then calling gstat. An overview is given by Hiemstra et al (2008) <doi:10.1016/j.cageo.2008.10.011>.
The goal of 'automatedRecLin' is to perform record linkage (also known as entity resolution) in unsupervised or supervised settings. It compares pairs of records from two datasets using selected comparison functions to estimate the probability or density ratio between matched and non-matched records. Based on these estimates, it predicts a set of matches that maximizes entropy. For details see: Lee et al. (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022001/article/00007-eng.htm>, Vo et al. (2023) <https://ideas.repec.org/a/eee/csdana/v179y2023ics0167947322002365.html>, Sugiyama et al. (2008) <doi:10.1007/s10463-008-0197-x>.
Automatically selects and runs the most appropriate statistical test for your data, returning clear, easy-to-read results. Ideal for all experience levels.
Provides R bindings to the 'Automerge' Conflict-free
    Replicated Data Type ('CRDT') library. 'Automerge' enables automatic
    merging of concurrent changes without conflicts, making it ideal for
    distributed systems, collaborative applications, and offline-first
    architectures. The approach of local-first software was proposed in
    Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019)
    <doi:10.1145/3359591.3359737>. This package supports all 'Automerge'
    data types (maps, lists, text, counters) and provides both low-level
    and high-level synchronization protocols for seamless interoperability
    with 'JavaScript' and other 'Automerge' implementations.
Intense parallel workloads can be difficult to monitor.
  Packages 'crew.cluster', 'clustermq', and 'future.batchtools'
  distribute hundreds of worker processes over multiple computers.
  If a worker process exhausts its available memory,
  it may terminate silently, leaving the underlying problem difficult
  to detect or troubleshoot.
  Using the 'autometric' package, a worker can proactively monitor
  itself in a detached background thread.
  The worker process itself runs normally,
  and the thread writes to a log every few seconds.
  If the worker terminates unexpectedly, 'autometric' can read and
  visualize the log file to reveal potential resource-related
  reasons for the crash. The 'autometric' package borrows heavily from
  the methods of packages 'ps' <doi:10.32614/CRAN.package.ps> and 'psutil'.
Fits from simple regression to highly customizable deep neural networks 
    either with gradient descent or metaheuristic, using automatic hyper parameters 
    tuning and custom cost function.
    A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.
Provides tools to summarize, analyze, and visualize results
    from Mendelian randomization studies using summarized genetic
    association data. The package includes functions for generating forest
    plots and scatter plots at the single-nucleotide polymorphism
    and Mendelian randomization method levels, and for fitting multiple
    estimators in a unified workflow, including inverse-variance weighted
    estimation, Mendelian randomization Egger regression,
    the weighted median estimator, the robust adjusted
    profile score, Mendelian randomization pleiotropy residual sum
    and outlier, Mendelian randomization with the genotype
    recoding invariance property, and a Bayesian horseshoe
    method. Related methods are described by Burgess (2013)
    <doi:10.1002/gepi.21758>, Bowden (2015) <doi:10.1093/ije/dyv080>,
    Bowden (2016) <doi:10.1002/gepi.21965>, Zhao (2020)
    <doi:10.1214/19-AOS1866>, Verbanck (2018)
    <doi:10.1038/s41588-018-0099-7>, Dudbridge (2025)
    <doi:10.1371/journal.pgen.1011967>, and Grant and Burgess (2024)
    <doi:10.1016/j.ajhg.2023.12.002>. Related open-source software
    includes 'TwoSampleMR' <https://github.com/MRCIEU/TwoSampleMR>,
    'mr.raps' <https://github.com/qingyuanzhao/mr.raps>,
    'MR-PRESSO' <https://github.com/rondolab/MR-PRESSO>, and
    'MR-Horse' <https://github.com/aj-grant/mrhorse>.
Automatically generate a changelog file (NEWS.md /
    CHANGELOG.md) from the git history using conventional commit messages
    (<https://www.conventionalcommits.org/en/v1.0.0/>).
Provides optimal combinations of input nodes and hidden neurons for fitting feedforward single-layer artificial neural networks in time series forecasting. Models are evaluated using root mean square error, mean absolute percentage error, and mean absolute error measures.
The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.
Create beautiful and interactive visualizations in a single function call. The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).
Make summary tables for descriptive statistics and select explanatory variables 
    automatically in various regression models. Support linear models, generalized linear 
    models and cox-proportional hazard models. Generate publication-ready tables summarizing 
    result of regression analysis and plots. The tables and plots can be exported in "HTML", 
    "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.
Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.
A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.
Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. 
    Including equal frequency binning, equidistant binning, 
    K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, 
    fully automatic generation of scorecards, etc. 
    This package is designed to make scorecard development easier and faster.
    References include:
    1. <http://shichen.name/posts/>. 
    2. Dong-feng Li(Peking University),Class PPT.
    3. <https://zhuanlan.zhihu.com/p/389710022>. 
    4. <https://www.zhangshengrong.com/p/281oqR9JNw/>.
For researchers to quickly and comprehensively acquire disease genes, so as to understand the mechanism of disease,
  we developed this program to acquire disease-related genes.
  The data is integrated from three public databases. The three databases are 'eDGAR', 'DrugBank'
  and 'MalaCards'. The 'eDGAR' is a comprehensive database, containing data on the relationship between disease and genes.
  'DrugBank' contains information on 13443 drugs and 5157 targets. 'MalaCards' integrates human disease information, including disease-related genes.
Static code compilation of a 'shiny' app given an R function (into 'ui.R' and 'server.R' files or into a 'shiny' app object). See examples at <https://github.com/alekrutkowski/autoshiny>.
The normal process of creating clinical study slides is that
    a statistician manually type in the numbers from outputs and a
    separate statistician to double check the typed in numbers. This
    process is time consuming, resource intensive, and error prone.
    Automatic slide generation is a solution to address these issues. It
    reduces the amount of work and the required time when creating slides,
    and reduces the risk of errors from manually typing or copying numbers
    from the output to slides. It also helps users to avoid unnecessary
    stress when creating large amounts of slide decks in a short time
    window.
Automatically do statistical exploration. Create formulas using 'tidyselect' syntax, and then determine cross-validated model accuracy and variable contributions using 'glm' and 'xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set.
Randomly splits data into testing and training sets. Then, uses stepwise selection to fit numerous multiple regression models on the training data, and tests them on the test data. Returned for each model are plots comparing model Akaike Information Criterion (AIC), Pearson correlation coefficient (r) between the predicted and actual values, Mean Absolute Error (MAE), and R-Squared among the models. Each model is ranked relative to the other models by the model evaluation metrics (i.e., AIC, r, MAE, and R-Squared) and the model with the best mean ranking among the model evaluation metrics is returned. Model evaluation metric weights for AIC, r, MAE, and R-Squared are taken in as arguments as aic_wt, r_wt, mae_wt, and r_squ_wt, respectively. They are equally weighted as default but may be adjusted relative to each other if the user prefers one or more metrics to the others, Field, A. (2013, ISBN:978-1-4462-4918-5).
Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. 
  Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
Build and train a variational autoencoder (VAE) for mixed-type
    tabular data (continuous, binary, categorical).
    Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' 
    interface, enabling reproducible VAE training for heterogeneous tabular 
    datasets.
Provides simple and intuitive functions for basic statistical analyses. 
    Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>),
    the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>),
    Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>),
    and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>).
    Functions are compatible with 'ggplot2' and 'dplyr'.
Algorithms for automatically finding appropriate thresholds
    for numerical data, with special functions for thresholding images.
    Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R
    users. See <https://imagej.net/plugins/auto-threshold> and Landini et
    al.  (2017) <DOI:10.1111/jmi.12474>.
A novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package 'AutoTransQF' based on Feng et al. (2016). Please read Feng et al. (2016) <doi:10.1002/sta4.104> for more details of the method.
Offers a set of functions to easily make predictions for univariate time series. 
             'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, 
             harmonising their outputs in tidy dataframes and using default values for each.
             The core function getBestModel() allows the user to effortlessly benchmark seven 
             algorithms along with a bagged estimator to identify which one performs the best 
             for a given time series.
Provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.
When many possible multiplier method estimates of a target population are available, a weighted sum of estimates from each back-calculated path can be achieved with this package.  Variance-minimizing weights are used and with any admissible tree-structured data.  The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.
Probability surveys often use auxiliary continuous data from administrative records, but the utility of this data is diminished when it is discretized for confidentiality. We provide a set of survey estimators to make full use of information from the discretized variables. See Williams, S.Z., Zou, J., Liu, Y., Si, Y., Galea, S. and Chen, Q. (2024), Improving Survey Inference Using Administrative Records Without Releasing Individual-Level Continuous Data. Statistics in Medicine, 43: 5803-5813. <doi:10.1002/sim.10270> for details.
Provides tools for assessing and selecting auxiliary variables using LASSO. 
    The package includes functions for variable selection and diagnostics, facilitating 
    survey calibration analysis with emphasis on robust auxiliary vector selection. For 
    more details see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x> and 
    Caughrey and Hartman (2017) <doi:10.2139/ssrn.3494436>.
Bindings to 'FFmpeg' <http://www.ffmpeg.org/> AV library for working with 
    audio and video in R. Generates high quality video from images or R graphics with 
    custom audio. Also offers high performance tools for reading raw audio, creating
    'spectrograms', and converting between countless audio / video formats. This package 
    interfaces directly to the C API and does not require any command line utilities.
Converts legacy microscopy video formats (H.264/H.265, AVI/MJPEG,
    TIFF stacks) to the modern 'AV1' codec with minimal quality loss. Typical
    use cases include compressing large TIFF stacks from confocal microscopy and
    time-lapse experiments from hundreds of gigabytes to manageable sizes,
    re-encoding MP4 files exported from 'CellProfiler', 'ImageJ'/'Fiji', and
    microscope software with approximately 2x better compression at the same
    visual quality, and converting legacy AVI (MJPEG) and H.265 recordings to a
    single patent-free format suited for long-term archival. Automatically selects
    the best available backend: GPU hardware acceleration via 'Vulkan'
    'VK_KHR_VIDEO_ENCODE_AV1' or 'VAAPI' (tested on AMD RDNA4; bundled headers,
    builds with any 'Vulkan' SDK >= 1.3.275), with automatic fallback to CPU
    encoding through 'FFmpeg' and 'SVT-AV1'. User controls quality via a single
    CRF parameter; each backend adapts automatically (CPU and Vulkan use CRF
    directly, VAAPI targets 55 percent of input bitrate). TIFF stacks use
    near-lossless CRF 5 by default, with optional proportional scaling via
    tiff_scale (multiplier or bounding box, aspect ratio always preserved).
    Small frames are automatically scaled up to meet hardware encoder minimums.
    Audio tracks are preserved automatically. Provides a simple R API for batch
    conversion of entire experiment folders.
Check if a given package name is available to use. It checks the
  name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks
  for unintended meanings by querying 'Wiktionary' and Wikipedia.
Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.
Average population attributable fractions are calculated for a set
    of risk factors (either binary or ordinal valued) for both prospective and case-
    control designs. Confidence intervals are found by Monte Carlo simulation. The
    method can be applied to either prospective or case control designs, provided an
    estimate of disease prevalence is provided. In addition to an exact calculation
    of AF, an approximate calculation, based on randomly sampling permutations has
    been implemented to ensure the calculation is computationally tractable when the
    number of risk factors is large.
Provides access to the species checklist published in
    'List of the Birds of Peru' by Plenge, M. A. and Angulo, F.
    (version 23-03-2026) <https://sites.google.com/site/boletinunop/checklist>.
    The package exposes the current Peru bird checklist as an R dataset and
    includes tools for species lookup, taxonomic reconciliation, and fuzzy
    matching of scientific names. These features help streamline taxonomic 
    validation for researchers and conservationists.
We provide a stage-wise selection method using genetic algorithm which can perform fast interaction selection in high-dimensional linear regression models with two-way interaction effects under strong, weak, or no heredity condition. Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.
Easy-to-use tools for performing complex queries on 'avidaDB', a
  semantic database that stores genomic and transcriptomic data of
  self-replicating computer programs (known as digital organisms) that mutate
  and evolve within a user-defined computational environment.
Provides easy access to the 'AviList' Global Avian Checklist, 
    the first unified global bird taxonomy that harmonizes previous 
    differences between International Ornithological Committee ('IOC'), 
    'Clements', and 'BirdLife' checklists. This package contains the 
    complete 'AviList' dataset as R data objects ready for ornithological 
    research and analysis. For more details see 'AviList' Core Team (2025) 
    <doi:10.2173/avilist.v2025>.
Tools to compute the center of gravity and moment of inertia tensor of any 
    flying bird. The tools function by modeling a bird as a composite structure 
    of simple geometric objects. This requires detailed morphological 
    measurements of bird specimens although those obtained for the associated 
    paper have been included in the package for use. Refer to the vignettes and 
    supplementary material for detailed information on the package function.
Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).
Anytime-valid inference for linear models, namely, sequential t-tests, sequential F-tests, and confidence sequences with time-uniform Type-I error and coverage guarantees. This allows hypotheses to be continuously tested without sacrificing false positive guarantees. It is based on the methods documented in Lindon et al. (2022) <doi:10.48550/arXiv.2210.08589>.
Provides a weekly summary of Hass Avocado sales for the 
    contiguous US from January 2017 through December 20204. 
    See the package website for more information, documentation,
    and examples. Data source: Haas Avocado Board 
    <https://hassavocadoboard.com/category-data/>.
Grafts the extinct bird species from the 'Avotrex' database (Sayol et al., in review) on to the 'BirdTree' phylogenies <https://birdtree.org>, using a set of different commands.
Anytime-valid sequential estimation of the p-value of a test calibrated by Monte-Carlo simulation, as described in Stoepker & Castro (2024) <doi:10.48550/arXiv.2409.18908>.
Data processing and generating stratigraphic sections 
    for volcanic deposits and tephrastratigraphy. Package was developed for
    studies on Alaska volcanoes ("av") where stratigraphic ("strat") figures 
    are needed for interpreting eruptive histories, but the methods are applicable
    to any sediment stratigraphy project. Plotting styles inspired by "SedLog"
    (Zervas et al. 2009) <doi:10.1016/j.cageo.2009.02.009> but with 
    more customizable outputs and flexible data input based on best practice
    recommendations for the tephra community (Wallace et al. 2022) 
    <doi:10.1038/s41597-022-01515-y>.
Query the four endpoints of the 'Air and Water Database (AWDB) REST
    API' maintained by the National Water and Climate Center (NWCC) at the 
    United States Department of Agriculture (USDA). Endpoints include data, 
    forecast, reference-data, and metadata. The package is extremely light 
    weight, with 'Rust' via 'extendr' doing most of the heavy lifting to 
    deserialize and flatten deeply nested 'JSON' responses. The AWDB can be 
    found at <https://wcc.sc.egov.usda.gov/awdbRestApi/swagger-ui/index.html>.
Which day a week starts depends heavily on the either the local or
  professional context. This package is designed to be a lightweight solution
  to easily switching between week-based date definitions.
Self-organizing maps (also known as SOM, see Kohonen (2001) <doi:10.1007/978-3-642-56927-2>) are a method for dimensionality reduction and clustering of continuous data. This package introduces interactive (html) graphics for easier analysis of SOM results. It also features an interactive interface, for push-button training and visualization of SOM on numeric, categorical or mixed data, as well as tools to evaluate the quality of SOM.
Collect  your data on digital marketing campaigns from 'Awin' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Make the compiled Java modules of the Amazon Web Services ('AWS') 'SDK' available to be used in downstream R packages interacting with 'AWS'. See <https://aws.amazon.com/sdk-for-java> for more information on the 'AWS' 'SDK' for Java.
Fetching data from Amazon 'Kinesis' Streams using the Java-based 'MultiLangDaemon' interacting with Amazon Web Services ('AWS') for easy stream processing from R. For more information on 'Kinesis', see <https://aws.amazon.com/kinesis>.
We provide a collection of R-functions implementing
    adaptive smoothing procedures in 1D, 2D and 3D. This includes the
    Propagation-Separation Approach to adaptive smoothing,
   the Intersecting Confidence Intervals (ICI), variational approaches and a non-local means filter.
   The package is described in detail in Polzehl J, Papafitsoros K, Tabelow K (2020). 
   Patch-Wise Adaptive Weights Smoothing in R. Journal of Statistical Software, 95(6), 1-27. 
   <doi:10.18637/jss.v095.i06>,
    Usage of the package in MR imaging is illustrated in Polzehl and Tabelow (2023),
   Magnetic Resonance Brain Imaging, 2nd Ed. Appendix A, Springer, Use R! Series.
   <doi:10.1007/978-3-031-38949-8>.
Providing the functions for communicating with Amazon Web Services(AWS)
    Elastic Compute Cloud(EC2) and Elastic Container Service(ECS).
    The functions will have the prefix 'ecs_' or 'ec2_' depending on the class 
    of the API. The request will be sent via the REST API and the parameters are
    given by the function argument. The credentials can be set via 'aws_set_credentials'.
    The EC2 documentation can be found at <https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Welcome.html>
    and ECS can be found at <https://docs.aws.amazon.com/AmazonECS/latest/APIReference/Welcome.html>.
A simple client for the Amazon Web Services ('AWS') Identity
    and Access Management ('IAM') 'API' <https://aws.amazon.com/iam/>.
Client package for the 'AWS Key Management Service' <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.
A simple client package for the Amazon Web Services ('AWS') Lambda 
    API <https://aws.amazon.com/lambda/>.
A client for AWS Polly <http://aws.amazon.com/documentation/polly>, a speech synthesis service.
A simple client package for the Amazon Web Services ('AWS') Simple
    Storage Service ('S3') 'REST' 'API' <https://aws.amazon.com/s3/>.
Generates version 2 and version 4 request signatures for Amazon Web Services ('AWS') <https://aws.amazon.com/> Application Programming Interfaces ('APIs') and provides a mechanism for retrieving credentials from environment variables, 'AWS' credentials files, and 'EC2' instance metadata. For use on 'EC2' instances, users will need to install the suggested package 'aws.ec2metadata' <https://cran.r-project.org/package=aws.ec2metadata>.
Client for 'AWS Transcribe' <https://aws.amazon.com/documentation/transcribe>, a cloud transcription service that can convert an audio media file in English and other languages into a text transcript.
A client for 'AWS Translate' <https://aws.amazon.com/documentation/translate>, a machine translation service that will convert a text input in one language into a text output in another language.
Exploration of Weather Research & Forecasting ('WRF') Model data
    of Servicio Meteorologico Nacional (SMN) from Amazon Web Services
    (<https://registry.opendata.aws/smn-ar-wrf-dataset/>) cloud. The package
    provides the possibility of data downloading, processing and correction
    methods. It also has map management and series exploration of available
    meteorological variables of 'WRF' forecast.
Defines the method extract and provides 'openMP' support as needed in several packages.
Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly.
Description: Computes maximum likelihood estimates of general, zero-inflated, and zero-altered models for discrete and continuous distributions. It also performs Kolmogorov-Smirnov (KS) tests and likelihood ratio tests for general, zero-inflated, and zero-altered data. Additionally, it obtains the inverse of the Fisher information matrix and confidence intervals for the parameters of general, zero-inflated, and zero-altered models. The package simulates random deviates from zero-inflated or hurdle models to obtain maximum likelihood estimates. Based on the work of Aldirawi et al. (2022) <doi:10.1007/s42519-021-00230-y> and Dousti Mousavi et al. (2023) <doi:10.1080/00949655.2023.2207020>.
It extends the functionality of 'logger' package. Additional
    logging metadata can be configured to be collected. Logging messages are
    displayed on console and optionally they are sent to 'Azure Log Analytics'
    workspace in real-time.
Implements a credential chain for 'Azure OAuth 2.0' authentication 
      based on the package 'httr2''s 'OAuth' framework. Sequentially attempts authentication 
      methods until one succeeds. During development allows interactive 
      browser-based flows ('Device Code' and 'Auth Code' flows) and non-interactive 
      flow ('Client Secret') in batch mode.
Imports Azure Application Insights for web pages into Shiny apps
    via Microsoft's JavaScript snippet. 
    Allows app developers to submit page tracking and submit events.
Provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's 'Azure' cloud <https://azure.microsoft.com/en-us>. Use this package to obtain 'OAuth' 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the 'rappdirs' package. The interface is based on the 'OAuth' framework in the 'httr' package, but customised and streamlined for Azure. Part of the 'AzureR' family of packages.
An interface to Azure Cognitive Services <https://learn.microsoft.com/en-us/azure/cognitive-services/>. Both an 'Azure Resource Manager' interface, for deploying Cognitive Services resources, and a client framework are supplied. While 'AzureCognitive' can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the 'AzureR' family of packages.
An interface to container functionality in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/products/category/containers/>. Manage 'Azure Container Instance' (ACI), 'Azure Container Registry' (ACR) and 'Azure Kubernetes Service' (AKS) resources, push and pull images, and deploy services. On the client side, lightweight shells to the 'docker', 'docker-compose', 'kubectl' and 'helm' commandline tools are provided. Part of the 'AzureR' family of packages.
An interface to 'Azure CosmosDB': <https://azure.microsoft.com/en-us/services/cosmos-db/>. On the admin side, 'AzureCosmosR' provides functionality to create and manage 'Cosmos DB' instances in Microsoft's 'Azure' cloud. On the client side, it provides an interface to the 'Cosmos DB' SQL API, letting the user store and query documents and attachments in 'Cosmos DB'. Part of the 'AzureR' family of packages.
A simple interface to the 'Microsoft Graph' API <https://learn.microsoft.com/en-us/graph/overview>. 'Graph' is a comprehensive framework for accessing data in various online Microsoft services. This package was originally intended to provide an R interface only to the 'Azure Active Directory' part, with a view to supporting interoperability of R and 'Azure': users, groups, registered apps and service principals. However it has since been expanded into a more general tool for interacting with Graph. Part of the 'AzureR' family of packages.
Manage keys, certificates, secrets, and storage accounts in Microsoft's 'Key Vault' service: <https://azure.microsoft.com/products/key-vault/>. Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the 'AzureAuth' package to enable authentication with a certificate, and with the 'openssl' package for importing and exporting cryptographic objects. Part of the 'AzureR' family of packages.
An interface to 'Azure Data Explorer', also known as 'Kusto', a fast, distributed data exploration service from Microsoft: <https://azure.microsoft.com/en-us/products/data-explorer/>. Includes 'DBI' and 'dplyr' interfaces, with the latter modelled after the 'dbplyr' package, whereby queries are translated from R into the native 'KQL' query language and executed lazily. On the admin side, the package extends the object framework provided by 'AzureRMR' to support creation and deletion of databases, and management of database principals. Part of the 'AzureR' family of packages.
Provides a wrapper for the Microsoft 'Azure Maps' REST APIs <https://learn.microsoft.com/en-us/rest/api/maps/route?view=rest-maps-2025-01-01>,
    enabling users to access mapping and geospatial services directly from R.
    This package simplifies authenticating, building, and sending requests for
    services like route directions. It handles conversions between R objects
    (such as 'sf' objects) and the GeoJSON+JSON format required by the API,
     making it easier to integrate 'Azure Maps' into R-based data analysis workflows.
Interface to the 'Azure Machine Learning' Software Development Kit
    ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and
    manage machine learning models on the 'Azure Machine Learning' service. To
    learn more about 'Azure Machine Learning' visit the website:
    <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
An interface to 'Azure Queue Storage'. This is a cloud service for storing large numbers of messages, for example from automated sensors, that can be accessed remotely via authenticated calls using HTTP or HTTPS. Queue storage is often used to create a backlog of work to process asynchronously. Part of the 'AzureR' family of packages.
A lightweight but powerful R interface to the 'Azure Resource Manager' REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting 'Azure' resource groups, resources and templates. While 'AzureRMR' can be used to manage any 'Azure' service, it can also be extended by other packages to provide extra functionality for specific services. Part of the 'AzureR' family of packages.
Manage storage in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/products/category/storage/>. On the admin side, 'AzureStor' includes features to create, modify and delete storage accounts. On the client side, it includes an interface to blob storage, file storage, and 'Azure Data Lake Storage Gen2': upload and download files and blobs; list containers and files/blobs; create containers; and so on. Authenticated access to storage is supported, via either a shared access key or a shared access signature (SAS). Part of the 'AzureR' family of packages.
An interface to the table storage service in 'Azure': <https://azure.microsoft.com/en-us/services/storage/tables/>. Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a 'CosmosDB' database with the table service API. Part of the 'AzureR' family of packages.
An interface to 'Azure Computer Vision' <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and 'Azure Custom Vision' <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages.
Functionality for working with virtual machines (VMs) in Microsoft's 'Azure' cloud: <https://azure.microsoft.com/en-us/services/virtual-machines/>. Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the 'AzureR' family of packages.
A simple interface to the instance metadata for a virtual machine running in Microsoft's 'Azure' cloud. This provides information about the VM's configuration, such as its processors, memory, networking, storage, and so on. Part of the 'AzureR' family of packages.
Fast, dependency free, and vectorized base32 encoding and
    decoding. 'b32' supports the Crockford, Z, RFC 4648 lower, hex, and
    lower hex alphabets.
Provides a fast, lightweight, and vectorized base 64 engine
    to encode and decode character and raw vectors as well as files stored
    on disk. Common base 64 alphabets are supported out of the box
    including the standard, URL-safe, bcrypt, crypt, 'BinHex', and
    IMAP-modified UTF-7 alphabets. Custom engines can be created to
    support unique base 64 encoding and decoding needs.
Approximate Bayesian inference and Monte Carlo validation for
    small-N repeated-measures designs with two time points and two raters.
    The package is intended for applications in which sample size is limited
    and the observed outcome may be affected by rater-specific bias.
    User-supplied data are standardised into a common long-format structure.
    Pre-post effects are analysed using difference scores in a linear model
    with a rater indicator as covariate. Posterior summaries for the
    regression coefficients are obtained from a large-sample normal
    approximation centred at the least-squares estimate with plug-in
    covariance under a flat improper prior. Evidence for a non-zero
    pre-post effect, adjusted for rater differences, is summarised using a
    BIC-based approximation to the Bayes factor for comparison between
    models with and without the pre-post effect. Monte Carlo validation uses
    design quantities estimated from the observed data, including sample
    size, mean pre-post change, and second-rater additive discrepancy, and
    summarises inferential performance in terms of bias, root mean squared
    error, credible interval coverage, posterior tail probabilities, and
    mean Bayes factor values. For background on the BIC approximation and
    Bayes factors, see Schwarz (1978)
    <doi:10.1214/aos/1176344136> and Kass and Raftery (1995)
    <doi:10.1080/01621459.1995.10476572>.
Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts.
Genomic analysis of model organisms frequently requires the
    use of databases based on human data or making comparisons to
    patient-derived resources. This requires harmonization of gene names
    into the same gene space. The 'babelgene' R package converts between
    human and non-human gene orthologs/homologs. The package integrates
    orthology assertion predictions sourced from multiple databases as
    compiled by the HGNC Comparison of Orthology Predictions (HCOP)
    (Wright et al. 2005 <doi:10.1007/s00335-005-0103-2>, Eyre et al. 2007
    <doi:10.1093/bib/bbl030>, Seal et al. 2011 <doi:10.1093/nar/gkq892>).
Run other estimation and simulation software via the 'nlmixr2' (Fidler et al (2019)
  <doi:10.1002/psp4.12445>) interface including 'PKNCA', 'NONMEM' and 'Monolix'. While not required, you can
  get/install the 'lixoftConnectors' package in the 'Monolix' installation, as
  described at the following url
  <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When
  'lixoftConnectors' is available, 'Monolix' can be run directly instead of setting up
  command line usage.
Provides a unified interface to interact with
    'docker' and 'singularity' containers.  You can execute a command
    inside a container, mount a volume or copy a file.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is commonly accompanied by 'BEAUti 2', 'Tracer' and 'DensiTree'.
    'babette' provides for an alternative workflow of using 
    all these tools separately. This allows doing complex Bayesian
    phylogenetics easily and reproducibly from 'R'.
US baby names provided by the SSA. This package contains all
    names used for at least 5 children of either sex.
Israeli baby names provided by Israel's Central Bureau of Statistics
    (CBS/LAMAS). Contains names used for at least 5 children in a given year,
    covering sectors "Jewish", "Muslim", "Christian-Arab", and "Druze" from
    1949-2024. Legacy 1948 data and archived "Other" sector data are provided
    as separate datasets. Primary data source: CBS Release 391/2025
    <https://www.cbs.gov.il/he/mediarelease/DocLib/2025/391/11_25_391t1.xlsx>.
'BabyTime' is an application for tracking infant and toddler
    care activities like sleeping, eating, etc. This package will take the
    outputted .zip files and parse it into a usable list object with
    cleaned data. It handles malformed and incomplete data gracefully and
    is designed to parse one directory at a time.
The BACCO bundle of packages is replaced by the BACCO
 package, which provides a vignette that illustrates the constituent
 packages (emulator, approximator, calibrator) in use.
Provides tools to facilitate the access and processing of data 
    from the Central Bank of Brazil API. The package allows users 
    to retrieve economic and financial data, transforming them into usable 
    tabular formats for further analysis. The data is obtained from the 
    Central Bank of Brazil API: <https://api.bcb.gov.br/dados/serie/bcdata.sgs.{series_code}/dados?formato=json&dataInicial={start_date}&dataFinal={end_date}>.
Provides functions to query, retrieve, and tidy economic and
    financial data from Brazilian Central Bank web services for use in R
    analyses and workflows. Active institutions information, balance sheets and normative acts.
Provides the design of multi-group phase
    II clinical trials with binary outcomes using the hierarchical Bayesian
    classification and information sharing (BaCIS) model. Subgroups are classified
    into two clusters on the basis of their outcomes mimicking the hypothesis
    testing framework. Subsequently, information sharing takes place within
    subgroups in the same cluster, rather than across all subgroups. This method can
    be applied to the design and analysis of multi-group clinical trials with binary
    outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.
An implementation of methods for extracting a sparse unweighted network (i.e. a backbone)
    from an unweighted network (e.g., Hamann et al., 2016 <doi:10.1007/s13278-016-0332-2>), a weighted network
    (e.g., Serrano et al., 2009 <doi:10.1073/pnas.0808904106>), or a weighted projection (e.g., Neal et al., 2021
    <doi:10.1038/s41598-021-03238-3>).
Provides a backward-pipe operator for 'magrittr' (%<%) or 
    'pipeR' (%<<%) that allows for a performing operations from right-to-left. 
    This allows writing more legible code where right-to-left ordering is 
    natural. This is common with hierarchies and nested structures such as 
    trees, directories or markup languages (e.g. HTML and XML). 
    The package also includes a R-Studio add-in that can be bound to a keyboard 
    shortcut.
Functions introduced or changed since R v3.0.0 are re-implemented in this
    package. The backports are conditionally exported in order to let R resolve
    the function name to either the implemented backport, or the respective base
    version, if available. Package developers can make use of new functions or
    arguments by selectively importing specific backports to
    support older installations.
The backtest package provides facilities for exploring
        portfolio-based conjectures about financial instruments
        (stocks, bonds, swaps, options, et cetera).
Creates an interactive graphics 
             interface to visualize backtest results of different financial 
             instruments, such as equities, futures, and credit default swaps.
             The package does not run backtests on the given data set but 
             displays a graphical explanation of the backtest results. Users can
             look at backtest graphics for different instruments, investment 
             strategies, and portfolios. Summary statistics of different 
             portfolio holdings are shown in the left panel, and interactive 
             plots of profit and loss (P&L), net market value (NMV) and 
             gross market value (GMV) are displayed in the right panel.
A novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework 
             Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) <doi:10.48550/arXiv.2408.14625>.
Decomposition for differences-in-differences with variation in
    treatment timing from Goodman-Bacon (2018) <doi:10.3386/w25018>.
Models discrete sequential data using Bayesian Context Trees.
    Context trees, also known as Variable Length Markov
    Chains (VLMCs), are parsimonious Markov models where the order of
    dependence can vary with the observed past. Provides a generic 'R6'
    class structure that exposes the full tree
    for building custom algorithms, exact Bayesian inference via a
    bottom-up recursive algorithm (closed-form marginal likelihood,
    Maximum A Posteriori (MAP) tree, exact posterior probabilities, and
    exact sampling from the posterior), a frequentist estimator via the
    context algorithm with likelihood-ratio pruning,
    simulation utilities, and a Metropolis-Hastings sampler.
    See Paulichen and Freguglia (2026) <doi:10.48550/arXiv.2603.25806>.
The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012)
 can be used to estimate the causal effect of a continuous exposure on a continuous outcome.
 This package provides an approximate sensitivity analysis of BAC with regards to the
 hyperparameter omega. BACprior also provides functions to guide the user in their choice
 of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).
Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.
Bindings to 'badgen' <https://www.npmjs.com/package/badgen> to
    generate beautiful 'svg' badges in R without internet access. Images can
    be converted to 'png' using the 'rsvg' package as shown in examples.
Query information and generate badge for using in README and GitHub Pages.
Implements Bayesian model averaging for dynamic panels with weakly
    exogenous regressors as described in the paper by
    Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>).
    The package provides functions to estimate dynamic panel data models
    and analyze the results of the estimation.
Download and read US Census Bureau data relationship files. Provides 
    support for cleaning and using block assignment files since 2010, as described in 
    <https://www.census.gov/geographies/reference-files/time-series/geo/block-assignment-files.html>. 
    Also includes support for working with block equivalency files, used for years 
    outside of decennial census years.
Waffle plots are rectangular pie charts that represent a quantity or abundances using
    colored squares or other symbol. This makes them better at transmitting information as the
    discrete number of squares is easier to read than the circular area of pie charts.
    While the original waffle charts were rectangular with 10 rows and columns, with a single square
    representing 1%, they are nowadays popular in various infographics to visualize any
    proportional ratios.
Fast Bayesian estimation and forecasting of age-specific
    rates, probabilities, and means, based on 'Template Model Builder'.
Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.
Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These 
  bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being 
    computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and 
  Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>.
Running and comparing meta-analyses of data with hierarchical 
    Bayesian models in Stan, including convenience functions for formatting
    data, plotting and pooling measures specific to meta-analysis. This implements many models
    from Meager (2019) <doi:10.1257/app.20170299>.
The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.
Tree- and rule-based models can be bagged
    (<doi:10.1007/BF00058655>) using this package and their predictions
    equations are stored in an efficient format to reduce the model
    objects size and speed.
Implementation of the Bag-and-Whisker Plot for bivariate data. Provides a single user-facing function bag_whisker() that wraps the computation and plotting helpers in this package. For more details, please refer to the paper "The Bag-and-Whisker Plot: A New Bagplot for Bivariate Data" by Qin, Gang, Tong and Cui (2025) <doi:10.48550/arXiv.2512.06314>.
The Philippines frequently experiences tropical cyclones (called
    'bagyo' in the Filipino language) because of its geographical position.
    These cyclones typically bring heavy rainfall, leading to widespread
    flooding, as well as strong winds that cause significant damage to human
    life, crops, and property. Data on cyclones are collected and curated by the
    Philippine Atmospheric, Geophysical, and Astronomical Services
    Administration or 'PAGASA' and made available through its website
    <https://bagong.pagasa.dost.gov.ph/tropical-cyclone/publications/annual-report>.
    This package contains Philippine tropical cyclones data in a
    machine-readable format. It is hoped that this data package provides an
    interesting and unique dataset for data exploration and visualisation.
A method to filter correlation and covariance matrices by averaging
     bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet,
     Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and
     Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning
     (2020) <arXiv:2005.08703>.
Computes approximated adjusted fractional Bayes factors for
    equality, inequality, and about equality constrained hypotheses.
    For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu,
    (2019) <doi:10.1037/met0000201>. For applications in structural equation
    modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, &
    Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>. For the statistical
    underpinnings, see Gu, Mulder, and Hoijtink (2018) <doi:10.1111/bmsp.12110>;
    Hoijtink, Gu, & Mulder, J. (2019) <doi:10.1111/bmsp.12145>; Hoijtink, Gu, 
    Mulder, & Rosseel, (2019) <doi:10.31234/osf.io/q6h5w>.
In ancient Chinese mythology, Bai Ze is a divine creature that knows the needs of everything.
    'baizer' provides data processing functions frequently used by the author.
    Hope this package also knows what you want!
Data about the bakers, challenges, and ratings 
    for "The Great British Bake Off", 
    from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.
Provides functions to specify, fit and visualize
    nested partially-latent class models (
    Wu, Deloria-Knoll, Hammitt, and Zeger (2016) <doi:10.1111/rssc.12101>; 
    Wu, Deloria-Knoll, and Zeger (2017) <doi:10.1093/biostatistics/kxw037>;
    Wu and Chen (2021) <doi:10.1002/sim.8804>) for 
    inference of population disease etiology and individual diagnosis. In the motivating
    Pneumonia Etiology Research for Child Health (PERCH) study, because both quantities
    of interest sum to one hundred percent, the PERCH scientists frequently refer to
    them as population etiology pie and individual etiology pie, hence the name of the package.
Easily launch, track, and control functions as
    background-parallel jobs. Includes robust utilities for job status,
    error handling, resource monitoring, and result collection. Designed
    for scalable workflows in interactive and automated settings (local or
    remote). Integrates with multiple backends; supports flexible
    automation pipelines and live job tracking. For more information, see
    <https://anirbanshaw24.github.io/bakerrr/>.
Several implementations of a novel Bayesian hierarchical statistical model of nucleotide
	recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.)
	for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) <doi:10.1261/rna.079451.122>).
	NR-seq is a powerful extension of RNA-seq that provides information about the kinetics
	of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking
	in standard RNA-seq data. The statistical model makes maximal use of these high-throughput
	datasets by sharing information across transcripts to significantly improve
	uncertainty quantification and increase statistical power. 'bakR' includes a maximally
	efficient implementation of this model for conservative initial investigations of datasets. 'bakR'
	also provides more highly powered implementations using the probabilistic programming language
	'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test
	adjusted statistical inference with the output of these model implementations to
	help biologists separate signal from background. Methods to automatically visualize key
	results and detect batch effects are also provided.
Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.
Select balanced and spatially balanced probability samples in multi-dimensional spaces
    with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of
    the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012)
    <doi:10.1111/j.1541-0420.2011.01699.x> and spatially correlated Poisson sampling by Grafström (2012)
    <doi:10.1016/j.jspi.2011.07.003> are included. Also the cube method (for balanced sampling) and
    the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013)
    <doi:10.1002/env.2194>.
Statistical decision in proteomics data using a hierarchical
    Bayesian model. There are two regression models for describing the 
    mean-variance trend, a gamma regression or a latent gamma mixture
    regression. The regression model is then used as an Empirical Bayes
    estimator for the prior on the variance in a peptide. Further, it assumes
    that each measurement has an uncertainty (increased variance) associated
    with it that is also inferred. Finally, it tries to estimate the posterior
    distribution (by Hamiltonian Monte Carlo) for the differences in means for
    each peptide in the data. Once the posterior is inferred, it integrates the
    tails to estimate the probability of error from which a statistical decision
    can be made.
    See Berg and Popescu for details (<doi:10.1016/j.mcpro.2023.100658>).
Hypothesis tests and sure independence screening (SIS) procedure based on ball statistics, including ball divergence <doi:10.1214/17-AOS1579>, ball covariance <doi:10.1080/01621459.2018.1543600>, and ball correlation <doi:10.1080/01621459.2018.1462709>, are developed to analyze complex data in metric spaces, e.g, shape, directional, compositional and symmetric positive definite matrix data. The ball divergence and ball covariance based distribution-free tests are implemented to detecting distribution difference and association in metric spaces <doi:10.18637/jss.v097.i06>. Furthermore, several generic non-parametric feature selection procedures based on ball correlation, BCor-SIS and all of its variants, are implemented to tackle the challenge in the context of ultra high dimensional data. A fast implementation for large-scale multiple K-sample testing with ball divergence <doi: 10.1002/gepi.22423> is supported, which is particularly helpful for genome-wide association study.
Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.
The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) <arXiv:1901.07410>. Please consult the following youtube video <https://www.youtube.com/watch?v=M9Dm1nl_zSQfor> the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data.
Provides pathwise estimation of regularized logistic propensity
    score models using covariate balancing loss functions rather than maximum
    likelihood. Regularization paths are fit via the 'adelie' elastic-net
    solver with a 'glmnet'-like interface, yielding balancing weights that target
    covariate balance for the ATE and ATT. Under lasso penalization, lambda
    bounds the maximum covariate imbalance, so the regularization path traces a
    sequence of decreasing imbalance tolerances. For details, see Sverdrup &
    Hastie (2026) <doi:10.48550/arXiv.2602.18577>.
Functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) <doi:10.1201/b17888>.
Perform mediation analysis in the presence of high-dimensional
    mediators based on the potential outcome framework. Bayesian Mediation
    Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and
    Song et al (2020) <doi:10.48550/arXiv.2009.11409>,
    relies on two Bayesian sparse linear mixed models to simultaneously analyze
    a relatively large number of mediators for a continuous exposure and outcome
    assuming a small number of mediators are truly active. This sparsity
    assumption also allows the extension of univariate mediator analysis by
    casting the identification of active mediators as a variable selection
    problem and applying Bayesian methods with continuous shrinkage priors on
    the effects.
Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) <doi:10.18637/jss.v099.i11>.
Enables a user to consume the 'BambooHR' API endpoints using R. The
  actual URL of the API will depend on your company domain, and will be handled
  by the package automatically once you setup the config file. The API documentation
  can be found here <https://documentation.bamboohr.com/docs>.
Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
  The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
  The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
  <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021)
  <doi:10.18637/jss.v100.i04>.
Species Distribution Modeling (SDM) is a practical
    methodology that aims to estimate the area of distribution of a
    species. However, most of the work has focused on estimating static
    expressions of the correlation between environmental variables. The
    outputs of correlative species distribution models can be interpreted
    as maps of the suitable environment for a species but not generally as
    maps of its actual distribution.  Soberón and Peterson (2005)
    <doi:10.17161/bi.v2i0.4> presented the BAM scheme, a heuristic
    framework that states that the occupied area of a species occurs on
    sites that have been accessible through dispersal (M) and have both
    favorable biotic (B) and abiotic conditions (A).  The 'bamm' package
    implements classes and functions to operate on each element of the BAM
    and by using a cellular automata model where the occupied area of a
    species at time t is estimated by the multiplication of three binary
    matrices: one matrix represents movements (M), another abiotic -niche-
    tolerances (A), and a third, biotic interactions (B).  The theoretical
    background of the package can be found in Soberón and Osorio-Olvera
    (2023) <doi:10.1111/jbi.14587>.
Provides functions for analyzing and visualizing complex
    macroevolutionary dynamics on phylogenetic trees. It is a companion
    package to the command line program BAMM (Bayesian Analysis of
    Macroevolutionary Mixtures) and is entirely oriented towards the analysis,
    interpretation, and visualization of evolutionary rates. Functionality
    includes visualization of rate shifts on phylogenies, estimating
    evolutionary rates through time, comparing posterior distributions of
    evolutionary rates across clades, comparing diversification models using
    Bayes factors, and more.
Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.
The network autocorrelation model (NAM) can be used for studying the degree of social influence 
    regarding an outcome variable based on one or more known networks. 
    The degree of social influence is quantified via the network autocorrelation parameters. In case of a single
    network, the Bayesian methods of Dittrich, Leenders, and Mulder
    (2017) <DOI:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019)
    <DOI:10.1177/0049124117729712> are implemented using a normal, flat, or independence  
    Jeffreys prior for the network autocorrelation. In the case of multiple 
    networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) 
    <DOI:10.1177/0081175020913899> are implemented using a multivariate normal prior for 
    the network autocorrelation parameters. Flat priors are implemented 
    for estimating the coefficients. For Bayesian testing of equality and order-constrained 
    hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) 
    <DOI:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance 
    matrix of the NAM parameters based on flat priors as input.
A light-weight object-oriented system with 'python'-like syntax which supports multiple inheritances and incorporates a 'python'-like method resolution order.
A set of functions for doing analysis of A/B split test data and web metrics in general.
Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.
Frequentist inference on adaptively generated data. The methods implemented are based on Zhan et al. (2021) <doi:10.48550/arXiv.2106.02029> and Hadad et al. (2021) <doi:10.48550/arXiv.1911.02768>. For illustration, several functions for simulating non-contextual and contextual adaptive experiments using Thompson sampling are also supplied.
Implements functions that calculate upper prediction 
  bounds on the false discovery proportion (FDP) in the list of discoveries 
  returned by competition-based setups, implementing Ebadi et al. (2022)
  <arXiv:2302.11837>. Such setups include target-decoy competition (TDC) 
  in computational mass spectrometry and the knockoff construction in linear 
  regression (note this package typically uses the terminology of TDC). Included 
  is the standardized (TDC-SB) and uniform (TDC-UB) bound on TDC's FDP, and the 
  simultaneous standardized and uniform bands. Requires 
  pre-computed Monte Carlo statistics available at 
  <https://github.com/uni-Arya/fdpbandsdata>. This data can be downloaded by
  running the command 'devtools::install_github("uni-Arya/fdpbandsdata")' in R
  and restarting R after installation. The size of this data is roughly 81Mb.
Assigns standardized diagnoses using the Banff Classification
    (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell
    mediated rejection as well as Active, Chronic active, and Chronic antibody
    mediated rejection). The main function considers a minimal dataset
    containing biopsies information in a specific format (described by a data
    dictionary), verifies its content and format (based on the data dictionary),
    assigns diagnoses, and creates a summary report. The package is developed on
    the reference guide to the Banff classification of renal allograft pathology
    Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) <doi:10.1097/TP.0000000000002366>.
    The full description of the Banff classification is available at <https://banfffoundation.org/>.
Provides functions for the Bayesian analysis of some simple 
    commonly-used models, without using Markov Chain Monte Carlo (MCMC) 
    methods such as Gibbs sampling.  The 'rust' package
    <https://cran.r-project.org/package=rust> is used to simulate a random 
    sample from the required posterior distribution, using the generalized 
    ratio-of-uniforms method.  See Wakefield, Gelfand and Smith (1991) 
    <DOI:10.1007/BF01889987> for details. At the moment three conjugate 
    hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way 
    analysis of variance (ANOVA).
Usually, it is difficult to plot choropleth maps for Bangladesh in 'R'.
    The 'bangladesh' package provides ready-to-use shapefiles for different administrative
    regions of Bangladesh (e.g., Division, District, Upazila, and Union).
    This package helps users to draw thematic maps of administrative regions
    of Bangladesh easily as it comes with the 'sf' objects for the boundaries.
    It also provides functions allowing users to efficiently get specific area
    maps and center coordinates for regions. Users can also search for
    a specific area and calculate the centroids of those areas.
A convenience package for use while drafting code.
    It facilitates making stand-out comment lines decorated with
    bands of characters.  The input text strings are converted into
    R comment lines, suitably formatted. These are then displayed in
    a console window and, if possible, automatically transferred to a
    clipboard ready for pasting into an R script.  Designed to save
    time when drafting R scripts that will need to be navigated and
    maintained by other programmers.
A client for the 'Base Adresses Nationale' ('BAN') API, which allows to (batch)
    geocode and reverse-geocode French addresses. For more information about the 'BAN' and its API, please see <https://adresse.data.gouv.fr/outils/api-doc/adresse>.
Create a hierarchical acoustic event species classifier out of  
  multiple call type detectors as described in 
  Rankin et al (2017) <doi:10.1111/mms.12381>.
Provides functions to scrape IQY calls to Bank of Mexico,
    downloading and ordering the data conveniently.
Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)<doi:10.1111/arcm.12984>). It handles event typo-chronology based timespans defined by start/end date as well as more complex user-provided vector of probabilities.
Various tools dealing with batch effects, in particular enabling the 
  removal of discrepancies between training and test sets in prediction scenarios.
  Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 
  2008) is implemented to enable integrating the quantile normalization step into 
  prediction rules. The following batch effect removal methods are implemented: 
  FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. 
  For each of these we provide  an additional function which enables a posteriori 
  ('addon') batch effect removal in independent batches ('test data'). Here, the
  (already batch effect adjusted) training data is not altered. For evaluating the
  success of batch effect adjustment several metrics are provided. Moreover, the 
  package implements a plot for the visualization of batch effects using principal 
  component analysis. The main functions of the package for batch effect adjustment 
  are ba() and baaddon() which enable batch effect removal and addon batch effect 
  removal, respectively, with one of the seven methods mentioned above. Another 
  important function here is bametric() which is a wrapper function for all implemented
  methods for evaluating the success of batch effect removal. For (addon) quantile 
  normalization and (addon) RMA normalization the functions qunormtrain(), 
  qunormaddon(), rmatrain() and rmaaddon() can be used.
Instructor-developed tools for Analytics and Quantitative Methods
    (AQM) courses at Babson College. Included are compact descriptive
    statistics for data frames and lists, expanded reporting and graphics for
    linear regressions, and formatted reports for best subsets analyses.
Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials.
Tool to find where a function has its lowest value(minimum). The
    functions can be any dimensions. Recommended use is with eps=10^-10, but can be
    run with 10^-20, although this depends on the function. Two more methods are in
    this package, simple gradient method (Gradmod) and Powell method (Powell). These
    are not recommended for use, their purpose are purely for comparison.
The function \code{barcode()}
        produces a histogram-like plot of a distribution that shows
        granularity in the data.
Tools to generate unique identifier codes and
    printable barcoded labels for the management of biological samples.
    The creation of unique ID codes and printable PDF files can be
    initiated by standard commands, user prompts, or through a GUI addin
    for R Studio. Biologically informative codes can be included for
    hierarchically structured sampling designs.
To perform species identification using DNA barcodes.
Simultaneously clusters the Periodontal diseases (PD) patients 
    and their tooth sites after taking the patient- and site-level covariates into consideration. 
    'BAREB' uses the determinantal point process (DPP) prior to induce diversity 
    among different biclusters to facilitate parsimony and interpretability. 
    Essentially, 'BAREB' is a cluster-wise linear model based on Yuliang (2020) <doi:10.1002/sim.8536>.
Bayesian Additive Regression Kernels (BARK) provides
	an implementation for non-parametric function estimation using Levy
	Random Field priors for functions that may be represented as a
	sum of additive multivariate kernels.  Kernels are located at
	every data point as in Support Vector Machines, however, coefficients 
	may be heavily shrunk to zero under the Cauchy process prior, or even, 
	set to zero.  The number of active features is controlled by priors on
	precision parameters within the kernels, permitting feature selection. For 
	more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels",
	Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, 
	and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy
	Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 
	<doi:10.1214/11-AOS889>.
Barnard's unconditional test for 2x2 contingency tables.
Provides tools to visualize ordination results in 'R' by adding covariance-based ellipses, centroids, vectors, and confidence regions to plots created with 'ggplot2'. 
    The package extends the 'vegan' framework and supports Principal Component Analysis (PCA), Redundancy Analysis (RDA), and Non-metric Multidimensional Scaling (NMDS). 
    Ellipses can represent either group dispersion (standard deviation, SD) or centroid precision (standard error, SE), following Wang et al. (2015) <doi:10.1371/journal.pone.0118537>. 
    Robust estimators of covariance are implemented, including the Minimum Covariance Determinant (MCD) method of Hubert et al. (2018) <doi:10.1002/wics.1421>. 
    This approach reduces the influence of outliers. 
    barrel is particularly useful for multivariate ecological datasets, promoting reproducible, publication-quality ordination graphics with minimal effort.
Calculate the bark beetle phenology based on raster data or
    point-related data. There are multiple models implemented for two bark
    beetle species. The models can be customized and their submodels (onset of
    infestation, beetle development, diapause initiation, mortality) can be
    combined. The following models are available in the package:
    PHENIPS-Clim (first-time release in this package),
    PHENIPS (Baier et al. 2007) <doi:10.1016/j.foreco.2007.05.020>,
    RITY (Ogris et al. 2019) <doi:10.1016/j.ecolmodel.2019.108775>,
    CHAPY (Ogris et al. 2020) <doi:10.1016/j.ecolmodel.2020.109137>,
    BSO (Jakoby et al. 2019) <doi:10.1111/gcb.14766>,
    Lange et al. (2008) <doi:10.1007/978-3-540-85081-6_32>,
    Jönsson et al. (2011) <doi:10.1007/s10584-011-0038-4>.
    The package may be expanded by models for other bark beetle species in the
    future.
Provides the 'C++' header-only library 'barry' for use in R packages. 
 'barry' is a 'C++' template library for counting sufficient statistics on binary 
 arrays and building discrete exponential-family models. It provides tools for 
 sparse arrays, user-defined count statistics, support set constraints, 
 power set generation, and includes modules for Discrete Exponential Family Models 
 (DEFMs) and network statistics. By placing these headers in this package, we 
 offer an efficient distribution system for CRAN as replication of this code in 
 the sources of other packages is avoided. This package follows the same 
 approach as the 'BH' package which provides 'Boost' headers for R packages.
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes.  For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
Contains a variety of methods to generate typical causal inference estimates using Bayesian Additive Regression Trees (BART) as the underlying regression model (Hill (2012) <doi:10.1198/jcgs.2010.08162>).
Fit Bayesian Regression Additive Trees (BART) models to
    select true confounders from a large set of potential confounders and
    to estimate average treatment effect. For more information, see Kim et
    al. (2023) <doi:10.1111/biom.13833>.
An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.
These are bartMachine's Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without bartMachine.
Investigating and visualising Bayesian Additive Regression Tree (BART) (Chipman, H. A., George, E. I., & McCulloch, R. E. 2010) <doi:10.1214/09-AOAS285> model fits.  We construct conventional plots to analyze a model’s performance and stability  as well as create new tree-based plots to analyze variable importance, interaction, and tree structure.  We employ Value Suppressing Uncertainty Palettes (VSUP) to construct heatmaps that display variable importance  and interactions jointly using colour scale to represent posterior uncertainty.  Our visualisations are designed to work with the most popular BART R packages available, namely 'BART' Rodney Sparapani and Charles Spanbauer and Robert McCulloch 2021 <doi:10.18637/jss.v097.i01>,  'dbarts'  (Vincent Dorie 2023) <https://CRAN.R-project.org/package=dbarts>,  and 'bartMachine' (Adam Kapelner and Justin Bleich 2016) <doi:10.18637/jss.v070.i04>.
Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>, this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: 'BART' <doi:10.18637/jss.v097.i01>, 'bartMachine' <doi:10.18637/jss.v070.i04>, and 'dbarts' <https://CRAN.R-project.org/package=dbarts>. For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) <doi:10.1038/s42256-019-0138-9> is also considered. The BARP model proposed by Bisbee (2019) <doi:10.1017/S0003055419000480> was implemented with reference to <https://github.com/jbisbee1/BARP> and is designed to work with modified functions based on that implementation. BARP extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods.
Intended to facilitate acoustic analysis of (animal) sound propagation experiments, which typically aim to quantify changes in signal structure when transmitted in a given habitat by broadcasting and re-recording animal sounds at increasing distances. The package offers a workflow with functions to prepare the data set for analysis as well as to calculate and visualize several degradation metrics, including blur ratio, signal-to-noise ratio, excess attenuation and envelope correlation among others (Dabelsteen et al 1993 <doi:10.1121/1.406682>).
Package for Bayesian Variable Selection and  Model Averaging 
    in linear models and generalized linear models using stochastic or
    deterministic sampling without replacement from posterior
    distributions.  Prior distributions on coefficients are
    from Zellner's g-prior or mixtures of g-priors
    corresponding to the Zellner-Siow Cauchy Priors or the
    mixture of g-priors from Liang et al (2008)
    <DOI:10.1198/016214507000001337>
    for linear models or mixtures of g-priors from  Li and Clyde
    (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models.
    Other model selection criteria include AIC, BIC and Empirical Bayes 
    estimates of g. Sampling probabilities may be updated based on the sampled
    models using sampling w/out replacement or an efficient MCMC algorithm which
    samples models using a tree structure of the model space 
    as an efficient hash table.  See  Clyde, Ghosh and Littman (2010) 
    <DOI:10.1198/jcgs.2010.09049> for  details on the sampling algorithms.
    Uniform priors over all models or beta-binomial prior distributions on
    model size are allowed, and for large p truncated priors on the model
    space may be used to enforce sampling models that are full rank.  
    The user may force variables to always be included in addition to imposing
    constraints that higher order interactions are included only if their 
    parents are included in the model.
    This material is based upon work supported by the National Science
    Foundation under Division of Mathematical Sciences grant 1106891.
    Any opinions, findings, and
    conclusions or recommendations expressed in this material are those of
    the author(s) and do not necessarily reflect the views of the
    National Science Foundation.
Provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.
We perform linear, logistic, and cox regression using the base functions lm(), 
    glm(), and coxph() in the R software and the 'survival' package. Likewise, we 
    can use ols(), lrm() and cph() from the 'rms' package for the same 
    functionality. Each of these two sets of commands has a different focus.
  In many cases, we need to use both sets of commands in the same situation, 
    e.g. we need to filter the full subset model using AIC, and we need to build 
    a visualization graph for the final model. 'base.rms' package can help you to switch 
    between the two sets of commands easily.
Compatibility wrapper to replace the orphaned package. New 
    applications should use base64 encoders from 'jsonlite' or 'openssl' or
    'base64enc'.
Tools for handling 'base64' encoding. It is more flexible than the orphaned 'base64' package.
In contrast to RFC3548, the 62nd character ("+") is replaced with
    "-", the 63rd character ("/") is replaced with "_". Furthermore, the encoder
    does not fill the string with trailing "=". The resulting encoded strings
    comply to the regular expression pattern "[A-Za-z0-9_-]" and thus are
    safe to use in URLs or for file names.
    The package also comes with a simple base32 encoder/decoder suited for
    case insensitive file systems.
Provides numerous utilities for acquiring and analyzing
    baseball data from online sources such as 'Baseball Reference' <https://www.baseball-reference.com/>,
    'FanGraphs' <https://www.fangraphs.com/>, and the 'MLB Stats' API <https://www.mlb.com/>.
Provides functions streamlining the data analysis workflow: 
  Outsourcing data import, renaming and type casting to a *.csv.
  Manipulating imputed datasets and fitting models on them. Summarizing models.
An R interface to the 'Base dos Dados' API <https://basedosdados.org/docs/api_reference_python/>). Authenticate your project, query our tables, save data to disk and memory, all from R.
Some very simple infrastructure for basis functions.
Collection of baseline correction algorithms, along with a framework and a Tcl/Tk enabled GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.
Nowcasting right-truncated epidemiological data is critical
  for timely public health decision-making, as reporting delays can create
  misleading impressions of declining trends in recent data. This package
  provides nowcasting methods based on using empirical
  delay distributions and uncertainty from past performance. It is also designed
  to be used as a baseline method for developers of new nowcasting methods.
  For more details on the performance of the method(s) in this package applied
  to case studies of COVID-19 and norovirus, see our recent paper at
  <https://wellcomeopenresearch.org/articles/10-614>. The package supports
  standard data frame inputs with reference date, report date, and
  count columns, as well as the direct use of reporting triangles, and is
  compatible with 'epinowcast' objects. Alongside
  an opinionated default workflow, it has a low-level pipe-friendly modular
  interface, allowing context-specific workflows. It can accommodate a wide
  spectrum of reporting schedules, including mixed patterns of reference and
  reporting (daily-weekly, weekly-daily). It also supports sharing delay
  distributions and uncertainty estimates between strata,
  as well as custom uncertainty models and delay estimation methods.
A lightweight package to access spatial basemaps from open sources such as 'OpenStreetMap', 'Carto', 'Mapbox' and others in R.
Providing equivalent functions for the dummy
    classifier and regressor used in 'Python' 'scikit-learn' library. Our goal
    is to allow R users to easily identify baseline performance for their
    classification and regression problems. Our baseline models use no
    predictors, and are useful in cases of class imbalance, multiclass
    classification, and when users want to quickly identify how much
    improvement their statistical and machine learning models are over several
    baseline models. We use a "better" default (proportional guessing) for
    the dummy classifier than the 'Python' implementation ("prior", which is
    the most frequent class in the training set). The functions in the
    package can be used on their own, or introduce methods named
    'dummy_regressor' or 'dummy_classifier' that can be used within the
    caret package pipeline.
From 'R' 4.5.0, the 'datasets' package includes the penguins and 
    penguins_raw data sets popularised in the 'palmerpenguins' package.
    'basepenguins' takes files that use the 'palmerpenguins' package and 
    converts them to work with the versions from 'datasets' ('R' >= 4.5.0). 
    It does this by removing calls to library(palmerpenguins) and making the 
    necessary changes to column names. Additionally, it provides helper 
    functions to define new files paths for saving the output and a directory
    of example files to experiment with.
Primarily created as an easy and understanding way to do basic sequences surrounding the central dogma of molecular biology.
Download typicality rating datasets, generate new stereotype-based typicality ratings using large language models via the Inference Providers API (<https://huggingface.co/docs/inference-providers>), and evaluate them against human-annotated validation data. Also includes functions to extract stereotype strength and base-rate items from typicality matrices. For more details see Beucler et al. (2025) <doi:10.31234/osf.io/eqrfu_v1>.
Provides various basis expansions for flexible regression modeling,
    including random Fourier features (Rahimi & Recht, 2007)
    <https://proceedings.neurips.cc/paper_files/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf>,
    exact kernel / Gaussian process feature maps, prior features for Bayesian
    Additive Regression Trees (BART) (Chipman et al., 2010) <doi:10.1214/09-AOAS285>,
    and a helpful interface for n-way interactions. The provided functions may
    be used within any modeling formula, allowing the use of kernel methods and
    other basis expansions in modeling functions that do not otherwise support
    them. Along with the basis expansions, a number of kernel functions are also
    provided, which support kernel arithmetic to form new kernels. Basic ridge
    regression functionality is included as well.
Implements a class and methods to work with sets, doing
    intersection, union, complementary sets, power sets, cartesian product
    and other set operations in a "tidy" way. These set operations are
    available for both classical sets and fuzzy sets. Import sets from
    several formats or from other several data structures.
All the seeds do not germinate at a single point in time due to physiological mechanisms determined by temperature which vary among individual seeds in the population. Seeds germinate by following accumulation of thermal time in degree days/hours, quantified by multiplying the time of germination with excess of base temperature required by each seed for its germination, which follows log-normal distribution. The theoretical germination course can be obtained by regressing the rate of germination at various fractions against temperature (Garcia et al., 1982), where the fraction-wise regression lines intersect the temperature axis at base temperature and the methodology of determining optimum base temperature has been described by Ellis et al. (1987). This package helps to find the base temperature of seed germination using algorithms of Garcia et al. (1982) and Ellis et al. (1982) <doi:10.1093/JXB/38.6.1033> <doi:10.1093/jxb/33.2.288>.
Functions to create and select graphical themes for the base plotting system. Contains: 1) several custom pre-made themes 2) mechanism for creating new themes by making persistent changes to the graphical parameters of base plots.
Includes modern base-R functions. Functions beginning with p_ are wrapper functions for existing base-R functions, supporting native piping. Other functions are wrapper functions for core base-R features, including bracket notation and dollar-sign notation. base_match() and base_when() mimic case_match() and case_when() from 'dplyr' but return a factor by default with levels ordered according to user input. et() mimics count() from 'dplyr'.
Resurrects the standard plot for shapes established by the
 'base' and 'graphics' packages. This is suited to workflows that require
 plotting using the established and traditional idioms of plotting spatially
 coincident data where it belongs. This package depends on 'sf' and only replaces 
 the plot method.
Evaluate, fit, and analyze Hill dose response models (Goutelle et 
    al., 2008 <doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred
    to as four-parameter log-logistic models.  Includes tools to invert Hill 
    models,  select models based on the Akaike information criterion
    (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>) or Bayesian information 
    criterion (Schwarz, 1978 <https://www.jstor.org/stable/2958889>), and 
    construct bootstrapped confidence intervals both
    on the Hill model parameters and values derived from the Hill model
    parameters.
Provides methods for examining posterior MCMC samples
    from a single chain using trace plots and density plots, and from
    multiple chains by comparing posterior medians and credible intervals
    from each chain.  These plotting functions have a variety of options,
    such as figure sizes, legends, parameters to plot, and saving plots to file.
    Functions interface with the NIMBLE software package, see
    de Valpine, Turek, Paciorek, Anderson-Bergman, Temple Lang and Bodik (2017)
    <doi:10.1080/10618600.2016.1172487>.
Provides functions to estimate latent dimensions of choice and judgment using Aldrich-McKelvey and Blackbox scaling methods, as described in Poole et al. (2016, <doi:10.18637/jss.v069.i07>). These techniques allow researchers (particularly those analyzing political attitudes, public opinion, and legislative behavior) to recover spatial estimates of political actors' ideal points and stimuli from issue scale data, accounting for perceptual bias, multidimensional spaces, and missing data. The package uses singular value decomposition and alternating least squares (ALS) procedures to scale self-placement and perceptual data into a common latent space for the analysis of ideological or evaluative dimensions. Functionality also include tools for assessing model fit, handling complex survey data structures, and reproducing simulated datasets for methodological validation.
Easily create tables from data 
    frames/matrices.  Create/manipulate tables 
    row-by-row, column-by-column or cell-by-cell.  
    Use common formatting/styling to output 
    rich tables as 'HTML', 'HTML widgets' or to 
    'Excel'.
Fetches, harmonizes, and analyses data from the Spanish National
    Forest Inventory for reproducible, design-aware forest inventory workflows.
    Computes tree- and stand-level metrics, applies sampling-based expansion
    factors, estimates volume, and supports extensible processing for external
    inventory designs with custom sampling schemes and volume equations.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.
Tool to perform Bayesian inference of carcass processing/transport strategy and bone attrition from archaeofaunal skeletal profiles characterized by percentages of MAU (Minimum Anatomical Units). The approach is based on a generative model for skeletal profiles that replicates the two phases of formation of any faunal assemblage: initial accumulation as a function of human transport strategies and subsequent attrition.Two parameters define this model: 1) the transport preference (alpha), which can take any value between - 1 (mostly axial contribution) and 1 (mostly appendicular contribution) following strategies constructed as a function of butchering efficiency of different anatomical elements and the results of ethnographic studies, and 2) degree of attrition (beta), which can vary between 0 (no attrition) and 10 (maximum attrition) and relates the survivorship of bone elements to their maximum bone density. Starting from uniform prior probability distribution functions of alpha and beta, a Monte Carlo Markov Chain sampling based on a random walk Metropolis-Hasting algorithm is adopted to derive the posterior probability distribution functions, which are then available for interpretation. During this process, the likelihood of obtaining the observed percentages of MAU given a pair of parameter values is estimated by the inverse of the Chi2 statistic, multiplied by the proportion of elements within a 1 percent of the observed value. See Ana B. Marin-Arroyo, David Ocio (2018).<doi:10.1080/08912963.2017.1336620>.
Implementation of multisource exchangeability models for Bayesian analyses of prespecified subgroups arising in the context of basket trial design and monitoring.  The R 'basket' package facilitates implementation of the binary, symmetric multi-source exchangeability model (MEM) with posterior inference arising from both exact computation and Markov chain Monte Carlo sampling. Analysis output includes full posterior samples as well as posterior probabilities, highest posterior density (HPD) interval boundaries, effective sample sizes (ESS), mean and median estimations, posterior exchangeability probability matrices, and maximum a posteriori MEMs. In addition to providing "basketwise" analyses, the package includes similar calculations for "clusterwise" analyses for which subgroups are combined into meta-baskets, or clusters, using graphical clustering algorithms that treat the posterior exchangeability probabilities as edge weights. In addition plotting tools are provided to visualize basket and cluster densities as well as their exchangeability.  References include Hyman, D.M., Puzanov, I., Subbiah, V., Faris, J.E., Chau, I., Blay, J.Y., Wolf, J., Raje, N.S., Diamond, E.L., Hollebecque, A. and Gervais, R (2015) <doi:10.1056/NEJMoa1502309>; Hobbs, B.P. and Landin, R. (2018) <doi:10.1002/sim.7893>; Hobbs, B.P., Kane, M.J., Hong, D.S. and Landin, R. (2018) <doi:10.1093/annonc/mdy457>; and Kaizer, A.M., Koopmeiners, J.S. and Hobbs, B.P. (2017) <doi:10.1093/biostatistics/kxx031>.
Contains data and code to accompany  the book 
             P. Zuccolotto and M. Manisera (2020) Basketball Data Science. Applications with R. CRC Press. ISBN 9781138600799.
Provides tools for Bayesian basket trial design and analysis using a novel three-component local power prior framework with global borrowing control, pairwise similarity assessment and a borrowing threshold. Supports simulation-based evaluation of operating characteristics and comparison with other methods. Applicable to both equal and unequal sample size settings in early-phase oncology trials. For more details see Zhou et al. (2023) <doi:10.48550/arXiv.2312.15352>.
Analytically calculates the operating characteristics of
    single-stage and two-stage basket trials with equal sample sizes using the
	power prior design by Baumann et al. (2024) <doi:10.48550/arXiv.2309.06988>
	and the design by Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>.
A unified framework for optimizing
    basket trial designs. To this end, the package supplies several utility
    functions and also a function for executing optimization algorithms on basket
    trial designs. The considered utility functions are discussed in
    Sauer et al. (2025) <doi:10.1371/journal.pone.0323097>.
Provides a unified syntax for the simulation-based comparison of 
  different single-stage basket trial designs with a binary endpoint and equal 
  sample sizes in all baskets. Methods include the designs by 
	Baumann et al. (2025) <doi:10.1080/19466315.2024.2402275>, 
	Schmitt and Baumann (2025) <doi:10.1080/19466315.2025.2486231>,
	Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>,
	Berry et al. (2020) <doi:10.1177/1740774513497539>, and
	Neuenschwander et al. (2016) <doi:10.1002/pst.1730>. For the 
	latter two designs, the functions are mostly 
	wrappers for functions provided by the package 'bhmbasket'.
A simple interface to switch between 
    two methods for calculating basket trial characteristics, numerical 
    integration ("exact") and Monte Carlo simulation ("simulated") for the
    basket trial design by 
    Fujikawa et al. 2020 <doi:10.1002/bimj.201800404>. 
    The exact implementation is from the 'baskexact' package, see
    Baumann (2024) <doi:10.1016/j.softx.2024.101793>. The simulated 
    implementation is from the 'basksim' package, which was developed for
    Baumann et al. (2024) <doi:10.1080/19466315.2024.2402275>. The package's 
    syntax is compatible with the 'basksim' syntax and easily extendable.
Bayesian fitting and sensitivity analysis methods for adaptive
    spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as
    functional or scalar output. An extension of the methodology in Denison, Mallick
    and Smith (1998) <doi:10.1023/A:1008824606259>.
Estimates survival and mortality with covariates from census or capture-recapture/recovery data in a Bayesian framework when many individuals are of unknown age. It includes tools for data checking, model diagnostics and outputs such as life-tables and plots, as described in Colchero, Jones, and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and Colchero et al. (2021) <doi:10.1038/s41467-021-23894-3>.
Includes algorithms to assess alpha and beta diversity
    in all their dimensions (taxonomic, phylogenetic and functional).
    It allows performing a number of analyses based on species
    identities/abundances, phylogenetic/functional distances, trees,
    convex-hulls or kernel density n-dimensional hypervolumes
    depicting species relationships.
    Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.
Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to
    specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm
    the required action. It also provides functions that will install 'Github' starred R packages whether available on 'CRAN' or not.
Functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(...)' for the main example of how to use this package.
Extends the BatchJobs package to run statistical experiments on
    batch computing clusters. For further details see the project web page.
Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.
Provides Map, Reduce and Filter variants to generate jobs on
    batch computing systems like PBS/Torque, LSF, SLURM and Sun Grid
    Engine.  Multicore and SSH systems are also supported. For further
    details see the project web page.
Provides consistent batch means estimation of Monte
    Carlo standard errors.
Semi-supervised and unsupervised Bayesian mixture models that
  simultaneously infer the cluster/class structure and a batch correction.
  Densities available are the multivariate normal and the multivariate t.
  The model sampler is implemented in C++. This package is aimed at analysis of
  low-dimensional data generated across several batches. See Coleman et al.
  (2022) <doi:10.1101/2022.01.14.476352> for details of the model.
Handy frameworks, such as error handling and log generation, for batch scripts.
	Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log.
Different adjustment methods for batch effects in biomarker data,
  such as from tissue microarrays. Some methods attempt to retain differences 
  between batches that may be due to between-batch differences in "biological"
  factors that influence biomarker values.
As a successor of the packages 'BatchJobs' and
    'BatchExperiments', this package provides a parallel implementation of
    the Map function for high performance computing systems managed by
    schedulers 'IBM Spectrum LSF'
    (<https://www.ibm.com/products/hpc-workload-management>), 'Univa Grid
    Engine'/'Oracle Grid Engine'
    (<https://altair.com/hpc-cloud-applications/>), 'Slurm'
    (<https://slurm.schedmd.com/>), 'TORQUE/PBS'
    (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>),
    or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>).  A
    multicore and socket mode allow the parallelization on a local
    machines, and multiple machines can be hooked up via SSH to create a
    makeshift cluster. Moreover, the package provides an abstraction
    mechanism to define large-scale computer experiments in a
    well-organized and reproducible way.
Compute bounds for the treatment effect 
    after adjusting for the presence of omitted variables in linear  
    econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. 
    You supply the data, identify the outcome and 
    treatment variables and additional regressors. The main functions
    will compute bounds for the bias-adjusted treatment effect. Many
    plot functions allow easy visualization of results.
Survey systems and other third-party data sources commonly use non-standard representations of logical values when
    it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals.
    It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian,
    Turkish, Chinese and Polish.
Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Batsch et al. (2008) <doi:10.1186/1471-2105-9-95>. This package
    provides five data sets, one for each PCR target: (i) rat SLC6A14,
    (ii) human SLC22A13, (iii) pig EMT, (iv) chicken ETT, and (v) human
    GAPDH. Each data set comprises a five-point, four-fold dilution series.
    For each concentration there are three replicates. Each amplification curve
    is 45 cycles long. Original raw data file:
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-95/MediaObjects/12859_2007_2080_MOESM5_ESM.xls>.
Defines operating characteristics of Bayesian Adaptive Trials considering a generalised linear model response via Monte Carlo simulations of Bayesian GLM fitted via integrated Laplace approximations (INLA).
Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.
Collection of tools to work with European basketball data. Functions available are related to friendly 
	web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, 
	<https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions 
        of their respectives robots.txt files, when available. Box score data are available for the three leagues. 
	Play-by-play and spatial shooting data are also available for the Spanish league. Methods for analysis include a 
	population pyramid, 2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, 
	team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games,
	maps of nationalities, combinations of lineups, possessions-related variables, timeouts,
	performance by periods, personal fouls, offensive rebounds and different types of shooting charts. 
	Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.
Simulating synthetic clumped isotope dataset, fitting
    linear regression models under Bayesian and non-Bayesian frameworks,
    and generating temperature reconstructions for the same two approaches.
    Please note that models implemented in this package are described
    in Roman-Palacios et al. (2021) <doi:10.1002/essoar.10507995.1>.
An approximate Bayesian method for inferring Directed Acyclic Graphs
    (DAGs) for continuous, discrete, and mixed data. The algorithm can use the 
    graph inferred by another more efficient graph inference method as input;
    the input graph may contain false edges or undirected edges but can help
    reduce the search space to a more manageable size. A Metropolis-Hastings-like 
    sampling algorithm is then used to infer the posterior probabilities of 
    edge direction and edge absence.
    References:
    Martin, Patchigolla and Fu (2026) <doi:10.48550/arXiv.1909.10678>.
Implements the Bayesian FDR control described by 
    Newton et al. (2004), <doi:10.1093/biostatistics/5.2.155>.
    Allows optimisation and visualisation of expected error rates based on
    tail posterior probability tests.
    Based on code written by Catalina Vallejos for BASiCS, see
    Beyond comparisons of means: understanding changes in gene expression at the
    single-cell level Vallejos et al. (2016) <doi:10.1186/s13059-016-0930-3>.
As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R.
Contains several Bayesian models for data analysis of psychological tests. A user friendly interface for these models should enable students and researchers to perform professional level Bayesian data analysis without advanced knowledge in programming and Bayesian statistics. This package is based on the Stan platform (Carpenter et el. 2017 <doi:10.18637/jss.v076.i01>).
Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).
Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.
Bayesian adaptive trial algorithm implements multiple-stage interim analysis. Package includes data generating function, and Bayesian hypothesis testing function.
Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.
Functions for performing the Bayesian bootstrap as introduced by
    Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result.
    The implementation can handle both summary statistics that works on a
    weighted version of the data and summary statistics that works on a
    resampled data set.
Smoothed lexis diagrams with Bayesian method specifically tailored to cancer 
             incidence data. Providing to calculating slope and constructing credible interval.
             LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>. 
             LH Chien et al. (2017) <doi:10.1002/cam4.1102>.
Implements Bayesian brain mapping with population-derived priors, 
    including the original model described in Mejia et al. (2020) 
    <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described
    in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model 
    with population-derived priors on functional connectivity described in Mejia
    et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors
    are based on templates representing established brain network maps, for 
    example derived from independent component analysis (ICA), parcellations, 
    or other methods.  Model estimation is based on expectation-maximization or 
    variational Bayes algorithms. Includes direct support for 'CIFTI', 'GIFTI', 
    and 'NIFTI' neuroimaging file formats.
Performs CACE (Complier Average Causal Effect analysis) on either a single study or meta-analysis of datasets with binary outcomes, using either complete or incomplete noncompliance information. Our package implements the Bayesian methods proposed in Zhou et al. (2019) <doi:10.1111/biom.13028>, which introduces a Bayesian hierarchical model for estimating CACE in meta-analysis of clinical trials with noncompliance, and Zhou et al. (2021) <doi:10.1080/01621459.2021.1900859>, with an application example on Epidural Analgesia.
Performs change point detection on univariate and multivariate time series (Martínez & Mena, 2014, <doi:10.1214/14-BA878> ; Corradin, Danese & Ongaro, 2022, <doi:10.1016/j.ijar.2021.12.019>) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, <doi:10.1007/s11222-025-10756-x>).
Combine diverse evidence across multiple studies to test a high level scientific theory. The methods can also be used as an alternative to a standard meta-analysis.
A set of functions to allow analysis of count data (such
        as faecal egg count data) using Bayesian MCMC methods.  Returns
        information on the possible values for mean count, coefficient
        of variation and zero inflation (true prevalence) present in
        the data.  A complete faecal egg count reduction test (FECRT)
        model is implemented, which returns inference on the true
        efficacy of the drug from the pre- and post-treatment data
        provided, using non-parametric bootstrapping as well as using
        Bayesian MCMC.  Functions to perform power analyses for faecal
        egg counts (including FECRT) are also provided.
A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.
Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.
A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) <doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos (2024b) <doi:10.48550/arXiv.2409.10221>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.
Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. <doi:10.1016/j.csda.2024.108053>) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn,  Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.
Bayesian estimation of dynamic conditional correlation GARCH model for multivariate time series volatility (Fioruci, J.A., Ehlers, R.S. and Andrade-Filho, M.G., (2014). <doi:10.1080/02664763.2013.839635>.
Estimates the density of a variable in a measurement error setup, potentially with an excess of zero values. For more details see Sarkar (2021) <doi:10.1080/01621459.2020.1782220>.
The proposed event-driven approach for Bayesian two-stage single-arm phase II trial design is a novel clinical trial design and can be regarded as an extension of the Simon’s two-stage design with the time-to-event endpoint. This design is motivated by cancer clinical trials with immunotherapy and molecularly targeted therapy, in which time-to-event endpoint is often a desired endpoint.
Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic 
    factor analysis is a dimension reduction tool for multivariate time series.
    'bayesdfa' extends conventional dynamic factor models in several ways. 
    First, extreme events may be estimated in the latent trend by modeling
    process error with a student-t distribution. Second, alternative constraints
    (including proportions are allowed). Third, the estimated
    dynamic factors can be analyzed with hidden Markov models to evaluate
    support for latent regimes.
Provides comprehensive tools for Bayesian model diagnostics 
    and comparison. Includes prior sensitivity analysis, posterior predictive 
    checks (Gelman et al. (2013) <doi:10.1201/b16018>), advanced model 
    comparison using Pareto-smoothed importance sampling leave-one-out 
    cross-validation (Vehtari et al. (2017) <doi:10.1007/s11222-016-9696-4>), 
    convergence diagnostics, and prior elicitation tools. Integrates with 
    'brms' (Burkner (2017) <doi:10.18637/jss.v080.i01>), 'rstan', and 
    'rstanarm' packages for comprehensive Bayesian workflow diagnostics.
Provide early termination phase II trial designs with a
    decreasingly informative prior (DIP) or a regular Bayesian prior
    chosen by the user. The program can determine the minimum planned
    sample size necessary to achieve the user-specified admissible
    designs. The program can also perform power and expected sample size
    calculations for the tests in early termination Phase II trials.
    See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>;
    Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.
Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.
Functions for data augmentation using the Bayesian discount prior 
    method for single arm and two-arm clinical trials, as described in Haddad 
    et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior 
    methodology was developed in collaboration with the The Medical Device 
    Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.
Exploratory Bayesian factor analysis of continuous, mixed-type, and bounded continuous variables using the mode-jumping algorithm of Man and Culpepper (2020) <doi:10.1080/01621459.2020.1773833>.
A  Bayesian smoothing method for post-processing of remote 
    sensing image classification which refines the
    labelling in a classified image in order to enhance its classification accuracy.
    Combines pixel-based classification methods with a spatial post-processing
    method to remove outliers and misclassified pixels.
Suite of tools that facilitate
    exposure-response analysis using Bayesian methods. The package
    provides a streamlined workflow for fitting types of models that are
    commonly used in exposure-response analysis - linear and Emax for continuous
    endpoints, logistic linear and logistic Emax for binary endpoints, as well
    as performing simulation and visualization. Learn more about the workflow
    at <https://genentech.github.io/BayesERbook/>.
Determines effective sample size of a parametric prior distribution
    in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.
A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.
A suite of functions for computing
    various Bayes factors for simple designs, including contingency tables,
    one- and two-sample designs, one-way designs, general ANOVA designs, and
    linear regression.
Allows Bayesian borrowing from a historical dataset for time-to-
    event data. A flexible baseline hazard function is achieved via a piecewise
    exponential likelihood with time varying split points and smoothing prior on the
    historic baseline hazards. The method is described in Scott and Lewin (2026)
    <doi:10.1093/biostatistics/kxag006>, and a paper focused on the software is
    in Scott, Axillus, Lewin and Izmirlian (2026) <doi:10.48550/arXiv.2408.04327>.
Implementation of 'BayesFlux.jl' for R; It extends the famous 
             'Flux.jl' machine learning library to Bayesian Neural Networks. 
             The goal is not to have the fastest production ready 
             library, but rather to allow more people to be able 
             to use and research on Bayesian Neural Networks.
Collection of procedures to perform Bayesian analysis on a variety
    of factor models. Currently, it includes: "Bayesian Exploratory Factor 
    Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, 
    R. Piatek (2014) <doi:10.1016/j.jeconom.2014.06.008>, an approach to 
    dedicated factor analysis with stochastic search on the structure of the 
    factor loading matrix. The number of latent factors, as well as the 
    allocation of the manifest variables to the factors, are not fixed a priori 
    but determined during MCMC sampling.
Performs a spatial Bayesian general linear model (GLM) for task 
    functional magnetic resonance imaging (fMRI) data on the cortical surface. 
    Additional models include group analysis and inference to detect thresholded
    areas of activation. Includes direct support for the 'CIFTI' neuroimaging 
    file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. 
    Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. 
    Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) 
    <doi:10.1016/j.neuroimage.2022.118908>.
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range 
  of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic 
  Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic 
  volatility models for univariate time series. Prior specifications are flexible and explicitly encourage 
  users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed 
  and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes 
  factor and leave-one-out cross-validation methods. References: Hyndman (2017)
    <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
A high-performance probabilistic programming library that aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU (Central Processing Unit), GPU (Graphics Processing Unit), or TPU (Tensor Processing Unit) using 'JAX' (Just-In-Time compiled Accelerated linear algebra) as the computational backend: Sosa (2026) <doi:10.64898/2026.01.19.700318>.
Easy application of Bayesian inference for functional responses via 'brms'. This package allows to fit various FR models for single- and multi-prey experiments by providing nonlinear prediction functions for 'brms'. It uses dynamical prediction models to correct for prey depletion. The 'brms' framework facilitates statistical modeling and enables users to conveniently incorporate covariates such as temperature gradients, experimental treatment variables, or random effects that account for grouping in experimental units. Default 'brms' functions make it easy to perform model checking, model comparison and hypothesis testing. Potential statistical issues with data from feeding trials, such as overdispersion, can be resolved by effortlessly switching between likelihood functions. This package, together with its tutorials, should provide students and researchers with a comprehensive and integrated statistical framework for easily testing their hypotheses on trophic interactions. References: Rosenbaum and Rall (2018) <doi:10.1111/2041-210X.13039>; Rosenbaum et al. (2024) <doi:10.1111/2041-210X.14372>.
Provides the bayesGARCH() function which performs the
    Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
Performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) <doi:10.5281/zenodo.1285275> and a demonstration of its application can be found at Yimer et al. (2022) <doi:10.48550/arXiv.2210.08499>.
Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.
Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.
Estimate fish length-at-age models using MCMC analysis with 
    'rstan' models. This package allows a multimodel approach to growth fitting
    to be applied to length-at-age data and is supported by further analyses to 
    determine model selection and result presentation. The core methods of this 
    package are presented in Smart and Grammer (2021) "Modernising fish and 
    shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2):
    e0246734 <doi:10.1371/journal.pone.0246734>.
Fits Bayesian grouped weighted quantile sum (BGWQS) regressions for one or more chemical groups with binary outcomes. Wheeler DC et al. (2019) <doi:10.1016/j.sste.2019.100286>.
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels'
    via 'bayesian' <doi:10.5281/zenodo.4426836>. 'tidymodels' is a collection of
    packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
    The technical details of 'brms' and 'Stan' are described in Bürkner (2017)
    <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>,
    and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Provides a Bayesian framework for Dynamic Energy Budget (DEB)
    modelling via 'Stan'. Implements the standard DEB model of Kooijman
    (2010, <doi:10.1017/CBO9780511805400>) as a state-space model with
    Hamiltonian Monte Carlo inference (Carpenter et al., 2017,
    <doi:10.18637/jss.v076.i01>). Includes individual-level growth models,
    growth-reproduction models, hierarchical multi-individual models with
    partial pooling, and toxicokinetic-toxicodynamic (TKTD) models for
    ecotoxicology following the DEBtox framework (Jager et al., 2006,
    <doi:10.1007/s10646-006-0060-x>). Supports prior specification from
    biological knowledge, convergence diagnostics (Vehtari et al., 2021,
    <doi:10.1214/20-BA1221>), posterior predictive checks, derived quantity
    estimation, and visualisation via 'ggplot2'.
Disaggregates an observed aggregate price index into sectoral
    components with a Bayesian state-space model in which the aggregate enters as
    a genuine observation density rather than as a renormalization identity. A
    random-walk-with-drift transition in log space (with partial pooling on the
    drift and the innovation scale) and an estimable cross-sectional
    concentration produce posterior draws of the sectoral indices with credible
    intervals, suitable as multiple-imputation input for downstream dynamic
    models. The Hamiltonian Monte Carlo engine follows Stan (Carpenter et al.,
    2017) <doi:10.18637/jss.v076.i01>; model comparison uses Pareto Smoothed
    Importance Sampling Leave-One-Out cross-validation (Vehtari, Gelman and
    Gabry, 2017) <doi:10.1007/s11222-016-9696-4>. A closed-form linear-Gaussian
    Kalman/RTS smoother provides an exact, MCMC-free Bayesian alternative for the
    same aggregate evidence.
The Epidemic Type Aftershock Sequence (ETAS) model is widely
    used for modelling and forecasting earthquake occurrences. This package
    implements Bayesian estimation routines for both the temporal and spatial ETAS
    model, allowing samples to be drawn from the full posterior distribution
    of the model parameters given an earthquake catalogue. The methods are
    described in Ross (2021) "Bayesian Estimation of the ETAS Model for
    Earthquake Occurrences" <doi:10.1785/0120200198>.
Methods for Bayesian parameter estimation and forecasting in epidemiological models. 
    Functions enable model fitting using Bayesian methods and generate forecasts with uncertainty quantification. 
    Implements approaches described in <doi:10.48550/arXiv.2411.05371> and <doi:10.1002/sim.9164>.
Fits double generalized Gamma regression models from a Bayesian perspective, where both the mean and shape parameters are modeled simultaneously using flexible link functions. The methodology is based on Cepeda-Cuervo and Urdinola (2012) <doi:10.1080/03610918.2011.600500> and extended in Cepeda-Cuervo (2026), 'Double Generalized Linear Models: Likelihood and Bayesian Methods' (ISBN: 9781041169970). The package provides parameter estimation, model fitting, and model comparison tools, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).
Beta version of 'Bayesian Inference' (BI) using 'python' and BI. It aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU, GPU, or TPU.
Implements Bayesian Lasso regression using efficient Gibbs sampling
               algorithms, including modified versions of the Hans and Park Casella (PC) samplers.
               Includes functions for working with the Lasso distribution, such as its density,
               cumulative distribution, quantile, and random generation functions, along with moment
               calculations. Also includes a function to compute the Mills ratio. Designed for sparse
               linear models and suitable for high-dimensional regression problems.
Functional differences between the cerebral hemispheres 
    are a fundamental characteristic of the human brain. Researchers 
    interested in studying these differences often infer underlying 
    hemispheric dominance for a certain function (e.g., language) from 
    laterality indices calculated from observed performance or brain 
    activation measures . However, any inference from observed measures 
    to latent (unobserved) classes has to consider the prior probability 
    of class membership in the population. The provided functions 
    implement a Bayesian model for predicting hemispheric dominance from
    observed laterality indices (Sorensen and Westerhausen, Laterality: 
    Asymmetries of Body, Brain and Cognition, 2020, <doi:10.1080/1357650X.2020.1769124>).
Bayesian MCPMod (Fleischer et al. (2022)
    <doi:10.1002/pst.2193>) is an innovative method that improves the
    traditional MCPMod by systematically incorporating historical data,
    such as previous placebo group data. This package offers functions
    for simulating, analyzing, and evaluating Bayesian MCPMod trials with
    normally and binary distributed endpoints. It enables the assessment of trial
    designs incorporating historical data across various true
    dose-response relationships and sample sizes. Robust mixture prior
    distributions, such as those derived with the Meta-Analytic-Predictive
    approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be
    specified for each dose group.  Resulting mixture posterior
    distributions are used in the Bayesian Multiple Comparison Procedure
    and modeling steps. The modeling step also includes a weighted model
    averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).
    Estimated dose-response relationships can be bootstrapped and
    visualized.
We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.
A "Shiny"" web application for creating interactive Bayesian Network models,
    learning the structure and parameters of Bayesian networks, and utilities for classic
    network analysis.
Fits Bayesian nonlinear Ornstein-Uhlenbeck models with cubic drift,
    stochastic volatility, and Student-t innovations. The package implements
    hierarchical priors for sector-specific parameters and supports parallel
    MCMC sampling via 'Stan'. Model comparison is performed using Pareto
    Smoothed Importance Sampling Leave-One-Out (PSIS-LOO) cross-validation
    following Vehtari, Gelman, and Gabry (2017)
    <doi:10.1007/s11222-016-9696-4>. Prior specifications follow recommendations
    from Gelman (2006) <doi:10.1214/06-BA117A> for scale parameters.
Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. 
    This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. 
    Additionally, it allows for the study of time trend problems in platform trials. 
    There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening.
    The Bayesian adaptive randomisation approaches are described in:
    Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and
    Wathen et al. (2017) <doi:10.1177/1740774517692302>.
    The randomisation algorithm is described in: 
    Zhao W <doi:10.1016/j.cct.2015.06.008>.
    The analysis methods of time trend effect in platform trial are described in:
    Saville et al. (2022) <doi:10.1177/17407745221112013> and
    Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.
A collection of methods to determine the required sample size for
    the evaluation of inequality constrained hypotheses by means of a Bayes 
    factor. Alternatively, for a given sample size, the unconditional error 
    probabilities or the expected conditional error probabilities can be
    determined. Additional material on the methods in this package is 
    available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) 
    <doi:10.31219/osf.io/d5kf3>.
Provides comprehensive methods to calculate posterior probabilities,
    posterior predictive probabilities, and Go/NoGo/Gray decision probabilities
    for quantitative decision-making under a Bayesian paradigm in clinical trials.
    The package supports both single and two-endpoint analyses for binary and
    continuous outcomes, with controlled, uncontrolled, and external designs.
    For single continuous endpoints, three calculation methods are
    available: numerical integration (NI), Monte Carlo simulation (MC), and
    Moment-Matching approximation (MM). For two continuous endpoints, a bivariate
    Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods.
    For two binary endpoints, a Dirichlet-multinomial model is implemented.
    External designs incorporate historical data through power priors
    using exact conjugate representations (Normal-Inverse-Chi-squared for single
    continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for
    binary endpoints), enabling closed-form posterior computation without Markov
    chain Monte Carlo (MCMC) sampling. This approach significantly reduces
    computational burden while preserving complete Bayesian rigor. The package
    also provides grid-search functions to find optimal Go and NoGo thresholds
    that satisfy user-specified operating characteristic criteria for all
    supported endpoint types and study designs. S3 print() and plot() methods
    are provided for all decision probability classes, enabling formatted display
    and visualisation of Go/NoGo/Gray operating characteristics across treatment
    scenarios.
    See Kang, Yamaguchi, and Han (2026) <doi:10.1080/10543406.2026.2655410>
    for the methodological framework.
Functions to plot and help understand positive and negative
    predictive values (PPV and NPV), and their relationship with
    sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007)
    <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of
    the technical concepts and Navarrete et al. (2015) for a practical
    explanation about the importance of their understanding
    <doi:10.3389/fpsyg.2015.01327>.
Implements Bayesian Surprise methodology for data visualization,
    based on Correll and Heer (2017) <doi:10.1109/TVCG.2016.2598839>
    "Surprise! Bayesian Weighting for De-Biasing Thematic Maps". Provides
    tools to weight event data relative to spatio-temporal models,
    highlighting unexpected patterns while de-biasing against known factors
    like population density or sampling variation. Integrates seamlessly with
    'sf' for spatial data and 'ggplot2' for visualization. Supports
    temporal/streaming data analysis.
General-purpose MCMC and SMC samplers, as well as plots and
    diagnostic functions for Bayesian statistics, with a particular focus on
    calibrating complex system models. Implemented samplers include various
    Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the
    T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential
    Monte Carlo (SMC) particle filter.
Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.
Efficient Markov Chain Monte Carlo (MCMC) algorithms for the
    fully Bayesian estimation of vectorautoregressions (VARs) featuring
    stochastic volatility (SV). Implements state-of-the-art shrinkage
    priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>.
    Efficient equation-per-equation estimation following Kastner & Huber
    (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021)
    <doi:10.1016/j.jeconom.2021.11.010>.
Various algorithms for segmentation of 2D and 3D images, such
    as computed tomography and satellite remote sensing. This package implements
    Bayesian image analysis using the hidden Potts model with external field
    prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>.
    Latent labels are sampled using chequerboard updating or Swendsen-Wang.
    Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
    the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
    and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to
    Moores, Pettitt & Mengersen (2020) <doi:10.1007/978-3-030-42553-1_6> for an overview
    and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for
    further details of specific algorithms.
Bayesian Latent Class Analysis using several different
        methods.
Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.
Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) <doi:10.1017/psrm.2025.10084> for examples and details of estimation.
Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <doi:10.48550/arXiv.1806.05738>.
Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.
Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>.  Useful for logistic regression.
Adjusts longitudinal regression models using Bayesian methodology 
            for covariance structures of composite symmetry (SC), 
            autoregressive ones of order 1 AR (1) and 
            autoregressive moving average of order (1,1) ARMA (1,1).
Covers many important models used
  in marketing and micro-econometrics applications. 
  The package includes:
  Bayes Regression (univariate or multivariate dep var),
  Bayes Seemingly Unrelated Regression (SUR),
  Binary and Ordinal Probit,
  Multinomial Logit (MNL) and Multinomial Probit (MNP),
  Multivariate Probit,
  Negative Binomial (Poisson) Regression,
  Multivariate Mixtures of Normals (including clustering),
  Dirichlet Process Prior Density Estimation with normal base,
  Hierarchical Linear Models with normal prior and covariates,
  Hierarchical Linear Models with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a mixture of normals prior
     and covariates,
  Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates,
  Hierarchical Negative Binomial Regression Models,
  Bayesian analysis of choice-based conjoint data,
  Bayesian treatment of linear instrumental variables models,
  Analysis of Multivariate Ordinal survey data with scale
     usage heterogeneity (as in Rossi et al, JASA (01)),
  Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see
  Jiang, Manchanda, Rossi 2009)
  For further reference, consult our book, Bayesian Statistics and
  Marketing by Rossi, Allenby and McCulloch (Wiley second edition 2024) and Bayesian Non- and Semi-Parametric
  Methods and Applications (Princeton U Press 2014).
An implementation of the Bayesian version of the Mallows rank model
    (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
    Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>;
    Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>;
    Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings
    and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
    Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
    analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
    as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
    posterior distributions of parameters are provided. The package also provides functions for estimating
    the partition function (normalizing constant) of the Mallows rank model, both with the importance
    sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
    (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).
Provides nested sequential Monte Carlo algorithms for performing
    sequential inference in the Bayesian Mallows model, which is a widely used
    probability model for rank and preference data. The package implements the
    SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially
    arriving rankings and pairwise preferences, including support for complete
    rankings, partial rankings, and pairwise comparisons. The methods are based
    on Sorensen (2025) <doi:10.1214/25-BA1564>.
Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.
A collection of functions allowing to derive the posterior distribution of the model parameters in random-effects meta-analysis or meta-regression, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>, or Roever C and Friede T (2022) <doi:10.1016/j.cmpb.2022.107303>.
Contains a split population survival estimator that models the 
    misclassification probability of failure versus right-censored events.  
    The split population survival estimator is described in 
    Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.
Producing probabilistic projections of net migration rate for all countries of the world
    or for subnational units using a Bayesian hierarchical model by Azose an Raftery (2015) <doi:10.1007/s13524-015-0415-0>.
Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.
Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and
        Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls
        overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components
        in the hazard function.
Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) <doi:10.1177/00811750221112398>; Zang, Emma, et al., (2022) <doi:10.1093/geronb/gbab149>.
Carry out Bayesian estimation and forecasting for a variety of stochastic mortality models using vague prior distributions. Models supported include numerous well-established approaches introduced in the actuarial and demographic literature, such as the Lee-Carter (1992) <doi:10.1080/01621459.1992.10475265>, the Cairns-Blake-Dowd (2009) <doi:10.1080/10920277.2009.10597538>, the Li-Lee (2005) <doi:10.1353/dem.2005.0021>, and the Plat (2009) <doi:10.1016/j.insmatheco.2009.08.006> models. The package is designed to analyse stratified mortality data structured as a 3-dimensional array of dimensions p × A × T (strata × age × year). Stratification can represent factors such as cause of death, country, deprivation level, sex, geographic region, insurance product, marital status, socioeconomic group, or smoking behavior. While the primary focus is on analysing stratified data (p > 1), the package can also handle mortality data that are not stratified (p = 1). Model selection via the Deviance Information Criterion (DIC) is supported.
Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>. Journal publication available at <doi:10.18637/jss.v113.i09>.
Methods for assessing animal movement from telemetry and biologging
    data using non-parametric Bayesian methods. This includes features for pre-
    processing and analysis of data, as well as the visualization of results
    from the models. This framework does not rely on standard parametric density
    functions, which provides flexibility during model fitting. Further details 
    regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.
Bayesian analysis of multivariate receptor modeling. The package consists of implementations of the methods of Park and Oh (2015) <doi:10.1016/j.chemolab.2015.08.021>.The package uses 'JAGS'(Just Another Gibbs Sampler) to generate Markov chain Monte Carlo samples of parameters.
Implements Bayesian marginal structural models for causal effect estimation with time-varying treatment and confounding.
             It includes an extension to handle informative right censoring. The Bayesian importance sampling weights are estimated using JAGS. See Saarela (2015) <doi:10.1111/biom.12269> for methodological details.
A two-step Bayesian approach for mode inference  
      (Baştürk et al., 2026) <doi:10.18637/jss.v116.i03>.
      First, a mixture distribution is fitted on the data using a sparse finite
      mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. The number of
      mixture components does not have to be known; the size of the mixture is
      estimated endogenously through the SFM approach. Second, the modes of the
      estimated mixture at each MCMC draw are retrieved using algorithms
      specifically tailored for mode detection. These estimates are then used to
      construct posterior probabilities for the number of modes, their locations
      and uncertainties, providing a powerful tool for mode inference.
Objective Bayesian inference procedures for the parameters of the
    multivariate random effects model with application to multivariate
    meta-analysis. The posterior for the model parameters, namely the overall
    mean vector and the between-study covariance matrix, are assessed by
    constructing Markov chains based on the Metropolis-Hastings algorithms as
    developed in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The
    Metropolis-Hastings algorithm is designed under the assumption of the
    normal distribution and the t-distribution when the Berger and Bernardo
    reference prior and the Jeffreys prior are assigned to the model parameters.
    Convergence properties of the generated Markov chains are investigated by
    the rank plots and the split hat-R estimate based on the rank normalization,
    which are proposed in Vehtari et al. (2021) (<DOI:10.1214/20-BA1221>).
Implementation of No-Effect-Concentration estimation that uses 'brms' (see Burkner (2017)<doi:10.18637/jss.v080.i01>; Burkner (2018)<doi:10.32614/RJ-2018-017>; Carpenter 'et al.' (2017)<doi:10.18637/jss.v076.i01> to fit concentration(dose)-response data using Bayesian methods for the purpose of estimating 'ECx' values, but more particularly 'NEC' (see Fox (2010)<doi:10.1016/j.ecoenv.2009.09.012>), 'NSEC' (see Fisher and Fox (2023)<doi:10.1002/etc.5610>), and 'N(S)EC (see Fisher et al. 2023<doi:10.1002/ieam.4809>). A full description of this package can be found in Fisher 'et al.' (2024)<doi:10.18637/jss.v110.i05>. This package expands and supersedes an original version implemented in 'R2jags' (see Su and Yajima (2020)<https://CRAN.R-project.org/package=R2jags>; Fisher et al. (2020)<doi:10.5281/ZENODO.3966864>).
Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.
Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the "nimble" package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.
Bayesian methods for predicting the calendar time at which a target number of events is reached in clinical trials. The methodology applies to both blinded and unblinded settings and jointly models enrollment, event-time, and censoring processes. The package provides tools for trial data simulation, model fitting using 'Stan' via the 'rstan' interface, and event time prediction under a wide range of trial designs, including varying sample sizes, enrollment patterns, treatment effects, and event or censoring time distributions. The package is intended to support interim monitoring, operational planning, and decision-making in clinical trial development. Methods are described in Fu et al. (2025) <doi:10.1002/sim.70310>.
Fits a piecewise exponential hazard to survival data using a
    Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive
    formulation for the spatial dependency in the hazard rates for each piece.
    This function uses Metropolis- Hastings-Green MCMC to allow the number of split
    points to vary and also uses Stochastic Search Variable Selection to determine
    what covariates drive the risk of the event. This function outputs trace plots
    depicting the number of split points in the hazard and the number of variables
    included in the hazard. The function saves all posterior quantities to the
    desired path.
Models time-to-event data from interval-censored
    screening studies. It accounts for latent prevalence at baseline and
    incorporates misclassification due to imperfect test sensitivity. For usage
    details, see the package vignette "BayesPIM_intro". Further details can be
    found in Klausch, Lissenberg-Witte and Coupé (2026)
    <doi:10.1002/sim.70433>.
A lightweight modelling syntax for defining likelihoods and priors
    and for computing Bayes factors for simple one parameter models. It includes
    functionality for computing and plotting priors, likelihoods, and model
    predictions. Additional functionality is included for computing and plotting
    posteriors.
Plotting functions for posterior analysis, MCMC diagnostics,
    prior and posterior predictive checks, and other visualizations
    to support the applied Bayesian workflow advocated in
    Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>.
    The package is designed not only to provide convenient functionality
    for users, but also a common set of functions that can be easily used by
    developers working on a variety of R packages for Bayesian modeling,
    particularly (but not exclusively) packages interfacing with 'Stan'.
The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" <doi:10.1080/00224065.2021.1916413>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" <doi:10.1080/00224065.2022.2161434>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" <doi:10.1080/00224065.2022.2161435>.
Performs Bayesian sample size, precision, and value-of-information analysis for external validation of existing multi-variable prediction models using the approach proposed by Sadatsafavi and colleagues (2026) <doi:10.1002/sim.70389>.
Presence-Only data is best modelled with a Point Process Model.
    The work of Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569> provides
    a way to use exact Bayesian inference to model this type of data, which is
    implemented in this package.
Implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian 
    framework for estimating causal effects of clinical interventions on bounded 
    continuous outcomes in longitudinal observational studies with irregular visits. 
    The methodology is specifically designed for periodontal disease research, where 
    discrete treatments and continuous disease states (e.g., proportion of periodontal 
    pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. 
    The package integrates a double-censored Tobit likelihood to handle boundary mass 
    at zero and one, subject-specific random effects to capture within-subject 
    correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) 
    to model complex nonlinear interactions without parametric restrictions. Causal 
    identification is established under the potential outcomes framework via the 
    G-computation formula, with key estimands including the Mixed Average Potential 
    Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The 
    package provides functions for model fitting, posterior inference, and causal estimand 
    estimation.
Generating population projections for all countries of the world using several probabilistic components,
     such as total fertility rate, life expectancy at birth and net migration (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>).
     The package can be also used for subnational population projections.
An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including 'JAGS', 'BUGS', 'MCMCpack', and 'Stan'.
The goal of 'BayesPower' is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive 'shiny' application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) <doi:10.31234/osf.io/pgdac_v3>.
Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for generalized linear models.
  Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>.
  Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'.
  The Bayesian clinical trial design methodology is described in Chen et al. (2011) 
  <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) 
  <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) 
  <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.
Bayesian power/type I error calculation and model fitting using 
  the power prior and the normalized power prior for proportional hazards models
  with piecewise constant hazard. The methodology and examples of 
  applying the package are detailed in <doi:10.48550/arXiv.2404.05118>.
  The Bayesian clinical trial design methodology is described in 
  Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, 
  and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. 
  The proportional hazards model with piecewise constant hazard is detailed in 
  Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.
Bayesian fitting of projection pursuit regression model. 
  Built to handle continuous and categorical inputs and
  scalar output (Collins et al., 2023 <DOI:10.1007/s11222-023-10334-z>).
Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) <doi:10.1007/s11222-020-09966-2>) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms.
A Bayesian factor-analytic framework for Q methodology. Fits a
    low-rank factor model to Q-sort data with a Student-t likelihood and a
    hierarchical normal prior on loadings, samples the posterior with Stan,
    resolves rotational ambiguity via the MatchAlign post-processing of
    Poworoznek et al. (2025) <doi:10.1214/25-BA1544>, and returns posterior
    summaries including credible intervals for loadings and factor scores,
    probabilistic dominant-factor membership, distinguishing and consensus
    statements, and PSIS-LOO-based factor enumeration following Vehtari et
    al. (2017) <doi:10.1007/s11222-016-9696-4> with the Sivula et al. (2025)
    <doi:10.1214/25-BA1569> parsimony rule.
Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.
Provides Bayesian quantile regression models for complex survey data 
    under informative sampling using survey-weighted estimators. Both single- and 
    multiple-output models are supported. To accelerate computation, all algorithms 
    are implemented in 'C++' using 'Rcpp', 'RcppArmadillo', and 'RcppEigen', and 
    are called from 'R'. See Nascimento and Gonçalves (2024) <doi:10.1093/jssam/smae015> 
    and Nascimento and Gonçalves (2025, in press) <https://academic.oup.com/jssam>.
Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. 
             The available methods include analytical Gaussian reconciliation (Corani et al., 2021) 
             <doi:10.1007/978-3-030-67664-3_13>, 
             MCMC reconciliation of count time series (Corani et al., 2024) 
             <doi:10.1016/j.ijforecast.2023.04.003>, 
             Bottom-Up Importance Sampling (Zambon et al., 2024) 
             <doi:10.1007/s11222-023-10343-y>,
             methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) 
             <https://proceedings.mlr.press/v244/zambon24a.html>,
             analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025)
             <doi: 10.48550/arXiv.2506.19554>.
Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <doi:10.48550/arXiv.1611.06649>.
Methods to estimate optimal dynamic treatment regimes using Bayesian
    likelihood-based regression approach as described in 
    Yu, W., & Bondell, H. D. (2023) <doi:10.1093/jrsssb/qkad016>
    Uses backward induction and dynamic programming theory for computing
    expected values. Offers options for future parallel computing.
Functionality for reliability estimates. For 'unidimensional' tests:
    Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
    bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. 
    For multidimensional tests: omega_t (total) and omega_h (hierarchical). 
    The results include confidence and credible intervals, the 
    probability of a coefficient being larger than a cutoff, 
    and a check for the factor models, necessary for the omega coefficients. 
    The method for the Bayesian 'unidimensional' estimates, except for omega_u, 
    is sampling from the posterior inverse 'Wishart' for the 
    covariance matrix based measures (see 'Murphy', 2007, 
    <https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. 
    The Bayesian omegas (u, t, and h) are obtained by 
    'Gibbs' sampling from the conditional posterior distributions of 
    (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, 
    (4) the correlated factor model
    ('Lee', 2007, <doi:10.1002/9780470024737>).
Provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) <doi:10.1111/rssb.12491>.
Provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) <doi:10.48550/arXiv.2211.02552>.
Computation and visualization of Bayesian Regions of Evidence
    to systematically evaluate the sensitivity of a superiority or
    non-inferiority claim against any prior assumption of its assessors.
    Methodological details are elaborated by Hoefler and Miller
    (<https://osf.io/jxnsv>).  Besides generic functions, the package also
    provides an intuitive 'Shiny' application, that can be run in local R
    environments.
Runs hierarchical linear Bayesian models. Samples from the posterior
    distributions of model parameters in JAGS (Just Another Gibbs Sampler;
	Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group
	parameters of interest with the Savage-Dickey density ratio (Wetzels,
	Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).
Provides tools for Markov chain Monte Carlo (MCMC) and Maximum A Posteriori (MAP) estimation utilizing the 'RTMB' package. It supports various statistical models including generalized linear mixed models, factor analysis, item response theory, and multidimensional unfolding. The package allows users to easily transition between frequentist and Bayesian paradigms using a unified interface. Automatic differentiation and Laplace approximation follow Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, and MCMC sampling uses the No-U-Turn Sampler described by Hoffman and Gelman (2014) <https://jmlr.org/papers/v15/hoffman14a.html>.
Provides datasets and functions used for analysis 
  and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). 
  The package contains a set of functions that summarize and plot Bayesian models from some conjugate families 
  and another set of functions for evaluation of some Bayesian models.
In p >> n settings, full posterior sampling using existing Markov chain Monte
    Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical
    perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.
Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and
    the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary
    information) - BLE_Ratio() - and to categorical data - BLE_Categorical().
    The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg()
    and it is also possible to achieve the design based estimators using vague prior distributions.    
    Based on Gonçalves, K.C.M, Moura, F.A.S and  Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in <doi:10.1016/j.annepidem.2006.04.001> and <doi:10.1177/0272989X09353452>.
Provides tools for fitting Bayesian single index models with
    flexible choices of priors for both the index and the link function.
    The package implements model estimation and posterior inference using
    efficient MCMC algorithms built on the 'nimble' framework, allowing
    users to specify, extend, and simulate models in a unified and
    reproducible manner. The following methods are implemented in the
    package: Antoniadis et al. (2004)
    <https://www.jstor.org/stable/24307224>, Wang (2009)
    <doi:10.1016/j.csda.2008.12.010>, Choi et al. (2011)
    <doi:10.1080/10485251003768019>, Dhara et al. (2019)
    <doi:10.1214/19-BA1170>, McGee et al. (2023) <doi:10.1111/biom.13569>.
Implements methods for Bayesian analysis of State Space Models. 
  Includes implementations of the Particle Marginal Metropolis-Hastings 
  algorithm described in Andrieu et al. (2010) 
  <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by 
  Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and
  T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.
Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.
Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models
             for censored data. Those can be not only right-censored but also interval-censored,
	     doubly-interval-censored or misclassified interval-censored. The methods implemented in the package
	     have been published in Komárek and Lesaffre (2006, Stat. Modelling) <doi:10.1191/1471082X06st107oa>,
	     Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) <doi:10.1002/sim.3083>, Komárek and Lesaffre (2007, Stat. Sinica) <https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, Komárek and Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>,
	     García-Zattera, Jara and Komárek (2016, Biometrics) <doi:10.1111/biom.12424>.
Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure.
    A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two.
    The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" <arXiv:2005.02889>. In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.
An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).
Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.
Provides utilities to describe posterior
    distributions and Bayesian models. It includes point-estimates such as
    Maximum A Posteriori (MAP), measures of dispersion (Highest Density
    Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and
    indices used for null-hypothesis testing (such as ROPE percentage, pd
    and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-011-0040-5> <doi:10.18637/jss.v106.i08>. Subnational probabilistic projections are also supported <doi:10.4054/DemRes.2018.38.60>.
Provides tools for conducting Bayesian analyses and Bayesian model averaging 
    (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, 
    Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains 
    functions for creating a wide range of prior distribution objects, mixing posterior 
    samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc...
    The tools for working with prior distribution span from visualization, generating 'JAGS' 
    and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.
This is an implementation of BART:Bayesian Additive Regression Trees,
        by Chipman, George, McCulloch (2010).
Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).
In screening programs, individuals are usually followed up and tested (screened) for the development of a disease, such as cancer. The target disease often develops progressively in stages; for example healthy (state 1), pre-state disease (state 2), and the disease state (state 3). When the pre-state disease is found during screening it is intervened upon, for example by surgical removal of a lesion, so that the progression of the pre-state disease to disease is interrupted. This is called censoring due to intervention. Researchers often want to estimate the time from baseline to the pre-state disease, the time from the pre-state disease to the disease, and the total time from baseline to the disease. In addition, researchers often want to regress these times on baseline covariates. To these ends, 'BayesTSM' estimates a progressive three-state model with censoring due to intervention using Bayesian estimation methods, as described in Klausch et al. (2023) <doi:10.1214/22-AOAS1669>.
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.
Bayes factors and posterior probabilities in Linear models, 
    aimed at provide a formal Bayesian answer to testing and variable 
    selection problems.
Provides users with its associated functions for pedagogical purposes in visually learning Bayesian networks and Markov chain Monte Carlo (MCMC) computations. It enables users to: a) Create and examine the (starting) graphical structure of Bayesian networks; b) Create random Bayesian networks using a dataset with customized constraints; c) Generate Stan code for structures of Bayesian networks for sampling the data and learning parameters; d) Plot the network graphs; e) Perform Markov chain Monte Carlo computations and produce graphs for posteriors checks. The package refers to one reference item, which describes the methods and algorithms: Vuong, Quan-Hoang and La, Viet-Phuong (2019) <doi:10.31219/osf.io/w5dx6> The 'bayesvl' R package. Open Science Framework (May 18).
Bayesian models are used to estimate effect sizes (e.g., gene expression changes,
    protein abundance differences, drug response effects) while accounting for uncertainty, 
    small sample sizes, and complex experimental designs.
    However, Bayesian posteriors of models with many parameters are often difficult to interpret at a glance.
    One way to quickly identify important biological changes based on frequentist analysis 
    are volcano plots (using fold-changes and p-values).
    Bayesian volcano plots bring together the explicit treatment of uncertainty in 
    Bayesian models and the familiar visualization of volcano plots.
Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities.  In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point.  Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point.  For further details, see: Alexander C. Murph et al. (2023) <doi:10.48550/arXiv.2310.02940>.
Functions for exploring and visualising estimation results
  obtained with BayesX, a free software for estimating structured additive
  regression models (<https://www.uni-goettingen.de/de/bayesx/550513.html>). In addition,
  functions that allow to read, write and manipulate map objects that are required in spatial
  analyses performed with BayesX.
'BayesX' performs Bayesian inference in structured additive regression (STAR) models.
	The R package BayesXsrc provides the 'BayesX' command line tool for easy installation.
	A convenient R interface is provided in package R2BayesX.
Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated
    and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.
A Bayesian, global planktic foraminifera core top calibration to 
    modern sea-surface temperatures. Includes four calibration models, 
    considering species-specific calibration parameters and seasonality.
This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.
Bayesian analysis of luminescence data and C-14 age
    estimates. Bayesian models are based on the following publications:
    Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003>
    and Combes et al. (2015) <doi:10.1016/j.quageo.2015.04.001>. This
    includes, amongst others, data import, export, application of age
    models and palaeodose model.
Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001)  <doi:10.1198/016214501753208690>.
BAYesian inference for MEDical designs in R. Functions for the 
    computation of Bayes factors for common biomedical research designs. 
    Implemented are functions to test the equivalence (equiv_bf), 
    non-inferiority (infer_bf), and superiority (super_bf) of an experimental 
    group compared to a control group on a continuous outcome measure, as well 
    as functions for simulating survival data and calculating a Bayes factor 
    for Cox proportional hazards models. Bayes factors for these tests can be 
    computed based on raw data or summary statistics.
Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using
    Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.
A toolkit for constructing, validating, and justifying Bayesian priors
  in clinical trial settings. Implements expert elicitation via quantile
  matching, the roulette method, and moment matching across six distribution
  families, linear and logarithmic expert pooling, prior-data conflict
  diagnostics including the Box p-value, surprise index, information
  divergence, and Mahalanobis distance, sensitivity analyses with tornado
  and influence heatmap plots, sceptical, robust, and power priors, and
  automated prior justification reports. Includes a fully modular 'Shiny'
  application for interactive use.
  Methods based on O'Hagan et al. (2006, ISBN:9780470029886),
  Box (1980) <doi:10.2307/2982063>,
  Oakley and O'Hagan (2010) <https://tonyohagan.co.uk/shelf/>,
  Schmidli et al. (2014) <doi:10.1111/biom.12242>,
  Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>,
  Spiegelhalter et al. (1994) <doi:10.2307/2983527>.
Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>).
Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.
Provides Bayesian age estimation for bioarchaeological skeletal 
    data using ordinal probit regression models implemented in 'JAGS' and 'NIMBLE'. 
    The package is designed to handle multiple ordinal traits of adult individuals
    and incorporates a Gompertz prior on age to reflect population-level mortality. 
    It accounts for estimation uncertainties and supports full customization of 
    model parameters and Markov Chain Monte Carlo settings. For more details
    see Müller-Scheeßel et al. (2026) <doi:10.1002/ajpa.70289>.
Enable users to evaluate long-term trends using a Generalized 
    Additive Modeling (GAM) approach. The model development includes selecting a 
    GAM structure to describe nonlinear seasonally-varying changes over time, 
    incorporation of hydrologic variability via either a river flow or salinity, 
    the use of an intervention to deal with method or laboratory changes 
    suspected to impact data values, and representation of left- and 
    interval-censored data. The approach has been applied to water quality data 
    in the Chesapeake Bay, a major estuary on the east coast of the United 
    States to provide insights to a range of management- and research-focused 
    questions.  Methodology described in Murphy (2019) 
    <doi:10.1016/j.envsoft.2019.03.027>.
Barzilai-Borwein spectral methods for solving nonlinear
        system of equations, and for optimizing nonlinear objective
        functions subject to simple constraints. A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("BB").
Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>).  
       The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. 
       For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.
We implemented a Bayesian-statistics approach for 
        subtraction of incoherent scattering from neutron total-scattering data. 
        In this approach, the estimated background signal associated with 
        incoherent scattering maximizes the posterior probability, which combines 
        the likelihood of this signal in reciprocal and real spaces with the prior 
        that favors smooth lines. The description of the corresponding approach 
        could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.
Set of functions to calculate Benthic Biotic Indices from
	composition data, obtained whether from morphotaxonomic inventories or
	sequencing data. Based on reference ecological weights publicly available for
	a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8>
	NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.
A client for retrieving data and metadata from central bank
    APIs including 'Banco de España' (BdE), 'Banco de Portugal' (BdP),
    'Bank for International Settlements' (BIS), 'Bank of Canada' (BoC),
    'Bank of England' (BoE), 'Bank of Japan' (BoJ), 'Banque de France'
    (BdF), 'Czech National Bank' (CNB), 'Deutsche Bundesbank' (BBk),
    'European Central Bank' (ECB), 'National Bank of Poland' (NBP),
    'Norges Bank' (NoB), 'Oesterreichische Nationalbank' (OeNB), 'Sveriges
    Riksbank' (SRb), and 'Swiss National Bank' (SNB).
A fast and intuitive batch effect removal tool for single-cell data. BBKNN is originally used in the 'scanpy' python package, and now can be used with 'Seurat' seamlessly.
Supervised learning using Boltzmann Bayes model inference, 
    which extends naive Bayes model to include interactions. Enables 
    classification of data into multiple response groups based on a large 
    number of discrete predictors that can take factor values of 
    heterogeneous levels. Either pseudo-likelihood or mean field 
    inference can be used with L2 regularization, cross-validation, and 
    prediction on new data. 
    <doi:10.18637/jss.v101.i05>.
Miscellaneous helper functions for and from B. Bischl and
    some other guys, mainly for package development.
The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing."
Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.
A system to build, visualise and evaluate Bayesian belief networks. The methods are described in Stafford et al. (2015) <doi:10.12688/f1000research.5981.1>.
Features highly configurable search spaces via the 'paradox'
    package and optimizes every user-defined objective function. The
    package includes several optimization algorithms e.g. Random Search,
    Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband
    (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning',
    'mlr3fselect' and 'miesmuschel'.
Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.
Provides comprehensive tools for blinded sample size re-estimation (BSSR) 
    in two-arm clinical trials with binary endpoints. Unlike traditional fixed-sample 
    designs, BSSR allows adaptive sample size adjustments during trials while 
    maintaining statistical integrity and study blinding. Implements five exact 
    statistical tests: Pearson chi-squared, Fisher exact, Fisher mid-p, Z-pooled 
    exact unconditional, and Boschloo exact unconditional tests. Supports restricted, 
    unrestricted, and weighted BSSR approaches with exact Type I error control.
    Statistical methods based on Mehrotra et al. (2003) <doi:10.1111/1541-0420.00051> 
    and Kieser (2020) <doi:10.1007/978-3-030-49528-2_21>.
The blocked weighted bootstrap (BBW) is an estimation technique 
    for use with data from two-stage cluster sampled surveys in which either 
    prior weighting (e.g. population-proportional sampling or PPS as used in 
    Standardized Monitoring and Assessment of Relief and Transitions or SMART 
    surveys) or posterior weighting (e.g. as used in rapid assessment method or
    RAM and simple spatial sampling method or S3M surveys) is implemented. See 
    Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of 
    bootstrap to cluster samples. See Aaron et al (2016) 
    <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) 
    <doi:10.1371/journal.pone.0162462> for application of the blocked weighted 
    bootstrap to estimate indicators from two-stage cluster sampled surveys.
Implementation of the BC3NET algorithm for gene regulatory network inference (de Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624, <doi:10.1371/journal.pone.0033624>).
Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.
Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).
Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.
Applies Beta Control Charts to defined values. The Beta Chart presents control limits based on the Beta probability distribution, making it suitable for monitoring fraction data from a Binomial distribution as a replacement for p-Charts. The Beta Chart has been applied in three real studies and compared with control limits from three different schemes. The comparative analysis showed that: (i) the Beta approximation to the Binomial distribution is more appropriate for values confined within the [0, 1] interval; and (ii) the proposed charts are more sensitive to the average run length (ARL) in both in-control and out-of-control process monitoring. Overall, the Beta Charts outperform the Shewhart control charts in monitoring fraction data. For more details, see Ângelo Márcio Oliveira Sant’Anna and Carla Schwengber ten Caten (2012) <doi:10.1016/j.eswa.2012.02.146>.
Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.
It is very common nowadays for a study to collect multiple
    features and appropriately integrating multiple longitudinal features
    simultaneously for defining individual clusters becomes increasingly
    crucial to understanding population heterogeneity and predicting
    future outcomes.  'BCClong' implements a Bayesian consensus clustering
    (BCC) model for multiple longitudinal features via a generalized
    linear mixed model. Compared to existing packages, several key
    features make the 'BCClong' package appealing: (a) it allows
    simultaneous clustering of mixed-type (e.g., continuous, discrete and
    categorical) longitudinal features, (b) it allows each longitudinal
    feature to be collected from different sources with measurements taken
    at distinct sets of time points (known as irregularly sampled
    longitudinal data), (c) it relaxes the assumption that all features
    have the same clustering structure by estimating the feature-specific
    (local) clusterings and consensus (global) clustering.
Developed for the following tasks. Simulating, computing maximum likelihood 
             estimator, computing the Fisher information matrix, computing goodness-of-fit measures,
             and correcting bias of the ML estimator for a wide range of distributions fitted to units
             placed on progressive type-I interval censoring and progressive type-II censoring plans.  
             The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and
             bootstrap method for computing the bias-corrected maximum likelihood estimator.
Implementation of bivariate binomial, geometric, and Poisson distributions based on conditional specifications. The package also includes tools for data generation and goodness-of-fit testing for these three distribution families. For methodological details, see Ghosh, Marques, and Chakraborty (2025) <doi:10.1080/03610926.2024.2315294>, Ghosh, Marques, and Chakraborty (2023) <doi:10.1080/03610918.2021.2004419>, and Ghosh, Marques, and Chakraborty (2021) <doi:10.1080/02664763.2020.1793307>.
A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables.
    References:
    F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>,
    F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>,
    F. Castelletti and A. Mascaro (2026) <doi:10.18637/jss.v116.i05>.
Search, query, and download tabular and
    'geospatial' data from the British Columbia Data Catalogue
    (<https://catalogue.data.gov.bc.ca/>).  Search catalogue data records
    based on keywords, data licence, sector, data format, and B.C.
    government organization. View metadata directly in R, download many
    data formats, and query 'geospatial' data available via the B.C.
    government Web Feature Service ('WFS') using 'dplyr' syntax.
Produces an economic evaluation of a sample of suitable variables of
             cost and effectiveness / utility for two or more interventions,
             e.g. from a Bayesian model in the form of MCMC simulations.
             This package computes the most cost-effective alternative and
             produces graphical summaries and probabilistic sensitivity analysis,
             see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.
A Bayesian model averaging approach to causal effect estimation
    based on the BCEE algorithm. Currently supports binary or continuous
    exposures and outcomes. For more details, see 
    Talbot et al. (2015) <doi:10.1515/jci-2014-0035>
    Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.
Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.
Implements the Bayesian Clustering Factor Models (BCFM) for 
    simultaneous clustering and latent factor analysis of multivariate longitudinal data. 
    The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. 
    Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via 'Rcpp'. 
    Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) <doi:10.1002/sim.70350>.
Fit semiparametric bivariate correlated frailty models.
Fit and simulate bivariate correlated frailty models with
    proportional hazard structure. Frailty distributions, such as gamma and
    lognormal models are supported semiparametric procedures. Frailty 
    variances of the two subjects can be varied or equal. Details on the models are available in book of 
    Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given 
    in Kifle et al (2023) <DOI: 10.4310/22-SII738> with modifications. Lognormal fit is based on the approach
    by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, 
    such as gamma, inverse gaussian and power variance frailty models are supported 
    for parametric approach.
Provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.
Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.
Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the 'Oxcal' function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished).
Performs inference for Bayesian conditional logistic regression with informative priors built from the concordant pair data. We include many options to build the priors. And we include many options during the inference step for estimation, testing and confidence set creation. For details, see Kapelner and Tennenbaum (2026) "Improved Conditional Logistic Regression using Information in Concordant Pairs with Software" <doi:10.48550/arXiv.2602.08212>.
Various layers of B.C., including administrative boundaries,
    natural resource management boundaries, census boundaries etc. All
    layers are available in BC Albers
    (<https://spatialreference.org/ref/epsg/3005/>) equal-area projection,
    which is the B.C. government standard. The layers are sourced from the
    British Columbia and Canadian government under open licenses,
    including B.C. Data Catalogue (<https://data.gov.bc.ca>), the
    Government of Canada Open Data Portal
    (<https://open.canada.ca/en/using-open-data>), and Statistics Canada
    (<https://www.statcan.gc.ca/en/terms-conditions/open-licence>).
Inference on the marginal model of the mixed effect model with 
    the Box-Cox transformation and on the model median differences between
    treatment groups for longitudinal randomized clinical trials. These 
    statistical methods are proposed by Maruo et al. (2017) 
    <doi:10.1002/sim.7279>.
Provides an implementation of the product partition model described in Barry and Hartigan (2019) <doi:10.2307/2290726> for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) <doi:10.48550/arXiv.1509.00817>, allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in 'bcp' v.3.0.0, is currently not implemented.
The Behavioral Change Point Analysis (BCPA) is a method of
    identifying hidden shifts in the underlying parameters of a time series,
    developed specifically to be applied to animal movement data which is
    irregularly sampled.  The method is based on: E. Gurarie, R. Andrews and 
    K. Laidre A novel method for identifying behavioural changes in animal 
    movement data (2009) Ecology Letters 12:5 395-408. A development version is 
    on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful 
    for any univariate, irregularly sampled Gaussian time-series, but animal 
    movement analysts are encouraged to apply correlated velocity change point 
    analysis as implemented in the smoove package, as of this writing on GitHub 
    at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis
    is provided in the UnivariateBCPA vignette.
Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases.
Functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals.
             Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. 
             Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.
The bias-corrected estimation methods for the receiver operating characteristics
  ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR)
  assumption.
Search and access more than ten thousand datasets included in 'BCRPDATA' (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).
Bindings to the 'blowfish' password hashing algorithm
    <https://www.openbsd.org/papers/bcrypt-paper.pdf> derived from
    the 'OpenBSD' implementation.
A collection of tools for regression analysis of non-negative data,
    including strictly positive and zero-inflated observations, based on the class of the Box-Cox
    symmetric (BCS) distributions and its zero-adjusted extension. The BCS distributions are a 
    class of flexible probability models capable of describing different levels of skewness and 
    tail-heaviness. The package offers a comprehensive regression modeling framework, including 
    estimation and tools for evaluating goodness-of-fit.
Methods for choosing the rank of an SVD (singular value decomposition) 
    approximation via cross validation. The package provides both Gabriel-style 
    "block" holdouts and Wold-style "speckled" holdouts. It also includes an 
    implementation of the SVDImpute algorithm. For more information about
    Bi-cross-validation, see Owen & Perry's 2009 AoAS article
    (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis
    (at <arXiv:0909.3052>).
Algorithms developed for binned data analysis,
  gene expression data analysis and
  measurement error models for ordinal data analysis.
The Bloom Detecting Algorithm enables the detection of blooms within a time series of species abundance and extracts 22 phenological variables. For details, see Karasiewicz et al. (2022) <doi:10.3390/jmse10020174>.
It brings together several aspects of biodiversity
    data-cleaning in one place. 'bdc' is organized in thematic modules
    related to different biodiversity dimensions, including 1) Merge
    datasets: standardization and integration of different datasets; 2)
    pre-filter: flagging and removal of invalid or non-interpretable
    information, followed by data amendments; 3) taxonomy: cleaning,
    parsing, and harmonization of scientific names from several taxonomic
    groups against taxonomic databases locally stored through the
    application of exact and partial matching algorithms; 4) space:
    flagging of erroneous, suspect, and low-precision geographic
    coordinates; and 5) time: flagging and, whenever possible, correction
    of inconsistent collection date. In addition, it contains
    features to visualize, document, and report data quality – which is
    essential for making data quality assessment transparent and
    reproducible. The reference for the methodology is Ribeiro and colleagues (2022)
    <doi:10.1111/2041-210X.13868>.
Fetches monthly financial tables and banking sector data published on the official website of the Banking Regulation and Supervision Agency of Turkey and also enables you to save it as an Excel file. It is a R implementation of the Python package <https://pypi.org/project/bddkdata/>.
The 'shiny' application 'bdDwC' makes biodiversity data field names Darwin Core compatible.
A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.
For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) <https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and Douglas C. Montgomery (2013, ISBN: 0849323312).
Provides functions to construct efficient block designs for 3-level factorial experiments in block size 3. The designs ensure the estimation of all main effects and two-factor interactions in minimum number of replications. For more details, see Dey and Mukerjee (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>.
Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.
Interface to Local Data Bank ('Bank Danych Lokalnych' - 'bdl') API 
    <https://api.stat.gov.pl/Home/BdlApi?lang=en> with set of useful tools like 
    quick plotting and map generating using data from bank.
Estimation and interpretation of Bayesian distributed lag interaction models (BDLIMs). A BDLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a categorical variable under four specific patterns of modification. The main function is bdlim(). There are also summary and plotting files. Details on methodology are described in Wilson et al. (2017) <doi:10.1093/biostatistics/kxx002>.
A Bayesian framework for estimating distributed lag linear and non-linear models. Model fitting is implemented using Integrated Nested Laplace Approximation (R package 'INLA'), together with prediction and visualization of exposure-lag-response associations. Additional functions allow estimation of optimal exposure values (e.g., minimum mortality temperature) and computation of attributable fractions and numbers. Models with 'crossbasis' or 'onebasis' terms are supported (R package 'dlnm').
The main function generateDataset() processes a user-supplied .R file that 
  contains metadata parameters in order to generate actual data. The metadata parameters 
  have to be structured in the form of metadata objects, the format of which is 
  outlined in the package vignette. This approach allows to generate artificial data 
  in a transparent and reproducible manner.
Analyze differences among time series curves with p-value
        adjustment for multiple comparisons introduced in Oleson et al
        (2015) <DOI:10.1177/0962280215607411>.
Provide a tool to easily build customized data flows to pre-process large volumes 
    of information from different sources. To this end, 'bdpar' allows to (i) easily use and 
    create new functionalities and (ii) develop new data source extractors according to the 
    user needs. Additionally, the package provides by default a predefined data flow 
    to extract and pre-process the most relevant information (tokens, dates, ... ) from some textual 
    sources (SMS, Email, YouTube comments).
Computation of asymptotic confidence intervals for negative and positive predictive values in binary diagnostic tests in case-control studies. Experimental design for hypothesis tests on predictive values.
Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2022) <doi:10.1002/env.2711>.
Implements Bayesian inference to detect signal from blinded 
    clinical trial when total number of adverse events of special 
    concerns and total risk exposures from all patients are available in the study.  
    For more details see the article by Mukhopadhyay et. al. (2018) 
    titled 'Bayesian Detection of Potential Risk Using Inference on Blinded Safety Data', 
    in Pharmaceutical Statistics (to appear).
Provides a continuous date scale, omitting weekends and holidays.
Implements methods for building and analyzing models based on panel
    data as described in the paper by 
    Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>).
    The package provides functions to estimate dynamic panel data models
    and analyze the results of the estimation.
This is a special case of sparse matrices, used by coxme.
Provides methods to perform block diagonal covariance matrix detection using singular vectors ('BD-SVD'), which can be extended to inherently sparse principal component analysis ('IS-PCA'). The methods are described in Bauer (2025) <doi:10.1080/10618600.2024.2422985> and Bauer (2026) <doi:10.48550/arXiv.2510.03729>.
A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
Provides an R interface for the Bureau of Economic Analysis (BEA) 
		API (see <http://www.bea.gov/API/bea_web_service_api_user_guide.htm> for 
		more information) that serves two core purposes - 
    1. To Extract/Transform/Load data [beaGet()] from the BEA API as R-friendly 
		formats in the user's work space [transformation done by default in beaGet() 
		can be modified using optional parameters; see, too, bea2List(), bea2Tab()].
		2. To enable the search of descriptive meta data [beaSearch()].
		Other features of the library exist mainly as intermediate methods 
		or are in early stages of development.
		Important Note - You must have an API key to use this library.  
		Register for a key at <http://www.bea.gov/API/signup/index.cfm> .
A platform for interactive data analysis designed to simplify development, deployment, interaction, and exploration (TEDDIE). The package enables users to create customized analyses and deploy them to end users, who can perform interactive analyses and export results to RTF or HTML files. It allows developers to focus on R code for analysis rather than managing HTML or Shiny application code.
Reproducible and automated analysis of multiplex bead assays such
    as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex
    (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi
    Biotec 2014; Application note: Data acquisition and analysis without the
    MACSQuant analyzer;
    <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The
    package provides functions for streamlined reading of fcs files, and
    identification of bead clusters and analyte expression. The package eases
    the calculation of standard curves and the subsequent calculation of the
    analyte concentration.
A minimalist web framework for developing application programming 
    interfaces in R that provides a flexible framework for handling common 
    HTTP-requests, errors, logging, and an ability to integrate any R code as 
    server middle-ware.
Fast Bayesian inference of marginal and conditional independence structures from high-dimensional data. Leday and Richardson (2019), Biometrics, <doi:10.1111/biom.13064>.
A suite of tools to mitigate sampling bias in species occurrence
    records by thinning data in the environmental space (E-space). This process
    can improve the accuracy and precision of species distribution models (SDM,
    also known as ecological niche models, ENM). The package offers a
    data-driven protocol to determine thinning parameters using kernel-density
    bandwidth selection. Two thinning methods are provided (stochastic and
    deterministic) to reduce over-sampled environmental conditions and down-weight
    outlier observations. The name 'bean' reflects the core principle of the
    method: each 'pod' (a grid cell in E-space) is allowed to contain only a
    limited number of 'beans' (occurrence points). See Silverman (1986,
    ISBN:978-0-412-24620-3) and Rousseeuw and Leroy (2003,
    ISBN:978-0-471-48855-2) for the underlying statistical methods.
Plots univariate comparison graphs, an alternative to
        boxplot/stripchart/violin plot.
These data contain morphological image measurements for dried
    beans from Koklu and Ozkan (2020) <doi:10.1016/j.compag.2020.105507>.
Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Juan A. Serur, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2019) <doi:10.1515/ijb-2018-0052> for a detailed presentation of the method.
'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is a command-line tool.
    This package provides a way to call 'BEAST2' 
    from an 'R' function call.
Provides JAR to perform Markov chain Monte Carlo (MCMC) inference using
    the popular Bayesian Evolutionary Analysis by Sampling Trees 'BEAST X' software
    library of Baele et al (2025) <doi:10.1038/s41592-025-02751-x>. 'BEAST X' supports
    auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte
    Carlo sampling for a large variety of composable standard and phylogenetic
    statistical models using high performance computing.  By placing the 'BEAST X' JAR in
    this package, we offer an efficient distribution system for 'BEAST X' use by other R
    packages using CRAN.
Bayesian dynamic borrowing with covariate adjustment via inverse probability 
  weighting for simulations and data analyses in clinical trials. This makes it easy to use
  propensity score methods to balance covariate distributions between external and internal 
  data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.
'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'BEAUti 2' (which is part of 'BEAST2') is a GUI tool 
  that allows users to specify the many possible setups
  and generates the XML file 'BEAST2' needs to run.
  This package provides a way to create 'BEAST2' input
  files without active user input, but using
  R function calls instead.
Provides a collection of utility functions for biostatistics,
    agricultural trial planning, and experimental design. Key features include
    generating experimental designs (like Latin Square, Alpha-Lattice by
    Patterson and Williams (1976) <doi:10.2307/2335087>, and Factorial),
    fieldbook creation, layout sketching, QR code-based label generation, and
    descriptive statistical tools to easily handle most common descriptive
    statistics for quantitative variables as described by Field, A., Miles, J.,
    & Field, Z. (2012, ISBN:978-1-4462-0045-2).
Dose-response modeling for negative-binomial distributed data with
  a variety of dose-response models. Covariate adjustment and Bayesian
  model averaging is supported. Functions are provided to easily obtain
  inference on the dose-response relationship and plot the dose-response curve.
An interface for the 'Neo4j' database providing
    mapping between different identifiers of biological entities.
    This Biological Entity Dictionary (BED)
    has been developed to address three main challenges.
    The first one is related to the completeness of identifier mappings.
    Indeed, direct mapping information provided by the different systems
    are not always complete and can be enriched by mappings provided by other
    resources.
    More interestingly, direct mappings not identified by any of these
    resources can be indirectly inferred by using mappings to a third reference.
    For example, many human Ensembl gene ID are not directly mapped to any
    Entrez gene ID but such mappings can be inferred using respective mappings
    to HGNC ID. The second challenge is related to the mapping of deprecated
    identifiers. Indeed, entity identifiers can change from one resource
    release to another. The identifier history is provided by some resources,
    such as Ensembl or the NCBI, but it is generally not used by mapping tools.
    The third challenge is related to the automation of the mapping process
    according to the relationships between the biological entities of interest.
    Indeed, mapping between gene and protein ID scopes should not be done
    the same way than between two scopes regarding gene ID.
    Also, converting identifiers from different organisms should be possible
    using gene orthologs information.
    The method has been published by
    Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.
Provides functions that allow users to quantify the relative 
	contributions of geographic and ecological distances to empirical patterns of genetic 
	differentiation on a landscape.  Specifically, we use a custom Markov chain 
	Monte Carlo (MCMC) algorithm, which is used to estimate the parameters of the 
	inference model, as well as functions for performing MCMC diagnosis and assessing 
	model adequacy.
A matrix-like data structure that allows for efficient,
    convenient, and scalable subsetting of binary genotype/phenotype files
    generated by PLINK (<https://www.cog-genomics.org/plink2>), the whole
    genome association analysis toolset, without loading the entire file into
    memory.
Genomic regions processing using open-source command line tools such as 'BEDTools', 'BEDOPS' and 'Tabix'.
  These tools offer scalable and efficient utilities to perform genome arithmetic e.g indexing, formatting and merging.
  bedr API enhances access to these tools as well as offers additional utilities for genomic regions processing.
Efficiently access the 'Bedrock Bio' library of open-access
    computational biology datasets. Lazily query datasets backed by 'DuckDB'
    and 'Apache Iceberg', with support for predicate pushdown and column
    projection to the cloud storage backend. This enables quick, iterative
    access to otherwise massive, unwieldy datasets without downloading them
    in full. See <https://bedrock.bio> for available datasets and
    documentation.
Flags and checks occurrence data that are in Darwin Core
    format.  The package includes generic functions and data as well as
    some that are specific to bees. This package is meant to build upon
    and be complimentary to other excellent occurrence cleaning packages,
    including 'bdc' and 'CoordinateCleaner'.  This package uses datasets
    from several sources and particularly from the Discover Life Website,
    created by Ascher and Pickering (2020).  For further information,
    please see the original publication and package website.  Publication
    - Dorey et al. (2023) <doi:10.1101/2023.06.30.547152> and package
    website - Dorey et al. (2023) <https://github.com/jbdorey/BeeBDC>.
Performs estimation of marginal treatment effects for binary 
  outcomes when using logistic regression working models with covariate 
  adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). 
  Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> 
  and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.
Tools to calibrate, validate, and make predictions with the
    General Unified Threshold model of Survival adapted for Bee species. The
    model is presented in the publication from Baas, J., Goussen, B., Miles, M.,
    Preuss, T.G., Roessing, I. (2022) <doi:10.1002/etc.5423> and 
    Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., 
    van den Berg, S., Roessink, I. (2024) <doi:10.1002/etc.5871>, and is based on the 
    GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) 
    <doi:10.1021/es103092a>.
    The authors are grateful to Bayer A.G. for its financial support.
The main function of this package is beep(), with the purpose to
    make it easy to play notification sounds on whatever platform you are on.
    It is intended to be useful, for example, if you are running a long analysis
    in the background and want to know when it is ready.
The bee swarm plot is a one-dimensional scatter plot like
    "stripchart", but with closely-packed, non-overlapping points.
Facilitates many of the analyses performed in studies of
    behavioral economic demand. The package supports commonly-used options for
    modeling operant demand including (1) data screening proposed by Stein,
    Koffarnus, Snider, Quisenberry, & Bickel (2015; <doi:10.1037/pha0000020>),
    (2) fitting models of demand such as linear (Hursh, Raslear, Bauman,
    & Black, 1989, <doi:10.1007/978-94-009-2470-3_22>), exponential (Hursh & Silberberg, 2008,
    <doi:10.1037/0033-295X.115.1.186>) and modified exponential (Koffarnus,
    Franck, Stein, & Bickel, 2015, <doi:10.1037/pha0000045>), and (3) calculating
    numerous measures relevant to applied behavioral economists (Intensity,
    Pmax, Omax). Also supports plotting and comparing data.
Facilitates some of the analyses performed in studies of
    behavioral economic discounting. The package supports scoring of the 27-Item Monetary Choice
    Questionnaire (see Kaplan et al., 2016; <doi:10.1007/s40614-016-0070-9>), calculating k
    values (Mazur's simple hyperbolic and exponential) using nonlinear regression, calculating
    various Area Under the Curve (AUC) measures, plotting regression curves for both fit-to-group and
    two-stage approaches, checking for unsystematic discounting
    (Johnson & Bickel, 2008; <doi:10.1037/1064-1297.16.3.264>) and scoring of the
    minute discounting task (see Koffarnus & Bickel, 2014; <doi:10.1037/a0035973>) using the
    Qualtrics 5-trial discounting template (see the Qualtrics Minute Discounting User Guide;
    <doi:10.13140/RG.2.2.26495.79527>), which is also available as a .qsf file in this package.
This is a sub national population projection model for calculating
    population development. The model uses a cohort component method.
    Further reading: Stanley K. Smith: A Practitioner's Guide to State and 
    Local Population Projections. 2013.  <doi:10.1007/978-94-007-7551-0>.
Contains specialised analyses and
    visualisation tools for behavior change science.
    These facilitate conducting determinant studies
    (for example, using confidence interval-based
    estimation of relevance, CIBER, or CIBERlite
    plots, see Crutzen, Noijen & Peters (2017)
    <doi:10/ghtfz9>),
    systematically developing, reporting,
    and analysing interventions (for example, using
    Acyclic Behavior Change Diagrams), and reporting
    about intervention effectiveness (for example, using
    the Numbers Needed for Change, see Gruijters & Peters
    (2017) <doi:10/jzkt>), and computing the
    required sample size (using the Meaningful Change
    Definition, see Gruijters & Peters (2020)
    <doi:10/ghpnx8>).
    This package is especially useful for
    researchers in the field of behavior change or
    health psychology and to behavior change
    professionals such as intervention developers and
    prevention workers.
Implements an S3 class based on 'data.table' to store and process efficiently ethomics (high-throughput behavioural) data.
Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.
Tools for downloading historical financial data from the www.belex.rs.
Calculates the Boltzmann entropy of a landscape gradient.
    This package uses the analytical method created by Gao, P., Zhang, H.
    and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019
    (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by
    allowing calculations on data with missing values.
A likelihood method is implemented to present evidence for evaluating bioequivalence (BE). The functions use bioequivalence data [area under the blood concentration-time curve (AUC) and peak concentration (Cmax)] from various crossover designs commonly used in BE studies including a fully replicated, a partially replicated design, and a conventional 2x2 crossover design. They will calculate the profile likelihoods for the mean difference, total standard deviation ratio, and within subject standard deviation ratio for a test and a reference drug. A plot of a standardized profile likelihood can be generated along with the maximum likelihood estimate and likelihood intervals, which present evidence for bioequivalence. See Liping Du and Leena Choi (2015) <doi:10.1002/pst.1661>.
Bell regression models for count data with overdispersion. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) <doi:10.1016/j.apm.2017.12.014> and Lemonte et al. (2020) <doi:10.1080/02664763.2019.1636940>.
Provides functions to download and work with the Bangladesh
    Environmental Mobility Panel (BEMP), a household panel survey tracing
    the impacts of riverbank erosion and flooding on (im)mobility,
    socio-economic outcomes, and political attitudes along the Jamuna River
    in Bangladesh (2021-2024). Wave datasets (20 files across 14 survey
    rounds) are hosted on Zenodo
    (<doi:10.5281/zenodo.18229497>) and downloaded on demand with
    local caching. Bundled data include a merged cross-wave codebook and
    wave metadata.
Tools to accurately benchmark and analyze execution times for
    R expressions.
Full implementation of the 28 distributions introduced as benchmarks for nonparametric density estimation by Berlinet and Devroye (1994) <https://hal.science/hal-03659919>. Includes densities, cdfs, quantile functions and generators for samples as well as additional information on features of the densities. Also contains the 4 histogram densities used in Rozenholc/Mildenberger/Gather (2010) <doi:10.1016/j.csda.2010.04.021>.
Methods for frontier
    analysis, Data Envelopment Analysis (DEA), under different
    technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+),
    and using different efficiency measures (input based, output based,
    hyperbolic graph, additive, super, and directional efficiency). Peers
    and slacks are available, partial price information can be included,
    and optimal cost, revenue and profit can be calculated. Evaluation of
    mergers is also supported.  Methods for graphing the technology sets
    are also included. There is also support for comparative methods based
    on Stochastic Frontier Analyses (SFA) and for convex nonparametric 
    least squares of convex functions (STONED). In general, the methods 
    can be used to solve not only standard models, but also many other 
    model variants. It complements the book, Bogetoft and Otto,
    Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of
    course also be used as a stand-alone package.
Benchmark your CPU and compare against other CPUs.  Also
    provides functions for obtaining system specifications, such as RAM,
    CPU type, and R version.
Crowd sourced benchmarks from running the 'benchmarkme'
    package.
Provides a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) <doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) <doi:10.1037/met0000358>).
Perform the Benford's Analysis to a data set in order to evaluate if it contains human fabricated data. For more details on the method see Moreau, 2021, Model Assist. Statist. Appl., 16 (2021) 73–79. <doi:10.3233/MAS-210517>.
Provides tools that make it easier to validate data using Benford's Law.
Several specialized statistical tests and support functions 
			for determining if numerical data could conform to Benford's law.
Bayesian model and associated tools for generating estimates of 
    total naloxone kit numbers distributed and used from naloxone kit orders 
    data. Provides functions for generating simulated data of naloxone kit use
    and functions for generating samples from the posterior.
Included are two main interfaces, bentcable.ar() and
	bentcable.dev.plot(), for fitting and diagnosing bent-cable
	regressions for autoregressive time-series data (Chiu and
	Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time
	series or otherwise - Chiu, Lockhart and Routledge 2006,
	<doi:10.1198/016214505000001177>). Some components in the package
	can also be used as stand-alone functions. The bent cable
	(linear-quadratic-linear) generalizes the broken stick
	(linear-linear), which is also handled by this package. Version
	0.2 corrected a glitch in the computation of confidence intervals
	for the CTP. References that were updated from Versions 0.2.1 and
	0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved
	robustness of the error-message producing mechanism. Version 0.3.1
	improves the NAMESPACE file of the package. It is the author's
	intention to distribute any future updates via GitHub.
Preprocessing tools and biodiversity measures 
    (species abundance, species richness, population heterogeneity and 
    sensitivity) for analysing marine benthic data. See Van Loon et al.
    (2015) <doi:10.1016/j.seares.2015.05.002> for an application of 
    these tools.
Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).
Bayesian analysis for exponential random graph models using
    advanced computational algorithms. More information can be found at:
    <https://acaimo.github.io/Bergm/>.
A suite of open-source R functions designed to produce standard 
    metrics for forest management and ecology from forest inventory data. The 
    overarching goal is to minimize potential inconsistencies introduced by the 
    algorithms used to compute and summarize core forest metrics. Learn more 
    about the purpose of the package and the specific algorithms used in the 
    package at <https://github.com/kearutherford/BerkeleyForestsAnalytics>.
Bayesian analysis for stochastic extensions of non-linear
    dynamic systems using advanced computational algorithms. Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) <doi:10.48550/arXiv.2211.15229>.
Draw horizontal histograms, color scattered points by 3rd dimension,
    enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, 
    use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, 
    fit multiple functions.
Interface to the Python package 'BERTopic' <https://maartengr.github.io/BERTopic/index.html> for transformer-based topic modeling. Provides R wrappers to fit BERTopic models, transform new documents, update and reduce topics, extract topic- and document-level information, and generate interactive visualizations. 'Python' backends and dependencies are managed via the 'reticulate' package.
Provides topic modeling and visualization by interfacing with the
    'BERTopic' library for 'Python' via 'reticulate'. See Grootendorst (2022)
    <doi:10.48550/arXiv.2203.05794>.
Calculates several entropy metrics for spatial data 
  inspired by Boltzmann's entropy formula.
  It includes metrics introduced by Cushman for landscape mosaics 
  (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), 
  and landscape gradients and point patterns
  (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for 
  landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>);
  and by Gao et al. for landscape gradients
  (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).
An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.
Computations for Bessel function for complex, real and partly
  'mpfr' (arbitrary precision) numbers; notably interfacing TOMS 644;
  approximations for large arguments, experiments, etc.
Best subset glm using information criteria or cross-validation,
        carried by using 'leaps' algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601>
        or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>.
        Implements PCR and PLS using AIC/BIC.
        Implements one-standard deviation rule for use with the 'caret' package.
Creates plots showing scored HR experiments and plots of distribution of means of ranks of HR score from bootstrapping.
An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.
Estimate a suite of normalizing transformations, including 
    a new adaptation of a technique based on ranks which can guarantee 
    normally distributed transformed data if there are no ties: ordered 
    quantile normalization (ORQ). ORQ normalization combines a rank-mapping
    approach with a shifted logit approximation that allows
    the transformation to work on data outside the original domain. It is 
    also able to handle new data within the original domain via linear 
    interpolation. The package is built to estimate the best normalizing 
    transformation for a vector consistently and accurately. It implements 
    the Box-Cox transformation, the Yeo-Johnson transformation, three types 
    of Lambert WxF transformations, and the ordered quantile normalization 
    transformation. It estimates the normalization efficacy of other
    commonly used transformations, and it allows users to specify 
    custom transformations or normalization statistics. Finally, functionality
    can be integrated into a machine learning workflow via recipes.
Decision tree algorithm with a major feature added. Allows for users to define an ordering on the partitioning process.
    Resulting in Branch-Exclusive Splits Trees (BEST). Cedric Beaulac and Jeffrey S. Rosentahl (2019) <arXiv:1804.10168>.
Provides a practical tool for estimating the burden of common communicable diseases in settlements of displaced populations. An online version of the tool can be found at <http://who-refugee-bod.ecdf.ed.ac.uk/shiny/app/>. Estimates of burden of disease aim to synthesize data about cause-specific morbidity and mortality through a systematic approach that enables evidence-based decisions and comparisons across settings. The focus of this tool is on four acute communicable diseases and syndromes, including Acute respiratory infections, Acute diarrheal diseases, Acute jaundice syndrome and Acute febrile illnesses.
Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, <doi:10.48550/arXiv.2103.00674>  and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report.
Fits Beta Autoregressive Moving Average (BARMA) models for time series data distributed in the standard unit interval (0, 1). The estimation is performed via the conditional maximum likelihood method using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton algorithm. A ridge penalization scheme is available to improve numerical stability of the estimation, as proposed by Cribari-Neto, Costa and Fonseca (2025) <doi:10.1214/25-BJPS645>. The package includes tools for model fitting, diagnostic checking, and forecasting, along with two hydro-environmental datasets from Brazil. Based on the work of Rocha and Cribari-Neto (2009) <doi:10.1007/s11749-008-0112-z> and the associated erratum Rocha and Cribari-Neto (2017) <doi:10.1007/s11749-017-0528-4>. The original code was developed by Fabio M. Bayer.
Provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) <doi:10.1016/j.csda.2021.107345>.
Three games: proton, frequon and regression. Each one 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.
  In proton 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.
  In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages.
  This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge?
  In regression you will test your modeling skills in a series of eight sub-tasks.
  Try only if ANOVA is your close friend.
  It's a part of Beta and Bit project.
  You will find more about the Beta and Bit project at <https://github.com/BetaAndBit/Charts>.
Fit beta calibration models and obtain calibrated probabilities from
    them.
A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.
Implements the four-parameter Beta-Danish distribution and its
    three-parameter submodel for survival and reliability analysis, based on
    Ahmad and Danish (2025) <doi:10.2478/jamsi-2025-0010>. Provides functions
    for density, distribution, quantile, hazard, and random generation.
    Includes maximum likelihood estimation for complete and right-censored
    data, goodness-of-fit assessment, comparison with standard lifetime
    distributions, and publication-quality visualizations. Advanced modules
    support Accelerated Failure Time (AFT) regression, mixture and
    promotion-time cure models, and competing risks analysis.
Generates confidence intervals for standardized regression coefficients
    using delta method standard errors for models fitted by lm()
    as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6>
    and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>.
    The package can also be used to generate confidence intervals for
    differences of standardized regression coefficients 
    and as a general approach to performing the delta method.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
Package providing a number of functions for working with Two- and 
    Four-parameter Beta and closely related distributions (i.e., the Gamma-
    Binomial-, and Beta-Binomial distributions).
        Includes, among other things: 
    - d/p/q/r functions for Four-Parameter Beta distributions and Generalized
    "Binomial" (continuous) distributions, and d/p/r- functions for Beta-
    Binomial distributions.
    - d/p/q/r functions for Two- and Four-Parameter Beta distributions
    parameterized in terms of their means and variances rather than their
    shape-parameters.
    - Moment generating functions for Binomial distributions, Beta-Binomial 
    distributions, and observed value distributions.
    - Functions for estimating classification accuracy and consistency, 
    making use of the Classical Test-Theory based 'Livingston and Lewis' (L&L) 
    and 'Hanson and Brennan' approaches.
      A shiny app is available, providing a GUI for the L&L approach when used 
    for binary classifications. For url to the app, see documentation for the 
    LL.CA() function.
    Livingston and Lewis (1995) <doi:10.1111/j.1745-3984.1995.tb00462.x>.
    Lord (1965) <doi:10.1007/BF02289490>.
    Hanson (1991) <https://files.eric.ed.gov/fulltext/ED344945.pdf>.
Generates Monte Carlo confidence intervals
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().
    'betaMC' combines ideas from Monte Carlo confidence intervals for the indirect effect
    (Pesigan and Cheung, 2024 <doi:10.3758/s13428-023-02114-4>)
    and the sampling covariance matrix of regression coefficients
    (Dudgeon, 2017 <doi:10.1007/s11336-017-9563-z>)
    to generate confidence intervals effect sizes in regression.
Generates nonparametric bootstrap confidence intervals
    (Efron and Tibshirani, 1993: <doi:10.1201/9780429246593>)
    for standardized regression coefficients (beta) and other effect sizes,
    including multiple correlation, semipartial correlations,
    improvement in R-squared, squared partial correlations,
    and differences in standardized regression coefficients,
    for models fitted by lm().
Functions to compute pair-wise dissimilarities (distance matrices) and multiple-site dissimilarities, separating the turnover and nestedness-resultant components of taxonomic (incidence and abundance based), functional and phylogenetic beta diversity.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. 
	Researchers often work with data taking the form of proportions that can be modeled with 
	a beta distribution. Here we present an R package, 'BetaPASS', that perform power and 
	sample size calculations for data following a beta distribution with comparative 
	nonparametric output. This package allows flexibility with multiple options for link 
	functions to fit the data and graphing functionality for visual comparisons.
Permutational method to incorporate taxonomic uncertainty  and some functions to assess its effects on parameters of some widely used multivariate methods in ecology, as explained in Cayuela et al. (2011) <doi:10.1111/j.1600-0587.2009.05899.x>.
Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1),
  e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>.
  Moreover, extended-support beta regression models can accommodate dependent variables with
  boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) <doi:10.1093/jrsssc/qlaf039>.
  For the classical beta regression model, alternative specifications are provided:
  Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for
  beta regression, see Grün, Kosmidis, and Zeileis (2012) <doi:10.18637/jss.v048.i11>.
Maximum-likelihood estimation of beta regression models for
    responses derived from bounded rating scales. Observations are treated
    as interval-censored on (0, 1) after a scale-to-unit transformation,
    and the likelihood is built from the difference of the beta CDF at the
    interval endpoints. The complete likelihood supports mixed censoring
    types: uncensored, left-censored, right-censored, and
    interval-censored observations. Both fixed- and
    variable-dispersion submodels are supported, with flexible link
    functions for the mean and precision components. A compiled C++ backend
    (via 'Rcpp' and 'RcppArmadillo') provides numerically stable,
    high-performance log-likelihood evaluation. Standard S3 methods
    (print(), summary(), coef(), fitted(), residuals(), predict(),
    plot(), confint(), vcov(), logLik(), AIC(), BIC()) are available
    for fitted objects.
Generates robust confidence intervals for standardized regression coefficients
    using heteroskedasticity-consistent standard errors for models fitted by lm()
    as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>.
    The package can also be used to generate confidence intervals for R-squared,
    adjusted R-squared, and differences of standardized regression coefficients.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.
It computes betas-select, coefficients after standardization in
  structural equation models and regression models, standardizing only selected
  variables. Supports models with moderation, with product terms formed after
  standardization. It also offers confidence intervals that account for
  standardization, including bootstrap confidence intervals as proposed by
  Cheung et al. (2022) <doi:10.1037/hea0001188>. An introduction to the package
  can be found in Sun et al. (2026) <doi:10.1080/00273171.2026.2672692>.
Quantify the stability of each community based on the beta 
    diversity between communities gathered in a single sampling event rather 
    than a series of continuous sampling activities.
Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
The sample size according to the Bethel's procedure.
Implements likelihood inference for early epidemic analysis. BETS is short for the four key epidemiological events being modeled: Begin of exposure, End of exposure, time of Transmission, and time of Symptom onset. The package contains a dataset of the trajectory of confirmed cases during the coronavirus disease (COVID-19) early outbreak. More detail of the statistical methods can be found in Zhao et al. (2020) <arXiv:2004.07743>.
Provides squared semi partial correlations, tolerance, Mahalanobis, Likelihood Ratio Chi Square, and Pseudo R Square. Aberson, C. L. (2022) <doi:10.31234/osf.io/s2yqn>.
A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.
Provides Bayesian early-warning surveillance methods for
    monitoring healthcare performance and patient safety outcomes.
    The package draws on risk-adjusted monitoring frameworks developed by
    Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441>,
    Spiegelhalter et al. (2003) <doi:10.1002/sim.1546>, Cook et al.
    (2011) <doi:10.1136/bmjqs.2008.031831>, and Neuburger et al. (2017)
    <doi:10.1136/bmjqs-2016-005511>. The package implements Bayesian
    predictive modelling, risk-adjusted monitoring, early-warning signal
    detection, and graphical tools for continuous quality improvement
    and healthcare performance assessment.
Provides functions for summarizing and plotting the output of the command-line tool 'BeXY' (<https://bitbucket.org/wegmannlab/bexy>), a tool that performs Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.
Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).
Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2025) <doi:10.1080/01621459.2025.2594191>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.
The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.
Decomposition of time series into
    trend, seasonal, and remainder components with methods for detecting and
    characterizing abrupt changes within the trend and seasonal components. 'BFAST'
    can be used to analyze different types of satellite image time series and can
    be applied to other disciplines dealing with seasonal or non-seasonal time
    series, such as hydrology, climatology, and econometrics. The algorithm can be
    extended to label detected changes with information on the parameters of the
    fitted piecewise linear models. 'BFAST' monitoring functionality is described
    in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor'
    provides functionality to detect disturbance in near real-time based on 'BFAST'-
    type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>.
    'BFAST Lite' approach is a flexible approach that handles missing data
    without interpolation, and will be described in an upcoming paper.
    Furthermore, different models can now be used to fit the
    time series data and detect structural changes (breaks).
Design and analysis of one- and two-stage binomial
    clinical phase II trials using Bayes factors. Implements Bayes factors
    for point-null and directional hypotheses, predictive densities under
    different hypotheses, and power and sample size calibration. Both one-arm
    trials with only a single treatment arm and two-arm trials with treatment
    and control arm are implemented for the one- and two-stage designs.
Calculate the operating characteristics of the 
    Bayesian Optimal Interval with Back Filling Design for dose escalation
    in early-phase oncology trials.
Implements the Backfill Bayesian Optimal Interval Design (BF-BOIN-ET), 
             a novel clinical trial methodology for dose optimization that simultaneously consider both efficacy 
             and toxicity outcome as described in (Takeda et al  (2025) <doi:10.1002/pst.2470>).
             The package has been extended to include a seamless two-stage phase I/II trial design with backfill 
             and joint efficacy and toxicity monitoring as described in (Takeda et al (2026) <doi:10.1002/pst.70092>).
Implements the distance measure for mixed-scale variables proposed by 
    Buttler and Fickel (1995), based on normalized mean pairwise distances 
    (Gini mean difference), and an R2 statistic to assess clustering quality.
Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies.
The Bayesian Federated Inference ('BFI') method combines inference results obtained from local data sets in the separate centers. In this version of the package, the 'BFI' methodology is programmed for linear, logistic and survival regression models. For GLMs, see Jonker, Pazira and Coolen (2024) <doi:10.1002/sim.10072>; for survival models, see Pazira, Massa, Weijers, Coolen and Jonker (2025) <doi:10.48550/arXiv.2404.17464>; and for heterogeneous populations, see Jonker, Pazira and Coolen (2025) <doi:10.1017/rsm.2025.6>.
Provides tools for factor analysis in financial and econometric settings under Beta factor models. It includes functions to simulate factor-model data with Beta-distributed idiosyncratic components (e.g., standard Beta, scaled Beta, and truncated Beta distributions) and to conduct model diagnostic assessments such as likelihood ratio tests for factor number selection and goodness-of-fit tests for Beta distribution assumptions. Estimation routines encompass maximum likelihood estimation for finite-dimensional Beta factor models, regularized Beta factor analysis for high-dimensional datasets, and shrinkage-based estimation for robust Beta factor loading recovery in noisy or incomplete data environments. The package's methodological framework is detailed in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Implements the Bayesian paradigm for fractional
 polynomial models under the assumption of normally distributed error terms, see
 Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.
Implementation of default Bayes factors
    for testing statistical hypotheses under various statistical models. The package is
    intended for applied quantitative researchers in the
    social and behavioral sciences, medical research,
    and related fields. The Bayes factor tests can be
    executed for statistical models such as 
    univariate and multivariate normal linear models,
    correlation analysis, generalized linear models, special cases of 
    linear mixed models, survival models, relational
    event models. Parameters that can be tested are
    location parameters (e.g., group means, regression coefficients),
    variances (e.g., group variances), and measures of 
    association (e.g,. polychoric/polyserial/biserial/tetrachoric/product
    moments correlations), among others.
    Relevant references on the methodology The statistical underpinnings are
    described in
    O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>,
    Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>,
    Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>,
    Mulder and Fox (2019) <DOI:10.1214/18-BA1115>,
    Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>,
    Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>,
    Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>,
    Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and
    Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the
    packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18>
    and the relevant methodological papers.
Implements z-test, t-test, and normal moment prior Bayes factors based on summary statistics, along with functionality to perform corresponding power and sample size calculations as described in Pawel and Held (2025) <doi:10.1080/00031305.2025.2467919>.
Search and download data from the Swiss Federal Statistical Office (BFS) APIs <https://www.bfs.admin.ch/>.
How to fit a straight line through a set of points with errors in
  both coordinates? The 'bfsl' package implements the York regression 
  (York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the 
  intercept, slope and standard errors for the best-fit straight line to 
  independent points with (possibly correlated) normally distributed errors in 
  both x and y. Other commonly used errors-in-variables methods, such as 
  orthogonal distance regression, geometric mean regression or Deming regression
  are special cases of the 'bfsl' solution.
At the Swiss Federal Statistical Office (SFSO), spatial maps of Switzerland are available free of charge as 'Cartographic bases for small-scale thematic mapping'. This package contains convenience functions to import ESRI (Environmental Systems Research Institute) shape files using the package 'sf' and to plot them easily and quickly without having to worry too much about the technical details.
      It contains utilities to combine multiple areas to one single polygon and to find neighbours for single regions. For any point on a map, a special locator can be used to determine to which municipality, district or canton it belongs.
Derived from the work of Kruschke (2015,
    <ISBN:9780124058880>), the present package aims to provide a framework
    for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC)
    sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer,
    2003, <https://mcmc-jags.sourceforge.io>).  The initial version
    includes several modules for conducting Bayesian equivalents of
    chi-squared tests, analysis of variance (ANOVA), multiple
    (hierarchical) regression, softmax regression, and for fitting data
    (e.g., structural equation modeling).
An umbrella package providing a phenotype/genotype data structure
    and scalable and efficient computational methods for large genomic datasets
    in combination with several other packages: 'BEDMatrix', 'LinkedMatrix',
    and 'symDMatrix'.
Density, distribution function, quantile function random generation and estimation of bimodal GEV distribution given in Otiniano et al. (2023) <doi:10.1007/s10651-023-00566-7>. This new generalization of the well-known GEV (Generalized Extreme Value) distribution is useful for modeling heterogeneous bimodal data from different areas.
Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.
Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes:
     a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and 
        actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>.
     b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, 
        properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. <doi:10.3934/math.2023352>.
     c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. <doi:10.3390/axioms11090438>.
Application of genome prediction for a continuous variable, focused 
             on genotype by environment (GE) genomic selection models (GS). It consists a group of functions 
             that help to create regression kernels for some GE genomic models proposed by Jarquín et al. (2014) <doi:10.1007/s00122-013-2243-1>
             and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches.
             The prediction function uses an orthogonal transformation of the data and specific priors
             present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.
Fit Bayesian Gaussian graphical models. The methods are separated into 
    two Bayesian approaches for inference: hypothesis testing and estimation. There are 
    extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, 
    and node wise predictability. These methods were recently introduced in the Gaussian 
    graphical model literature, including 
    Williams (2019) <doi:10.31234/osf.io/x8dpr>, 
    Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
    Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Provides a Metropolis-coupled Markov chain Monte Carlo sampler,
    post-processing and parameter estimation functions, and plotting utilities
    for the generalized graded unfolding model of Roberts, Donoghue, and
    Laughlin (2000) <doi:10.1177/01466216000241001>.
A collection of box-geometry model (BGM) files for the Atlantis 
    ecosystem model. Atlantis is a deterministic, biogeochemical, 
    whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).
Provides functions for behavior genetics analysis, 
    including variance component model identification [Hunter et al. (2021) <doi:10.1007/s10519-021-10055-x>],
    calculation of relatedness coefficients using path-tracing methods 
    [Wright (1922) <doi:10.1086/279872>; McArdle & McDonald (1984) <doi:10.1111/j.2044-8317.1984.tb00802.x>], 
    inference of relatedness, pedigree conversion, and simulation of multi-generational family data 
    [Lyu et al. (2025) <doi:10.1007/s10519-025-10225-1>]. For a full overview, 
    see [Garrison et al. (2024) <doi:10.21105/joss.06203>]. For a big data application see [Burt et al. (2025) <doi: 10.1016/j.ebiom.2025.105911>.
Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables.
Computes the hazard rate estimate as described by
    Nieto-Barajas & Walker (2002), Nieto-Barajas (2003), Nieto-Barajas &
    Walker (2007) and Nieto-Barajas & Yin (2008).
Bimodal Gumbel distribution. General functions for performing extreme value analysis.
Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.
Boost provides free peer-reviewed portable C++ source 
 libraries.  A large part of Boost is provided as C++ template code
 which is resolved entirely at compile-time without linking.  This 
 package aims to provide the most useful subset of Boost libraries 
 for template use among CRAN packages. By placing these libraries in 
 this package, we offer a more efficient distribution system for CRAN 
 as replication of this code in the sources of other packages is 
 avoided. As of release 1.84.0-0, the following Boost libraries are
 included: 'accumulators' 'algorithm' 'align' 'any' 'atomic' 'beast'
 'bimap' 'bind' 'circular_buffer' 'compute' 'concept' 'config'
 'container' 'date_time' 'detail' 'dynamic_bitset' 'exception'
 'flyweight' 'foreach' 'functional' 'fusion' 'geometry' 'graph' 'heap'
 'icl' 'integer' 'interprocess' 'intrusive' 'io' 'iostreams'
 'iterator' 'lambda2' 'math' 'move' 'mp11' 'mpl' 'multiprecision'
 'numeric' 'pending' 'phoenix' 'polygon' 'preprocessor' 'process'
 'propery_tree' 'qvm' 'random' 'range' 'scope_exit' 'smart_ptr' 'sort'
 'spirit' 'tuple' 'type_traits' 'typeof' 'unordered' 'url' 'utility'
 'uuid'.
Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.
Provides functions for Maximum Likelihood
             Estimation, Markov Chain Monte Carlo, finding confidence
             intervals.  The implementation is heavily based on the
             original Fortran source code translated to R.
Performs Bayesian posterior inference for heteroskedastic Gaussian processes.
    Models are trained through MCMC including elliptical slice sampling (ESS) of 
    latent noise processes and Metropolis-Hastings sampling of 
    kernel hyperparameters. Replicates are handled efficientyly through a
    Woodbury formulation of the joint likelihood for the mean and noise process 
    (Binois, M., Gramacy, R., Ludkovski, M. (2018) <doi:10.1080/10618600.2018.1458625>)
    For large data, Vecchia-approximation for faster 
    computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R.,
    (2023), <doi:10.1080/10618600.2022.2129662>). Incorporates 'OpenMP' and 
    SNOW parallelization and utilizes 'C'/'C++' under the hood.
Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
Provides functions for the evaluation of basket
    trial designs with binary endpoints. Operating characteristics of a
    basket trial design are assessed by simulating trial data according to
    scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
    assessing decision probabilities on stratum and trial-level based on Go / No-go decision making.
    The package is build for high flexibility regarding decision rules,
    number of interim analyses, number of strata, and recruitment.
    The BHMs proposed by
    Berry et al. (2013) <doi:10.1177/1740774513497539>
    and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
    as well as a model that combines both approaches are implemented.
    Functions are provided to implement Bayesian decision rules as for example
    proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
    In addition, posterior point estimates (mean/median) and credible intervals
    for response rates and some model parameters can be calculated.
    For simulated trial data, bias and mean squared errors of posterior
    point estimates for response rates can be provided.
Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.
Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations (Carragher et al. (2020) <doi:10.1002/sim.8563>). This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (<https://gtr.ukri.org/>). Principal Investigator: Raymond Carragher.
Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.
Generate the James Blinding Index, as described in James et al (1996) 
             <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, 
             as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>.
             These are measures to assess whether or not satisfactory blinding has been 
             maintained in a randomized, controlled, clinical trial. These can be generated 
             for trial subjects, research coordinators and principal investigators, based 
             upon standardized questionnaires that have been administered, to assess whether
             or not they can correctly guess to which treatment arm (e.g. placebo or treatment) 
             subjects were assigned at randomization.
A GUI to correct measurement bias in DNA methylation
    analyses. The 'BiasCorrector' package just wraps the functions
    implemented in the 'R' package 'rBiasCorrection' into a shiny web
    application in order to make them more easily accessible. Publication:
    Kapsner et al. (2021) <doi:10.1002/ijc.33681>.
Statistical models of biased sampling in the form of 
  univariate and multivariate noncentral hypergeometric distributions, 
  including Wallenius' noncentral hypergeometric distribution and
  Fisher's noncentral hypergeometric distribution. 
  See vignette("UrnTheory") for explanation of these distributions.
  Literature: 
  Fog, A. (2008a). Calculation Methods for Wallenius' Noncentral Hypergeometric Distribution, Communications in Statistics, Simulation and Computation, 37(2) <doi:10.1080/03610910701790269>.
  Fog, A. (2008b). Sampling methods for Wallenius’ and Fisher’s noncentral hypergeometric distributions, Communications in Statistics—Simulation and Computation, 37(2)
  <doi:10.1080/03610910701790236>.
Parse a BibTeX file to a data.frame to make it accessible for further analysis and visualization.
Reading and writing BibTeX files using data frames in R sessions.
Tool for quantitative research in scientometrics and bibliometrics.
    It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and 
    Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>.
    'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS',
    'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' 
	(<https://www.dimensions.ai/>), 'OpenAlex' (<https://openalex.org/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>),  'Lens' (<https://lens.org>), 
	and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis 
    and building networks for co-citation, coupling, scientific collaboration and co-word analysis.
It contains some example datasets used in 'bibliometrix'. The data are bibliographic datasets exported from the 'SCOPUS' (<https://scopus.com>) and 'Clarivate Analytics Web of Science' (<https://www.webofscience.com/>) databases. They can be used to test the different features of the package 'bibliometrix' (<https://bibliometrix.org>).
Functions to find edges for bibliometric networks like bibliographic coupling network, co-citation network and co-authorship network. The weights of network edges can be calculated according to different methods, depending on the type of networks, the type of nodes, and what you want to analyse. These functions are optimized to be be used on large dataset. The package contains functions inspired by: Leydesdorff, Loet and Park, Han Woo (2017) <doi:10.1016/j.joi.2016.11.007>; Perianes-Rodriguez, Antonio, Ludo Waltman, and Nees Jan Van Eck (2016) <doi:10.1016/j.joi.2016.10.006>; Sen, Subir K. and Shymal K. Gan (1983) <http://nopr.niscair.res.in/handle/123456789/28008>; Shen, Si, Zhu, Danhao, Rousseau, Ronald, Su, Xinning and Wang, Dongbo (2019) <doi:10.1016/j.joi.2019.01.012>; Zhao, Dangzhi and Strotmann, Andreas (2008) <doi:10.1002/meet.2008.1450450292>.
Generates a list, with a size defined by the user, containing the main scientific references and the frequency distribution of authors and journals in the list obtained.
    The database is a dataframe with academic production metadata made available by bibliographic collections such as Scopus, Web of Science, etc.
    The temporal evolution of scientific production on a given topic is presented and ordered lists of articles are constructed by number of citations and of authors and journals by level of productivity.
    Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>.
    Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>.
Identifies and visualizes document overlap in any number of bibliographic datasets.
    This package implements the identification of overlapping documents through the exact match of a unique 
    identifier (e.g. Digital Object Identifier - DOI) and, for records where the identifier is absent, through a score calculated 
    from a set of fields commonly found in bibliographic datasets (Title, Source, Authors and Publication Year).
    Additionally, it provides functions to visualize the results of the document matching through a Venn diagram 
    and/or UpSet plot, as well as a summary of the matching procedure.
Imports, constructs, and exports bibliometric networks from
    scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX',
    'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref'
    exports. Goes beyond standard co-networks with attention-weighted
    networks (lead, last, proximity, circular position weights),
    position-aware counting (harmonic, arithmetic, geometric,
    golden-ratio), similarity and dissimilarity normalisations,
    temporal networks with fixed, sliding, and cumulative windows,
    disparity-filter backbone extraction, historiograph construction,
    and local citation scoring. Methods described in López-Pernas,
    Saqr & Apiola (2023) <doi:10.1007/978-3-031-25336-2_5>.
Currently, the package provides several functions for plotting and analyzing bibliometric data (JIF, Journal Impact Factor, and paper percentile values), beamplots with citations and percentiles, and three plot functions to visualize the result of a reference publication year spectroscopy (RPYS) analysis performed in the free software 'CRExplorer' (see <http://crexplorer.net>). Further extension to more plot variants is planned.
A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j.  The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.
Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.
Implementation of a collection of MCMC methods for Bayesian structure learning
    of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
    inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
    the search space, the algorithm employs a hybrid approach, combining constraint-based 
    learning with search and score. A reduced search space is initially defined on the basis
    of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
    search and score. Search and score is then performed following two approaches:
    Order MCMC, or Partition MCMC.
    The BGe score is implemented for continuous data and the BDe score is implemented
    for binary data or categorical data. The algorithms may provide the maximum a posteriori 
    (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data.
    All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks.
    References:
    J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
    N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, 
    J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, 
    M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>,
    D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, 
    P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.
Implements the efficient estimator of bid-ask spreads from open, high, low, and close prices
  described in Ardia, Guidotti, & Kroencke (JFE, 2024) <doi:10.1016/j.jfineco.2024.103916>. 
  It also provides an implementation of the estimators described in 
  Roll (JF, 1984) <doi:10.1111/j.1540-6261.1984.tb03897.x>, 
  Corwin & Schultz (JF, 2012) <doi:10.1111/j.1540-6261.2012.01729.x>,
  and Abdi & Ranaldo (RFS, 2017) <doi:10.1093/rfs/hhx084>.
Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).
A selection of distances measures for bioinformatics data. Other important distance measures for bioinformatics data are selected from the R package 'parallelDist'. A special distance measure for the Gene Ontology is available.
Tools for working with 'BIDS' (Brain Imaging Data Structure)
    formatted neuroimaging datasets. The package provides functionality for
    reading and querying 'BIDS'-compliant projects, creating mock 'BIDS'
    datasets for testing, and extracting preprocessed data from 'fMRIPrep'
    derivatives. It supports searching and filtering 'BIDS' files by various
    entities such as subject, session, task, and run to streamline
    neuroimaging data workflows. See Gorgolewski et al. (2016)
    <doi:10.1038/sdata.2016.44> for the 'BIDS' specification.
Parse and read the files that comply with the brain imaging data 
    structure, or 'BIDS' format, see the publication from Gorgolewski, K., 
    Auer, T., Calhoun, V. et al. (2016) <doi:10.1038/sdata.2016.44>.
    Provides query functions to extract and check the 'BIDS' entity 
    information (such as subject, session, task, etc.) from the file paths
    and suffixes according to the specification. The package is developed 
    and used in the reproducible analysis and visualization of intracranial 
    electroencephalography, or 'RAVE', see Magnotti, J. F., Wang, Z., and 
    Beauchamp, M. S. (2020) <doi:10.1016/j.neuroimage.2020.117341>; see
    'citation("bidsr")' for details and attributions.
Provides a framework and toolkit to guide R dashboard developers in
    implementing the Behavioral Insight Design (BID) framework. The package offers
    functions for documenting each of the five stages (Interpret, Notice,
    Anticipate, Structure, and Validate), along with a comprehensive concept
    dictionary. Works with both 'shiny' applications and 'Quarto' dashboards.
Provides Tools for Accessing the Botanical Information and Ecology Network Database.  The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data (See <https://bien.nceas.ucsb.edu/bien/> for more Information).  This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.
The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the 'Excel' based 'Bifactor Indices Calculator' (Dueber, 2017)  <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.
Estimates fixed effects binary choice models (logit and probit) with potentially many
  individual fixed effects and computes average partial effects. Incidental parameter bias can be
  reduced with an asymptotic bias correction proposed by Fernandez-Val (2009)
  <doi:10.1016/j.jeconom.2009.02.007>.
Contains tools for survey statistics (especially in educational
    assessment) for datasets with replication designs (jackknife, 
    bootstrap, replicate weights; see Kolenikov, 2010;
    Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>,
    <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, 
    <doi:10.1080/02331889708802523>). 
    Descriptive statistics, linear and logistic regression, 
    path models for manifest variables with measurement error 
    correction and two-level hierarchical regressions for weighted 
    samples are included. Statistical inference can be conducted for 
    multiply imputed datasets and nested multiply imputed datasets
    and is in particularly suited for the analysis of plausible values
    (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; 
    Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham &
    Yanagida, 2016). The package development was supported by BIFIE 
    (Federal Institute for Educational Research, Innovation and Development 
    of the Austrian School System; Salzburg, Austria).
Methods for detecting and visualizing cladogenic shifts in multivariate trait data on phylogenies. Implements penalized-likelihood multivariate generalized least squares models, enabling analyses of high-dimensional trait datasets and large trees via searchOptimalConfiguration(). Includes a greedy step-wise shift-search algorithm following approaches developed in Smith et al. (2023) <doi:10.1111/nph.19099> and Berv et al. (2024) <doi:10.1126/sciadv.adp0114>. Methods build on multivariate GLS approaches described in Clavel et al. (2019) <doi:10.1093/sysbio/syy045> and implemented in the mvgls() function from the 'mvMORPH' package. Documentation and vignettes are available at <https://jakeberv.com/bifrost/>, including worked examples for the jaw-shape dataset.
Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023)
             <doi:10.6339/23-JDS1092>.
Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects.
Extend the 'bigmemory' package with various analytics.
    Functions 'bigkmeans' and 'binit' may also be used with native R objects.
    For 'tapply'-like functions, the bigtabulate package may also be helpful.
    For linear algebra support, see 'bigalgebra'.  For mutex (locking) support
    for advanced shared-memory usage, see 'synchronicity'.
Approximate Euclidean k-nearest neighbour search routines that
    operate on 'bigmemory::big.matrix' data through Annoy indexes created with
    'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar
    metadata from streamed 'big.matrix' rows, supports euclidean, angular,
    Manhattan, and dot-product Annoy metrics, and can either return in-memory
    results or stream neighbour indices and distances into destination
    'bigmemory' matrices. Explicit index life cycle helpers, stronger metadata
    validation, descriptor-aware file-backed workflows, and benchmark helpers
    are also included.
Enhanced message functions (cat() / message() / warning() / error()) 
    using wrappers around sprintf(). Also, multiple assertion functions 
    (e.g. to check class, length, values, files, arguments, etc.).
A set of Boolean operators which accept integers of any size, in any base from 2 to 36, including 2's complement format, and perform actions like "AND," "OR", "NOT", "SHIFTR/L" etc. The output can be in any base specified. A direct base to base converter is included.
Format dates and times flexibly and to whichever locales
    make sense. Parses dates, times, and date-times in various formats
    (including string-based ISO 8601 constructions). The formatting syntax gives
    the user many options for formatting the date and time output in a precise
    manner. Time zones in the input can be expressed in multiple ways and there
    are many options for formatting time zones in the output as well. Several of
    the provided helper functions allow for automatic generation of locale-aware
    formatting patterns based on date/time skeleton formats and standardized
    date/time formats with varying specificity.
Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) <doi:10.3233/IDA-140706>; Martos et al (2018) <doi:10.3390/e20010033>; Hernandez et al (2018, submitted); Martos et al (2018, submitted).
Designed to simplify the process of retrieving datasets from the 'Big Data PE' platform using secure token-based authentication. It provides functions for securely storing, retrieving, and managing tokens associated with specific datasets, as well as fetching and processing data using the 'httr2' package.
A framework for 'scalable' statistical computing on large on-disk 
    matrices stored in 'HDF5' files. It provides efficient block-wise 
    implementations of core linear-algebra operations (matrix multiplication, 
    SVD, PCA, QR decomposition, and canonical correlation analysis) written 
    in C++ and R. These building blocks are designed not only for direct use, 
    but also as foundational components for developing new statistical methods 
    that must operate on datasets too large to fit in memory. The package 
    supports data provided either as 'HDF5' files or standard R objects, and is 
    intended for high-dimensional applications such as 'omics' and 
    precision-medicine research.
Data sets and functions for chi-squared Hardy-Weinberg and case-control association tests of highly polymorphic genetic data [e.g., human leukocyte antigen (HLA) data]. Performs association tests at multiple levels of polymorphism (haplotype, locus and HLA amino-acids) as described in Pappas DJ, Marin W, Hollenbach JA, Mack SJ (2016) <doi:10.1016/j.humimm.2015.12.006>. Combines rare variants to a common class to account for sparse cells in tables as described by Hollenbach JA, Mack SJ, Thomson G, Gourraud PA (2012) <doi:10.1007/978-1-61779-842-9_14>.
Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).
A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.
Distributes Gaussian process calculations across nodes
    in a distributed memory setting, using Rmpi. The bigGP class 
    provides high-level methods for maximum likelihood with normal data, 
    prediction, calculation of uncertainty (i.e., posterior covariance 
    calculations), and simulation of realizations. In addition, bigGP 
    provides an API for basic matrix calculations with distributed 
    covariance matrices, including Cholesky decomposition, back/forwardsolve, 
    crossproduct, and matrix multiplication.
Exact nearest-neighbour and radius-search routines that operate
    directly on 'bigmemory::big.matrix' objects. The package streams row
    blocks through 'BLAS' kernels, supports self-search and external-query
    search, exposes prepared references for repeated queries, and can build
    exact k-nearest-neighbour, radius, mutual k-nearest-neighbour, and
    shared-nearest-neighbour graphs. Version 0.3.0 adds execution plans,
    serializable prepared caches, resumable streamed graph jobs, coercion
    helpers, exact candidate reranking, and recall summaries for evaluating
    approximate neighbours.
Response surface methods for drug synergy analysis. Available
    methods include generalized and classical Loewe formulations as well as Highest
    Single Agent methodology. Response surfaces can be plotted in an interactive
    3-D plot and formal statistical tests for presence of synergistic effects are
    available. Implemented methods and tests are described in the article 
    "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction 
    of the expected combined effect compatible with partial agonism and antagonism"
    by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, 
    Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) 
    <doi:10.1038/s41598-017-18068-5>.
Extend lasso and elastic-net model fitting for large data sets that
  cannot be loaded into memory. Designed to be more memory- and
  computation-efficient than existing lasso-fitting packages like 'glmnet' and
  'ncvreg', thus allowing the user to analyze big data with limited RAM
  <doi:10.32614/RJ-2021-001>.
Calculation of physical (e.g. aerodynamic conductance, surface temperature), 
             and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties
			 from eddy covariance data and accompanying meteorological measurements. Calculations
			 assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.
Regression for data too large to fit in memory.
Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R.
Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. 
    Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). <DOI:10.1214/19-sts699>.
MDS is a statistic tool for reduction of dimensionality, using as input a distance
    matrix of dimensions n × n. When n is large, classical algorithms suffer from
    computational problems and MDS configuration can not be obtained.
    With this package, we address these problems by means of six algorithms, being two of them 
    original proposals:
        - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004).
        - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Reduced MDS proposed by Paradis E (2018).
        - Pivot MDS proposed by Brandes U. and C. Pich (2007)
        - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).
Create, store, access, and manipulate massive matrices.
    Matrices are allocated to shared memory and may use memory-mapped
    files.  Packages 'biganalytics', 'bigtabulate', 'synchronicity', and
    'bigalgebra' provide advanced functionality.
A shared resource interface for the bigmemory and synchronicity packages.
A computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using 'Apache Spark'. The methodology is described in Morvan et al. (2026) <doi:10.48550/arXiv.2601.21613>.
Classes for storing and manipulating arbitrary-precision
    integer vectors and high-precision floating-point vectors. These
    extend the range and precision of the 'integer' and 'double' data
    types found in R. This package utilizes the 'Boost.Multiprecision' C++
    library. It is specifically designed to work well with the 'tidyverse'
    collection of R packages.
Utility functions for easy parallelism in R. Include some reexports
    from other packages, utility functions for splitting and parallelizing over
    blocks, and choosing and setting the number of cores used.
High performance principal component analysis routines
       that operate directly on bigmemory::big.matrix() objects. The
       package avoids materialising large matrices in memory by
       streaming data through 'BLAS' and 'LAPACK' kernels and provides
       helpers to derive scores, loadings, correlations, and
       contribution diagnostics, including utilities that stream
       results into 'bigmemory'-backed matrices for file-based
       workflows. Additional interfaces expose 'scalable' singular value
       decomposition, robust PCA, and robust SVD algorithms so that
       users can explore large matrices while tempering the influence
       of outliers. 'Scalable' principal component analysis is also implemented,
       Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) 
       <doi:10.1145/2723372.2751520>.
Provides Partial least squares Regression and various regular, sparse 
    or kernel, techniques for fitting Cox models for big data. Provides a Partial 
    Least Squares (PLS) algorithm adapted to Cox proportional hazards models that 
    works with 'bigmemory' matrices without loading the entire dataset in memory. 
    Also implements a gradient-descent based solver for Cox proportional hazards 
    models that works directly on 'bigmemory' matrices.     
    Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and 
    <https://hal.science/hal-05352061> highlighted 
    fitting and cross-validating PLS-based Cox models to censored big data.
Tools for pedigree quality control and genomic breed/line
    composition estimation in diploid and polyploid breeding populations.
    'BIGpopA' provides functions to check and correct common pedigree
    errors, assign parentage from SNP genotype data using Mendelian error
    rates, validate parent-offspring trios, and estimate genome-wide
    breed or line composition using quadratic programming. Supports both
    diploid and polyploid species. For more details about the included 'breedTools' 
    functions, see Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>.
A computationally-efficient leading-eigenvalue approximation to tail probabilities and quantiles of large quadratic forms, in particular for the Sequence Kernel Association Test (SKAT) used in genomics <doi:10.1002/gepi.22136>. Also provides stochastic singular value decomposition for dense or sparse matrices.
Use Newton's method, coordinate descent, and METIS clustering
    to solve the L1 regularized Gaussian MLE inverse covariance
    matrix estimation problem.
Functions developed within Breeding Insight to analyze
    diploid and polyploid breeding and genetic data. 'BIGr' provides the
    ability to filter variant call format (VCF) files, extract single nucleotide polymorphisms (SNPs) 
    from diversity arrays technology missing allele discovery count (DArT MADC) files, 
    and manipulate genotype data for both diploid and polyploid species. It
    also serves as the core dependency for the 'BIGapp' 'Shiny' app, which
    provides a user-friendly interface for performing routine genotype
    analysis tasks such as dosage calling, filtering, principal component analysis (PCA), 
    genome-wide association studies (GWAS), and
    genomic prediction. For more details about the included 'breedTools' 
    functions, see Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>, and
    the 'updog' output format, see Gerard et al. (2018) <doi:10.1534/genetics.118.301468>.
Read large text files by splitting them in smaller files.
    Package 'bigreadr' also provides some convenient wrappers around fread()
    and fwrite() from package 'data.table'.
Allows the user to carry out GLM on very large
    data sets. Data can be created using the data_frame() function and appended
    to the object with object$append(data); data_frame and data_matrix objects
    are available that allow the user to store large data on disk. The data is
    stored as doubles in binary format and any character columns are transformed
    to factors and then stored as numeric (binary) data while a look-up table is
    stored in a separate .meta_data file in the same folder. The data is stored in
    blocks and GLM regression algorithm is modified and carries out a MapReduce-
    like algorithm to fit the model. The functions bglm(), and summary()
    and bglm_predict() are available for creating and post-processing of models.
    The library requires Armadillo installed on your system. It may not 
    function on windows since multi-core processing is done using mclapply() 
    which forks R on Unix/Linux type operating systems.
Easily talk to Google's 'BigQuery' database from R.
Easily talk to Google's 'BigQuery Storage' API from R
    (<https://cloud.google.com/bigquery/docs/reference/storage/rpc>).
Simulate multivariate data with arbitrary marginal distributions.
  'bigsimr' is a package for simulating high-dimensional multivariate data with 
  a target correlation and arbitrary marginal distributions via Gaussian copula. 
  It utilizes the Julia package 'Bigsimr.jl' for its core routines.
Easy-to-use, efficient, flexible and scalable tools for analyzing 
    massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.
Provide a sparse matrix format with data stored on disk, to be
    used in both R and C++. This is intended for more efficient use of sparse 
    data in C++ and also when parallelizing, since data on disk does not need
    copying. Only a limited number of features will be implemented. For now,
    conversion can be performed from a 'dgCMatrix' or a 'dsCMatrix' from R 
    package 'Matrix'. A new compact format is also now available.
Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.
Easy-to-use, efficient, flexible and scalable statistical tools.
  Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
  It provides for instance matrix operations, Principal Component Analysis,
  sparse linear supervised models, utility functions and more
  <doi:10.1093/bioinformatics/bty185>.
Selecting linear and generalized linear models for large data sets
    using modified stepwise procedure and modern selection criteria (like
    modifications of Bayesian Information Criterion). Selection can be 
    performed on data which exceed RAM capacity. Bogdan et al., (2004)
    <doi:10.1534/genetics.103.021683>.
Fits Cox model via stochastic gradient descent. This implementation avoids computational instability of the standard Cox Model when dealing large datasets. Furthermore, it scales up with large datasets that do not fit the memory. It also handles large sparse datasets using proximal stochastic gradient descent algorithm. For more details about the method, please see Aliasghar Tarkhan and Noah Simon (2020) <arXiv:2003.00116v2>.
Extend the bigmemory package with 'table', 'tapply', and 'split'
    support for 'big.matrix' objects. The functions may also be used with native R
    matrices for improving speed and memory-efficiency.
For studying recurrent disease and death with competing
    risks, comparisons based on the well-known cumulative incidence function
    can be confounded by different prevalence rates of the competing events.
    Alternatively, comparisons of the conditional distribution of the survival
    time given the failure event type are more relevant for investigating the
    prognosis of different patterns of recurrence disease. This package implements
    a nonparametric estimator for the conditional cumulative incidence function
    and a nonparametric conditional bivariate cumulative incidence function for the
    bivariate gap times proposed in Huang et al. (2016) <doi:10.1111/biom.12494>.
Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.
Utility functions for large-scale data. For now, package 'bigutilsr'
    mainly includes functions for outlier detection and unbiased PCA projection.
Estimates VAR and VARX models with Structured Penalties.
Anonymised Bay Area bike share trip data for the year 2014. 
    Also contains additional metadata on stations and weather.
Co-clustering of the rows and columns of a contingency or binary matrix, or double binary matrices and model selection for the number of row and column clusters. Three models are considered: the Poisson latent block model for contingency matrix, the binary latent block model for binary matrix and a new model we develop: the multiple latent block model for double binary matrices. A new procedure named bikm1 is implemented to investigate more efficiently the grid of numbers of clusters. Then, the studied model selection criteria are the integrated completed likelihood (ICL) and the Bayesian integrated likelihood (BIC). Finally, the co-clustering adjusted Rand index (CARI) to measure agreement between co-clustering partitions is implemented. Robert Valerie, Vasseur Yann, Brault Vincent (2021) <doi:10.1007/s00357-020-09379-w>.
Performs logistic regression for binary longitudinal
  data, allowing for serial dependence among observations from a given
  individual and a random intercept term. Estimation is via maximization
  of the exact likelihood of a suitably defined model. Missing values and 
  unbalanced data are allowed, with some restrictions. 
  M. Helena Goncalves et al.(2007) <DOI: 10.18637/jss.v046.i09>.
Contains data sets regarding songs on the Billboard Hot 100 list
    from 1960 to 2016. The data sets include the ranks for the given year,
    musical features of a lot of the songs and lyrics for several of the songs
    as well.
Provides an 'htmlwidgets' interface to 'billboard.js', 
    a re-usable easy interface JavaScript chart library, based on D3 v4+.
    Chart types include line charts, scatterplots, bar/lollipop charts, histogram/density plots, pie/donut charts and gauge charts.
    All charts are interactive, and a proxy method is implemented to smoothly update a chart without rendering it again in 'shiny' apps.
Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control, by A. Luciani (2022) <doi:10.13140/RG.2.2.31160.83202>.
Defines the functions used to compute the
  bimodal index as defined by Wang et al. (2009)
  <https://pmc.ncbi.nlm.nih.gov/articles/PMC2730180/>,
  <doi:10.4137/CIN.S2846>.
Provides likelihood-based and hierarchical estimation methods
    for thresholded (binomial-probit) data. Supports fixed-mean and random-mean
    models with maximum likelihood estimation (MLE), generalized linear mixed model
    (GLMM), and Bayesian Markov chain Monte Carlo (MCMC) implementations.
    For methodological background, see Albert and Chib (1993)
    <doi:10.1080/01621459.1993.10476321> and McCulloch (1994)
    <doi:10.2307/2297959>.
R client to the 'Binance' Public Rest API for data collection on cryptocurrencies, portfolio management and trading: <https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.
Provides methods for the binarization of one-dimensional data and some visualization functions.
Allows to view the optimal probability cut-off point at which the Sensitivity and Specificity meets and its a best way to minimize both Type-1 and Type-2 error for a binary Classifier in determining the Probability threshold.
Tools to create binary dosage files from either VCF
    or GEN files, merge binary dosage files, and read
    binary dosage files. Binary dosage files tend to have quicker
    read times than VCF and GEN formats. There is a small increase
    in size compared to compressed VCF and GEN files.
Implements variable selection for high dimensional datasets with a binary response
    variable using the EM algorithm.  Both probit and logit models are supported.  Also included 
    is a useful function to generate high dimensional data with correlated variables.
Under- and over-dispersed binary data are modeled using an extended Poisson 
 process model (EPPM) appropriate for binary data. A feature of the model is that the 
 under-dispersion relative to the binomial distribution only needs to be greater than
 zero, but the over-dispersion is  restricted compared to other distributional models  
 such as the beta and correlated binomials. Because of this, the examples focus on 
 under-dispersed data and how, in combination with the beta or correlated distributions,
 flexible models can be fitted to data displaying both under- and over-dispersion. Using
 Generalized Linear Model (GLM)  terminology, the functions utilize linear predictors for
 the probability of success and scale-factor with various link functions for p, and log 
 link for scale-factor, to fit a variety of models relevant to areas such as bioassay. 
 Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214> and 
 Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.
Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.
Statistical methods for analyzing binary replicates, which are noisy
    binary measurements of latent binary states. Provides scoring functions
    (average, median, likelihood-based, and Bayesian) to estimate the probability
    that an individual is in the positive state. Includes maximum a posteriori
    estimation via the EM algorithm and full Bayesian inference via Stan.
    Supports classification with inconclusive decisions and prevalence estimation.
Tools for building Rescorla-Wagner Models for Two-Alternative 
  Forced Choice tasks, commonly employed in psychological research. 
  Most concepts and ideas within this R package are referenced from 
  Sutton and Barto (2018) <ISBN:9780262039246>. 
  The package allows for the intuitive definition of RL models using simple 
  if-else statements and three basic models built into this R package are 
  referenced from 
  Niv et al. (2012) <doi:10.1523/JNEUROSCI.5498-10.2012>. 
  Our approach to constructing and evaluating these computational models 
  is informed by the guidelines proposed in 
  Wilson & Collins (2019) <doi:10.7554/eLife.49547>. 
  Example datasets included with the package are sourced from the work of
  Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.
A collection of 'LaTeX' styles using 'Beamer' customization for
 pdf-based presentation slides in 'RMarkdown'. At present it contains
 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme')
 theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the
 'IQSS' them by Ista Zahn (which is included here), and the 'Monash' theme by
 Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers
 'Fira', and 'IQSS' requires 'Libertinus'.
Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series). A description of the method and package is provided in Polanco-Martínez et al. (2019), <doi:10.32614/RJ-2019-035>.
Implements functions for multi-class
   discriminant analysis using binary predictors, for corresponding 
   variable selection, and for dichotomizing continuous data.
Generation of correlated artificial binary data.
Provides a simple interface for creating active bindings where the
    bound function accepts additional arguments.
Provides an easy way to fill an environment with active
    bindings that call a C++ function.
Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored.  We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results.  We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
Estimates group-level means and standard deviations from
    binned (coarsened) count data, where the within-bin scores are
    unobserved.  The package implements three methods that share a
    common output structure: bin_means() (a fast estimator that
    assumes within-district normality and uses pooled bin proportions
    to derive bin-conditional truncated-normal expectations),
    mle_hetop() (maximum likelihood for the heteroskedastic ordered
    probit model of Reardon, Shear, Castellano and Ho 2017
    <doi:10.3102/1076998616666279>), and fh_hetop() (the Bayesian
    Fay-Herriot variant of Lockwood, Castellano and Shear 2018
    <doi:10.3102/1076998618795124>).  The mle_hetop() and fh_hetop()
    functions are forked from the 'HETOP' package by J. R. Lockwood
    ('CRAN', last released 2019).  mle_hetop() has been modified to
    speed up the runtime via a vectorized inner loop and to remove
    two user-facing arguments (fixedcuts and svals) that some users
    found confusing; cutpoints and starting values are now derived
    internally from the data.
Binford's hunter-gatherer data includes more than 200 variables
    coding aspects of hunter-gatherer subsistence, mobility, and social organization
    for 339 ethnographically documented groups of hunter-gatherers.
Provides maximum likelihood estimates of the 
    performance parameters that drive a binomial distribution of observed 
    errors, and takes full advantage of zero error observations. High performance 
    communications systems typically have inherent noise sources and other 
    performance limitations that need to be estimated. Measurements made at 
    high signal to noise ratios typically result in zero 
    errors due to limitation in available measurement time. Package includes 
    theoretical performance functions for common modulation schemes (Proakis, 
    "Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted 
    QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility 
    functions to work with the performance functions.
Collect  your data on digital marketing campaigns from bing Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Methods for estimation and hypothesis testing of proportions
 in group testing designs: methods for estimating a proportion in a single
        population (assuming sensitivity and specificity equal to 1 in designs
        with equal group sizes), as well as hypothesis tests and
        functions for experimental design for this situation. For
        estimating one proportion or the difference of proportions, a
        number of confidence interval methods are included, which can
        deal with various different pool sizes. Further, regression
        methods are implemented for simple pooling and matrix pooling
        designs.
        Methods for identification of positive items in group
        testing designs: Optimal testing configurations can be found 
        for hierarchical and array-based algorithms. Operating 
        characteristics can be calculated for testing configurations 
        across a wide variety of situations.
Consider an at-most-K-stage group sequential design with only an upper bound for the last analysis and non-binding lower bounds.With binary endpoint, two kinds of test can be applied, asymptotic test based on normal distribution and exact test based on binomial distribution. This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design.
Binomial Haar-Fisz transforms for Gaussianization as in Nunes and Nason (2009).
Tools and functions for managing the download of binary files.
    Binary repositories are defined in 'YAML' format. Defining new 
    pre-download, download and post-download templates allow additional 
    repositories to be added.
A molecular genetics tool that processes binary data from fragment analysis. It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the publication available here: <doi:10.1016/j.biocontrol.2020.104426>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "clarkevansteenderen") into the console in R. Two real-world datasets accompany the package: an AFLP dataset of Bunias orientalis samples from Tewes et. al. (2017) <doi:10.1111/1365-2745.12869>, and an ISSR dataset of Nymphaea specimens from Reid et. al. (2021) <doi:10.1016/j.aquabot.2021.103372>. The authors of these publications are thanked for allowing the use of their data.
Asymptotic simultaneous confidence intervals for comparison of many treatments with one control,
 for the difference of binomial proportions, allows for Dunnett-like-adjustment, Bonferroni or unadjusted intervals.
 Simulation of power of the above interval methods, approximate calculation of any-pair-power, and sample size
 iteration based on approximate any-pair power. Exact conditional maximum test for many-to-one comparisons to a control.
Generation of multiple binary and continuous non-normal variables simultaneously 
             given the marginal characteristics and association structure based on the methodology 
             proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.
Constructs confidence intervals on the probability of
        success in a binomial experiment via several parameterizations.
Twelve confidence intervals for one binomial proportion or a vector of binomial proportions are computed. The confidence intervals are: Jeffreys,  Wald, Wald corrected, Wald, Blyth and Still,  Agresti and Coull, Wilson, Score, Score corrected, Wald logit, Wald logit corrected, Arcsine and Exact binomial. References include, among others: Vollset, S. E. (1993). "Confidence intervals for a binomial proportion". Statistics in Medicine, 12(9): 809-824. <doi:10.1002/sim.4780120902>.
Detection of a statistically significant trend in the data provided by the user.
    This is based on the a signed test based on the binomial distribution.
    The package returns a trend test value, T, and also a p-value. A T value 
    close to 1 indicates a rising trend, whereas a T value close to -1 indicates a decreasing
    trend. A T value close to 0 indicates no trend. There is also a command to visualize
    the trend. A test data set called gtsa_data is also available, which has global mean 
    temperatures for January, April, July, and October for the years 1851 to 2022.
    Reference: Walpole, Myers, Myers, Ye. (2007, ISBN: 0-13-187711-9).
Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) <doi:10.1167/tvst.9.3.8>. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included 'shiny' apps.
Implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details.
Package binr (pronounced as "binner") provides algorithms for cutting numerical values exhibiting a potentially highly skewed distribution into evenly distributed groups (bins). This functionality can be applied for binning discrete values, such as counts, as well as for discretization of continuous values, for example, during generation of features used in machine learning algorithms.
Provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.
Standard template library 
 containers are used to implement an efficient binary segmentation
 algorithm, which is log-linear on average and quadratic in the
 worst case.
For a series of binary responses, create stopping boundary with exact results after stopping, allowing updating for missing assessments.
Provides several methods for generating density functions
    based on binned data. Methods include step function, recursive
    subdivision, and optimized spline. Data are assumed to be nonnegative, 
    the top bin is assumed to have no upper bound, but the bin widths need
    be equal. All PDF smoothing methods maintain the areas specified by 
    the binned data. (Equivalently, all CDF smoothing methods interpolate 
    the points specified by the binned data.) In practice, an estimate for 
    the mean of the distribution should be supplied as an optional argument.
    Doing so greatly improves the reliability of statistics computed from 
    the smoothed density functions. Includes methods for estimating the Gini 
    coefficient, the Theil index, percentiles, and random deviates from a 
    smoothed distribution. Among the three methods, the optimized spline 
    (splinebins) is recommended for most purposes. The percentile and 
    random-draw methods should be regarded as experimental, and these methods 
    only support splinebins.
The Bayesian MCMC estimation of parameters for Thomas-type cluster
    point process with various inhomogeneities. It allows for inhomogeneity in
    (i) distribution of parent points, (ii) mean number of points in a cluster,
    (iii) cluster spread. The package also allows for the Bayesian MCMC
    algorithm for the homogeneous generalized Thomas process. The cluster size
    is allowed to have a variance that is greater or less than the expected
    value (cluster sizes are over or under dispersed). Details are described in
    Dvořák, Remeš, Beránek & Mrkvička (2022) <arXiv: 10.48550/arXiv.2205.07946>.
Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_AER.pdf>, Cattaneo, Crump, Farrell and Feng (2025) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2025_Stata.pdf> and Cattaneo, Crump, Farrell and Feng (2026) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2026_RESTAT.pdf>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.
Various supervised and unsupervised binning tools
    including using entropy, recursive partition methods
    and clustering.
A recently proposed Bayesian BIN model disentangles the underlying processes 
    that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into 
    three components: bias, partial information, and noise. By describing the differences between two 
    groups of forecasters, the model allows the user to carry out useful inference, such as calculating 
    the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information.
    It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient 
    extraction of valid information from the environment improves forecasting accuracy. This package provides 
    easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov,
    Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN 
    Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
Client for the 'Binance' <https://www.binance.com/> Spot,
    Futures, and Options REST APIs. Provides helper functions for signed
    and unsigned requests, market data retrieval, account access, and order
    management with 'data.table' output by default.
Imports benthic count data, reformats this data, and
        computes environmental inferences from this data.
Utilities to process, organize and explore protein structure,
    sequence and dynamics data. Features include the ability to read and write
    structure, sequence and dynamic trajectory data, perform sequence and structure
    database searches, data summaries, atom selection, alignment, superposition,
    rigid core identification, clustering, torsion analysis, distance matrix
    analysis, structure and sequence conservation analysis, normal mode analysis,
    principal component analysis of heterogeneous structure data, and correlation
    network analysis from normal mode and molecular dynamics data. In addition,
    various utility functions are provided to enable the statistical and graphical
    power of the R environment to work with biological sequence and structural data.
    Please refer to the URLs below for more information.
Contains all the necessary tools to process audio recordings of
             various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, 
             display audio signals, detect and extract automatically acoustic
             features for further analysis such as classification.
Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.
Provides an integrated data management solution for assets installed via the 'Biobricks.ai' platform. Streamlines the process of loading and interacting with diverse datasets in a consistent manner. A list of bricks is available at <https://status.biobricks.ai>. Documentation for 'Biobricks.ai' is available at <https://docs.biobricks.ai>.
Download stats reported from the BioConductor.org stats website.
Using numeric or raster data, this package contains functions to 
    calculate: complete water balance, bioclimatic balance, bioclimatic 
    intensities, reports for individual locations, multi-layered rasters for
    spatial analysis.
A convenient tool to install and update Bioconductor packages.
Tools to create, validate, and export BioCompute Objects
    described in King et al. (2019) <doi:10.17605/osf.io/h59uh>.
    Users can encode information in data frames, and compose
    BioCompute Objects from the domains defined by the standard.
    A checksum validator and a JSON schema validator are provided.
    This package also supports exporting BioCompute Objects as JSON,
    PDF, HTML, or 'Word' documents, and exporting to cloud-based platforms.
A cross-platform representation of models as sets of equations
    that facilitates modularity in model building and allows users to harness
    modern techniques for numerical integration and data visualization.
    Documentation is provided by several vignettes included in this package;
    also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.
The Biodem package provides a number of functions for Biodemographic analysis.
Graphical User Interface (via the R-Commander) and utility functions (often based on the vegan package) for statistical analysis of biodiversity and ecological communities, including species accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of species abundance and presence-absence, distance matrices, Mantel tests, and cluster, constrained and unconstrained ordination analysis. A book on biodiversity and community ecology analysis is available for free download from the website. In 2012, methods for (ensemble) suitability modelling and mapping were expanded in the package.
A tool to perform all different statistical tests and calculations
    needed by Biological dosimetry Laboratories. Detailed documentation is
    available in <https://biodosetools-team.github.io/documentation/>.
Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity.  Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.
Fits simultaneous regression models to compare two sources
    (reference and test) and estimates relative bioefficiency. Includes
    simultaneous exponential model with common asymptote (model = 1),
    slope-ratio model (model = 2), quadratic model (model = 3),
    linear-response plateau model (model = 4), and Michaelis-Menten
    model (model = 5). Output style follows the 'easyreg' package.
    Methods are based on Finney (1978, ISBN:0-85264-252-0),
    Mercer et al. (1978) <doi:10.1093/jn/108.8.1244>,
    Robbins et al. (1979) <doi:10.1093/jn/109.10.1710>,
    Noll et al. (1984) <doi:10.3382/ps.0632458>,
    Gallant and Fuller (1973) <doi:10.1080/01621459.1973.10481356>,
    Littell et al. (1997) <doi:10.2527/1997.75102672x>, and
    Burnham and Anderson (2002, ISBN:978-0-387-95364-9).
Functions for calculating biochemical methane potential (BMP) from laboratory measurements and other types of data processing and prediction useful for biogas research. Raw laboratory measurements for diverse methods (volumetric, manometric, gravimetric, gas density) can be processed to calculate BMP. Theoretical maximum BMP or methane or biogas yield can be predicted from various measures of substrate composition. Molar mass and calculated oxygen demand (COD') can be determined from a chemical formula. Measured gas volume can be corrected for water vapor and to standard (or user-defined) temperature and pressure. Gas quantity can be converted between volume, mass, and moles. A function for planning BMP experiments can consider multiple constraints in suggesting substrate or inoculum quantities, and check for problems. Inoculum and substrate mass can be determined for planning BMP experiments. Finally, a set of first-order models can be fit to measured methane production rate or cumulative yield in order to extract estimates of ultimate yield and kinetic constants. See Hafner et al. (2018) <doi:10.1016/j.softx.2018.06.005> for details. OBA is a web application that provides access to some of the package functionality: <https://biotransformers.shinyapps.io/oba1/>. The Standard BMP Methods website documents the calculations in detail: <https://www.dbfz.de/en/BMP>.
Is used to simulate and fit biological geometries. 'biogeom' incorporates several novel universal parametric equations that can generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings (Gielis (2003) <doi:10.3732/ajb.90.3.333>; Shi et al. (2020) <doi:10.3390/sym12040645>), three growth-rate curves representing the ontogenetic growth trajectories of animals and plants against time, and the axially symmetrical and integral forms of all these functions (Shi et al. (2017) <doi:10.1016/j.ecolmodel.2017.01.012>; Shi et al. (2021) <doi:10.3390/sym13081524>). The optimization method proposed by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> was used to estimate model parameters. 'biogeom' includes several real data sets of the boundary coordinates of natural shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings, seeds, and sea stars,and can be potentially applied to other natural shapes. 'biogeom' can quantify the conspecific or interspecific similarity of natural outlines, and provides information with important ecological and evolutionary implications for the growth and form of living organisms. Please see Shi et al. (2022) <doi:10.1111/nyas.14862> for details.
Tools for extraction and analysis of various
    n-grams (k-mers) derived from biological sequences (proteins
    or nucleic acids). Contains QuiPT (quick permutation test) for fast
    feature-filtering of the n-gram data.
Modelling of population growth under static and dynamic environmental conditions. 
    Includes functions for model fitting and making prediction under isothermal and 
    dynamic conditions. The methods (algorithms & models) are based on
    predictive microbiology (See Perez-Rodriguez and Valero (2012, ISBN:978-1-4614-5519-6)).
Implementation of Graph Signal Processing (GSP) methods including Spectral 
    Graph Wavelet Transform (SGWT) for analyzing spatial patterns in biological data. 
    Based on Hammond, Vandergheynst, and Gribonval (2011) <doi:10.1016/j.acha.2010.04.005>. Provides tools for multi-scale analysis of biology spatial signals, including forward and inverse transforms, energy analysis, and visualization functions tailored for biological applications. Biological application example is on Stephanie, Yao, Yuzhou (2024) <doi:10.1101/2024.12.20.629650>.
Single linkage clustering and connected component analyses are often performed on biological images. 'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.
Tools for 3D imaging, mostly for biology/microscopy. 
    Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns.
Functions for modelling microbial inactivation under
    isothermal or dynamic conditions. The calculations are based on several mathematical models broadly
    used by the scientific community and industry. Functions enable to make predictions for cases where the
    kinetic parameters are known. It also implements functions for parameter estimation for isothermal and
    dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian
    approach to parameter estimation.
Supports the standardized analysis of Mediterranean 
    International Bottom Trawl Survey (MEDITS) data and the calculation 
    of biological indicators for selected species and population 
    components. The package provides functions to estimate abundance 
    and biomass indices, analyse size structure and length frequency 
    distributions, derive sex ratio and maturity related metrics, 
    explore spatial patterns, and assess temporal trends across 
    surveys. Developed for integration within the Regional Database 
    for Fisheries (RDBFIS) framework, it is intended to work on 
    quality checked input data and to produce reproducible outputs 
    that can support monitoring, comparative analyses among 
    Geographical Sub-Areas (GSAs) and countries, and fishery management.
Analyze and plot the abundance of different RNA biotypes present in a count matrix, this evaluation can be useful if you want to test different strategies of normalization or analyze a particular biotype in a differential gene expression analysis.
Prevents and detects information leakage in biomedical machine learning.
  Provides leakage-resistant split policies (subject-grouped, batch-blocked, study leave-out, time-ordered),
  guarded preprocessing (train-only imputation, normalization, filtering, feature selection),
  cross-validated fitting with common learners, permutation-gap auditing, batch and fold association tests,
  and duplicate detection.
Generate urls and hyperlinks to commonly used biological databases
    and resources based on standard identifiers. This is primarily useful when
    writing dynamic reports that reference things like gene symbols in text or
    tables, allowing you to, for example, convert gene identifiers to hyperlinks
    pointing to their entry in the 'NCBI' Gene database. Currently supports
    'NCBI' Gene, 'PubMed', Gene Ontology, 'KEGG', CRAN and Bioconductor.
Ecological alteration of degraded lands can improve their sustainability by addition of large amount of biomass to soil resulting in improved soil health. Soil biological parameters (such as carbon, nitrogen and phosphorus cycling enzyme activity) are reactive to minute variations in soils [Ghosh et al. (2021) <doi:10.1016/j.ecoleng.2021.106176> ]. Hence, biological activity index combining Urease, Alkaline Phosphatase, Dehydrogenase (DHA) & Beta-Glucosidase activity will assist in detecting early changes in restored land use systems [Patidar et al. (2023) <doi:10.3389/fsufs.2023.1230156>]. This package helps to calculate Biological Activity Index (BAI) based on vectors of Land Use System/treatment and control/reference Land Use System containing four values of Urease, Alkaline Phosphatase, DHA & Beta-Glucosidase. (DHA), urease (URE), fluorescein diacetate hydrolysis (FDA) and alkaline phosphatase (ALP) activities are measured in soil samples using triphenyl tetrazolium chloride, urea, fluorescein diacetate and p-nitro phenyl-phosphate as substrates, respectively.
Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.
Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized
                way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome
                retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', 
                and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database
                (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve
                functional annotation for genomic loci. In addition, users can download entire databases such
                as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
                'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. with only one command.
Contains functions for estimating above-ground biomass/carbon and its uncertainty in tropical forests. These functions allow to (1) retrieve and correct taxonomy, (2) estimate wood density and its uncertainty, (3) build height-diameter models, (4) manage tree and plot coordinates, (5) estimate above-ground biomass/carbon at stand level with associated uncertainty. To cite ‘BIOMASS’, please use citation(‘BIOMASS’). For more information, see Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.
A system of functions and datasets to carry out quantitative analyses in the biological  
    sciences. The package facilitates data management, exploratory analyses, and model assessment. 
    Although it currently focuses on forest ecology, silviculture and decision-making, most of
    the package functions are applicable across several disciplines, including economics,
    environmental science, and healthcare.
Provides functions to aid in the design and analysis of
    agronomic and agricultural experiments through easy access to
    documentation and helper functions, especially for users who are
    learning these concepts. While not required for most functionality,
    this package enhances the `asreml` package which provides a 
    computationally efficient algorithm for fitting mixed models 
    using Residual Maximum Likelihood. It is a commercial package 
    that can be purchased as 'ASReml-R' from 'VSNi' 
    <https://vsni.co.uk/>, who will supply a zip file for local 
    installation/updating (see <https://asreml.kb.vsni.co.uk/>).
Functions for species distribution modeling, calibration and evaluation, 
  ensemble of models, ensemble forecasting and visualization. The package permits to run
  consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences)
  dataset and to combine them in ensemble models and ensemble projections. Some bench of other 
  evaluation and visualisation tools are also available within the package.
An aid for manipulating data associated with biomonitoring and bioassessment. Calculations include metric calculation, marking of excluded taxa, 
    subsampling, and multimetric index calculation.  Targeted communities are benthic macroinvertebrates, fish, periphyton, and coral.  As described in the Revised Rapid Bioassessment Protocols (Barbour et al. 1999) <https://archive.epa.gov/water/archive/web/html/index-14.html>.
Tools for bioinformatics modeling using recursive transformer-inspired 
    architectures, autoencoders, random forests, XGBoost, and stacked ensemble models. 
    Includes utilities for cross-validation, calibration, benchmarking, and threshold 
    optimization in predictive modeling workflows. The methodology builds on ensemble 
    learning (Breiman 2001 <doi:10.1023/A:1010933404324>), gradient boosting (Chen and 
    Guestrin 2016 <doi:10.1145/2939672.2939785>), autoencoders (Hinton and Salakhutdinov 
    2006 <doi:10.1126/science.1127647>), and recursive transformer efficiency approaches 
    such as Mixture-of-Recursions (Bae et al. 2025 <doi:10.48550/arXiv.2507.10524>).
Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) <DOI:10.1038/clpt.2010.233>). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. This package is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials.
Prognostic Enrichment is a strategy of enriching a clinical trial for testing an intervention intended to prevent or delay an unwanted clinical event.  A prognostically enriched trial enrolls only patients who are more likely to experience the unwanted clinical event than the broader patient population (R. Temple (2010) <doi:10.1038/clpt.2010.233>). By testing the intervention in an enriched study population, the trial may be adequately powered with a smaller sample size, which can have both practical and ethical advantages.
    This package provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival/time-to-event outcomes.
Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially 
    microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) 
    <doi:10.21037/jlpm.2019.04.05>).
Provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.
Several tools for analyzing diagnostic tests and 2x2 contingency tables are provided. In particular, positive and negative predictive values for a diagnostic tests can be calculated from prevalence, sensitivity and specificity values. For contingency tables, relative risk and odds ratio measures are estimated. Furthermore, confidence intervals are provided.
Extract, visualize and summarize aerial movements of birds and
    insects from weather radar data. See Dokter, A. M. et al. (2018) 
    "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028>
    for a software paper describing package and methodologies.
The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) <doi:10.1111/j.1365-2699.2010.02375.x>) and network algorithms (Lenormand et al. (2019) <doi:10.1002/ece3.4718> and Leroy et al. (2019) <doi:10.1111/jbi.13674>).
A robust framework for analyzing mortality data from bioassays for one or several strains/lines/populations.
Utilities for computation and analysis of correlation/covariation in multiple sequence alignments and in side chain motions during molecular dynamics simulations. Features include the computation of correlation/covariation scores using a variety of scoring functions between either sequence positions in alignments or side chain dihedral angles in molecular dynamics simulations and utilities to analyze the correlation/covariation matrix through a variety of tools including network representation and principal components analysis. In addition, several utility functions are based on the R graphical environment to provide friendly tools for help in data interpretation. Examples of sequence covariation analysis are provided in: (1) Pele J, Moreau M, Abdi H, Rodien P, Castel H, Chabbert M (2014) <doi:10.1002/prot.24570> and (2) Taddese B, Deniaud M, Garnier A, Tiss A, Guissouma H, Abdi H, Henrion D, Chabbert M (2018) <doi:10.1371/journal.pcbi.1006209>. An example of side chain correlated motion analysis is provided in: Taddese B, Garnier A, Abdi H, Henrion D, Chabbert M (2020) <doi:10.1038/s41598-020-72766-1>. This work was supported by the French National Research Agency (Grant number: ANR-11-BSV2-026) and by GENCI (Grant number: 100567).
Utilities dedicated to the analysis of
        biological sequences by metric MultiDimensional Scaling with
        projection of supplementary data. It contains functions for
        reading multiple sequence alignment files, calculating distance
        matrices, performing metric multidimensional scaling and
        visualizing results.
Provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016) <doi:10.1111/2041-210X.12613>.
Unified and user-friendly framework for using new 
             distributional representations of biosensors data in different statistical modeling 
             tasks: regression models, hypothesis testing, cluster analysis, visualization, and 
             descriptive analysis. Distributional representations are a functional extension of 
             compositional time-range metrics and we have used them successfully so far in modeling 
             glucose profiles and accelerometer data. However, these functional representations can 
             be used to represent any biosensor data such as ECG or medical imaging such as fMRI.
             Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of 
             glucose profiles using distributional data analysis" (2021) 
             <doi:10.1177/0962280221998064>.
Classes and functions to work with biological sequences (DNA, RNA and amino acid sequences).
    Implements S3 infrastructure to work with biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>.
    Provides a collection of functions to perform biological conversion among classes
    (transcription, translation) and basic operations on sequences
    (detection, selection and replacement based on positions or patterns).
    The package also provides functions to import and export sequences from and to other package formats.
Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.
A beginners toolbox to help those in ecology who want to deepen their understanding or utilize Bioacoustics in their work. The package has a number of utilizations from calculating frequency from waveform, performing operations in dB, and determining acoustic range of recorders. The majority of this package is based on key concepts learned from the K. Lisa Yang Center for Conservation Bioacoustics at Cornell University and their associated course: Introduction to Bioacoustics course. More information can be found within the walk through vignettes at <https://github.com/MattyD797/bioSNR/tree/main/vignettes>.
Provides some tools for developing and validating prediction models, estimate expected survival of patients and visualize them graphically. Most of the implemented methods are based on penalized regressions such as: the lasso (Tibshirani R (1996)), the elastic net (Zou H et al. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>), the adaptive lasso (Zou H (2006) <doi:10.1198/016214506000000735>), the stability selection (Meinshausen N et al. (2010) <doi:10.1111/j.1467-9868.2010.00740.x>), some extensions of the lasso (Ternes et al. (2016) <doi:10.1002/sim.6927>), some methods for the interaction setting (Ternes N et al. (2016) <doi:10.1002/bimj.201500234>), or others. A function generating simulated survival data set is also provided.
Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".
Datasets and functions for the book "Initiation à la Statistique avec R", F. Bertrand and M. Maumy-Bertrand (2022, ISBN:978-2100782826 Dunod, fourth edition).
Biostatistical and clinical data analysis, including descriptive 
    statistics, exploratory data analysis, sample size and power calculations, 
    statistical inference, and data visualization. Normality tests are 
    implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, 
    omnibus test procedures are based on Blanca et al. (2017) 
    <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), 
    while sample size and power calculation methods follow Chow et al. (2017) 
    <doi:10.1201/9781315183084>.
A set of user-friendly functions designed to fill gaps in existing introductory biostatistics R tools, making it easier for newcomers to perform basic biostatistical analyses without needing advanced programming skills.
 The methods implemented in this package are based on the works:
 Connor (1987) <doi:10.2307/2531961>
 Fleiss, Levin, & Paik (2013, ISBN:978-1-118-62561-3)
 Levin & Chen (1999) <doi:10.1080/00031305.1999.10474431>
 McNemar (1947) <doi:10.1007/BF02295996>.
Tools and code snippets for summarizing nested data, adverse events 
  and REDCap study information.
A modular framework for standardized analysis of thermal imaging data in
    animal experimentation. The package integrates thermographic data import
    (FLIR, raw, CSV), automated region of interest (ROI) segmentation based on
    'EBImage' (Pau et al., 2010 <doi:10.1093/bioinformatics/btq046>), interactive
    ROI refinement, and high-throughput batch processing.
Calculates a range of UK freshwater invertebrate biotic indices
    including BMWP, Whalley, WHPT, Habitat-specific BMWP, AWIC, LIFE and PSI.
The 'BioTIME' database was first published in
    2018 and inspired ideas, questions, project and research
    article. To make it even more accessible, an R package
    was created.
    The 'BioTIMEr' package provides tools designed to interact with the
    'BioTIME' database. The functions provided include the 'BioTIME' recommended
    methods for preparing (gridding and rarefaction) time series data, a
    selection of standard biodiversity metrics (including species richness,
    numerical abundance and exponential Shannon) alongside examples on how to
    display change over time. It also includes a sample subset of both the query
    and meta data, the full versions of which are freely available on the 'BioTIME'
    website <https://biotime.st-andrews.ac.uk/home.php>.
Tools designed to perform and evaluate cluster analysis (including Tocher's algorithm), 
	discriminant analysis and path analysis (standard and under collinearity), as well as some 
	useful miscellaneous tools for dealing with sample size and optimum plot size calculations. 
	A test for seed sample heterogeneity is now available. Mantel's permutation test can be found in this package. 
	A new approach for calculating its power is implemented. biotools also contains tests for genetic covariance components.
	Heuristic approaches for performing non-parametric spatial predictions of generic response variables and 
	spatial gene diversity are implemented.
Tools to process the information obtained from experiments conducted in the Barnes Maze. These tools enable the detection of trajectories generated by subjects during trials, as well as the acquisition of precise coordinates and relevant statistical data regarding the results. Through this approach, it aims to facilitate the analysis and interpretation of observed behaviors, thereby contributing to a deeper understanding of learning and memory processes in such experiments.
Provides tools for the analysis and visualization of gene
    expression data from the NCBI Gene Expression Omnibus (GEO). Implements
    a complete workflow including data import, quality control, differential
    expression analysis, co-expression network analysis, pathway enrichment,
    and multi-gene biomarker discovery. Differential expression uses the
    empirical Bayes moderated t-statistic of Smyth (2004)
    <doi:10.2202/1544-6115.1027>. Gene set enrichment analysis follows
    Subramanian et al. (2005) <doi:10.1073/pnas.0506580102>. Multi-gene
    biomarker selection uses the LASSO method of Tibshirani (1996)
    <doi:10.1111/j.2517-6161.1996.tb02080.x>. Effect sizes are computed
    as Cohen's d following Cohen (1988).
Creates an area-proportional Venn diagram of 2 or 3 circles. 'BioVenn' is the only R package that can automatically generate an accurate area-proportional Venn diagram by having only lists of (biological) identifiers as input. Also offers the option to map Entrez and/or Affymetrix IDs to Ensembl IDs. In SVG mode, text and numbers can be dragged and dropped. Based on the BioVenn web interface available at <https://www.biovenn.nl>. Hulsen (2021) <doi:10.3233/DS-210032>.
Visualizing the types and distribution of elements within
  bio-sequences. At the same time, We have developed a geom layer,
  geom_rrect(), that can generate rounded rectangles. No external references
  are used in the development of this package.
Provides a curated collection of biodiversity and species-related datasets (birds, plants, reptiles, turtles, mammals, bees, marine data and related biological measurements), together with small utilities to load and explore them.
    The package gathers data sourced from public repositories (including Kaggle and well-known ecological/biological R packages) and standardizes access for researchers, educators, and data analysts working on biodiversity, biogeography, ecology and comparative biology.
    It aims to simplify reproducible workflows by packaging commonly used example datasets and metadata so they can be easily inspected, visualized, and used for teaching, testing, and prototyping analyses.
Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.
Generates interactive bipartite graphs using the D3 library.
    Designed for use with the 'bipartite' analysis package.
    Includes open source 'viz-js' library 
    Adapted from examples at <https://bl.ocks.org/NPashaP> (released under GPL-3).
Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) <doi:10.1103/PhysRevE.76.066102> using the bipartite version of the algorithm described in Treviño (2015) <doi:10.1088/1742-5468/2015/02/P02003>.
We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.
A modern view on the principal component analysis biplot with 
    calibrated axes. Create principal component analysis biplots rendered in 
    HTML with significant reactivity embedded within the plot. Furthermore, 
    the traditional biplot view is enhanced by translated axes with inter-class
    kernel densities superimposed. For more information on biplots, see 
    Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0).
A GUI with which the user can construct and interact with Bootstrap methods on Classical Biplots and with Clustering and/or Disjoint Biplot. This GUI is also aimed for estimate any numerical data matrix using the Clustering and Disjoint Principal component (CDPCA) methodology.
Provides users with an EZ-to-use platform for representing data
             with biplots. Currently principal component analysis (PCA), canonical variate
             analysis (CVA) and simple correspondence analysis (CA) biplots are included. 
             This is accompanied by various formatting
             options for the samples and axes. Alpha-bags and concentration ellipses
             are included for visual enhancements and interpretation. For an extensive
             discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J.
             (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.
Provides a GUI with which users can construct and interact
        with biplots.
Implements methods for fitting logistic biplot models to
    multivariate binary data. The logistic biplot represents individuals
    as points and binary variables as directed vectors in a low-dimensional
    subspace; the orthogonal projection of each individual onto a variable
    vector approximates the expected probability that the corresponding
    characteristic is present. Available fitting methods include conjugate
    gradient algorithms, a coordinate descent Majorization-Minimization
    (MM) algorithm, and a block coordinate descent algorithm based on data
    projection that supports matrices with missing values and allows new
    individuals to be projected as supplementary rows without refitting
    the model. A cross-validation procedure is provided to select the
    number of latent dimensions k.
    References: Babativa-Marquez and Vicente-Villardon (2021)
    <doi:10.3390/math9162015>;
    Vicente-Villardon and Galindo (2006, ISBN:9780470973196).
Highly efficient functions for estimating various rank (centrality) measures of nodes in bipartite graphs (two-mode networks). Includes methods for estimating HITS, CoHITS, BGRM, and BiRank with implementation primarily inspired by He et al. (2016) <doi:10.1109/TKDE.2016.2611584>. Also provides easy-to-use tools for efficiently estimating PageRank in one-mode graphs, incorporating or removing edge-weights during rank estimation, projecting two-mode graphs to one-mode, and for converting edgelists and matrices to sparseMatrix format. Best of all, the package's rank estimators can work directly with common formats of network data including edgelists (class data.frame, data.table, or tbl_df) and adjacency matrices (class matrix or dgCMatrix).
Create attractive palettes based on the colors of the world's birds. Palettes are composed of 2 to 9 colors, with options to expand palettes via interpolation. Compatible with the package 'ggplot2' and base R graphics.
Provides a unified set of methods to detect scientific emergence
    and technological trajectories in academic papers and patents. The package
    combines citation network analysis with community detection and attribute
    extraction, also applying natural language processing (NLP) and structural
    topic modeling (STM) to uncover the contents of research communities. It
    implements metrics and visualizations of community trajectories, including
    novelty indicators, citation cycle time, and main path analysis, allowing
    researchers to map and interpret the dynamics of emerging knowledge fields.
    Applications of the method include: Souza et al. (2022)
    <doi:10.1002/bbb.2441>, Souza et al. (2022)
    <doi:10.14211/ibjesb.e1742>, Matos et al. (2023)
    <doi:10.1007/s43938-023-00036-3>, Maria et al. (2023)
    <doi:10.3390/su15020967>, Biazatti et al. (2024)
    <doi:10.1016/j.envdev.2024.101074>, Felizardo et al. (2025)
    <doi:10.1007/s12649-025-03136-z>, and Miranda et al. (2025)
    <doi:10.1016/j.ijhydene.2025.01.089>.
Bayesian models for accurately estimating conditional 
    distributions by race, using Bayesian Improved Surname Geocoding (BISG) 
    probability estimates of individual race. Implements the methods described
    in McCartan, Fisher, Goldin, Ho and Imai (2025) <doi:10.1080/01621459.2025.2526695>.
Use 'BirdNET', a state-of-the-art deep learning classifier, to automatically identify (bird) sounds.
    Analyze bioacoustic datasets without any computer science background using a pre-trained model or a custom trained classifier.
    Predict bird species occurrence based on location and week of the year.
    Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) <doi:10.1016/j.ecoinf.2021.101236>.
R functions to read EURING data and analyse re-encounter data of birds marked by metal rings. For a tutorial, go to <doi:10.1080/03078698.2014.933053>.
Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) <doi:10.5281/zenodo.5734960>). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) <doi:10.1111/ecog.04025>.
Collection of tools to make R more convenient. Includes tools to
    summarize data using statistics not available with base R and manipulate
    objects for analyses.
R functions for "The Basics of Item Response Theory Using R" by Frank B. Baker and Seock-Ho Kim (Springer, 2017, ISBN-13: 978-3-319-54204-1) including iccplot(), icccal(), icc(), iccfit(), groupinv(), tcc(), ability(), tif(), and rasch().  For example, iccplot() plots an item characteristic curve under the two-parameter logistic model.
Provides an interface to data provided by the Bank for
  International Settlements <https://www.bis.org>, allowing for programmatic
  retrieval of a large quantity of (central) banking data.
Provides a 'ggplot2' centric approach to bivariate mapping. This is a 
    technique that maps two quantities simultaneously rather than the single value 
    that most thematic maps display. The package provides a suite of tools 
    for calculating breaks using multiple different approaches, a selection of 
    palettes appropriate for bivariate mapping and scale functions for 'ggplot2' 
    calls that adds those palettes to maps. Tools for creating bivariate legends 
    are also included.
Functions for downloading data from the Bank for
  International Settlements (BIS; <https://www.bis.org/>) in
  Basel.  Supported are only full datasets in (typically) CSV
  format.  The package is lightweight and without dependencies;
  suggested packages are used only if data is to be transformed
  into particular data structures, for instance into 'zoo'
  objects. Downloaded data can optionally be cached, to avoid
  repeated downloads of the same files.
A collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.
Estimates cumulative history for time-series for continuously
    viewed bistable perceptual rivalry displays. Computes cumulative history
    via a homogeneous first order differential process. I.e., it assumes
    exponential growth/decay of the history as a function time and perceptually
    dominant state, Pastukhov & Braun (2011) <doi:10.1167/11.10.12>.
    Supports Gamma, log normal, and normal distribution families.
    Provides a method to compute history directly and example of using the
    computation on a custom Stan code.
Provided are classes for boolean and skewed boolean vectors,
    fast boolean methods, fast unique and non-unique integer sorting,
    fast set operations on sorted and unsorted sets of integers, and
    foundations for ff (range index, compression, chunked processing).
Package 'bit64' provides serializable S3 atomic 64bit (signed) integers.
  These are useful for handling database keys and exact counting in +-2^63.
  WARNING: do not use them as replacement for 32bit integers, integer64 are not
  supported for subscripting by R-core and they have different semantics when
  combined with double, e.g. integer64 + double => integer64.
  Class integer64 can be used in vectors, matrices, arrays and data.frames.
  Methods are available for coercion from and to logicals, integers, doubles,
  characters and factors as well as many elementwise and summary functions.
  Many fast algorithmic operations such as 'match' and 'order' support inter-
  active data exploration and manipulation and optionally leverage caching.
Record algorithmic and analytic meta data along a workflow to store 
    that in a bitfield, which can be published alongside any (modelled) data 
    products.
A client for cryptocurrency exchange BitMEX
    <https://www.bitmex.com/> including the ability to obtain historic
    trade data and place, edit and cancel orders. BitMEX's Testnet and
    live API are both supported.
Functions for bitwise operations on integer vectors.
Provides methods for the binarization and trinarization of one-dimensional data and some visualization functions.
Implements bidirectional two-stage least squares (Bi-TSLS) estimation 
    for identifying bidirectional causal effects between two variables 
    in the presence of unmeasured confounding. The method uses proxy 
    variables (negative control exposure and outcome) along with 
    at least one covariate to handle confounding.
Bit-level reading and writing are necessary when dealing with 
  many file formats e.g. compressed data and binary files.  Currently, R 
  connections are manipulated at the byte level.  This package wraps existing
  connections and raw vectors so that it is possible to read bits, bit sequences,
  unaligned bytes and low-bit representations of integers.
Provides functions for creating, modifying, and displaying bitmaps including printing them in the terminal.   There is a special emphasis on monochrome bitmap fonts and their glyphs as well as colored pixel art/sprites.  Provides native read/write support for the 'hex' and 'yaff' bitmap font formats and if 'monobit' <https://github.com/robhagemans/monobit> is installed can also read/write several additional bitmap font formats.
Provides tools for fitting bivariate hurdle negative binomial 
    models with horseshoe priors, Bayesian Model Averaging (BMA) via stacking, 
    and comprehensive causal inference methods including G-computation, 
    transfer entropy, Threshold Vector Autoregressive (TVAR) and Smooth 
    Transition Autoregressive (STAR) models, Dynamic Bayesian Networks (DBN), 
    Hidden Markov Models (HMM), and sensitivity analysis.
Simplify bivariate and regression analyses by automating
    result generation, including summary tables, statistical tests, and
    customizable graphs. It supports tests for continuous and dichotomous
    data, as well as stepwise regression for linear, logistic, and Firth
    penalized logistic models.  While not a substitute for tailored
    analysis, 'BiVariAn' accelerates workflows and is expanding features
    like multilingual interpretations of results.The methods for selecting
    significant statistical tests, as well as the predictor selection in
    prediction functions, can be referenced in the works of Marc Kery
    (2003) <doi:10.1890/0012-9623(2003)84[92:NORDIG]2.0.CO;2> and Rainer
    Puhr (2017) <doi:10.1002/sim.7273>.
Perform competing risks analysis under bivariate Pareto models. See Shih et al. (2019) <doi:10.1080/03610926.2018.1425450> for details.
Creates bivariate choropleth maps using 'Leaflet'. This package provides
    tools for visualizing the relationship between two variables through a color
    matrix representation on an interactive map.
Contains functions mainly focused to plotting bivariate maps.
Provides functions to compute the joint probability mass function (pmf), cumulative distribution function (cdf), and survival function (sf) of the Basu-Dhar bivariate geometric distribution. Additional functionalities include the calculation of the correlation coefficient, covariance, and cross-factorial moments, as well as the generation of random variates. The package also implements parameter estimation based on the method of moments.
Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
Implements methods for bivariate and univariate Laplace transforms
    of residual lives and reversed residual lives, associated stochastic ordering
    concepts, and entropy measures for reliability analysis. The package covers:
    (1) Bivariate Laplace transform of residual lives and stochastic comparisons
    based on the bivariate Laplace transform order of residual lives (BLt-rl),
    including weak bivariate hazard rate, mean residual life, and relative mean
    residual life orders, nonparametric estimation, and NBUHR/NWUHR aging class
    characterisation; Jayalekshmi, Rajesh, and Nair (2022)
    "Bivariate Laplace Transform of Residual Lives and Their Properties"
    <doi:10.1080/03610926.2022.2085874>;
    (2) Bivariate Laplace transform order of reversed residual lives (BLt-Rrl),
    reversed hazard gradient, reversed mean residual life, and the associated
    stochastic orders (weak bivariate reversed hazard rate, weak bivariate
    reversed mean residual life); Jayalekshmi, Rajesh, and Nair (2022)
    "Bivariate Laplace Transform Order and Ordering of Reversed Residual Lives"
    <doi:10.1142/S0218539322500061>;
    (3) Univariate Laplace transform of residual life, hazard rate, mean residual
    life, and the corresponding stochastic orders (Lt-rl order, hazard rate order,
    MRL order), together with a nonparametric estimator.
    Shannon entropy and Golomb's (1966) information generating function are also
    provided. Parametric families supported include the Gumbel bivariate
    exponential, Farlie-Gumbel-Morgenstern (FGM), bivariate power, and
    Schur-constant distributions. Plotting utilities and a simulation framework
    for evaluating estimator performance are also provided.
Maximum likelihood estimation, random values generation, density computation and other functions for the bivariate Poisson distribution. References include: Kawamura K. (1984). "Direct calculation of maximum likelihood estimator for the bivariate Poisson distribution". Kodai Mathematical Journal, 7(2): 211--221. <doi:10.2996/kmj/1138036908>. Kocherlakota S. and Kocherlakota K. (1992). "Bivariate discrete distributions". CRC Press. <doi:10.1201/9781315138480>. Karlis D. and Ntzoufras I. (2003). "Analysis of sports data by using bivariate Poisson models". Journal of the Royal Statistical Society: Series D (The Statistician), 52(3): 381--393. <doi:10.1111/1467-9884.00366>.
A collection of models for bivariate alternating recurrent event data analysis. 
             Includes non-parametric and semi-parametric methods.
Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.
Generates bivariate residual plots with simulation polygons for any diagnostics and bivariate model from which functions to extract the desired diagnostics, simulate new data and refit the models are available.
Simulation of bivariate uniform data with a full range of correlations based on 
             two beta densities and computation of the tetrachoric correlation 
             (correlation of bivariate uniform data) from the phi coefficient 
             (correlation of bivariate binary data) and vice versa.
This is a port of the WTC MATLAB package written by Aslak Grinsted
    and the wavelet program written by Christopher Torrence and Gibert P.
    Compo. This package can be used to perform univariate and bivariate
    (cross-wavelet, wavelet coherence, wavelet clustering) analyses.
Compute multivariate location, scale, and correlation
        estimates based on Tukey's biweight M-estimator.
Business days calculations based on a list of holidays and
    nonworking weekdays. Quite useful for fixed income and derivatives pricing.
Maximum likelihood estimation of copula-based zero-inflated 
    (and non-inflated) Poisson and negative binomial count models, based on the 
    article <doi:10.18637/jss.v109.i01>. Supports Frank and Gaussian copulas. 
    Allows for mixed margins (e.g., one margin Poisson, the other zero-inflated 
    negative binomial), and several marginal link functions. Built-in methods for 
    publication-quality tables using 'texreg', post-estimation diagnostics using 
    'DHARMa', and testing for marginal zero-modification via <doi:10.1177/0962280217749991>. 
    For information on copula regression for count data, see Genest and Nešlehová (2007) 
    <doi:10.1017/S0515036100014963> as well as Nikoloulopoulos (2013) <doi:10.1007/978-3-642-35407-6_11>. 
    For information on zero-inflated count regression generally, see Lambert (1992) 
    <https://www.jstor.org/stable/1269547>. The author acknowledges 
    support by NSF DMS-1925119 and DMS-212324.
Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures, as described in Bobb et al (2015) 
  <doi:10.1093/biostatistics/kxu058>.
Bayesian kernel machine regression (from the 'bkmr' package)
    is a Bayesian semi-parametric generalized linear model approach under
    identity and probit links. There are a number of functions in this
    package that extend Bayesian kernel machine regression fits to allow
    multiple-chain inference and diagnostics, which leverage functions
    from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
    F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
    software for analyzing the health effects of multiple concurrent
    exposures via Bayesian kernel machine regression. ;
    <doi:10.1186/s12940-018-0413-y>.
Implements the Beta Kernel Process (BKP) for nonparametric modeling of spatially varying binomial probabilities, together with its extension, the Dirichlet Kernel Process (DKP), for categorical or multinomial data.
 The package provides functions for model fitting, predictive inference with uncertainty quantification, posterior simulation, and visualization in one-and two-dimensional input spaces.
 Multiple kernel functions (Gaussian, Matern 5/2, and Matern 3/2) are supported, with hyperparameters optimized through multi-start gradient-based search.
 For more details, see Zhao, Qing, and Xu (2025) <doi:10.48550/arXiv.2508.10447>.
Bayesian kernel projection classifier (Domijan and Wilson,2011) <doi:10.1007/s11222-009-9161-8> is a nonlinear multicategory classifier which performs the classification of the projections of the data to the principal axes of the feature space. A Gibbs sampler is implemented to find the posterior distributions of the parameters.
Facilitates scalable spatiotemporally varying coefficient
    modelling with Bayesian kernelized tensor regression.
    The important features of this package are:
    (a) Enabling local temporal and spatial modeling of the relationship between
    the response variable and covariates.
    (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>.
    (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior
    distribution of the model parameters.
    (d) Employing a tensor decomposition to reduce the number of estimated parameters.
    (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration
    with the 'torch' package.
Fits boundary line models to datasets as proposed by Webb
    (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical
    inferences about their parameters. Provides additional tools for
    testing datasets for evidence of boundary presence and selecting
    initial starting values for model optimization prior to fitting the
    boundary line models. It also includes tools for conducting post-hoc
    analyses such as predicting boundary values and identifying the most
    limiting factor (Miti, Milne, Giller, Lark (2024)
    <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive
    analysis for datasets that exhibit upper boundary structures.
Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.
Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.
Can be used to read and write a fwf with an accompanying 'Blaise' datamodel.
    Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a 
    way to write and collect surveys and perform statistical analysis on the data. It stores its data in 
    fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically 
    interprets this metadata and reads the file into an R dataframe.
    When supplying a datamodel for writing, the dataframe will be automatically converted 
    to that format and checked for compatibility.
    Supports dataframes, tibbles and LaF objects.
    For more information about 'Blaise', see <https://blaise.com/products/general-information>.
Fast and accurate calculation of Blaker's binomial and Poisson confidence limits (and some related stuff).
Bland-Altman Plots using either base graphics or ggplot2,
    augmented with confidence intervals, with detailed return values and
    a sunflowerplot option for data with ties.
Carries out Bland Altman analyses (also known as a Tukey
    mean-difference plot) as described by JM Bland and DG Altman in
    1986 <doi:10.1016/S0140-6736(86)90837-8>. This package was created in 
    2015 as existing Bland-Altman analysis functions did not calculate 
    confidence intervals. This package was created to rectify this, 
    and create reproducible plots. This package is also available as a module
    for the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org>
    for more information).
Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) <https://github.com/p-mq/Percentile_based_averaging>.
Laplace approximations and penalized B-splines are combined
    for fast Bayesian inference in latent Gaussian models. The routines can be
    used to fit survival models, especially proportional hazards and promotion 
    time cure models (Gressani, O. and Lambert, P. (2018) 
    <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
    be implemented for inference in (generalized) additive models
    (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>).
    See the associated website for more information and examples.
Implementation of an efficient BLAST-like sequence
             comparison algorithm, written in 'C++11' and using
             native R datatypes. Blaster is based on 'nsearch' -
             Schmid et al (2018) <doi:10.1101/399782>.
Compose and send out responsive HTML email messages that render
    perfectly across a range of email clients and device sizes. Helper functions
    let the user insert embedded images, web link buttons, and 'ggplot2' plot
    objects into the message body. Messages can be sent through an 'SMTP'
    server, through the 'Posit Connect' service, or through the 'Mailgun' API
    service <https://www.mailgun.com/>.
Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) <doi:10.1007/s11336-008-9089-5>.
A wrapper around the 'Blat' command line SMTP mailer for Windows.
   'Blat' is public domain software, but be sure to read the license before use.
    It can be found at the Blat website http://www.blat.net.
Fit a variety of Bayesian latent variable models, including confirmatory
   factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.
Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.
Create a blended curve from two survival curves, which is particularly useful
          for survival extrapolation in health technology assessment. The main idea is to
          mix a flexible model that fits the observed data well with a parametric model that
          encodes assumptions about long-term survival. The two curves are blended into a
          single survival curve that is identical to the first model over the range of 
          observed times and gradually approaches the parametric model over the extrapolation
          period based on a given weight function. This approach allows for the inclusion of
          external information, such as data from registries or expert opinion, to guide
          long-term extrapolations, especially when dealing with immature trial data.
          See Che et al. (2022) <doi:10.1177/0272989X221134545>.
Colour blend functions. These functions make it easier to blend 
    colours and palettes using digital blend modes such as multiply, screen, 
    and overlay.
Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
Computation of key characteristics and plots for blinded sample size recalculation.
   Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
   See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description.
   The implemented methods include the approaches by
   Lu, K. (2016) <doi:10.1002/pst.1737>,
   Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
   Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, 
   Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
   Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.
A blind spike program provides samples to a laboratory in order to  perform quality control (QC) checks. The samples provided are of a known quantity to the tester. The laboratory is typically uninformed of that the sample provided is a QC sample.
An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
Identifies cell-cell communication hotspots in spatial
    transcriptomics data using bivariate Local Moran's I statistics
    on hexagonally binned cells. Provides functions for spatial
    weighting, ligand-receptor pair filtering, hotspot detection,
    and visualisation of sender-receiver cell-type interactions.
A method for the Bayesian functional linear regression model (scalar-on-function),
  including two estimators of the coefficient function and an estimator of its support.
  A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., 
  Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.
Implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) <doi:10.1007/s11336-013-9328-2>. Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.
A chemical speciation and toxicity prediction model for the 
    toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) 
    engine was originally programmed in 'PowerBasic' by Robert Santore and 
    others. The main way the BLM can be used is to predict the toxicity of a 
    metal to an organism with a known sensitivity (i.e., it is known how much 
    of that metal must accumulate on that organism's biotic ligand to cause a 
    physiological effect in a certain percentage of the population, such as a 
    20% loss in reproduction or a 50% mortality rate). The second way the BLM 
    can be used is to estimate the chemical speciation of the metal and other 
    constituents in water, including estimating the amount of metal accumulated 
    to an organism's biotic ligand during a toxicity test. In the first 
    application of the BLM, the amount of metal associated with a toxicity 
    endpoint, or regulatory limit will be predicted, while in the 
    second application, the amount of metal is known and the portions of that 
    metal that exist in various forms will be determined. This version of the 
    engine has been re-structured to perform the calculations in a different 
    way that will make it more efficient in R, while also making it more 
    flexible and easier to maintain in the future. Because of this, it does 
    not currently match the desktop model exactly, but we hope to improve 
    this comparability in the future.
Posterior distribution in the Black-Litterman model is computed from a prior distribution given in the form of a time series of asset returns and a continuous distribution of views provided by the user as an external function.
R's raw vector is useful for storing a single binary object.
    What if you want to put a vector of them in a data frame? The 'blob'
    package provides the blob object, a list of raw vectors, suitable for
    use as a column in data frame.
Computation of large covariance matrices having a block structure up to a permutation of their columns and rows 
    from a small number of samples with respect to the dimension of the matrix.
 The method is described in the paper Perrot-Dockès et al. (2019) <arXiv:1806.10093>.
Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.
Provides computational tools to generate efficient blocked and unblocked
    fractional factorial designs for two-level and three-level factors using the
    generalized Minimum Aberration (MA) criterion and related optimization algorithms.
    Methodological foundations include the general theory of minimum aberration as
    described by Cheng and Tang (2005) <doi:10.1214/009053604000001228>, and the 
    catalogue of three-level regular fractional factorial designs developed by Xu 
    (2005) <doi:10.1007/s00184-005-0408-x>. The main functions dol2() and dol3() generate
    blocked two-level and three-level fractional factorial designs, respectively, using
    beam search, optimization-based ranking, confounding assessment, and structured 
    output suitable for complete factorial situations.
A random forest variant 'block forest' ('BlockForest') tailored to the 
  prediction of binary, survival and continuous outcomes using block-structured 
  covariate data, for example, clinical covariates plus measurements of a certain 
  omics data type or multi-omics data, that is, data for which measurements of 
  different types of omics data and/or clinical data for each patient exist. Examples 
  of different omics data types include gene expression measurements, mutation data
  and copy number variation measurements.
  Block forest are presented in Hornung & Wright (2019). The package includes four
  other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 
  'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), 
  but performed worse than block forest in their comparison study based on 20 real 
  multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') 
  in applications. The other random forest variants can, however, be consulted for 
  academic purposes, for example, in the context of further methodological 
  developments. 
  Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.
The goal of 'blocking' is to provide blocking methods for record linkage and deduplication using approximate nearest neighbour (ANN) algorithms and graph techniques. It supports multiple ANN implementations via 'rnndescent', 'RcppHNSW', 'RcppAnnoy', and 'mlpack' packages, and provides integration with the 'reclin2' package. The package generates shingles from character strings and similarity vectors for record comparison, and includes evaluation metrics for assessing blocking performance including false positive rate (FPR) and false negative rate (FNR) estimates. For details see: Papadakis et al. (2020) <doi:10.1145/3377455>, Steorts et al. (2014) <doi:10.1007/978-3-319-11257-2_20>, Dasylva and Goussanou (2021) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X202100200002>, Dasylva and Goussanou (2022) <doi:10.1007/s42081-022-00153-3>.
A set of functions to select the optimal block-length for a 
    dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing
    (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the 
    Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density 
    Plug-in method, including the Patton, Politis, and White (2009)
    <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee
    (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with 
    a corresponding set of S3 plot methods.
Some elementary matrix algebra tools are implemented to manage
    block matrices or partitioned matrix, i.e. "matrix of matrices"
    (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here
    defined as a new S3 object. In this package, some methods for "matrix"
    object are rewritten for "blockmatrix" object. New methods are implemented.
    This package was created to solve equation systems with block matrices for
    the analysis of environmental vector time series .
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) <doi:10.1080/01621459.2020.1751176>.
This is primarily meant as an implementation of generalized blockmodeling for valued networks.
 In addition, measures of similarity or dissimilarity based on structural equivalence and 
 regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted:
 Žiberna (2007)<doi:10.1016/j.socnet.2006.04.002>, Žiberna (2008)<doi:10.1080/00222500701790207>,
 Žiberna (2014)<doi:10.1016/j.socnet.2014.04.002>.
This app provides some useful tools for Offering an accessible GUI for generalised blockmodeling of single-relation, one-mode networks. The user can execute blockmodeling without having to write a line code by using the app's visual helps. Moreover, there are several ways to visualisations networks and their partitions. Finally, the results can be exported as if they were produced by writing code. The development of this package is financially supported by the Slovenian Research Agency (www.arrs.gov.si) within the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Latent and Stochastic Block Model estimation by a Variational EM algorithm.
             Various probability distribution are provided (Bernoulli,
             Poisson...), with or without covariates.
A framework for building interactive dashboards and document-based
    reports. Underlying data manipulation and visualization is possible using a
    web-based point and click user interface.
A framework for data manipulation and visualization using a
    web-based point and click user interface where analysis pipelines are decomposed into re-usable and parameterizable blocks.
Building on the docking layout manager provided by 'blockr.dock', this
    provides an extension that allows for visualizing and manipulating a 'blockr'
    board using a DAG-based user interface powered by the 'g6R' 
    graph visualisation HTML widget.
Building on the docking layout manager provided by 'dockViewR',
    this provides a flexible front-end to 'blockr.core'. It provides an extension mechanism which allows for providing means to manipulate a board
    object via panel-based user interface components.
Extends 'blockr.core' with interactive blocks for visual data
    wrangling using 'dplyr' and 'tidyr' operations. Users can build data
    transformation pipelines through a graphical interface without writing code
    directly. Includes blocks for filtering, selecting, mutating, summarizing,
    joining, and arranging data, with support for complex expressions, grouping
    operations, and real-time validation.
Extends 'blockr.core' with interactive blocks for data
    visualization using 'ggplot2'. Users can build charts through a graphical
    interface without writing code directly. Includes common chart types
    (bar charts, line charts, pie charts, scatter plots) as well as statistical
    plots (boxplots, histograms, density plots, violin plots) with
    rich customization options and intuitive user interfaces.
Extends 'blockr.core' with interactive blocks for reading and
    writing data files. Supports CSV, Excel, Parquet, RDS, and other formats
    through a graphical interface without writing code directly. Includes
    file browser integration and configurable import/export options.
Create randomizations for block random clinical trials.  Can also produce a pdf file of randomization cards.
Computes power for response-adaptive randomization with a block
    design that captures both the time and treatment effect. T. Chandereng,
    R. Chappell (2019) <doi:10.48550/arXiv.1904.07758>.
Constructs treatment and block designs for linear treatment models
  with crossed or nested block factors. The treatment design can be any feasible 
  linear model and the block design can be any feasible combination of crossed or 
  nested block factors. The block design is a sum of one or more block factors
  and the block design is optimized sequentially with the levels of each successive
  block factor optimized conditional on all previously optimized block factors. 
  D-optimality is used throughout except for square or rectangular lattice block designs 
  which are constructed algebraically using mutually orthogonal Latin squares.
  Crossed block designs with interaction effects are optimized using a weighting scheme
  which allows for differential weighting of first and second-order block effects. 
  Outputs include a table showing the allocation of treatments to blocks and tables showing
  the achieved D-efficiency factors for each block and treatment design.  
  Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. 
  JABES 25, 500–522 (2020) <doi:10.1007/s13253-020-00416-0>.
Sample dataframes by group, in the form of a 'block bootstrap'. Entire groups are returned allowing for a single 'observation' to span multiple rows of the dataframe.
Blocks units into experimental blocks, with one unit per treatment condition, by creating a measure of multivariate distance between all possible pairs of units. Maximum, minimum, or an allowable range of differences between units on one variable can be set. Randomly assign units to treatment conditions. Diagnose potential interference between units assigned to different treatment conditions.  Write outputs to .tex and .csv files. For more information on the methods implemented, see Moore (2012) <doi:10.1093/pan/mps025>.
Supervised learning on tabular data with blockwise missing
    patterns, using the Blockwise Reduced Modeling (BRM) method of
    Srinivasan, Currim, and Ram (2025) <doi:10.1287/ijds.2022.9016>.
    BRM partitions the training data into overlapping subsets based on
    per-row feature-missing patterns, fits one user-supplied learner per
    subset with minimal imputation, and at prediction time routes each
    test instance to the best-matching subset model. The interface is
    learner-agnostic: any fit-and-predict pair can be plugged in, and
    convenience specifications are provided for linear models, tree
    models, random forests, and gradient boosting.
Implements the Block-wise Rank in Similarity Graph Edge-count 
    test (BRISE), a rank-based two-sample test designed for block-wise 
    missing data. The method constructs (pattern) pair-wise similarity graphs 
    and derives quadratic test statistics with asymptotic chi-square 
    distribution or permutation-based p-values. It provides both 
    vectorized and congregated versions for flexible inference. 
    The methodology is described in Zhang, Liang, Maile, and 
    Zhou (2025) <doi:10.48550/arXiv.2508.17411>.
Functions to combine data on voting blocs' 
    size, turnout, and vote choice to estimate each bloc's
    vote contributions to the Democratic and Republican parties.
    The package also includes functions for uncertainty estimation and
    plotting. Users may define voting blocs along a discrete or continuous variable.
    The package implements methods described in Grimmer, Marble, and Tanigawa-Lau (2023) <doi:10.31235/osf.io/c9fkg>.
Write blog posts and web pages in R Markdown. This package
    supports the static site generator 'Hugo' (<https://gohugo.io>) best,
    and it also supports 'Jekyll' (<https://jekyllrb.com>) and 'Hexo'
    (<https://hexo.io>).
Provides methods for estimating the area under the concentration
    versus time curve (AUC) and its standard error in the presence of Below the
    Limit of Quantification (BLOQ) observations. Two approaches are implemented:
    direct estimation using censored maximum likelihood, and a two-step approach
    that first imputes BLOQ values using various methods and then computes the
    AUC using the imputed data. Technical details are described in Barnett et al.
    (2020), "Methods for Non-Compartmental Pharmacokinetic Analysis With
    Observations Below the Limit of Quantification," Statistics in
    Biopharmaceutical Research. <doi:10.1080/19466315.2019.1701546>.
Tools designed to make it easier for beginner and intermediate users to build and validate 
    binary logistic regression models. Includes bivariate analysis, comprehensive regression output, 
    model fit statistics, variable selection procedures, model validation techniques and a 'shiny' 
    app for interactive model building.
Arrays of structured data types can require large volumes of disk
    space to store. 'Blosc' is a library that provides a fast and efficient way
    to compress such data. It is often applied in storage of n-dimensional
    arrays, such as in the case of the geo-spatial 'zarr' file format. This
    package can be used to compress and decompress data using 'Blosc'.
Finds the best block diagonal matrix approximation of a symmetric matrix. This can be exploited for divisive hierarchical clustering using singular vectors, named HC-SVD. The method is described in Bauer (202Xa) <doi:10.48550/arXiv.2308.06820>.
Provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.
Design dose escalation using Bayesian logistic regression modeling in Phase I oncology trial.
An interactive document on  the topic of binary logistic regression  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.
Allows users to easily visualize data from the BLS (United States of America Bureau of Labor Statistics) <https://www.bls.gov>. Currently unemployment data series U1-U6 are available. Not affiliated with the Bureau of Labor Statistics or United States Government.
These functions provide a convenient interface for downloading data from the U.S. Bureau of Labor Statistics <https://www.bls.gov>.  The functions in this package utilize flat files produced by the Bureau of Labor Statistics, which contain full series history.  These files include employment, unemployment, wages, prices, industry and occupational data at a national, state, and sub-state level, depending on the series.  Individual functions are included for those programs which have data available at the state level.  The core functions provide direct access to the Current Employment Statistics (CES) <https://www.bls.gov/ces/>, Local Area Unemployment Statistics (LAUS) <https://www.bls.gov/lau/>, Occupational Employment and Wage Statistics (OEWS) <https://www.bls.gov/oes/> and Alternative Measures of Labor Underutilization (SALT) <https://www.bls.gov/lau/stalt.htm> data produced by the Bureau of Labor Statistics.
Provides a Bayesian latent space 
    model for complex networks, either weighted or unweighted.
    Given an observed input graph, the estimates for the latent coordinates 
    of the nodes are obtained through a Bayesian MCMC algorithm. 
    The overall likelihood of the graph depends on a fundamental probability 
    equation, which is defined so that ties are more likely to exist 
    between nodes whose latent space coordinates are close. 
    The package is mainly based on the model by Hoff, Raftery and Handcock (2002)
    <doi:10.1198/016214502388618906> and contains some extra features 
    (e.g., removal of the Procrustean step, weights implemented as 
    coefficients of the latent distances, 3D plots). 
    The original code related to the above model was retrieved from
    <https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
    Users can inspect the MCMC simulation, create and customize insightful 
    graphical representations or apply clustering techniques.
Implements v2 of the B.L.S. API for requests of survey information
  and time series data through 3-tiered API that allows users to interact with
  the raw API directly, create queries through a functional interface, and
  re-shape the data structures returned to fit common uses. The API definition 
  is located at: <https://www.bls.gov/developers/api_signature_v2.htm>.
Facilitates the importation of the Boston Blue Bike trip data
    since 2015. Functions include the computation of trip distances of
    given trip data. It can also map the location of stations within a
    given radius and calculate the distance to nearby stations. Data is
    from <https://www.bluebikes.com/system-data>.
Tools to estimate soil organic carbon stocks and sequestration rates 
    in blue carbon ecosystems. 'BlueCarbon' contains functions to estimate and correct
    for core compaction, estimate sample thickness, estimate organic carbon content 
    from organic matter content, estimate organic carbon stocks and 
    sequestration rates, and visualize the error of carbon stock extrapolation.
Estimates Boltzmann–Lotka–Volterra (BLV) interaction model 
    efficiently. Enables programmatic and graphical exploration of the solution
    space of BLV models when parameters are varied. See Wilson, A. (2008) 
    <dx.doi.org/10.1098/rsif.2007.1288>.
Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.
Package for Bayesian model averaging and variable selection for linear models,
        generalized linear models and survival models (cox
        regression).
Used for Bayesian mediation analysis based on Bayesian additive Regression Trees (BART).
	The analysis method is described in Yu and Li (2025) "Mediation Analysis with Bayesian Additive Regression Trees", submitted for publication.
Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.
Multivariate tool for analyzing genome-wide association
    study results in the form of univariate summary statistics. The 
    goal of 'bmass' is to comprehensively test all possible multivariate
    models given the phenotypes and datasets provided. Multivariate
    models are determined by assigning each phenotype to being either
    Unassociated (U), Directly associated (D) or Indirectly associated
    (I) with the genetic variant of interest. Test results for each model 
    are presented in the form of Bayes factors, thereby allowing direct
    comparisons between models. The underlying framework implemented
    here is based on the modeling developed in "A Unified Framework 
    for Association Analysis with Multiple Related Phenotypes",
    M. Stephens (2013) <doi:10.1371/journal.pone.0065245>.
Provides Bayesian methods for comparing groups on multiple binary
     outcomes. Includes basic tests using multivariate Bernoulli distributions,
     subgroup analysis via generalized linear models, and multilevel models
     for clustered data.
     For statistical underpinnings, see Kavelaars, Mulder, and Kaptein (2020)
     <doi:10.1177/0962280220922256>, Kavelaars, Mulder, and Kaptein (2024)
     <doi:10.1080/00273171.2024.2337340>, and Kavelaars, Mulder, and Kaptein (2023)
     <doi:10.1186/s12874-023-02034-z>.
     An interactive shiny app to perform sample size computations is available.
The four functions svdcp() ('cp' for column partitioned), svdbip() or svdbip2() ('bip' for bipartitioned), and svdbips() ('s' for a simultaneous optimization of a set of 'r' solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) <https://eudml.org/doc/106424> and Hanafi M. & Lafosse, R. (2001) <https://eudml.org/doc/106494>.
Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.
Provides an accessible and robust implementation of core BME 
    methodologies for spatial prediction. It enables the systematic integration 
    of heterogeneous data sources including both hard data (precise 
    measurements) and soft interval data (bounded or uncertain observations) 
    while incorporating prior knowledge and supporting variogram-based spatial 
    modeling. The BME methodology is described in Christakos (1990) 
    <doi:10.1007/BF00890661> and Serre and Christakos (1999) 
    <doi:10.1007/s004770050029>.
Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).
Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. 
             Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) 
             <doi:10.1002/sta4.645>, but more models and equivalence testing features may be added over time.
Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.
Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using 'rstan' (See Stan Development Team (2020) <https://mc-stan.org/>).
    Functions are provided for estimation, result extraction, model fit statistics, and plottings.
These are miscellaneous functions for working with panel data, quantiles, and printing results.  For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.  Additional utilities support staggered treatment adoption settings, including functions for identifying treatment groups, recovering pre-treatment outcomes and covariate averages, and computing lagged outcomes and first differences in panel data.
Provides a suite of Bayesian MI-LASSO for variable selection methods for multiply-imputed datasets. The package includes four Bayesian MI-LASSO models using shrinkage (Multi-Laplace, Horseshoe, ARD) and Spike-and-Slab (Spike-and-Laplace) priors, along with tools for model fitting via MCMC, four-step projection predictive variable selection, and hyperparameter calibration. Methods are suitable for both continuous and binary covariates under missing-at-random or missing-completely-at-random assumptions. See Zou, J., Wang, S. and Chen, Q. (2025), Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data. ArXiv, 2211.00114. <doi:10.48550/arXiv.2211.00114> for more details. We also provide the frequentist`s MI-LASSO function.
Provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.
Implements Bayesian multiple-membership multilevel models with parameterizable weight functions via 'JAGS' to model how lower-level units jointly shape higher-level outcomes (micro-macro link) across a range of outcome types (e.g., linear, logit, and survival models). Supports estimation and comparison of alternative aggregation mechanisms, allows weight matrices to be endogenized through parameters and covariates, and accommodates complex dependence structures that extend beyond traditional multilevel frameworks. For details, see Rosche (2026) "A Multilevel Model for Coalition Governments. Uncovering Party-Level Dependencies Within and Between Governments" <doi:10.31235/osf.io/4bafr_v2>.
Easy estimation of Bayesian multilevel mediation models with Stan.
Fit computational and measurement models using full Bayesian
    inference. The package provides a simple and accessible interface by
    translating complex domain-specific models into 'brms' syntax, a
    powerful and flexible framework for fitting Bayesian regression models
    using 'Stan'. The package is designed so that users can easily apply
    state-of-the-art models in various research fields, and so that
    researchers can use it as a new model development framework.
    References: Frischkorn and Popov (2025) <doi:10.3758/s13428-025-02643-0>.
Reads Windows BMP format images. Currently limited to 8 bit
    greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
    external dependencies.
The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.
Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.
Fits, validates and compares a number of Bayesian models for
    spatial and space time point referenced and areal unit data. Model fitting
    is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer',
    'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using
    the DIC and WAIC,  and K-fold cross-validation where the user is free
    to select their own subset of data rows for validation. Sahu (2022)
    <doi:10.1201/9780429318443> describes the methods in detail.
Density, distribution, quantile function, random number generation for the BMT (Bezier-Montenegro-Torres) distribution. Torres-Jimenez C.J. and Montenegro-Diaz A.M. (2017) <doi:10.48550/arXiv.1709.05534>. Moments, descriptive measures and parameter conversion for different parameterizations of the BMT distribution. Fit of the BMT distribution to non-censored data by maximum likelihood, moment matching, quantile matching, maximum goodness-of-fit, also known as minimum distance, maximum product of spacing, also called maximum spacing, and minimum quantile distance, which can also be called maximum quantile goodness-of-fit. Fit of univariate distributions for non-censored data using maximum product of spacing estimation and minimum quantile distance estimation is also included.
State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.
Functions to implement a Hwang(2021) <doi:10.2139/ssrn.3866876> estimator, which bounds an omitted variable bias using auxiliary data.
Bayesian network structure learning, parameter learning and inference.
  This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC,
  Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu
  Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete,
  Gaussian and conditional Gaussian networks, along with many score functions and
  conditional independence tests.
  The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented.
  Some utility functions (model comparison and manipulation, random data generation, arc
  orientation testing, simple and advanced plots) are included, as well as support for
  parameter estimation (maximum likelihood and Bayesian) and inference, conditional
  probability queries, cross-validation, bootstrap and model averaging.
  Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.
Network meta-analyses using Bayesian framework following Dias et al. (2013) <DOI:10.1177/0272989X12458724>. Based on the data input, creates prior, model file, and initial values needed to run models in 'rjags'. Able to handle binomial, normal and multinomial arm-level data. Can handle multi-arm trials and includes methods to incorporate covariate and baseline risk effects. Includes standard diagnostics and visualization tools to evaluate the results.
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.
Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by 'Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) <doi:10.1007/978-1-4612-0745-0>.
Implements a bootstrap aggregated (bagged) version of
    the k-nearest neighbors survival probability prediction method (Lowsky et
    al. 2013). In addition to the bootstrapping of training samples, the
    features can be subsampled in each baselearner to break the correlation
    between them. The Rcpp package is used to speed up the computation.
This project aims to enable the method of Path Analysis to infer causalities 
             from data. For this we propose a hybrid approach, which uses Bayesian network 
             structure learning algorithms from data to create the input file for creation of a 
             PA model. The process is performed in a semi-automatic way by our intermediate 
             algorithm, allowing novice researchers to create and evaluate their own PA models
             from a data set. The references used for this project are: 
             Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press. <doi:10.1017/S0269888910000275>. 
             Nagarajan, R., Scutari, M., & Lèbre, S. (2013). Bayesian networks in r. Springer, 122, 125-127. Scutari, M., & Denis, J. B. <doi:10.1007/978-1-4614-6446-4>.
             Scutari M (2010). Bayesian networks: with examples in R. Chapman and Hall/CRC. <doi:10.1201/b17065>. 
             Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. <doi:10.18637/jss.v048.i02>.
Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.
Bayesian Nonparametric sensitivity analysis of multiple testing procedures for p values with arbitrary dependencies, based on the Dirichlet process prior distribution.
The Pritchard-Stephens-Donnelly (PSD) admixture model has k intermediate subpopulations from which n individuals draw their alleles dictated by their individual-specific admixture proportions.  The BN-PSD model additionally imposes the Balding-Nichols (BN) allele frequency model to the intermediate populations, which therefore evolved independently from a common ancestral population T with subpopulation-specific FST (Wright's fixation index) parameters.  The BN-PSD model can be used to yield complex population structures.  This simulation approach is now extended to subpopulations related by a tree.  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Performs the algorithm for time series clustering described in Nieto-Barajas and Contreras-Cristan (2014).
A collection of Bayesian networks (discrete, Gaussian, and conditional linear Gaussian) collated from recent academic literature. The 'bnRep_summary' object provides an overview of the Bayesian networks in the repository and the package documentation includes details about the variables in each network. A Shiny app to explore the repository can be launched with 'bnRep_app()' and is available online at <https://manueleleonelli.shinyapps.io/bnRep>. Reference: 'M. Leonelli' (2025) <doi:10.1016/j.neucom.2025.129502>.
Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters.
MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2022) <doi: 10.1002/sim.9376>, and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.
Bayesian Network Structure Learning from Data with Missing Values.
    The package implements the Silander-Myllymaki complete search,
    the Max-Min Parents-and-Children, the Hill-Climbing, the
    Max-Min Hill-climbing heuristic searches, and the Structural
    Expectation-Maximization algorithm. Available scoring functions are
    BDeu, AIC, BIC. The package also implements methods for generating and using
    bootstrap samples, imputed data, inference.
Bayesian networks provide an intuitive framework for probabilistic reasoning 
             and its graphical nature can be interpreted quite clearly. Graph based methods 
             of machine learning are becoming more popular because they offer a richer model 
             of knowledge that can be understood by a human in a graphical format. The 'bnviewer' 
             is an R Package that allows the interactive visualization of Bayesian Networks. 
             The aim of this package is to improve the Bayesian Networks visualization over 
             the basic and static views offered by existing packages.
A menu-driven program and library of functions for carrying out
    convergence diagnostics and statistical and graphical analysis of Markov
    chain Monte Carlo sampling output.
Tools for constructing board/grid based games, as well as readily available game(s) for your entertainment.
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
A wrapper to allow users to download Bus Open Data Service 'BODS' transport information from the API (<https://www.bus-data.dft.gov.uk/>).
    This includes timetable and fare metadata (including links for full datasets), timetable data at line level,
    and real-time location data.
Skinfold measurements is one of the most popular and practical methods for estimating percent body fat. Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. <doi:10.4324/9780203868744>.
Get z-scores, percentiles, absolute values, and percent of predicted of a reference cohort.
  Functionality requires installing the data packages 'adiposerefdata' and 'musclerefdata'.
  For more information on the underlying research, please visit our website which also includes a graphical interface.
  The models and underlying data are described in
  Marquardt JP et al.(planned publication 2025; reserved doi 10.1097/RLI.0000000000001104),
  "Subcutaneous and Visceral adipose tissue Reference Values from Framingham Heart Study Thoracic and Abdominal CT",
  *Investigative Radiology*
  and
  Tonnesen PE et al. (2023),
  "Muscle Reference Values from Thoracic and Abdominal CT for Sarcopenia Assessment [column] The Framingham Heart Study",
  *Investigative Radiology*,
  <doi:10.1097/RLI.0000000000001012>.
Provides functions to download and tidy statistical data
    published by the 'Bank of England' <https://www.bankofengland.co.uk>.
    Covers Bank Rate, 'SONIA', gilt yields, exchange rates, mortgage rates,
    mortgage approvals, consumer credit, and money supply. Series are
    fetched from the 'Bank of England Interactive Statistical Database'
    using its CSV endpoint. Data is cached locally between sessions.
Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package
    provides a dilution series for one PCR target: a random sequence that
    minimizes secondary structure and off-target primer binding. The data set
    is a six-point, ten-fold dilution series.
    For each concentration there are two replicates. Each amplification curve
    is 40 cycles long. Original raw data file:
    <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.
Provides deterministic two- and three-dimensional boids and swarm
    simulations for R. The package implements Reynolds-style separation,
    alignment, and cohesion rules with optional obstacles, attractors,
    predators, species parameters, and reproducible frame export. Simulation
    state is renderer-neutral; optional adapters can hand frame data to
    visualization packages such as 'ggWebGL'. The model follows Reynolds
    (1987) <doi:10.1145/37402.37406>.
Generic Extraction of main text content from HTML files; removal
    of ads, sidebars and headers using the boilerpipe 
    <https://github.com/kohlschutter/boilerpipe> Java library. The
    extraction heuristics from boilerpipe show a robust performance for a wide
    range of web site templates.
Managing and generating standardised text for methods and results sections of scientific reports. It handles template variable substitution and supports hierarchical organisation of text through dot-separated paths. The package supports both RDS and JSON database formats, enabling version control and cross-language compatibility.
The Bayesian optimal interval (BOIN) design is a novel phase I
    clinical trial design for finding the maximum tolerated dose (MTD). It can be
    used to design both single-agent and drug-combination trials. The BOIN design
    is motivated by the top priority and concern of clinicians when testing a new
    drug, which is to effectively treat patients and minimize the chance of exposing
    them to subtherapeutic or overly toxic doses. The prominent advantage of the
    BOIN design is that it achieves simplicity and superior performance at the same
    time. The BOIN design is algorithm-based and can be implemented in a simple
    way similar to the traditional 3+3 design. The BOIN design yields an average
    performance that is comparable to that of the continual reassessment method
    (CRM, one of the best model-based designs) in terms of selecting the MTD, but
    has a substantially lower risk of assigning patients to subtherapeutic or overly
    toxic doses. For tutorial, please check Yan et al. (2020) <doi:10.18637/jss.v094.i13>.
Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Perform seasonal adjustment and forecasting of weekly data.
    The package provides a user-friendly interface for computing seasonally
    adjusted estimates and forecasts of weekly time series and includes
    functions for the construction of country-specific prior adjustment
    variables, as well as diagnostic tools to assess the quality of the
    adjustments. The methodology is described in more detail in
    Ginker (2024) <doi:10.13140/RG.2.2.12221.44000>.
Provides an interface to Bank of Japan <https://www.boj.or.jp>
  statistics.
Implements the bolasso algorithm for consistent variable selection 
    and estimation accuracy. Includes support for many parallel backends via the 
    future package. For details see: Bach (2008),
    'Bolasso: model consistent Lasso estimation through the bootstrap', 
    <doi:10.48550/arXiv.0804.1302>.
Facilitates retrieval, transformation and analysis of the data
    from the Barcode of Life Data Systems (BOLD) database <https://boldsystems.org/>. 
    This package allows both public and private user data to be easily downloaded into the R
    environment using a variety of inputs such as: IDs (processid, sampleid), BINs, dataset codes, 
    project codes, taxonomy, geography etc. It provides frictionless data conversion  
    into formats compatible with other R-packages and third-party tools, 
    as well as functions for sequence alignment & clustering, biodiversity analysis and spatial mapping.
Provides 'RStudio' add-ins and helper functions for converting the
    current editor line, or a supplied character string, into formatted section
    titles for 'R' scripts. When used as an add-in, it uses 'rstudioapi' to read
    the active document context and replace only the selected line, so users can
    bind the add-ins to keyboard shortcuts. The formatter trims common comment
    and heading markers, converts the label to uppercase, and renders three
    styles: a five-line boxed title, a framed subtitle, and a simple inline
    header. Generated titles are padded or centered to a configurable width,
    using the package-specific width option when set and the session display
    width otherwise, which helps keep analysis scripts visually consistent and
    easier to navigate.
Provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.
A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.
A set of R functions and data sets for the book "Understanding Computational Bayesian Statistics." This book was written by Bill (WM) Bolstad and published in 2009 by John Wiley & Sons (ISBN 978-0470046098).
Querying, extracting, and processing large-scale network data from Neo4j databases using the 'Neo4j Bolt' <https://neo4j.com/docs/bolt/current/bolt/> protocol. This interface supports efficient data retrieval, batch processing for large datasets, and seamless conversion of query results into R data frames, making it ideal for bioinformatics, computational biology, and other graph-based applications.
Provides probability computation, data generation,
    and model estimation for fully-visible Boltzmann machines. It follows the methods
    described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> 
    and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.
Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return.
    CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577).
    Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132).
    Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143).
    Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).
Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.
Allows local bone density estimates to be derived from CT data and mapped to 3D bone models in a reproducible manner. Processing can be performed at the individual bone or group level. Also includes tools for visualizing the bone density estimates. Example methods are described in Telfer et al., (2021) <doi:10.1002/jor.24792>, Telfer et al., (2021) <doi:10.1016/j.jse.2021.05.011>.
Bone Profiler is a scientific method and a software used to model 
    bone section for paleontological and ecological studies. See Girondot and Laurin 
    (2003) <https://www.researchgate.net/publication/280021178_Bone_profiler_A_tool_to_quantify_model_and_statistically_compare_bone-section_compactness_profiles>
    and Gônet, Laurin and Girondot (2022) <https://palaeo-electronica.org/content/2022/3590-bone-section-compactness-model>.
An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. It controls false discovery rates through controlling the expected number of false discoveries.
Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.
Bindings for additional tree-based model engines for use with
    the 'parsnip' package. Models include gradient boosted decision trees
    with 'LightGBM' (Ke et al, 2017.), conditional inference trees and
    conditional random forests with 'partykit' (Hothorn and Zeileis, 2015.
    and Hothorn et al, 2006. <doi:10.1198/106186006X133933>), and
    accelerated oblique random forests with 'aorsf' (Jaeger et al, 2022
    <doi:10.5281/zenodo.7116854>).
Subgroup analyses are routinely performed in clinical trial
    analyses. From a methodological perspective, two key issues of
    subgroup analyses are multiplicity (even if only predefined subgroups
    are investigated) and the low sample sizes of subgroups which lead to
    highly variable estimates, see e.g. Yusuf et al (1991)
    <doi:10.1001/jama.1991.03470010097038>.  This package implements
    subgroup estimates based on Bayesian shrinkage priors, see Carvalho et
    al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In
    addition, estimates based on penalized likelihood inference are
    available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>.
    The corresponding shrinkage based forest plots address the
    aforementioned issues and can complement standard forest plots in
    practical clinical trial analyses.
Component-wise gradient boosting for analysis of multiply
    imputed datasets. Implements the algorithm Boosting after Multiple
    Imputation (MIBoost), which enforces uniform variable selection across
    imputations and provides utilities for pooling. Includes a cross-validation
    workflow that first splits the data into training and validation sets and
    then performs imputation on the training data, applying the learned
    imputation models to the validation data to avoid information leakage.
    Supports Gaussian and logistic loss. Methods relate to gradient boosting
    and multiple imputation as in Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>,
    Friedman (2001) <doi:10.1214/aos/1013203451>, and van Buuren (2018, ISBN:9781138588318)
    and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; see also Kuchen (2025)
    <doi:10.48550/arXiv.2507.21807>.
Make some distributions from the 'C++' library 'Boost'
    available in 'R'. In addition, the normal-inverse Gaussian distribution
    and the generalized inverse Gaussian distribution are provided. The 
    distributions are represented by 'R6' classes. The method to sample from 
    the generalized inverse Gaussian distribution is the one given in 
    "Random variate generation for the generalized inverse Gaussian 
    distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.
Output formats and utilities for authoring books and technical documents with R Markdown.
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.
Functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.
A C++ library for Bayesian modeling, with an emphasis on Markov
   chain Monte Carlo.  Although boom contains a few R utilities (mainly plotting
   functions), its primary purpose is to install the BOOM C++ library on your
   system so that other packages can link against it.
Spike and slab regression with a variety of residual error
  distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a
  few others.  Spike and slab regression is Bayesian regression with prior
  distributions containing a point mass at zero.  The posterior updates the
  amount of mass on this point, leading to a posterior distribution that is
  actually sparse, in the sense that if you sample from it many coefficients are
  actually zeros.  Sampling from this posterior distribution is an elegant way
  to handle Bayesian variable selection and model averaging.  See
  <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.
Includes functions to estimate production frontiers 
    and make ideal output predictions in the Data Envelopment Analysis (DEA) 
    context using both standard models from DEA and Free Disposal Hull (FDH)
    and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 
    <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package 
    includes code for estimating several technical efficiency measures using 
    different models such as the input and output-oriented radial measures, the
    input and output-oriented Russell measures, the Directional Distance 
    Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based 
    Measure (SBM).
'R' bindings for the various functions and statistical distributions
    provided by the 'Boost' Math library <https://www.boost.org/doc/libs/latest/libs/math/doc/html/index.html>.
Jointly models the multivariate longitudinal responses and multiple covariates and time using gradient boosting approach.
Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal.  A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.
Boosting Regression Quantiles is a component-wise boosting algorithm, 
             that embeds all boosting steps in the well-established framework 
             of quantile regression. It is initialized with the corresponding quantile, 
             uses a quantile-specific learning rate, and uses quantile regression as its base learner.
             The package implements this algorithm and allows cross-validation and stability selection.
Functions and datasets for bootstrapping from the
  book "Bootstrap Methods and Their Application" by A. C. Davison and 
  D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package 'metafor' to obtain parameter estimates and likelihoods, so installation of R package 'metafor' is required. This approach refers to the studies of Anscombe (1956) <doi:10.2307/2332926>, Haldane (1940) <doi:10.2307/2332614>, Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).
Computation of bootstrap p-values through inversion of confidence intervals, including convenience functions for regression models and tests of location.
Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.
Propagate uncertainty from several estimates when combining these estimates via a function.
    This is done by using the parametric bootstrap to simulate values from the distribution of each estimate to build up an empirical distribution of the combined parameter.
    Finally either the percentile method is used or the highest density interval is chosen to derive a confidence interval for the combined parameter with the desired coverage.
    Gaussian copulas are used for when parameters are assumed to be dependent / correlated.
    References: Davison and Hinkley (1997,ISBN:0-521-57471-4) for the parametric bootstrap and percentile method, Gelman et al. (2014,ISBN:978-1-4398-4095-5) for the highest density interval, Stockdale et al. (2020)<doi:10.1016/j.jhep.2020.04.008> for an example of combining conditional prevalences.
The bootstrap ARDL tests for cointegration is the main functionality of this package. It also acts as a wrapper of the most commond ARDL testing procedures for cointegration: the bound tests of Pesaran, Shin and Smith (PSS; 2001 - <doi:10.1002/jae.616>) and the asymptotic test on the independent variables of Sam, McNown and Goh (SMG: 2019 - <doi:10.1016/j.econmod.2018.11.001>). Bootstrap and bound tests are performed under both the conditional and unconditional ARDL models.
Calculate robust measures of effect sizes using the bootstrap.
Compare dissolution profiles with confidence interval of similarity
  factor f2 using bootstrap methodology as described in the literature, such as 
  Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997,
  ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. 
  The package can also be used to simulate dissolution profiles based on 
  mathematical modelling and multivariate normal distribution.
Bootstrap based goodness-of-fit tests. It allows
    to perform rigorous statistical tests to check if a chosen
    model family is correct based on the marked empirical
    process. The implemented algorithms are described in
    (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>)
    and can be applied
    to generalized linear models without any further implementation
    effort. As far as certain linearity conditions are fulfilled
    the resampling scheme are also applicable beyond generalized
    linear models. This is reflected in the software architecture
    which allows to reuse the resampling scheme by implementing
    only certain interfaces for models that are not supported
    natively by the package.
Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.
Implementation of the bootkmeans algorithm, a bootstrap augmented k-means algorithm that returns probabilistic cluster assignments. From paper by Ghashti, J.S., Andrews, J.L. Thompson, J.R.J., Epp, J. and H.S. Kochar (2025), "A bootstrap augmented k-means algorithm for fuzzy partitions" (Submitted).
Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.
Implements three test procedures using bootstrap resampling techniques for assessing treatment effects in one-way ANOVA models with unequal variances (heteroscedasticity). It includes a parametric bootstrap likelihood ratio test (PB_LRT()), a pairwise parametric bootstrap mean test (PPBMT()), and a Rademacher wild pairwise non-parametric bootstrap test (RWPNPBT()). These methods provide robust alternatives to classical ANOVA and standard pairwise comparisons when the assumption of homogeneity of variances is violated.
Functions for bootstrapping with multilevel 
    data and models (and mixed-effect models). It implements multiple 
    bootstrap methods under the parametric, residual, and case bootstrap 
    categories, as discussed in Van der Leeden, Meijer, and Busing (2008) <doi:10.1007/978-0-387-73186-5_11>
    and Carpenter, Goldstein, and Rasbash (2003) <doi:10.1111/1467-9876.00415>.
    Currently it supports fitted objects from the 'lme4' package.
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Bootstrap methods to assess accuracy and stability of estimated network structures
              and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible 
              specification of any undirected network estimation procedure in R, and offers 
              default sets for various estimation routines.
Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, 'The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, 'Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, 'Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).
Contains functions for bias-Corrected Forecasting and Bootstrap Prediction Intervals for Autoregressive Time Series.
We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.
Provides a streamlined and user-friendly framework for
    bootstrapping in state space models, particularly when the number of
    subjects/units (n) exceeds one, a scenario commonly encountered in
    social and behavioral sciences. The parametric bootstrap implemented
    here was developed and applied in Pesigan, Russell, and Chow (2025)
    <doi:10.1037/met0000779>.
Model selection by bootstrapping the stepAIC() procedure.
Software (bootstrap, cross-validation, jackknife) and data
        for the book "An Introduction to the Bootstrap" by B. Efron and
        R. Tibshirani, 1993, Chapman and Hall. This package is
        primarily provided for projects already based on it, and for
        support of the book. New projects should preferentially use the
        recommended package "boot".
Finite Population bootstrap algorithms to estimate the variance
    of the Horvitz-Thompson estimator for single-stage sampling. 
    For a survey of bootstrap methods for finite populations, see Mashreghi et Al. (2016) <doi:10.1214/16-SS113>.
Identifies genome-related molecular traits with significant 
  evidence of genetic regulation and performs a bootstrap procedure to 
  correct estimated effect sizes for over-estimation present in cis-QTL
  mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 
  2018 <doi: 10.1093/nar/gky780>.
Perform bootstrap-based hypothesis testing procedures on
    three statistical problems. In particular, it covers independence
    testing, testing the slope in a linear regression setting, and
    goodness-of-fit testing, following (Derumigny, Galanis, Schipper and Van der Vaart, 2025)
    <doi:10.48550/arXiv.2512.10546>.
Bootstrap resampling methods have been widely studied in the context of survey data. This package implements various bootstrap resampling techniques tailored for survey data, with a focus on stratified simple random sampling and stratified two-stage cluster sampling. It provides tools for precise and consistent bootstrap variance estimation for population totals, means, and quartiles. Additionally, it enables easy generation of bootstrap samples for in-depth analysis.
Implements fast, exact bootstrap Principal Component Analysis and
    Singular Value Decompositions for high dimensional data, as described in
    <doi:10.1080/01621459.2015.1062383> (see also <doi:10.48550/arXiv.1405.0922>). For data matrices that are too large to operate
    on in memory, users can input objects with class 'ff' (see the 'ff'
    package), where the actual data is stored on disk. In response, this
    package will implement a block matrix algebra procedure for calculating the
    principal components (PCs) and bootstrap PCs. Depending on options set by
    the user, the 'parallel' package can be used to parallelize the calculation of
    the bootstrap PCs.
Set of functions to perform various bootstrap unit root tests for both individual time series
  (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see
  Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>,
  Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>,
  Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, 
  Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, 
  Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and 
  Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.
The card game War is simple in its rules but can be lengthy. In
    another domain, the nonparametric bootstrap test with pooled resampling
    (nbpr) methods, as outlined in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>,
    is optimal for comparing paired or unpaired means in non-normal data,
    especially for small sample size studies. However, many researchers are
    unfamiliar with these methods. The 'bootwar' package bridges this gap by
    enabling users to grasp the concepts of nbpr via Boot War, a variation of the
    card game War designed for small samples. The package provides functions like
    score_keeper() and play_round() to streamline gameplay and scoring. Once a
    predetermined number of rounds concludes, users can employ the analyze_game()
    function to derive game results. This function leverages the 'npboottprm'
    package's nonparboot() to report nbpr results and, for comparative analysis,
    also reports results from the 'stats' package's t.test() function. Additionally,
    'bootwar' features an interactive 'shiny' web application, bootwar(). This
    offers a user-centric interface to experience Boot War, enhancing understanding
    of nbpr methods across various distributions, sample sizes, number of bootstrap
    resamples, and confidence intervals.
Provides significance tests for second-order stationarity
	for time series using bootstrap wavelet packet tests.
	Provides functionality to visualize the time series with
	the results of the hypothesis tests superimposed.
	The methodology is described in Cardinali, A and Nason, G P (2016)
	"Practical powerful wavelet packet tests for second-order stationarity."
	Applied and Computational Harmonic Analysis, 44, 558-585
	<doi:10.1016/j.acha.2016.06.006>.
Bayesian optimal design with futility and efficacy stopping boundaries (BOP2-FE) is a novel statistical framework for single-arm Phase II clinical trials. It enables early termination for efficacy when interim data are promising, while explicitly controlling Type I and Type II error rates. The design supports a variety of endpoint structures, including single binary endpoints, nested endpoints, co-primary endpoints, and joint monitoring of efficacy and toxicity. The package provides tools for enumerating stopping boundaries prior to trial initiation and for conducting simulation studies to evaluate the design’s operating characteristics. Users can flexibly specify design parameters to suit their specific applications. For methodological details, refer to Xu et al. (2025) <doi:10.1080/10543406.2025.2558142>.
Computes Bayesian A- and D-optimal block designs under the linear mixed effects model settings using block/array exchange algorithm of Debusho, Gemechu and Haines (2018) <doi:10.1080/03610918.2018.1429617> and Gemechu, Debusho and Haines (2025) <doi:10.5539/ijsp.v14n1p50> where the interest is in a 
	comparison of all possible elementary treatment contrasts. The package also provides an optional 
	method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov Chain Monte Carlo (MCMC) methods via Three. JAGS types of models may be fitted: 1) With explanatory variables only, boral fits independent column Generalized Linear Models (GLMs) to each column of the response matrix; 2) With latent variables only, boral fits a purely latent variable model for model-based unconstrained ordination; 3) With explanatory and latent variables, boral fits correlated column GLMs with latent variables to account for any residual correlation between the columns of the response matrix.
Comprehensive toolkit for valid spatial, temporal, and grouped model
    evaluation. Automatically detects data dependencies (spatial autocorrelation,
    temporal structure, clustered observations), generates appropriate
    cross-validation schemes (spatial blocking, checkerboard, hexagonal, KNNDM,
    environmental blocking, leave-location-out, purged CV), and validates
    evaluation pipelines for leakage. Includes area of applicability (AOA)
    assessment following Meyer & Pebesma (2021) <doi:10.1111/2041-210X.13650>,
    forward feature selection with blocked CV, spatial thinning, block-permutation
    variable importance, extrapolation detection, and interactive visualizations.
    Integrates with 'tidymodels', 'caret', 'mlr3', 'ENMeval', and 'biomod2'.
    Based on evaluation principles described in
    Roberts et al. (2017) <doi:10.1111/ecog.02881>,
    Kaufman et al. (2012) <doi:10.1145/2382577.2382579>,
    Kapoor & Narayanan (2023) <doi:10.1016/j.patter.2023.100804>, and
    Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>.
Estimate population average treatment effects from a primary data source 
  with borrowing from supplemental sources. Causal estimation is done with either a 
  Bayesian linear model or with Bayesian additive regression trees (BART) to adjust 
  for confounding. Borrowing is done with multisource exchangeability models (MEMs). For 
  information on BART, see Chipman, George, & McCulloch (2010) <doi:10.1214/09-AOAS285>. 
  For information on MEMs, see Kaizer, Koopmeiners, & 
  Hobbs (2018) <doi:10.1093/biostatistics/kxx031>.
An all relevant feature selection wrapper algorithm.
 It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).
Computes exact bounds of Spearman's footrule in the presence of missing data, and performs independence test based on the bounds with controlled Type I error regardless of the values of missing data. Suitable only for distinct, univariate data where no ties is allowed.
Classical Boson Sampling using the algorithm of
 Clifford and Clifford (2017) <arXiv:1706.01260>. Also provides functions for
 generating random unitary matrices, evaluation of matrix permanents (both
 real and complex) and evaluation of complex permanent minors.
Reads and plots phylogenetic placements.
The Biomarker Optimal Segmentation System R package, 'bossR', is designed for precision medicine, helping to identify individual traits using biomarkers. It focuses on determining the most effective cutoff value for a continuous biomarker, which is crucial for categorizing patients into two groups with distinctly different clinical outcomes. The package simultaneously finds the optimal cutoff from given candidate values and tests its significance. Simulation studies demonstrate that 'bossR' offers statistical power and false positive control non-inferior to the permutation approach (considered the gold standard in this field), while being hundreds of times faster.
Fork-safe, raw access to the 'Amazon Web Services' ('AWS') 'SDK' via the 'boto3' 'Python' module, and convenient helper functions to query the 'Simple Storage Service' ('S3') and 'Key Management Service' ('KMS'), partial support for 'IAM', the 'Systems Manager Parameter Store' and 'Secrets Manager'.
Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.
Computes uniform bounds on the distance between
  the cumulative distribution function of a standardized sum
  of random variables and its first-order Edgeworth expansion,
  following the article Derumigny, Girard, Guyonvarch (2023)
  <doi:10.1007/s13171-023-00320-y>.
Implements unit root tests for bounded time series following
    Cavaliere and Xu (2014) <doi:10.1016/j.jeconom.2013.08.012>. Standard 
    unit root tests (ADF, Phillips-Perron) have non-standard limiting 
    distributions when the time series is bounded. This package provides 
    modified ADF and M-type tests (MZ-alpha, MZ-t, MSB) with p-values computed 
    via Monte Carlo simulation of bounded Brownian motion. Supports one-sided 
    (lower bound only) and two-sided bounds, with automatic lag selection 
    using the MAIC criterion of Ng and Perron (2001) 
    <doi:10.1111/1468-0262.00256>.
Generate ground truth cases for object localization algorithms. 
    Cycle through a list of images, select points around which to generate bounding 
    boxes and assign classifiers. Output the coordinates, and images annotated with 
    boxes and labels. For an example study that uses bounding boxes for image 
    localization and classification see Ibrahim, Badr, Abdallah, and Eissa (2012)
    "Bounding Box Object Localization Based on Image Superpixelization"
    <doi:10.1016/j.procs.2012.09.119>.
Bounded continuous data are encountered in many areas of test application. 
 Examples include visual analogue scales used in the measurement of personality, mood, 
 depression, and quality of life; item response times from tests with item deadlines; 
 confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) 
 models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. 
 The package draws on posterior sampling facilities provided by R-package 'rstan' (Stan Development Team, 2025)<https://mc-stan.org/>. 
 Available models include the Beta IRT model by Noel and Dauvier (2007)<doi:10.1177/0146621605287691>, the continuous response 
 model by Samejima (1973)<doi:10.1007/BF03372160>, the unbounded normal model by Mellenbergh (1994)<doi:10.1207/s15327906mbr2903_2>, 
 and the Simplex IRT model by Flores et al. (2020)<doi:10.1007/978-3-030-43469-4_8>. All models can be 
 fitted with or without zero-one inflation (Molenaar et al., 2022)<doi:10.3102/10769986221108455>. 
 Model fit comparisons can be conducted using the Watanabe-Akaike information criterion (WAIC), leave-one-out 
 cross-validation information citerion (LOOIC) and the fully marginalized likelihood (i.e., Bayes factors).
A collection of R functions were implemented
        from published and available analytic solutions for the
        One-Dimensional Boussinesq Equation (ground-water). In
        particular, the function "beq.lin()" is the analytic solution of
        the linearized form of Boussinesq Equation between two
        different head-based boundary (Dirichlet) conditions;
        "beq.song" is the non-linear power-series analytic solution of
        the motion of a wetting front over a dry bedrock (Song at al,
        2007, see complete reference on function documentation).
        Bugs/comments/questions/collaboration of any kind are warmly
        welcomed.
Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).
Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.
A modern module system for R. Organise code into hierarchical,
    composable, reusable modules, and use it effortlessly across projects via a
    flexible, declarative dependency loading syntax.
Static code analysis of 'box' modules.
  The package enhances code quality by providing linters that check for common issues,
  enforce best practices, and ensure consistent coding standards.
A 'box' compatible custom language parser for the 'languageserver' package to provide
  completion and signature hints in code editors.
Box-Cox-type transformations for linear and logistic models with random effects using non-parametric profile maximum likelihood estimation, as introduced in Almohaimeed (2018) <http://etheses.dur.ac.uk/12831/> and Almohaimeed and Einbeck (2022) <doi:10.1177/1471082X20966919>. The main functions are 'optim.boxcox()' for linear models with random effects and 'boxcoxtype()' for logistic models with random effects.
Noise filter based on determining the proportion of neighboring points. A false point will be rejected if it has only few neighbors, but accepted if the proportion of neighbors in a rectangular frame is high. The size of the rectangular frame as well as the cut-off value, i.e. of a minimum proportion of neighbor-points, may be supplied or can be calculated automatically. Originally designed for the cleaning of heart rates, but suitable for filtering any slowly-changing physiological variable.For more information see Signer (2010)<doi:10.1111/j.2041-210X.2009.00010.x>.
Interactive box plot using 'plotly' for clinical trial analysis.
Following Arroyo-Maté-Roque (2006), the function calculates the distance between rows or columns of the dataset using the generalized Minkowski metric as described by Ichino-Yaguchi (1994). The distance measure gives more weight to differences between quartiles than to differences between extremes, making it less sensitive to outliers. Further,the function calculates the silhouette width (Rousseeuw 1987) for different numbers of clusters and selects the number of clusters that maximizes the average silhouette width, unless a specific number of clusters is provided by the user. The approach implemented in this package is based on the following publications: Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7>; Ichino-Yaguchi (1994) <doi:10.1109/21.286391>; Arroyo-Maté-Roque (2006) <doi:10.1007/3-540-34416-0_7>.
Correlation chart of two set (x and y) of data.  
  Using Quartiles with boxplot style. 
  Visualize the effect of factor.
An R interface for the remote file hosting service 'Box'
    (<https://www.box.com/>). In addition to uploading and downloading files,
    this package includes functions which mirror base R operations for local
    files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well
    as 'git' style functions for entire directories (e.g. box_fetch(),
    box_push()).
Provides functions to create side-by-side boxplots for a continuous variable grouped by a two-level categorical variable, check normality assumptions using the Shapiro-Wilk test (Shapiro and Wilk (1965) <doi:10.2307/2333709>), and perform appropriate statistical tests such as the independent two-sample t-test (Student (1908) <doi:10.1093/biomet/6.1.1>) or the Mann–Whitney U test ( Mann–Whitney (1947) <doi:10.1214/aoms/1177730491>). Returns a publication-ready plot and test statistics including test statistic, degrees of freedom, and p-value.
A comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.
Run basic pattern analyses on character sets, digits, or combined
    input containing both characters and numeric digits. Useful for data
    cleaning and for identifying columns containing multiple or nonstandard
    formats.
A comprehensive statistical analysis of the accuracy of blood
         pressure devices based on the method of AAMI/ANSI SP10 standards
         developed by the AAMI Sphygmomanometer Committee for indirect
         measurement of blood pressure, incorporated into IS0 81060-2. The 
         'bpAcc' package gives the exact probability 'of accepting a device D'
         derived from the join distribution of the sample standard deviation
         and a non-linear transformation of the sample mean for a specified
         sample size introduced by Chandel et al. (2023) and by the Association
         for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8).
Implementation of the nonparametric bounds for the average
    causal effect under an instrumental variable model by Balke and Pearl
    (Bounds on Treatment Effects from Studies with Imperfect Compliance,
    JASA, 1997, 92, 439, 1171-1176, <doi:10.1080/01621459.1997.10474074>). The package
    can calculate bounds for a binary outcome, a binary
    treatment/phenotype, and an instrument with either 2 or 3 categories.
    The package implements bounds for situations where these 3 variables
    are measured in the same dataset (trivariate data) or where the
    outcome and instrument are measured in one study and the
    treatment/phenotype and instrument are measured in another study
    (bivariate data).
Provides tools for creating 2D and 3D biplots of multivariate data
    based on principal components analysis, together with diagnostics for
    reduction quality and enhanced visualization of variables and objects.
Calculates nonparametric pointwise confidence intervals for the survival distribution for right censored data, and for medians [Fay and Brittain <DOI:10.1002/sim.6905>]. Has two-sample tests for dissimilarity (e.g., difference, ratio or odds ratio) in survival at a fixed time, and differences in medians [Fay, Proschan, and Brittain <DOI:10.1111/biom.12231>]. Basically, the package gives exact inference methods for one- and two-sample exact inferences for Kaplan-Meier curves (e.g., generalizing Fisher's exact test to allow for right censoring), which are especially important for latter parts of the survival curve, small sample sizes or heavily censored data. Includes mid-p options.
Functions for drawing boxplots for data on (the boundary of) a unit circle (i.e., circular and axial data), from Buttarazzi D., Pandolfo G., Porzio G.C. (2018) <doi:10.1111/biom.12889>.
Model-based clustering using Bayesian parsimonious Gaussian mixture models.
  MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. 
  GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.
Fits the Bayesian partial least squares regression model
    introduced in Urbas et al. (2024) <doi:10.1214/24-AOAS1947>. Suitable
    for univariate and multivariate regression with high-dimensional data.
Bayesian purity model to estimate tumor purity using methylation 
    array data (DNA methylation Infinium 450K array data) without reference samples.
To visualize the execution data of the processes on 'BPMN' (Business Process Model and Notation) diagrams, using overlays, style customization and interactions, with the 'bpmn-visualization' 'TypeScript' library.
Fitting Bayesian multiple and mixed-effect regression models for 
    circular data based on the projected normal distribution. Both continuous 
    and categorical predictors can be included. Sampling from the posterior is 
    performed via an MCMC algorithm. Posterior descriptives of all parameters, 
    model fit statistics and Bayes factors for hypothesis tests for inequality 
    constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) 
    <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) 
    <doi:10.1016/j.csda.2012.07.025>.
Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.
Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.
Bayesian models to estimate causal effects of biological 
    treatments on time-to-event endpoints in clinical trials with principal
    strata defined by the occurrence of antidrug antibodies. 
    The methodology is based on Frangakis and Rubin (2002) 
    <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997)
    <doi:10.1214/aos/1034276631>, and here adapted to a specific 
    time-to-event setting.
A collection of functions that allow servers (specifically BPKP) to 
    summarize and verify shift earnings, identify composition of tips and hourly wage, and
    track performance through several metrics.
Package provides functions for estimation and inference in Bayesian quantile regression with ordinal outcomes. An ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings (MH) algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using a Gibbs sampling algorithm. The summary output presents the posterior mean, posterior standard deviation, 95% credible intervals, and the inefficiency factors along with the two model comparison measures – logarithm of marginal likelihood and the deviance information criterion (DIC). The package also provides functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models.
    Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, 11(1): 1-24 <doi: 10.1214/15-BA939>.
    Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>.
    Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>.
    Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>.
    Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.
QTL mapping toolkit for inbred crosses and recombinant
        inbred lines. Includes maximum likelihood and Bayesian tools.
Adjusting the bias due to residual confounding (often called
    treatment selection bias) in estimating the treatment effect in a
    proportional hazard model, as described in Williamson et al.
    (2022) <doi:10.1158/1078-0432.ccr-21-2468>.
Performs brace expansions on strings.  Made popular by Unix shells, brace expansion allows users to concisely generate certain character vectors by taking a single string and (recursively) expanding the comma-separated lists and double-period-separated integer and character sequences enclosed within braces in that string.  The double-period-separated numeric integer expansion also supports padding the resulting numbers with zeros.
Create and manage tournament brackets for various competition formats
    including single elimination, double elimination, round robin, Swiss system,
    and group-stage-to-knockout tournaments. Provides tools for seeding,
    scheduling, recording results, and tracking standings.
The goal of this method is to identify associations between bacteria and an environmental variable in 16S or other compositional data. The environmental variable is any variable which is measure for each microbiome sample, for example, a butyrate measurement paired with every sample in the data. Microbiome data is compositional, meaning that the total abundance of each sample sums to 1, and this introduces severe statistical distortions. This method takes a Bayesian approach to correcting for these statistical distortions, in which the total abundance is treated as an unknown variable. This package runs the python implementation using reticulate.
Provides access to a range of functions for analyzing, applying and visualizing Bayesian response-adaptive trial designs for a binary endpoint. Includes the predictive probability approach and the predictive evidence value designs for binary endpoints.
Specify and fit the Bradley-Terry model, including structured
    versions in which the parameters are related to explanatory variables
    through a linear predictor and versions with contest-specific effects,
    such as a home advantage.
Forecasters predicting the chances of a future event may disagree due to
    differing evidence or noise. To harness the collective evidence of the crowd, 
    Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions"
    <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian
    aggregator that is regularized by analyzing the forecasters' disagreement and ascribing 
    over-dispersion to noise. This aggregator requires no user intervention and can be computed
    efficiently even for a large numbers of predictions. The author evaluates
    the aggregator on subjective probability predictions collected during
    a four-year forecasting tournament sponsored by the US intelligence community.
    The aggregator improves the accuracy of simple averaging by around 20% and
    other state-of-the-art aggregators by 10-25%. The advantage stems almost
    exclusively from improved calibration. This aggregator -- know as "the revealed
    aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event
    and b) the forecasters' common prior (p0) of the future event. In this R-package,
    the function sample_aggregator(p,p0,...) allows the user to calculate the revealed
    aggregator. Its use is illustrated with a simple example.
Provides functions to visualize combined action data in 'ggplot2'.
    Also provides functions for producing full BRAID analysis reports with 
    custom layouts and aesthetics, using the BRAID method originally described 
    in Twarog et al. (2016) <doi:10.1038/srep25523>.
Contains functions for evaluating, analyzing, and fitting combined
    action dose response surfaces with the Bivariate Response to Additive
    Interacting Doses (BRAID) model of combined action, along with tools for
    implementing other combination analysis methods, including Bliss independence,
    combination index, and additional response surface methods.
Deals with the braid groups. Includes creation of some
    specific braids, group operations, free reduction, and Bronfman
    polynomials. Braid theory has applications in fluid mechanics and
    quantum physics. The code is adapted from the 'Haskell' library
    'combinat', and is based on Birman and Brendle (2005)
    <doi:10.48550/arXiv.math/0409205>.
A statistical tool to inference the multi-level partial correlations based on multi-subject time series data, especially for brain functional connectivity. It combines both individual and population level inference by using  the methods of Qiu and Zhou. (2021)<DOI: 10.1080/01621459.2021.1917417> and Genovese and Wasserman. (2006)<DOI: 10.1198/016214506000000339>. It realizes two reliable estimation methods of partial correlation coefficients, using scaled lasso and lasso. It can be used to estimate individual- or population-level partial correlations, identify nonzero ones, and find out unequal partial correlation coefficients between two populations.
A set of tools for performing graph theory analysis of brain MRI
    data. It works with data from a Freesurfer analysis (cortical thickness,
    volumes, local gyrification index, surface area), diffusion tensor
    tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from
    DPABI). It contains a graphical user interface for graph visualization and
    data exploration, along with several functions for generating useful
    figures.
It is designed to calculate connection between (among) brain regions and plot connection lines. Also, the summary function is included to summarize group-level connectivity network. Kang, Jian (2016) <doi:10.1016/j.neuroimage.2016.06.042>.
Non-parametric hypothesis testing for populations of brain
    networks represented as graphs, following the L1-distance ANOVA
    framework of Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-21688-0>.
    The package builds on this nonparametric graph-comparison framework,
    extending it with procedures for edge-level inference and identification
    of the specific connections driving group differences. In particular, it
    provides utilities to compute central (mean) graphs, pairwise Manhattan
    distances between adjacency matrices, the group test statistic T, and a
    fast permutation procedure to identify the critical edges that drive
    between-group differences. Helper functions to generate synthetic
    community-structured graphs and to visualise brain networks with
    communities are also included.
This includes functions for creating 3D and 4D images using 
    'WebGL', 'rgl', and 'JavaScript' commands.  
    This package relies on the X toolkit ('XTK',
    <https://github.com/xtk/X#readme>).
Performs efficient and scalable glm best 
	subset selection using a novel implementation of a branch and bound algorithm.
	To speed up the model fitting process, a range of optimization
	methods are implemented in 'RcppArmadillo'. Parallel computation 
	is available using 'OpenMP'.
Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.
Quantify outbreak risk posed by individual importers of a transmissible
    pathogen. Input parameters of negative binomial offspring distributions for the
    number of transmissions from each infected individual and initial number of
    infected. Calculate probabilities of final outbreak size and generations of
    transmission, as described in Toth et al. (2015) <doi:10.3201/eid2108.150170> 
    and Toth et al. (2016) <doi:10.1016/j.epidem.2016.04.002>.
Read and process 'brand.yml' 'YAML' files. 'brand.yml' is a
    simple, portable 'YAML' file that codifies your company's brand
    guidelines into a format that can be used by 'Quarto', 'Shiny' and 'R'
    tooling to create branded outputs. Maintain unified, branded theming
    for web applications to printed reports to dashboards and
    presentations with a consistent look and feel.
A system to facilitate brand identity management using the
    brand.yml standard, providing functions to consistently access and apply
    brand colors, typography, and other visual elements across your R projects.
Interact with the 'Brandwatch' API <https://developers.brandwatch.com/docs>. 
  Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories.
  Also allows you to directly obtain mentions and aggregate data for a specified query or query group.
Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package 'MASS'.
Test the robustness of a user's Qualitative Comparative Analysis
    solutions to randomness, using the bootstrapped assessment: baQCA(). This
    package also includes a function that provides recommendations for improving
    solutions to reach typical significance levels: brQCA(). Data included 
    come from McVeigh et al. (2014) <doi:10.1177/0003122414534065>.
Implements Bayesian response-adaptive randomization methods based on Bayesian hypothesis testing for multi-arm settings (Pawel and Held, 2025, <doi:10.48550/arXiv.2510.01734>).
Executes BASIC programs from the 1970s, for historical and
    educational purposes. This enables famous examples of early machine
    learning, artificial intelligence, natural language processing, cellular
    automata, and so on, to be run in their original form.
Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.Also contains an user friendly web application to perform multi trait GWAS.
Allows access to data from the Brazilian Public Security Information System (SINESP) by state and municipality. It should be emphasized that the package only extracts the data and facilitates its manipulation in R. Therefore, its sole purpose is to support empirical research. All data credits belong to SINESP, an integrated information platform developed and maintained by the National Secretariat of Public Security (SENASP) of the Ministry of Justice and Public Security. <https://www.gov.br/mj/pt-br/assuntos/sua-seguranca/seguranca-publica/sinesp-1>.
Provides functions to access data from the 'BrasilAPI', 'REST Countries API',
    'Nager.Date API', and 'World Bank API', related to Brazil's postal codes, banks, holidays,
    company registrations, international country indicators, public holidays information, and
    economic development data. Additionally, the package includes curated datasets related to
    Brazil, covering topics such as demographic data (males and females by state and year),
    river levels, environmental emission factors, film festivals, and yellow fever outbreak
    records. The package supports research and analysis focused on Brazil by integrating open
    APIs with high-quality datasets from multiple domains. For more information on the APIs, see: 
    'BrasilAPI' <https://brasilapi.com.br/>, 
    'Nager.Date' <https://date.nager.at/Api>, 
    'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, 
    and 'REST Countries API' <https://restcountries.com/>.
A collection of functions for downloading and processing automatic weather station (AWS) data from INMET (Brazil’s National Institute of Meteorology), designed to support the estimation of reference evapotranspiration (ETo). The package facilitates streamlined access to meteorological data and aims to simplify analyses in agricultural and environmental contexts.
Boldness-recalibration maximally spreads out probability predictions while maintaining a user specified level of calibration, facilitated the brcal() function. Supporting functions to assess calibration via Bayesian and Frequentist approaches, Maximum Likelihood Estimator (MLE) recalibration, Linear in Log Odds (LLO)-adjust via any specified parameters, and visualize results are also provided. Methodological details can be found in Guthrie & Franck (2024) <doi:10.1080/00031305.2024.2339266>.
Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects.
    Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.
A unified framework for identification and ecological interpretation of core microbiomes across time and space, enhancing robustness and reproducibility in microbiome data analysis. 'BRCore' implements the workflow proposed by Shade and Stopnisek (2019) and incorporates additional rarefaction steps. The proposed workflow aims to identify persistent microbiomes using abundance-occupancy distributions and neutral community model fitting. For more details on abundance-occupancy distributions see Shade A, Stopnisek N (2019) <doi:10.1016/j.mib.2019.09.008>, for neutral models, see Sloan et al. (2006) <doi:10.1111/j.1462-2920.2005.00956.x> and Burns et al. (2015) <doi:10.1038/ismej.2015.142>.
Functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.
A simple set of wrapper functions for data.table::fread() that allows subsetting or
    filtering rows and selecting columns of table-formatted files too large for the available RAM.
    'b stands for 'big files'.
    bread makes heavy use of Unix commands like 'grep', 'sed', 'wc', 'awk' and 'cut'. They are available 
    by default in all Unix environments.
    For Windows, you need to install those commands externally in order to simulate a 
    Unix environment and make sure that the executables are in the Windows PATH variable.
    To my knowledge, the simplest ways are to install 'RTools', 'Git' or 'Cygwin'. If they have been 
    correctly installed (with the expected registry entries), they should be detected on loading
    the package and the correct directories will be added automatically to the PATH.
The goal of the package is to provide an easy-to-use method 
  for estimating degrees of relatedness (up to the second degree) 
  for extreme low-coverage data. The package also allows users to 
  quantify and visualise the level of confidence in the estimated 
  degrees of relatedness.
Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.
Model agnostic tool for decomposition of predictions from black boxes.
    Break Down Table shows contributions of every variable to a final prediction. 
    Break Down Plot presents variable contributions in a concise graphical way. 
    This package work for binary classifiers and general regression models.
A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences.
Compute Buishand Range Test, Pettit Test, SNHT, Student t-test, and Mann-Whitney Rank Test, to identify breakpoints in series. For all functions NA is allowed. Since all of the mention methods identify only one breakpoint in a series, a general function to look for N breakpoint is given. Also, the Yamamoto test for climate jump is available. Alexandersson, H. (1986) <doi:10.1002/joc.3370060607>, Buishand, T. (1982) <doi:10.1016/0022-1694(82)90066-X>, Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020) <doi:10.1016/j.atmosres.2020.104955>, Mann, H. B., Whitney, D. R. (1947) <doi:10.1214/aoms/1177730491>, Pettitt, A. N. (1979) <doi:10.2307/2346729>, Ruxton, G. D., jul (2006) <doi:10.1093/beheco/ark016>, Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985) <doi:10.2151/jmsj1965.63.6_1157>.
Reads several formats of 13C data (IRIS/Wagner,
    BreathID) and CSV.  Creates artificial sample data for testing.  Fits
    Maes/Ghoos, Bluck-Coward self-correcting formula using 'nls', 'nlme'.
    Methods to fit breath test curves with Bayesian Stan methods are
    refactored to package 'breathteststan'. For a Shiny GUI, see package
    'dmenne/breathtestshiny' on github.
Stan-based curve-fitting function
  for use with package 'breathtestcore' by the same author.
  Stan functions are refactored here for easier testing.
A collection of functions to analyse, visualize and interpret wind data
         and to calculate the potential energy production of wind turbines.
Easily processes batches of univariate or multivariate
    regression models. Returns results in a tidy format and generates
    visualization plots for straightforward interpretation (Wang,
    Shixiang, et al. (2025) <DOI:10.1002/mdr2.70028>).
Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.
Implements a templating framework for mixing text and R code
    for report generation. brew template syntax is similar to PHP, Ruby's
    erb module, Java Server Pages, and Python's psp module.
Provides simplified access to selected Brazilian macroeconomic and financial time series from official sources, primarily the Central Bank of Brazil through the SGS (Sistema Gerenciador de Séries Temporais) API. The package enables users to quickly retrieve and visualize indicators such as the unemployment rate and the Selic interest rate using a standardized data structure. It is designed for data access and visualization purposes, without performing forecasts or statistical modeling. For more information, see the official API: <https://dadosabertos.bcb.gov.br/dataset/>.
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'.  More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit()' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reducing adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression). The 'mdyplFit()' fitting method fits logistic regression models using maximum Diaconis-Ylvisaker prior penalized likelihood, which also guarantees finite estimates. High-dimensionality corrections under proportional asymptotics can be applied to the resulting objects; see Sterzinger and Kosmidis (2024) <doi:10.48550/arXiv.2311.07419> for details.
Interface with the 'Brickset' API
    <https://brickset.com/article/52664/api-version-3-documentation> for
    getting data about LEGO sets. Data sets that can be used for teaching
    and learning without the need of a 'Brickset' account and API key are
    also included. Includes all LEGO since through the end of 2025.
Collection of utilities that improve using 'Databricks' from
    R.  Primarily functions that wrap specific 'Databricks' APIs
    (<https://docs.databricks.com/api>), 'RStudio' connection pane
    support, quality of life functions to make 'Databricks' simpler to
    use.
An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.
BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.
Provides functions for estimating marginal likelihoods, Bayes
    factors, posterior model probabilities, and normalizing constants in general,
    via different versions of bridge sampling (Meng & Wong, 1996, 
    <https://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
    Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.
Implements bridge models for nowcasting and forecasting macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Simplifies forecasting and aggregating indicator variables to match the target frequency, enabling timely predictions ahead of official data releases. For more on bridge models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Burri (2023) <https://www5.unine.ch/RePEc/ftp/irn/pdfs/WP23-02.pdf> or Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>.
Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through 'OpenMP' <https://gcc.gnu.org/wiki/openmp>.
Connect to the D2L 'Brightspace' Data Sets ('BDS') API via
    'OAuth2', download all available datasets as tidy data frames with proper
    types, join them using convenience functions that know the foreign key
    relationships, and analyse student engagement, performance, and retention
    with ready-made analytics functions.
Implementation of the BRIk, FABRIk and FDEBRIk algorithms 
        to initialise k-means. These methods are intended for the 
        clustering of multivariate and functional data, respectively.
        They make use of the Modified Band Depth and bootstrap to 
        identify appropriate initial seeds for k-means, which are 
        proven to be better options than many techniques in the 
        literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3>
        It makes use of the functions kma and kma.similarity, from the 
        archived package fdakma, by Alice Parodi et al.
An automated graphical exploratory data analysis (EDA) tool that introduces: 
  a.) wideplot graphics for exploring the structure of a dataset through a grid of variables 
      and graphic types. 
  b.) longplot graphics, which present the entire catalog of available graphics for representing 
      a particular variable using a grid of graphic types and variations on these types.
  c.) plotup function, which presents a particular graphic for a specific variable of a dataset. 
      The plotup() function also makes it possible to obtain the code used to generate the graphic, 
      meaning that the user can adjust its properties as needed.
  d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships
      between all pairs of variables of a certain(s) type(s) in a multivariate data set.
Functions to handle basic input output, these functions
    always read and write UTF-8 (8-bit Unicode Transformation Format)
    files and provide more explicit control over line endings.
An interface to the 'Briq' API <https://briq.github.io>. 'Briq' is
    a tool that aims to promote employee engagement by helping employees
    recognize and reward each other. Employees can praise and thank one another
    (for achieving a company goal, for example) by giving virtual credits (known
    as 'briqs' or 'bqs') that can be redeemed for various rewards. The 'Briq'
    API lets you create, read, update and delete users, user groups,
    transactions and messages. This package provides functions that simplify
    getting the users, user groups and transactions of your organization into R.
Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.
Quantitative methods for benefit-risk analysis help to condense
    complex decisions into a univariate metric describing the overall benefit
    relative to risk.  One approach is to use the multi-criteria decision
    analysis framework (MCDA), as in Mussen, Salek, and Walker
    (2007) <doi:10.1002/pds.1435>.  Bayesian benefit-risk
    analysis incorporates uncertainty through posterior distributions which are
    inputs to the benefit-risk framework.  The brisk package provides functions
    to assist with Bayesian benefit-risk analyses, such as MCDA.
    Users input posterior samples, utility functions, weights, and the package
    outputs quantitative benefit-risk scores.  The posterior of the benefit-risk
    scores for each group can be compared.  Some plotting capabilities are also
    included.
Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>.  It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.
Fits novel models for the conditional relative risk, risk difference and odds ratio <doi:10.1080/01621459.2016.1192546>.
Fit Bayesian generalized (non-)linear multivariate multilevel models
    using 'Stan' for full Bayesian inference. A wide range of distributions
    and link functions are supported, allowing users to fit -- among others --
    linear, robust linear, count data, survival, response times, ordinal,
    zero-inflated, hurdle, and even self-defined mixture models all in a
    multilevel context. Further modeling options include both theory-driven and
    data-driven non-linear terms, auto-correlation structures, censoring and 
    truncation, meta-analytic standard errors, and quite a few more. 
    In addition, all parameters of the response distribution can be predicted 
    in order to perform distributional regression. Prior specifications are 
    flexible and explicitly encourage users to apply prior distributions that
    actually reflect their prior knowledge. Models can easily be evaluated and
    compared using several methods assessing posterior or prior predictions. 
    References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; 
    Bürkner (2018) <doi:10.32614/RJ-2018-017>;
    Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017)
    <doi:10.18637/jss.v076.i01>.
The mixed model for repeated measures (MMRM) is a popular
  model for longitudinal clinical trial data with continuous endpoints,
  and 'brms' is a powerful and versatile package for fitting Bayesian
  regression models.  The 'brms.mmrm' R package leverages 'brms' to run
  MMRMs, and it supports a simplified interfaced to reduce difficulty
  and align with the best practices of the life sciences.  References:
  Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008)
  <doi:10.1177/009286150804200402>.
Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).
Bayesian regularization for feed-forward neural networks.
Implements efficient 'NumPy'-like broadcasted operations for atomic and recursive arrays.
    In the context of operations involving 2 (or more) arrays,
    “broadcasting” (AKA singleton expansion) refers to efficiently recycling array dimensions,
    without making copies.
		Besides linking to 'Rcpp',
		'broadcast' does not use any external libraries in any way;
		'broadcast' was essentially made from scratch and can be installed out-of-the-box.
		The implementations available in 'broadcast' include, but are not limited to, the following.
		  1) Broadcasted element-wise operations on any 2 arrays;
		  they support a large set of
		  relational, arithmetic, Boolean, string, and bit-wise operations.
		  2) A faster, more memory efficient, and broadcasted abind-like function,
		  for binding arrays along an arbitrary dimension.
		  3) Broadcasted ifelse-like and apply-like functions.
		  4) Casting functions,
		  that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa.
		  5) A few linear algebra functions for statistics.
		The functions in the 'broadcast' package strive to minimize computation time and memory usage
		(which is not just better for efficient computing, but also for the environment).
Very large numbers in R.  Real numbers are held
        using their natural logarithms, plus a logical flag indicating
        sign.  Functionality for complex numbers is also provided.  The
        package includes a vignette that gives a step-by-step
        introduction to using S4 methods.
Approximates best-subset selection (L0) regression with
   an iteratively adaptive Ridge (L2) penalty for large-scale models.
   This package uses Cyclops for an efficient implementation and the
   iterative method is described in Kawaguchi et al (2020)
   <doi:10.1002/sim.8438> and Li et al (2021)
   <doi:10.1016/j.jspi.2020.12.001>.
Data on multiple individuals through time are often sampled at 
    times that differ between persons. Irregular observation times can severely 
    complicate the statistical analysis of the data. The broken stick model 
    approximates each subject’s trajectory by one or more connected line segments. 
    The times at which segments connect (breakpoints) are identical for all 
    subjects and under control of the user. A well-fitting broken stick model 
    effectively transforms individual measurements made at irregular times into 
    regular trajectories with common observation times. Specification of the 
    model requires three variables: time, measurement and subject. The 
    model is a special case of the linear mixed model, with time as a linear 
    B-spline and subject as the grouping factor. The main assumptions are: 
    subjects are exchangeable, trajectories between consecutive breakpoints are 
    straight, random effects follow a multivariate normal distribution, and 
    unobserved data are missing at random. The package contains functions for 
    fitting the broken stick model to data, for predicting curves in new data 
    and for plotting broken stick estimates. The package supports two 
    optimization methods, and includes options to structure the 
    variance-covariance matrix of the random effects. The analyst may use the 
    software to smooth growth curves by a series of connected straight lines, to 
    align irregularly observed curves to a common time grid, to create synthetic 
    curves at a user-specified set of breakpoints, to estimate the time-to-time 
    correlation matrix and to predict future observations. See 
    <doi:10.18637/jss.v106.i07> for additional documentation on background, 
    methodology and applications.
Provides a framework of tools to summarise, visualise, and explore 
  longitudinal data. It builds upon the tidy time series data frames used in the
  'tsibble' package, and is designed to integrate within the 'tidyverse', and
  'tidyverts' (for time series) ecosystems. The methods implemented include 
  calculating features for understanding longitudinal data, including 
  calculating summary statistics such as quantiles, medians, and numeric ranges,
  sampling individual series, identifying individual series representative of a 
  group, and extending the facet system  in 'ggplot2' to facilitate exploration of samples of data. These methods are
  fully described in the paper "brolgar: An R package to Browse Over 
  Longitudinal Data Graphically and Analytically in R", Nicholas Tierney, 
  Dianne Cook, Tania Prvan (2020) <doi:10.32614/RJ-2022-023>.
Miscellaneous R functions, including functions related to
    graphics (mostly for base graphics), permutation tests, running
    mean/median, and general utilities.
Summarizes key information about statistical objects in tidy
    tibbles. This makes it easy to report results, create plots and
    consistently work with large numbers of models at once.  Broom
    provides three verbs that each provide different types of information
    about a model. tidy() summarizes information about model components
    such as coefficients of a regression. glance() reports information
    about an entire model, such as goodness of fit measures like AIC and
    BIC. augment() adds information about individual observations to a
    dataset, such as fitted values or influence measures.
Provides suite of functions to work with regression model
    'broom::tidy()' tibbles.  The suite includes functions to group
    regression model terms by variable, insert reference and header rows
    for categorical variables, add variable labels, and more.
Convert fitted objects from various R mixed-model packages
    into tidy data frames along the lines of the 'broom' package.
    The package provides three
    S3 generics for each model: tidy(), which summarizes a model's statistical findings such as
    coefficients of a regression; augment(), which adds columns to the original
    data such as predictions, residuals and cluster assignments; and glance(), which
    provides a one-row summary of model-level statistics.
A lossless compressed data format that uses a combination 
    of the LZ77 algorithm and Huffman coding <https://www.rfc-editor.org/rfc/rfc7932>.
    Brotli is similar in speed to deflate (gzip) but offers more dense compression.
Provides tools for identifying subgroups within populations based on individual response patterns to specific interventions or treatments. Designed to support researchers and clinicians in exploring heterogeneous treatment effects and developing personalized therapeutic strategies. Offers functionality for analyzing and visualizing the interplay between two variables, thereby enhancing the interpretation of social sustainability metrics. The package focuses on bivariate discriminant analysis and aims to clarify relationships between indicator variables.
Bayesian estimation and variable selection for quantile regression models.
Tests for a linear relationship in the log ratio between an observed and simulated series and an independent variable. Typically this the error in modelled streamflow at an annual time scale, and a rainfall input. The approach allows for multiple sites as random factors and for multiple replicates of the simulated values. The approach is outlined in Gibbs et al. (2026) in review.
Provides the facility to calculate the Brainerd-Robinson similarity coefficient for the rows of an input table, and to calculate the significance of each coefficient based on a permutation approach; a heatmap is produced to visually represent the similarity matrix. Optionally, hierarchical agglomerative clustering can be performed and the silhouette method is used to identify an optimal number of clusters; the results of the clustering can be optionally used to sort the heatmap.
Broadly useful convenient and efficient R functions
    that bring users concise and elegant R data analyses.
    This package includes easy-to-use functions for
    (1) basic R programming
    (e.g., set working directory to the path of currently opened file;
    import/export data from/to files in any format;
    print tables to Microsoft Word);
    (2) multivariate computation
    (e.g., compute scale sums/means/... with reverse scoring);
    (3) reliability analyses and factor analyses;
    (4) descriptive statistics and correlation analyses;
    (5) t-test, multi-factor analysis of variance (ANOVA),
    simple-effect analysis, and post-hoc multiple comparison;
    (6) tidy report of statistical models
    (to R Console and Microsoft Word);
    (7) mediation and moderation analyses (PROCESS);
    and (8) additional toolbox for statistics and graphics.
Provides high-level modeling functions to define and train
    models using the 'torch' R package. Models include linear, logistic,
    and multinomial regression as well as multilayer perceptrons.
Provides spatial data for mapping Brunei, including boundaries for districts, mukims, and kampongs, as well as locations of key infrastructure such as masjids, hospitals, clinics, and schools. The package supports researchers, analysts, and developers working with Brunei’s geographic and demographic data, offering a quick and accessible foundation for creating maps and conducting spatial studies.
Provides the functions for Brunner-Munzel test and
    permuted Brunner-Munzel test,
    which enable to use formula, matrix, and table as argument.
    These functions are based on Brunner and Munzel (2000)
     <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>
    and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>,
    and are written with FORTRAN.
When samples contain missing data, are small, or are suspected of bias,
    estimation of scale reliability may not be trustworthy.  A recommended solution
    for this common problem has been Bayesian model estimation.  Bayesian methods
    rely on user specified information from historical data or researcher intuition 
    to more accurately estimate the parameters.  This package provides a user friendly
    interface for estimating test reliability.  Here, reliability is modeled as a beta
    distributed random variable with shape parameters alpha=true score variance and
    beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.
Allows the user to generate bootstrap cards within
    R markdown documents. Intended for use in conjunction with
    R markdown HTML outputs and other formats that support the 
    bootstrap 4 library.
Make 'Bootstrap 4' Shiny dashboards. Use the full power of
    'AdminLTE3', a dashboard template built on top of 'Bootstrap 4'
    <https://github.com/ColorlibHQ/AdminLTE>.
Provides branding, theme application, and navigation utilities for
    applications built with 'bs4Dash' and 'shiny'. Supports configurable
    sidebar brand display modes, hover-expand behavior, and theme
    customization using CSS variables. Includes standardized navigation
    components such as refresh and help controls, along with helpers for
    common navigation bar and footer layouts.
Collection of functions, data sets and code examples 
 for evaluations of field trials with the objective of equivalence assessment.
Contains functions to perform Bayesian inference
    using a spectral analysis of Gaussian process priors.
    Gaussian processes are represented with a Fourier series 
    based on cosine basis functions. Currently the package
    includes parametric linear models, partial linear additive
    models with/without shape restrictions, generalized linear
    additive models with/without shape restrictions, and  
    density estimation model. To maximize computational 
    efficiency, the actual Markov chain Monte Carlo sampling 
    for each model is done using codes written in FORTRAN 90.
    This software has been developed using funding supported by
    Basic Science Research Program through the National Research
    Foundation of Korea (NRF) funded by the Ministry of Education
    (no. NRF-2016R1D1A1B03932178 and no. NRF-2017R1D1A3B03035235).
Render SVG as interactive figures to display contextual
   information, with selectable and clickable user interface elements.
   These figures can be seamlessly integrated into 'rmarkdown' and 'Quarto'
   documents, as well as 'shiny' applications, allowing manipulation of elements
   and reporting actions performed on them.
   Additional features include pan, zoom in/out functionality, and the ability
   to export the figures in SVG or PNG formats.
Data sets for book "Basic Statistics and Data Analysis" by
    Larry J. Kitchens.
Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.
An implementation of the Bayesian Surrogate Evaluation Test
    (BSET) for assessing the validity of surrogate markers in clinical
    trials. Provides hypothesis testing tools to evaluate whether a
    surrogate can reliably estimate the causal effect of a treatment on a
    primary outcome. Implements the imputation-based Bayesian methodology
    of Carlotti and Parast (2026) <doi:10.48550/arXiv.2603.14381>,
    extending the frequentist rank-based approach of Parast et al. (2024)
    <doi:10.1093/biomtc/ujad035>. Addresses key limitations of the
    frequentist method, including the lack of causal interpretability and
    the inability to adjust for covariates in the estimation process.
Performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).
Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.
The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). 
	     The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed. 
	     The package is based on the work in Rebora P (2014) <doi:10.32614/RJ-2014-028>.
A collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica 'bSi' in inland and marine sediments. These tools share common data representations and follow a consistent 'API' design. The primary goal of the 'bSi' package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire 'bSi' analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.
Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R
    Markdown' documents. More generally, icons can be inserted in any
    'htmltools' document through inline 'SVG'.
A highly scientific and utterly addictive 
  bird point count simulator
  to test statistical assumptions, aid survey design,
  and have fun while doing it (Solymos 2024 <doi:10.1007/s42977-023-00183-2>).
  The simulations follow time-removal and distance sampling models 
  based on Matsuoka et al. (2012) <doi:10.1525/auk.2012.11190>,
  Solymos et al. (2013) <doi:10.1111/2041-210X.12106>,
  and Solymos et al. (2018) <doi:10.1650/CONDOR-18-32.1>,
  and sound attenuation experiments by 
  Yip et al. (2017) <doi:10.1650/CONDOR-16-93.1>.
The Super Imposition by Translation and Rotation (SITAR) model 
    is a shape-invariant nonlinear mixed effect model that fits a natural cubic 
    spline mean curve to the growth data and aligns individual-specific growth 
    curves to the underlying mean curve via a set of random effects (see Cole, 
    2010 <doi:10.1093/ije/dyq115> for details). The non-Bayesian version of the 
    SITAR model can be fit by using the already available R package 'sitar'. Unlike 
    the 'sitar' package which allows modelling of a single outcome only, the 'bsitar' 
    package offers great flexibility in fitting models of varying complexities, 
    including joint modelling of multiple outcomes such as height and weight 
    (multivariate model). Additionally, the 'bsitar' package allows for the simultaneous  
    analysis of an outcome separately for subgroups defined by a factor variable such 
    as gender. This is achieved by fitting separate models for each subgroup 
    (for example males and females for gender variable). An advantage of this approach 
    is that posterior draws for each subgroup are part of a single model object, 
    making it possible to compare coefficients across subgroups and test hypotheses. 
    Since the 'bsitar' package is a front-end to the R package 'brms', it offers excellent 
    support for post-processing of posterior draws via various functions that are 
    directly available from the 'brms' package. In addition, the 'bsitar' package 
    includes various customized functions that allow for the visualization of distance 
    (increase in size with age) and velocity (change in growth rate as a function of age), 
    as well as the estimation of growth spurt parameters such as age at peak growth velocity  
    and peak growth velocity.
Collect data from and make posts on 'Bluesky' Social
    via the Hypertext Transfer Protocol (HTTP) Application Programming 
    Interface (API), as documented at <https://atproto.com/specs/xrpc>. This 
    further supports broader queries to the Authenticated Transfer (AT) Protocol
    <https://atproto.com/> which 'Bluesky' Social relies on. Data is returned in a
    tidy format and posts can be made using a simple interface.
Simplifies custom 'CSS' styling of both 'shiny' and
    'rmarkdown' via 'Bootstrap' 'Sass'. Supports 'Bootstrap' 3, 4 and 5 as
    well as their various 'Bootswatch' themes. An interactive widget is
    also provided for previewing themes in real time.
Bayes screening and model discrimination follow-up designs.
Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) <arXiv:2205.15263> for technical details. Source code and more data sets are at <https://github.com/profyliu/bsnsing/>.
Provides an S4 class and methods for analyzing microbial social
    behavior in bacterial consortia. Includes growth parameter extraction,
    social behavior classification (cooperators/cheaters/neutrals),
    diversity effect analysis, consortium assembly path finding, and
    stability analysis via coefficient of variation. Methods are described
    in Purswani et al. (2017) <doi:10.3389/fmicb.2017.00919>.
The purpose of this package is to fit the three Spatial Econometric Models proposed
  in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms
  and observational working variables approach.
Gibbs sampling for Bayesian spatial blind source separation (BSP-BSS). BSP-BSS is designed for spatially dependent signals in high dimensional and large-scale data, such as neuroimaging. The method assumes the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, and constructs a Bayesian nonparametric prior by thresholding Gaussian processes. Details can be found in our paper: Wu, B., Guo, Y., & Kang, J. (2024). Bayesian spatial blind source separation via the thresholded gaussian process. Journal of the American Statistical Association, 119(545), 422-433.
Bayesian estimations of a covariance matrix for multivariate 
        normal data. Assumes that the covariance matrix is sparse or band 
        matrix and positive-definite. Methods implemented include the beta-mixture 
        shrinkage prior (Lee et al. (2022) <doi:10.1016/j.jmva.2022.105067>), 
        screened beta-mixture prior (Lee et al. (2024) <doi:10.1214/24-BA1495>), 
        and post-processed posteriors for banded and sparse covariances 
        (Lee et al. (2023) <doi:10.1214/22-BA1333>; Lee and Lee (2023) 
        <doi:10.1016/j.jeconom.2023.105475>). This software has been developed using 
        funding supported by Basic Science Research Program through the National 
        Research Foundation of Korea ('NRF') funded by the Ministry of Education
        ('RS-2023-00211979', 'NRF-2022R1A5A7033499', 'NRF-2020R1A4A1018207' 
        and 'NRF-2020R1C1C1A01013338').
Bayesian inference on the (discrete) power spectrum of time series.
Build and use B-splines for interpolation and regression.
  In case of regression, equality constraints as well as monotonicity
  and/or positivity of B-spline weights can be imposed. Moreover, 
  knot positions can be on regular grid or be part of 
  optimized parameters too (in addition to the spline weights).
  For this end, 'bspline' is able to calculate
  Jacobian of basis vectors as function of knot positions. User is provided with 
  functions calculating spline values at arbitrary points. These 
  functions can be differentiated and integrated to obtain B-splines calculating 
  derivatives/integrals at any point. B-splines of this package can 
  simultaneously operate on a series of curves sharing the same set of 
  knots. 'bspline' is written with concern about computing 
  performance that's why the basis and Jacobian calculation is implemented in C++.
  The rest is implemented in R but without notable impact on computing speed.
Quantile regression with cubic B-splines under monotonicity
    and convexity constraints using the Karlin-Studden SOCP formulation.
    The method is described in Abbes (2026) <doi:10.5281/zenodo.17427913>.
    This R implementation is intended for demonstration and prototyping;
    all B-spline and polynomial functions have been rewritten for consistency.
    A faster version written in 'Python' is available at
    <https://github.com/alexandreabbes/Constrained-Quantile-Regression-with-cubic-splines>.
The Bootstrap framework lets you add some JavaScript functionality to your web site by
  adding attributes to your HTML tags - Bootstrap takes care of the JavaScript
  <https://getbootstrap.com/docs/3.3/javascript/>. If you are using R Markdown or Shiny, you can
  use these functions to create collapsible sections, accordion panels, modals, tooltips,
  popovers, and an accordion sidebar framework (not described at Bootstrap site).
  Please note this package was designed for Bootstrap 3.3.
Enables binary package installations on Linux distributions.
    Provides functions to manage packages via the distribution's package
    manager. Also provides transparent integration with R's install.packages()
    and a fallback mechanism. When installed as a system package, interacts
    with the system's package manager without requiring administrative
    privileges via an integrated D-Bus service; otherwise, uses sudo.
    Currently, the following backends are supported: DNF, APT, ALPM.
Fit Bayesian models with a focus on the spatial econometric models.
Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in 
    Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes
    to data, and functions to estimate the stochastic volatility process of a BSS process.
Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.
Computation of the minimum sample size using the Average
    Coverage Criterion or the Average Length Criterion for estimating
    binomial proportions using beta prior distributions. For more details
    see Costa (2025) <DOI:10.1007/978-3-031-72215-8_14>.
Efficient methods for Bayesian inference of state space models 
    via Markov chain Monte Carlo (MCMC) based on parallel 
    importance sampling type weighted estimators 
    (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), 
    particle MCMC, and its delayed acceptance version. 
    Gaussian, Poisson, binomial, negative binomial, and Gamma
    observation densities and basic stochastic volatility models 
    with linear-Gaussian state dynamics, as well as general non-linear Gaussian 
    models and discretised diffusion models are supported. 
    See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.
Provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. 'BSSoverSpace' is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.
Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods.
Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
Implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes.  Functions are based on and extended from methods described in Berrett, et al. (2020) <doi:10.1002/env.2609>.
Functions are pre-configured to utilize Bootstrap 5 classes and 
  HTML structures to create Bootstrap-styled HTML quickly and easily. Includes 
  functions for creating common Bootstrap elements such as containers, rows, cols,
  navbars, etc. Intended to be used with the html5 package. Learn more at 
  <https://getbootstrap.com/>.
Perform record linkage on streaming files using recursive Bayesian updating.
Time series regression using dynamic linear models fit using
  MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many
  other sources.
Estimation of association between disease or death counts (e.g. COVID-19) and socio-environmental risk factors using a zero-inflated Bayesian spatiotemporal model. Non-spatiotemporal models and/or models without zero-inflation are also included for comparison. Functions to produce corresponding maps are also included. See Chakraborty et al. (2022) <doi:10.1007/s13253-022-00487-1> for more details on the method.
It submits R code/R scripts/shell commands to 'LSF cluster' 
  (<https://en.wikipedia.org/wiki/Platform_LSF>, the 'bsub' system) without 
  leaving R. There is also an interactive 'shiny' application for monitoring job status.
Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.
Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.
Implements the Bayesian Synthetic Control method for causal inference
    in comparative case studies. This package provides tools for estimating
    treatment effects in settings with a single treated unit and multiple control
    units, allowing for uncertainty quantification and flexible modeling of
    time-varying effects. The methodology is based on the paper by Vives and
    Martinez (2022) <doi:10.48550/arXiv.2206.01779>.
The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. 
        The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid.
        The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid.
        The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid.
        The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.
        Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, 
        Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.
We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.
Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures.
              This package utilizes 'runjags' to run Gibbs sampling, parallelizing the chains.
              Options for different covariances/relationship structures between parameters of interest.
Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Biterm Topic Models find topics in collections of short texts. 
    It is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns which are called biterms.
    This in contrast to traditional topic models like Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis 
    which are word-document co-occurrence topic models.
    A biterm consists of two words co-occurring in the same short text window.  
    This context window can for example be a twitter message, a short answer on a survey, a sentence of a text or a document identifier. 
    The techniques are explained in detail in the paper 'A Biterm Topic Model For Short Text' by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng (2013) <https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf>.
Generalization of the Bayesian classification and regression tree model that partitions subjects into terminal nodes and tailors predictive model to each terminal node.
Generalization of the Bayesian classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors regression model to each terminal node.
Provides advanced Bayesian methods to estimate
	     abundance and run-timing from temporally-stratified
	     Petersen mark-recapture experiments. Methods include
	     hierarchical modelling of the capture probabilities
  	     and spline smoothing of the daily run size. Theory
  	     described in Bonner and Schwarz (2011)
         <doi:10.1111/j.1541-0420.2011.01599.x>.
Simulate, estimate and forecast a wide range of regression
    based dynamic models for bounded time series, covering the most
    commonly applied models in the literature. The main calculations are
    done in FORTRAN, which translates into very fast algorithms.
Large panel data sets are often subject to common trends. However, it can be difficult to determine the exact number of these common factors and analyse their properties.
  The package implements the Barigozzi and Trapani (2022) <doi:10.1080/07350015.2021.1901719> test, which not only provides an efficient way of estimating the number of common factors in large nonstationary panel data sets, but also gives further insights on factor classes. The routine identifies the existence of (i) a factor subject to a linear trend, (ii) the number of zero-mean I(1) and (iii) zero-mean I(0) factors.
  Furthermore, the package includes the Integrated Panel Criteria by Bai (2004) <doi:10.1016/j.jeconom.2003.10.022> that provide a complementary measure for the number of factors.
Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) <doi:10.48550/arXiv.2408.10558>, and allows for the statistical modeling of multi-attribute pairwise comparison data.
A complete toolkit for connecting 'R' environments with Large
    Language Models (LLMs). Provides utilities for describing 'R' objects,
    package documentation, and workspace state in plain text formats
    optimized for LLM consumption. Supports multiple workflows:
    interactive copy-paste to external chat interfaces, programmatic tool
    registration with 'ellmer' chat clients, batteries-included chat
    applications via 'shinychat', and exposure to external coding agents
    through the Model Context Protocol. Project configuration files enable
    stable, repeatable conversations with project-specific context and
    preferred LLM settings.
Implements the Butterworth-Induced Autoregressive ('BTWAR')
    model, where autoregressive coefficients are obtained from analog
    Butterworth filter prototypes mapped into the discrete-time domain
    using the Matched Z-Transform. The framework establishes a structured
    connection between frequency-domain filter design and time-domain
    autoregressive modeling. Model order selection is performed via
    nested rolling-origin cross-validation. Method described in
    Bras-Geraldes, Rocha and Martins (2026) <doi:10.3390/math14030479>.
Functions for data preparation, parameter estimation, scoring, and plotting for the 
    BG/BB (Fader, Hardie, and Shang 2010 <doi:10.1287/mksc.1100.0580>), 
    BG/NBD (Fader, Hardie, and Lee 2005 <doi:10.1287/mksc.1040.0098>) and 
    Pareto/NBD and Gamma/Gamma (Fader, Hardie, and Lee 2005 <doi:10.1509/jmkr.2005.42.4.415>) models.
Plotting package based on the grid system, combining elements of 
    a bubble plot and heatmap to conveniently display two numerical variables, 
    (represented by color and size) grouped by categorical variables on the x 
    and y axes. This is a useful alternative to a forest plot when the data can 
    be grouped in two dimensions, such as predictors x outcomes. It has 
    particular advantages for visualising the metabolic measures produced by 
    the 'Nightingale Health' metabolomics platform, and templates are included 
    for automatically generating figures from these datasets.
Creates bubbles within 'shiny' and 'rmarkdown' backgrounds using the 'bubbly-bg' 'JavaScript' library.
Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and
    uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).
Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber (2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al. (1998) <DOI:10.1109/5.726791> for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data.
A tuneable and interpretable method for relaxing 
	the instrumental variables (IV) assumptions to infer treatment effects in the presence
	of unobserved confounding.
	For a treatment-associated covariate to be a valid IV, it must be (a) unconfounded with the outcome
	and (b) have a causal effect on the outcome that is exclusively mediated by the exposure. 
	There is no general test of the validity of these IV assumptions for any particular pre-treatment 
	covariate. 
	However, if different pre-treatment covariates give differing causal effect estimates 
	when treated as IVs, then we know at least some of the covariates violate these assumptions. 
	'budgetIVr' exploits this fact by taking as input a minimum budget of pre-treatment covariates assumed
	to be valid IVs and idenfiying the set of causal effects that are consistent with the user's data and budget assumption.
	The following generalizations of this principle can be used in this package:
	(1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; 
	(2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis;
	(3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost).
	The methods in this package require only summary statistics. 
	Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature.
	For further methodological details, please refer to Penn et al. (2024) <doi:10.48550/arXiv.2411.06913>.
Finds the largest possible regression model that will still converge
    for various types of regression analyses (including mixed models and generalized
    additive models) and then optionally performs stepwise elimination similar to the
    forward and backward effect-selection methods in SAS, based on the change in
    log-likelihood or its significance, Akaike's Information Criterion, the Bayesian
    Information Criterion, the explained deviance, or the F-test of the change in R².
Working with reproducible reports or any other
    similar projects often require to run the script that builds the
    output file in a specified way. 'buildr' can help you organize, modify
    and comfortably run those scripts. The package provides a set of
    functions that interactively guides you through the process and that
    are available as 'RStudio' Addin, meaning you can set up the keyboard
    shortcuts, enabling you to choose and run the desired build script
    with one keystroke anywhere anytime.
A build system based on 'GNU make' that creates and
    maintains (simply) make files in an R session and provides
    GUI debugging support through 'Microsoft Visual Code'.
Buckley-James regression for right-censoring survival data with high-dimensional covariates. Implementations for survival data include boosting with componentwise linear least squares, componentwise smoothing splines, regression trees and MARS. Other high-dimensional tools include penalized regression for survival data. See Wang and Wang (2010) <doi:10.2202/1544-6115.1550>.
Multicenter randomized trials involve the collection and analysis of data from numerous study participants across multiple sites. Outliers may be present. To identify outliers, this package examines data at the individual level (univariate and multivariate) and site-level (with and without covariate adjustment). Methods are outlined in further detail in Rigdon et al (to appear).
Designed to simplify and streamline the process of reading
    and processing large volumes of data in R, this package offers a
    collection of functions tailored for bulk data operations. It enables
    users to efficiently read multiple sheets from Microsoft Excel and
    Google Sheets workbooks, as well as various CSV files from a
    directory. The data is returned as organized data frames, facilitating
    further analysis and manipulation. Ideal for handling extensive data
    sets or batch processing tasks, bulkreadr empowers users to manage
    data in bulk effortlessly, saving time and effort in data preparation
    workflows. Additionally, the package seamlessly works with labelled
    data from SPSS and Stata.
Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step
    of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option
    in the function from the package 'bulletxtrctr' which identifies the groove locations.
Stock, Options and Futures Trading Strategies for Traders and Investors with Bullish Outlook are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
We provide a tidy data structure and visualisations for multiple or grouped  variable correlations, 
    general association measures scagnostics and other pairwise scores  suitable for numerical, ordinal and nominal variables. 
    Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.
The bullwhipgame is an educational game that has as purpose the illustration and exploration of the bullwhip effect,i.e, the increase in demand variability along the supply chain. Marchena Marlene (2010) <arXiv:1009.3977>.
Bumblebee colonies grow during worker production, then
    decline after switching to production of reproductive individuals
    (drones and gynes).  This package provides tools for modeling and
    visualizing this pattern by identifying a switchpoint with a growth
    rate before and a decline rate after the switchpoint. The mathematical
    models fit by bumbl are described in Crone and Williams (2016)
    <doi:10.1111/ele.12581>.
A simple tool to quantify the amount of transmission
   of an infectious disease of interest occurring within and between 
   population groups. 'bumblebee' uses counts of observed directed 
   transmission pairs, identified phylogenetically from deep-sequence data or 
   from epidemiological contacts, to quantify transmission flows within and 
   between population groups accounting for sampling heterogeneity. Population 
   groups might include: geographical areas (e.g. communities, regions), 
   demographic groups (e.g. age, gender) or arms of a randomized clinical 
   trial. See the 'bumblebee' website for statistical theory, documentation 
   and examples <https://magosil86.github.io/bumblebee/>.
Implementation of the bunching estimator for kinks and notches. 
        Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). 
        It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.
View and analyze data where bunching is expected. Estimate counter-
    factual distributions. For earnings data, estimate the compensated
    elasticity of earnings w.r.t. the net-of-tax rate.
Provides a registry of APIs listed on <https://bund.dev> and a core
    'OpenAPI' client layer to explore specs and perform requests. Adapter helpers
    return tidy data frames for supported APIs, with optional response caching and
    rate limiting guidance.
Download data from the time-series
  databases of the Bundesbank, the German central
  bank. See the overview at the Bundesbank website
  (<https://www.bundesbank.de/en/statistics/time-series-databases>)
  for available series. The package provides only a
  single function, getSeries(), which supports both
  traditional and real-time datasets; it will also
  download meta data if available. Downloaded data
  can automatically be arranged in various formats,
  such as data frames or 'zoo' series. The data
  may optionally be cached, so as to avoid repeated
  downloads of the same series.
All final tables of Germany's highest football (soccer!) league, the Bundesliga. Contains data from 1964 to 2016.
Typically, models in 'R' exist in memory and can be saved via
    regular 'R' serialization. However, some models store information in
    locations that cannot be saved using 'R' serialization alone. The goal
    of 'bundle' is to provide a common interface to capture this
    information, situate it within a portable object, and restore it for
    use in new settings.
Provides an efficient and robust implementation for estimating marginal Hazard Ratio (HR) and 
  Restricted Mean Survival Time (RMST) with covariate adjustment using Daniel et al. (2021) <doi:10.1002/bimj.201900297> and 
  Karrison et al. (2018) <doi:10.1177/1740774518759281>.
Provides tools to deploy R web server applications that
    follow the '_server.yml' standard. This standard allows different R
    server frameworks ('plumber2', 'fiery', etc.) to be deployed using a
    common interface.  The package supports deployment to 'DigitalOcean'
    and includes validation tools to ensure '_server.yml' files are
    correctly formatted.
Comprehensive Business Process Analysis toolkit. Creates S3-class for event log objects, and related handler functions. Imports related packages for filtering event data, computation of descriptive statistics, handling of 'Petri Net' objects and visualization of process maps. See also packages 'edeaR','processmapR', 'eventdataR' and 'processmonitR'.
Provides a way to reduce model objects to necessary parts, making them easier to work with, store, share and simulate multiple values for new responses while allowing for parameter uncertainty.
Tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.
Call the data wrappers for Bursa Metropolitan Municipality's Open Data Portal <https://acikyesil.bursa.bel.tr/>. This will return all datasets stored in different formats.
A suite of functions for finance, including the estimation
	of variance matrices via a statistical factor model or
	Ledoit-Wolf shrinkage.
Script search, corner, genetic optimization, permutation tests, write expect test.
An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>).  Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.
Get a current financial year, start of current
    month, End of current month, start of financial year and end of it.
    Allow for offset from the date.
Data on the first 24 seasons of the UK TV show 'I'm a Celebrity, 
  Get Me Out of Here', broadcast from 2002-2024. Taken from the Wikipedia pages
  for each season and the main page available at
  <https://en.wikipedia.org/wiki/I%27m_a_Celebrity...Get_Me_Out_of_Here!_(British_TV_series)>.
Calculates business duration between two dates. This excluding weekends, public holidays and non-business hours.
A collection of S4 classes, methods and functions to create
        and visualize business plans. Different types of cash flows can be
        defined, which can then be used and tabulated to create profit and
        loss statements, cash flow plans, investment and depreciation
        schedules, loan amortization schedules, etc. The methods are
        designed to produce handsome tables in both PDF and HTML using
        'RMarkdown' or 'Shiny'.
Provides a set of S3 generics to axe components of fitted
    model objects and help reduce the size of model objects saved to disk.
Verification of continually updating time series data where we
    expect new values, but want to ensure previous data remains unchanged. Data
    previously recorded could change for a number of reasons, such as discovery
    of an error in model code, a change in methodology or instrument recalibration.
    Monitoring data sources for these changes is not always possible. Other unnoticed
    changes could include a jump in time or measurement frequency, due to instrument
    failure or software updates. Functionality is provided that can be
    used to check and flag changes to previous data to prevent changes going
    unnoticed, as well as unexpected jumps in time.
Trading of Butterfly Options Strategies is represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Provides tools to download and extract data from the
    'OzButterflies' Database <doi:10.5281/zenodo.19019034>, which
    contains reflectance spectra, calibrated photographs, and
    cytochrome c oxidase subunit I (COI) sequences of Australian
    butterflies. The main function get_Oz_butterflies() downloads the
    database and extracts selected files to a specified directory using
    user-defined filters.
Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.
A collection of quantitative tools for selecting mediating effects within exploratory Bayesian mediation models. The package accommodates both continuous and dichotomous outcomes, including the dependent variables and the mediators for identifying and analyzing mediation pathways.
Calculates B-value and empirical equivalence bound. B-value is defined as the maximum magnitude of a confidence interval; and the empirical equivalence bound is the minimum B-value at a certain level. A new two-stage procedure for hypothesis testing is proposed, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using the introduced empirical equivalence bound. See Zhao et al. (2019) "B-Value and Empirical Equivalence Bound: A New Procedure of Hypothesis Testing" <arXiv:1912.13084> for details.
Estimation of hierarchical Bayesian vector autoregressive models
    following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>.
    Implements hierarchical prior selection for conjugate priors in the fashion
    of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>.
    Functions to compute and identify impulse responses, calculate forecasts,
    forecast error variance decompositions and scenarios are available.
    Several methods to print, plot and summarise results facilitate analysis.
Bayesian estimation of multilevel Vector Autoregression (VAR) models
    using Stan. Supports Gaussian, Binary, and Ordinal (adjacent category) outcome
    variables with random effects and customizable priors.
Provides fast and efficient procedures for Bayesian estimation and forecasting using state-of-the-art Vector Autoregressions. This package includes the model proposed by Chan (2020) <doi:10.1080/07350015.2018.1451336>, that is, a Bayesian Vector Autoregression with Minnesota priors and a flexible structure of the error term specification. The latter includes: conditional multivariate normal or Student’s t distributions, as well as homoskedastic or heteroskedastic specifications with a common volatility modelled by centred or non-centred Stochastic Volatility. Additionally, the package facilitates predictive analyses using density forecasting and forecast-error variance decompositions. All this is complemented by simple workflows, useful plots and summary functions, and comprehensive documentation. The 'bvars' package aligns with R packages 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and 'bpvars' by Woźniak (2025) <doi:10.32614/CRAN.package.bpvars> regarding objects, workflows, and code structure, and they constitute an integrated toolset.
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Functions to prepare tidy objects from estimated models via 'BVAR'
    (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and
    visualisation thereof. Bridges the gap between estimating models with 'BVAR'
    and plotting the results in a more sophisticated way with 'ggplot2' as well
    as passing them on in a tidy format.
Tools to model and forecast multivariate time series
    including Bayesian Vector heterogeneous autoregressive (VHAR) model
    by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>).
    'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.
An R interface to the Stark-Parker implementation of an
        algorithm for bounded-variable least squares
Implements the EM algorithm with one-step Gradient Descent method to estimate
             the parameters of the Block-Basu bivariate Pareto distribution with location
             and scale. We also found parametric bootstrap and asymptotic confidence 
             intervals based on the observed Fisher information of scale and shape parameters,
             and exact confidence intervals for location parameters. Details are in 
             Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199>
             "An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto
             distribution with location and scale"; 
             E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation";
             Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593> 
             "An Introduction to the Bootstrap"; 
             A P Dempster, N M Laird and D B Rubin
             (1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete
             Data via the EM Algorithm".
Implements a backward procedure for single and multiple change point detection proposed by Shin et al. <arXiv:1812.10107>. The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.
Whole-genome regression methods on Bayesian framework fitted via EM
    or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), 
    univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>),
    with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).
Package for Breed Wheat Genomic Selection Pipeline. 
    The R package 'BWGS' is developed by Louis Gautier Tran <louis.gautier.tran@gmail.com> and Gilles Charmet <gilles.charmet@inra.fr>.
    This repository is forked from original repository <https://forgemia.inra.fr/umr-gdec/bwgs>
    and modified as a R package.
A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.
Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.
An interface to the Bayesian Weighted Sums model implemented in 'RStan'.
    It estimates the summed effect of multiple, often moderately to highly correlated,
    continuous predictors. Its applications can be found in analysis of exposure mixtures.
    The model was proposed by Hamra, Maclehose, Croen, Kauffman, and
    Newschaffer (2021) <doi:10.3390/ijerph18041373>.
    This implementation includes an extension to model binary outcome.
Performs the 'Baumgartner-Weiss-Schindler' two-sample test of equal
   probability distributions, <doi:10.2307/2533862>. Also performs
   similar rank-based tests for equal probability distributions due to
   Neuhauser <doi:10.1080/10485250108832874> and
   Murakami <doi:10.1080/00949655.2010.551516>.
Functions to get and download city bike data from 
    the website and API service of each city bike service in Norway. The
    package aims to reduce time spent on getting Norwegian city bike data,
    and lower barriers to start analyzing it. The data is retrieved from
    Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is 
    made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.
Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).
Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.
The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.
Dataset containing cumulative COVID-19 deaths (absolute and per 100,000 pop) at the regional level (mostly NUTS 3) for 31 EU/EFTA countries.
Provides a self-contained set of methods to aid clinical trial
    safety investigators, statisticians and researchers, in the early
    detection of adverse events using groupings by body-system or system
    organ class. This work was supported by the Engineering and Physical
    Sciences Research Council (UK) (EPSRC) [award reference 1521741] and
    Frontier Science (Scotland) Ltd. The package title c212 is in reference
    to the original Engineering and Physical Sciences Research Council (UK)
    funded project which was named CASE 2/12.
Compare two classifications or clustering solutions that may or may
    not have the same number of classes, and that might have hard or soft
    (fuzzy, probabilistic) membership. Calculate various metrics to assess how
    the clusters compare to each other. The calculations are simple, but provide
    a handy tool for users unfamiliar with matrix multiplication. This package
    is not geared towards traditional accuracy assessment for classification/
    mapping applications - the motivating use case is for comparing a
    probabilistic clustering solution to a set of reference or existing class
    labels that could have any number of classes (that is, without having to
    degrade the probabilistic clustering to hard classes).
'Cristin' to 'Zotero' ('c2z') aims at obtaining total
    dominion over 'Cristin' ('Current Research Information SysTem in
    Norway') and 'Zotero'. The package enables manipulating 'Zotero'
    libraries using 'R'.  Import references from 'Cristin', 'Regjeringen',
    'CRAN', 'ISBN' ('Alma', 'LoC'), and 'DOI' ('CrossRef', 'DataCite') to
    a 'Zotero' library. Add, edit, copy, or delete items, including
    attachments and collections, and export references to 'BibLaTeX' (and
    other formats).
Create interactive charts with the 'C3.js' <http://c3js.org/> charting library. All plot 
    types in 'C3.js' are available and include line, bar, scatter, and mixed geometry plots. Plot 
    annotations, labels and axis are highly adjustable. Interactive web based charts can be embedded 
    in R Markdown documents or Shiny web applications.
A wrapper for the 'EZC3D' library to work with C3D motion capture data.
Allows inferring gene regulatory networks
        with direct physical interactions from microarray expression
        data using C3NET.
Get insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020) <doi:10.1007/s00357-019-09350-4>.
C5.0 decision trees and rule-based models for pattern
    recognition that extend the work of Quinlan (1993,
    ISBN:1-55860-238-0).
Computation and visualization of simple, multiple and joint correspondence analysis.
Provides four variants of three-way correspondence analysis (ca):
 three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca
 and three-way ordered non-symmetrical ca.
Identify the most relative data points by dividing a numeric data set into three classes A, B, and C, where class A items are the "import few", class C items are the "trivial many" with class B items being something in between, resembling the idea of the Pareto principle.
 This ABC classification is done using an ABC curve, which plots cumulative "Yield" against "Effort", similar to a Lorenz curve. Class borders are then precisely mathematically defined on that curve, aiding in interpretation. Based on: Ultsch A, Lotsch J (2015) "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data". PLoS ONE 10(6): e0129767. <doi:10.1371/journal.pone.0129767>.
Creates project specific directory and file templates that are 
 written to a .Rprofile file. Upon starting a new R session, these templates 
 can be used to streamline the creation of new directories that are 
 standardized to the user's preferences and can include the initiation of a 
 git repository, an RStudio R project, and project-local dependency management 
 with the 'renv' package.
Performs simple correspondence analysis on a two-way contingency table, 
    or multiple correspondence analysis (homogeneity analysis) 
    on data with p categorical variables,
    and produces bootstrap-based elliptical confidence regions around the 
    projected coordinates for the category points. 
    Includes routines to plot the results in a variety of styles. 
    Also reports the standard numerical output for correspondence analysis.
A set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) <doi:10.1007/s10940-008-9044-8>, and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) <doi:10.1177/0011128719866123>. Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.
Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.
Key-value stores with automatic pruning. Caches can limit
    either their total size or the age of the oldest object (or both),
    automatically pruning objects to maintain the constraints.
Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.
Converts customer transaction data (ID, purchase date) into a R6 class called customer.  The class stores various customer analytics calculations at the customer level. The package also contains functionality to convert data in the R6 class to data.frames that can serve as inputs for various customer analytics models.
Hansen's (1995) Covariate-Augmented
        Dickey-Fuller (CADF) test. The only required argument is y, the
        Tx1 time series to be tested. If no stationary covariate X is
        passed to the procedure, then an ordinary ADF test is
        performed. The p-values of the test are computed using the
        procedure illustrated in Lupi (2009).
Encrypts and decrypts strings using either the Caesar cipher or a
    pseudorandom number generation (using set.seed()) method.
Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.
Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.
Provides rank-based compositional differential abundance analysis for microbiome count data with zero cells using the compositional accelerated failure time model of Satten, Li and Zhao (2025) "CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells" <doi:10.1101/2025.11.26.690468>. Zero counts are treated as censored observations below sample-specific detection limits, avoiding the use of pseudocounts. The package implements estimation and hypothesis testing procedures for assessing associations between microbial taxa and covariates while accounting for the compositional structure of sequencing count data. It supports taxon-level differential abundance analysis, estimation of regression effects under censoring induced by detection limits, and inference based on rank-based methods that remain applicable in the presence of excess zeros. Functions are provided for model fitting, significance testing, extraction of effect estimates, and summarization of results across taxa. The package also provides optional bootstrap calibration of taxon-level p-values for sensitivity analysis in small-taxon settings.
A time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.
Provides functions for the estimation of the conditional Akaike
    information in generalized mixed-effect models fitted with (g)lmer() 
    from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'.
    For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.
Allows to plot a number of information related to the interpretation of Correspondence Analysis' results. It provides the facility to plot the contribution of rows and columns categories to the principal dimensions, the quality of points display on selected dimensions, the correlation of row and column categories to selected dimensions, etc. It also allows to assess which dimension(s) is important for the data structure interpretation by means of different statistics and tests. The package also offers the facility to plot the permuted distribution of the table total inertia as well as of the inertia accounted for by pairs of selected dimensions. Different facilities are also provided that aim to produce interpretation-oriented scatterplots. Reference: Alberti 2015 <doi:10.1016/j.softx.2015.07.001>.
R graphics device using cairographics library that can be used to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG,JPEG,TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.
Standard metrics converter and comparator for consumer electronics.
    Provides utility functions for converting battery capacity (mAh to Wh),
    comparing wall charger output times, and validating product specifications
    using standard formulas. Includes a sample dataset of electronic accessories
    compiled from CairoVolt's catalog.
Functions for performing experimental comparisons of algorithms 
             using adequate sample sizes for power and accuracy. Implements the 
             methodology originally presented in Campelo and Takahashi (2019) 
             <doi:10.1007/s10732-018-9396-7> 
             for the comparison of two algorithms, and later generalised in 
             Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.
Implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.
A first-principle, phylogeny-aware comparative genomics tool for 
             investigating associations between terms used to annotate genomic
             components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative 
             or rank variables such as number of cell types, genome size, or 
             density of specific genomic elements. See the project website for 
             more information, documentation and examples, and 
             <doi:10.1016/j.patter.2023.100728> for the full paper.
Fast 'C++'-backed tools for computing conspecific and total neighborhood basal area in mapped forest plots. Includes unweighted and distance-weighted neighborhoods, multiple radii, decay kernels, and basic edge correction. Outputs are model-ready covariates for forest competition, growth, and survival models, following neighborhood modeling workflows commonly used in spatial ecology (e.g., Hülsmann et al. 2024 <doi:10.1038/s41586-024-07118-4>).
An R implementation of the algorithms described in Reingold and
    Dershowitz (4th ed., Cambridge University Press, 2018) <doi:10.1017/9781107415058>,
    allowing conversion between many different calendar systems. Cultural and religious holidays
    from several calendars can be calculated.
Provides access to the 'Calcite Design System' 'javascript'
    components via integration with the 'htmltools' and 'shiny' packages.
    Pre-built and interactive components can be used to generate either
    static html or interactive web applications. Learn more about the
    'Calcite Design System' at
    <https://developers.arcgis.com/calcite-design-system/>.
Contains functions which can be used to calculate Pesticide Risk Metric 
    values in aquatic environments from concentrations of multiple pesticides with known species sensitive 
    distributions (SSDs). Pesticides provided by this package have all be validated
    however if the user has their own pesticides with SSD values they can append them
    to the pesticide_info table to include them in estimates.
Efficient C++ optimized functions for numerical and symbolic calculus as described in Guidotti (2022) <doi:10.18637/jss.v104.i05>. It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors.
This is a one-function package that will pass only unique values to a computationally-expensive function that returns an output of the same length as the input.
    In importing and working with tidy data, it is common to have index columns, often including time stamps that are far from unique. Some functions to work with these such as text conversion to other variable types (e.g. as.POSIXct()), various grep()-based functions, and often the cut() function are relatively slow when working with tens of millions of rows or more.
Provides function to create, read, write, and work with
    'iCalendar' files (which typically have '.ics' or '.ical' extensions),
    and the scheduling data, calendars and timelines of people,
    organisations and other entities that they represent.  'iCalendar' is
    an open standard for exchanging calendar and scheduling information
    between users and computers, described at <https://icalendar.org/>.
Contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.
Fork of 'calendR' R package to generate ready to print
  calendars with 'ggplot2' (see <https://r-coder.com/calendar-plot-r/>)
  with additional features (backwards compatible).
  'calendRio' provides a 'calendR()' function that serves as a drop-in
  replacement for the upstream version but allows for additional
  parameters unlocking extra functionality.
Contains greedy algorithms for coarse approximation linear functions.
Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) <doi:10.1093/aje/kwt312>.
Assess the calibration of an existing (i.e. previously developed) multistate
  model through calibration plots. 
  Calibration is assessed using one of three methods. 1) Calibration methods for 
  binary logistic regression models applied at a fixed time point in conjunction 
  with inverse probability of censoring weights. 2) Calibration methods for 
  multinomial logistic regression models applied at a fixed time point in conjunction 
  with inverse probability of censoring weights. 3) Pseudo-values estimated using 
  the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction
  with landmarking when required. These calibration plots evaluate the calibration 
  (in a validation cohort of interest) of the transition probabilities estimated from an 
  existing multistate model. While package development has focused on multistate 
  models, calibration plots can be produced for any model which utilises information 
  post baseline to update predictions (e.g. dynamic models); competing risks models; 
  or standard single outcome survival models, where predictions can be made at 
  any landmark time. Please see Pate et al. (2024) <doi:10.1002/sim.10094>
  and Pate et al. (2024) <https://alexpate30.github.io/calibmsm/articles/Overview.html>.
General optimisation and specific tools for the parameter estimation (i.e. calibration) of complex models, including stochastic ones. It implements generic functions that can be used for fitting any type of models, especially those with non-differentiable objective functions, with the same syntax as base::optim. 
  It supports multiple phases estimation (sequential parameter masking), constrained optimization (bounding box restrictions) and automatic parallel computation of numerical gradients. 
  Some common maximum likelihood estimation methods and automated construction of the objective function from simulated model outputs is provided.  
  See <https://roliveros-ramos.github.io/calibrar/> for more details.
Package for drawing calibrated scales with tick marks on (non-orthogonal) 
             variable vectors in scatterplots and biplots. Also provides some functions for biplot creation and
	     for multivariate analysis such as principal coordinate analysis.
Performs the calibration procedure proposed by Sung et al. (2018+) <arXiv:1806.01453>. This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest.
Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the 'survey' package is used for this purpose. It is also possible to use 'ReGenesees', which can be installed from <https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) <http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.
Package to assess the calibration of probabilistic classifiers using confidence bands for monotonic functions. Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests.  For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) <arXiv:2203.04065>.
Plots calibration curves and computes statistics for assessing calibration performance. See Lasai et al. (2025) <doi:10.48550/arXiv.2503.08389>, De Cock Campo (2023) <doi:10.48550/arXiv.2309.08559> and Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.
Performs Bayesian calibration of computer models as per
 Kennedy and O'Hagan 2001.  The package includes routines to find the
 hyperparameters and parameters; see the help page for stage1() for a
 worked example using the toy dataset.  A tutorial is provided in the
 calex.Rnw vignette; and a suite of especially simple one dimensional
 examples appears in inst/doc/one.dim/.
Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.
Assesses the quality of estimates made by complex sample designs,
  following the methodology developed by the National Institute of Statistics Chile (Household Survey Standard 2020, <https://www.ine.cl/docs/default-source/institucionalidad/buenas-pr%C3%A1cticas/clasificaciones-y-estandares/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-publicaci%C3%B3n-27022020.pdf>), (Economics Survey Standard 2024, <https://www.ine.gob.cl/docs/default-source/buenas-practicas/directrices-metodologicas/estandares/documentos/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-econ%C3%B3micas.pdf?sfvrsn=201fbeb9_2>)
  and by Economic Commission  for Latin America and 
  Caribbean (2020, <https://repositorio.cepal.org/bitstream/handle/11362/45681/1/S2000293_es.pdf>), (2024, <https://repositorio.cepal.org/server/api/core/bitstreams/f04569e6-4f38-42e7-a32b-e0b298e0ab9c/content>).
In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.
Compile inline 'C' code and easily call with
 automatically generated wrapper functions.  By allowing user-defined headers
 and compilation flags (preprocessor, compiler and linking flags) the user 
 can configure optimization options and linking to third party 
 libraries. Multiple functions may be defined in a single block of code - which 
 may be defined in a string or a path to a source file.
It is sometimes useful to perform a computation in a separate
    R process, without affecting the current R process at all.  This
    packages does exactly that.
Intended to analyse recordings from multiple microphones (e.g., backpack
  microphones in captive setting). It allows users to align recordings even if there is non-linear 
  drift of several minutes between them. A call detection and assignment pipeline can be used 
  to find vocalisations and assign them to the vocalising individuals (even if the vocalisation
  is picked up on multiple microphones). The tracing and measurement functions allow for detailed
  analysis of the vocalisations and filtering of noise. Finally, the package includes a function
  to run spectrographic cross correlation, which can be used to compare vocalisations. It also 
  includes multiple other functions related to analysis of vocal behaviour.
The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.
Implementations of canonical associative learning models,
    with tools to run experiment simulations, estimate model parameters,
    and compare model representations. Experiments and results are
    represented using S4 classes and methods.
Provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, 
 measurement invariance analysis, and assessment of latent mean differences of 
 equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the 'MatchIt' package [Ho et al. (2011) <doi:10.18637/jss.v042.i08>], 
 ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the 'lavaan' package [Rosseel (2012) <doi:10.18637/jss.v048.i02>], 
 providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) <doi:10.1002/hrdq.21452> 
 to support factor ratio testing and the list-and-delete procedure.
Implements methods for querying data from CalPASS using its API.
 CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.
Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.
Record and generate a 'gif' of your 'R' sessions plots. When creating 
    a visualization, there is inevitably iteration and refinement that occurs. 
    Automatically save the plots made to a specified directory, previewing them 
    as they would be saved. Then combine all plots generated into a 'gif'
    to show the plot refinement over time.
A tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) <doi:10.48550/arXiv.2505.20168>. Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the 'metafor' package <https://CRAN.R-project.org/package=metafor>.
Computes a composite year-on-year index for bank performance
    assessment using the CAMEL framework (Capital Adequacy, Asset Quality,
    Management Efficiency, Earnings, Liquidity). The multivariate
    weighting scheme employs factor analysis with robust covariance
    estimation to derive communality-based weights from the correlation
    matrix of CAMEL ratios.  Provides functions for index computation,
    visualization, and comparison across banks and time periods.The methodology is described in Ayimah et al. (2023a)
    <doi:10.9734/bpi/mono/978-81-19315-32-1> and Ayimah et al. (2023b)
    <https://ajtem.com/index.php/ajtem/article/view/53>.
Implements the board game 'CamelUp' for use in introductory statistics classes using a Shiny app.
Quick and easy access to datasets that let you replicate the
    empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and
    Applications" (ISBN: 9780521848053).The data are available as soon as you install
    and load the package (lazy-loading) as data frames. The documentation includes
    reference to chapter sections and page numbers where the datasets are used.
Collect  marketing data from Campaign Manager using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Explore and normalize American campaign finance
    data. Created by the Investigative Reporting Workshop to facilitate
    work on The Accountability Project, an effort to collect public data
    into a central, standard database that is more easily searched:
    <https://publicaccountability.org/>.
A generic, easy-to-use and intuitive
    pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on
    the R packages 'rxode2' and 'mrgsolve'. Campsis provides an
    abstraction layer over the underlying processes of defining a PK/PD
    model, assembling a custom dataset and running a simulation. The
    package has a strong dependency on the R package 'campsismod', which
    allows models to be read from and written to files, including through
    a JSON-based interface, and to be adapted further on the fly in the R
    environment. In addition, 'campsis' allows users to assemble datasets
    in an intuitive manner, including via a JSON-based interface to import
    Campsis datasets defined using formal JSON schemas distributed with
    the package. Once the dataset is ready, the package prepares the
    simulation, calls 'rxode2' or 'mrgsolve' (at the user's choice), and
    returns the results for the given model, dataset and desired
    simulation settings. The package itself is licensed under the GPL (>=
    3); the JSON schema files shipped in inst/extdata are licensed
    separately under the Creative Commons Attribution 4.0 International
    (CC BY 4.0).
A generic, easy-to-use and expandable implementation of a
    pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4
    class system. This package allows the user to read and write
    pharmacometric models from and to files, including a JSON-based
    interface to import Campsis models defined using a formal JSON schema
    distributed with the package. Models can be adapted further on the fly
    in the R environment using an intuitive API to add, modify or delete
    equations, ordinary differential equations (ODEs), model parameters or
    compartment properties (such as infusion duration or rate,
    bioavailability and initial values). The package also provides export
    facilities for use with the simulation packages 'rxode2' and
    'mrgsolve'. The package itself is licensed under the GPL (>= 3); the
    JSON schema file shipped in inst/extdata is licensed separately under
    the Creative Commons Attribution 4.0 International (CC BY 4.0).  This
    package is designed and intended to be used with the package
    'campsis', a PK/PD simulation platform built on top of 'rxode2' and
    'mrgsolve'.
Copernicus Atmosphere Monitoring Service (CAMS) radiations service 
    provides time series of global, direct, and diffuse irradiations on horizontal
    surface, and direct irradiation on normal plane for the actual weather 
    conditions as well as for clear-sky conditions.
    The geographical coverage is the field-of-view of the Meteosat satellite,
    roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage
    of data is from 2004-02-01 up to 2 days ago. Data are available with a time step
    ranging from 15 min to 1 month. For license terms and to create an account,
    please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.
Read and manipulate Camera Trap Data Packages ('Camtrap DP').
    'Camtrap DP' (<https://camtrap-dp.tdwg.org>) is a data exchange format
    for camera trap data. With 'camtrapdp' you can read, filter and
    transform data (including to Darwin Core) before further analysis in
    e.g. 'camtraptor' or 'camtrapR'.
Management and analysis of camera trap wildlife data through
    an integrated workflow. Provides functions for image/video
    organization and metadata extraction, species/individual
    identification. Creates detection histories for occupancy and spatial
    capture-recapture analyses, with support for multi-season studies.
    Includes tools for fitting community occupancy models in JAGS and
    NIMBLE, and an interactive dashboard for survey data visualization and
    analysis. Features visualization of species distributions and activity
    patterns, plus export capabilities for GIS and reports. Emphasizes
    automation and reproducibility while maintaining flexibility for
    different study designs.
Terrestrial maps with simplified topologies for Census Divisions,
    Agricultural Regions, Economic Regions, Federal Electoral Divisions and
    Provinces.
Simple and seamless access to a variety of 'StatCan' shapefiles for mapping Canadian provinces, regions, forward sortation areas, census divisions, and subdivisions using the popular 'ggplot2' package.
Provides functions to analyze the spatial distribution of
    biodiversity, in particular categorical analysis of neo- and paleo-endemism
    (CANAPE) as described in Mishler et al (2014) <doi:10.1038/ncomms5473>.
    'canaper' conducts statistical tests to determine the types of endemism that
    occur in a study area while accounting for the evolutionary relationships of
    species.
Integrated, convenient, and uniform access to Canadian
    Census data and geography retrieved using the 'CensusMapper' API. This package produces analysis-ready 
    tidy data frames and spatial data in multiple formats, as well as convenience functions
    for working with Census variables, variable hierarchies, and region selection. API
    keys are freely available with free registration at <https://censusmapper.ca/api>.
    Census data and boundary geometries are reproduced and distributed on an "as
    is" basis with the permission of Statistics Canada (Statistics Canada 1996; 2001; 2006;
    2011; 2016; 2021).
Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.
Functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network).  Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.
Predicts anticancer peptides using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI. The CancerGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/BioGenies/CancerGramModel>. For more information see: 
    Burdukiewicz et al. (2020) <doi:10.3390/pharmaceutics12111045>.
Classifies the type of cancer using routinely collected data
    commonly found in cancer registries from pathology reports. The
    package implements the International Classification of Diseases for
    Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type 
    <https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology>. 
    Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) <doi:10.1002/cncr.20910>.
    Adolescent and young adult cancer classification is based on Barr et al. (2020) <doi:10.1002/cncr.33041>.
Cancer RADAR is a project which aim is to develop an
    infrastructure that allows quantifying the risk of cancer by migration
    background across Europe.  This package contains a set of functions
    cancer registries partners should use to reshape 5 year-age group
    cancer incidence data into a set of summary statistics (see Boyle &
    Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data
    protections rules.
Functions for computing and visualizing 
	generalized canonical discriminant analyses and canonical correlation analysis
	for a multivariate linear model.
	Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA'
	design and is equivalent to canonical correlation analysis between a set of quantitative
	response variables and a set of dummy variables coded from the factor variable.
	The 'candisc' package generalizes this to higher-way 'MANOVA' designs
	for all factors in a multivariate linear model,
	computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) 
	visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are
	now provided for canonical correlation analysis when all predictors are quantitative. Methods for
	linear discriminant analysis are now included.
Non-linear/linear hybrid method for batch-effect correction
    that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between 
    datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.
Extra family objects in "weird" scenarios, particularly
    logistic or log-linear model with unbounded or non-binary/non-integer
    outcomes. Provides binomial_extra() and poisson_extra() as
    generalizations of binomial() and poisson(). The use of canonical link
    with the corresponding working likelihood in glm() ensures convexity,
    making model fitting reliable and independent of starting value.
    Robert WM Wedderburn (1974) <doi:10.1093/biomet/61.3.439> and Peter McCullagh (1983) <doi:10.1214/aos/1176346056> justified this method to fit generalized linear (mean) models with quasi-/working likelihood.
Chemical analysis of proteins based on their amino acid
  compositions. Amino acid compositions can be read from FASTA files and used to
  calculate chemical metrics including carbon oxidation state and stoichiometric
  hydration state, as described in Dick et al. (2020) <doi:10.5194/bg-17-6145-2020>. 
  Other properties that can be calculated include protein length, grand average of
  hydropathy (GRAVY), isoelectric point (pI), molecular weight (MW), standard
  molal volume (V0), and metabolic costs (Akashi and Gojobori, 2002
  <doi:10.1073/pnas.062526999>; Wagner, 2005 <doi:10.1093/molbev/msi126>;
  Zhang et al., 2018 <doi:10.1038/s41467-018-06461-1>). A database of amino acid
  compositions of human proteins derived from UniProt is provided.
Searches for, accesses, and retrieves Statistics Canada data 
    tables, as well as individual vectors, as tidy data frames.
    This package enriches the tables with metadata, deals
    with encoding issues, allows for bilingual English or French language data retrieval, and bundles
    convenience functions to make it easier to work with retrieved table data. For more efficient data
    access the package allows for caching data in a local database and database level filtering, data
    manipulation and summarizing.
Enables creation of visualizations using the CanvasXpress framework
    in R. CanvasXpress is a standalone JavaScript library for reproducible research
    with complete tracking of data and end-user modifications stored in a single
    PNG image that can be played back. See <https://www.canvasxpress.org> for more
    information.
Contains the prepared data that is needed for the 'shiny' application examples in the 
    'canvasXpress' package.  This package also includes datasets used for automated 'testthat' tests.
    Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>.
    Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.
Provides the official administrative boundaries of the Azores 
    (Região Autónoma dos Açores (RAA)) as defined in the 2024 edition of the 
    Carta Administrativa Oficial de Portugal (CAOP), published by the 
    Direção-Geral do Território (DGT). The package includes convenience
    functions to import these boundaries as 'sf' objects for spatial analysis in
    R.
    Source: <https://geo2.dgterritorio.gov.pt/caop/CAOP_RAA_2024-gpkg.zip>.
Functions for performing phylogenetic comparative analyses.
Information on activities to promote scholarships in Brazil and abroad for international mobility programs, recorded in Capes' computerized payment systems. The CAPES database refers to international mobility programs for the period from 2010 to 2019 <https://dadosabertos.capes.gov.br/dataset/>.
Provides simplified access to the data from the Catalog of Theses and Dissertations 
    of the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES, <https://catalogodeteses.capes.gov.br>) 
    for the years 1987 through 2022. The dataset includes variables such as Higher Education 
    Institution (institution), Area of Concentration (area), Graduate Program Name (program_name), 
    Type of Work (type), Language of Work (language), Author Identification (author), Abstract 
    (abstract), Advisor Identification (advisor), Development Region (region), State (state).
Provides Capital Budgeting Analysis functionality and the essential Annuity loan functions.
    Also computes Loan Amortization Schedules including schedules with irregular payments.
A toolkit for computing and visualizing CAPL-2
    (Canadian Assessment of Physical Literacy, Second Edition;
    <https://www.capl-eclp.ca>) scores and interpretations from raw data.
Performs Correspondence Analysis on the given dataframe and plots the results in a scatterplot that emphasizes the geometric interpretation aspect of the analysis, following Borg-Groenen (2005) and Yelland (2010). It is particularly useful for highlighting the relationships between a selected row (or column) category and the column (or row) categories. See Borg-Groenen (2005, ISBN:978-0-387-28981-6); Yelland (2010) <doi:10.3888/tmj.12-4>.
Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel 'Shiny' apps, 
    building upon on a previously published mathematical model written in C, 
    to ensure efficient computations. The underlying model is accessible
    here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>.
    The first application explores the fundamentals of Ca-Pi homeostasis, 
    while the second provides interactive case studies for in-depth exploration of the topic, 
    thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation.
Covariate Assisted Principal Regression (CAPR) for multiple 
    covariance-matrix outcomes. The method identifies (principal) projection 
    directions that maximize the log-likelihood of a log-linear regression model
    of the covariates. See Zhao et al. (2021), "Covariate Assisted Principal
    Regression for Covariance Matrix Outcomes" <doi:10.1093/biostatistics/kxz057>.
A comprehensive reproducibility framework designed for R and bioinformatics
    workflows. Automatically captures the entire analysis environment including R session
    info, package versions, external tool versions ('Samtools', 'STAR', 'BWA', etc.), 'conda'
    environments, reference genomes, data provenance with smart checksumming for large files,
    parameter choices, random seeds, and hardware specifications. Generates executable scripts
    with 'Docker', 'Singularity', and 'renv' configurations. Integrates with workflow managers
    ('Nextflow', 'Snakemake', 'WDL', 'CWL') to ensure complete reproducibility of computational
    research workflows.
Git hook scripts are useful for identifying simple issues before submission to code review. 'captain' (hook) is an R package to manage and run git pre-commit hooks.
Calibration of penalized criteria for model selection. The calibration methods available in this package are based on the slope heuristics.
Fast and user-friendly estimation of generalized linear
    models with multiple fixed effects and cluster the standard errors.
    The method to obtain the estimated fixed-effects coefficients is based
    on Stammann (2018) <doi:10.48550/arXiv.1707.01815>, Gaure (2013)
    <doi:10.1016/j.csda.2013.03.024>, Berge (2018)
    <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al.
    (2020) <doi: 10.1177/1536867X20909691>. This implementation is
    described in Vargas Sepulveda (2025)
    <doi:10.1371/journal.pone.0331178>.
Functions to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage, 2019.
Computer algebra via the 'SymPy' library (<https://www.sympy.org/>). 
  This makes it possible to solve equations symbolically, 
  find symbolic integrals, symbolic sums and other important quantities.
Tools for implementing covariate-adjusted response-adaptive procedures for binary, continuous and survival responses. Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references:
    Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007)<doi:10.1214/009053606000001424>
    Zhang, L. X. & Hu, F. (2009) <doi:10.1007/s11766-009-0001-6>
    Hu, J., Zhu, H., & Hu, F. (2015) <doi:10.1080/01621459.2014.903846>
    Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) <doi:10.1002/pst.2160>
    Mukherjee, A., Jana, S., & Coad, S. (2024) <doi:10.1177/09622802241287704>.
The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets
     that are optimal with regard to a multi-objective target (Monteil et al. <doi:10.5194/hess-24-3189-2020>).
Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. 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. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.
Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.
Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ)  that make up the 2001 definition of the  Greater Glasgow and Clyde health board.
Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by  random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) <doi:10.1016/j.sste.2014.05.001>. Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council  (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1.
Unified access to carbon market data from compliance
    emissions trading systems ('EU ETS', 'UK ETS', 'RGGI', California
    Cap-and-Trade) and voluntary carbon markets (Verra, Gold Standard,
    American Carbon Registry, Climate Action Reserve, via the Berkeley
    Voluntary Registry Offsets Database and the 'CarbonPlan' 'OffsetsDB'
    API). Includes cross-market price data from the 'International
    Carbon Action Partnership' ('ICAP') Allowance Price Explorer
    <https://icapcarbonaction.com/en/ets-prices>, global carbon pricing
    from the World Bank Carbon Pricing Dashboard
    <https://carbonpricingdashboard.worldbank.org/>, and the historical
    'RFF' World Carbon Pricing Database following Dolphin, Pollitt and
    Newbery (2020) <doi:10.1038/s41597-022-01659-x>. Data is downloaded
    from public sources on first use and cached locally.
Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). 
  Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.
Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data, as well as Scope 1 carbon emissions for UK farms. The 'carbonpredict' package provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. It also provides Scope 1 carbon emissions predictions for UK farms of types: Cereals ex. rice, Dairy, Mixed farming, Sheep and goats, Cattle & buffaloes, Poultry, Animal production and Support for crop production. The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publications: Phillpotts, A., Owen. A., Norman, J., Trendl, A.,  Gathergood, J., Jobst, Norbert., Leake, D. (2025) <doi:10.1111/jiec.70106> "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions" and Wells, J., Trendl, A., Owen, A., Barrett, J., Gridley, J., Jobst, N., Leake, D. (2025) <doi:10.1088/1748-9326/ae20ab> "A Scalable Tool for Farm-Level Carbon Accounting: Evidence from UK Agriculture".
Provides a flexible tool for calculating carbon-equivalent emissions. Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report <https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024>, it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for 'shiny' applications and community-driven extensions.
The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.
A collection of cardiovascular research datasets and analytical
		tools, including methods for cardiovascular procedural data, such as 
		electrocardiography, echocardiography, and catheterization data. Additional
		methods exist for analysis of procedural billing codes.
Responsive and modern 'HTML' card essentials for 'shiny' applications and dashboards. This novel card component in 'Bootstrap' provides a flexible and extensible content container with multiple variants and options for building robust 'R' based apps e.g for graph build or machine learning projects. The features rely on a combination of 'JQuery' <https://jquery.com> and 'CSS' styles to improve the card functionality.
Create self-contained SVG information cards with embedded 'Google 
    Fonts', shields-style badges, and custom logos. Cards are fully 
    portable SVG files ideal for dashboards, reports, and web applications.
    Includes functions to export cards to PNG format and display them in 
    'R Markdown' and 'Quarto' documents.
Datasets to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage (2019).
An algorithm developed to efficiently and accurately process complex and variable cardiac data with three key features: 1. employing autocorrelation to identify recurrent heartbeats and use their periods to compute heart rates; 2. incorporating a genetic algorithm framework to minimize data loss due to noise interference and accommodate within-sequence variations; and 3. introducing a tracking index as a moving reference to reduce errors. Lau, Wong, & Gu (2026) <https://ssrn.com/abstract=5153081>.
Identification of cardinal dates
             (begin, time of maximum, end of mass developments)
             in ecological time series using fitted Weibull functions.
A collection of functions to generate a large variety of
    structures in high dimensions. These data structures are useful for
    testing, validating, and improving algorithms used in dimensionality
    reduction, clustering, machine learning, and visualization.
Automated and robust framework for analyzing R-R interval (RRi) signals using advanced nonlinear modeling and preprocessing techniques. The package implements a dual-logistic model to capture the rapid drop and subsequent recovery of RRi during exercise, as described by Castillo-Aguilar et al. (2025) <doi:10.1038/s41598-025-93654-6>. In addition, 'CardioCurveR' includes tools for filtering RRi signals using zero-phase Butterworth low-pass filtering and for cleaning ectopic beats via adaptive outlier replacement using local regression and robust statistics. These integrated methods preserve the dynamic features of RRi signals and facilitate accurate cardiovascular monitoring and clinical research.
Offers a diverse collection of datasets focused on cardiovascular and heart disease research, including heart failure, myocardial infarction, aortic dissection, transplant outcomes, cardiovascular risk factors, drug efficacy, and mortality trends. 
    Designed for researchers, clinicians, epidemiologists, and data scientists, the package features clinical, epidemiological, and simulated datasets covering a wide range of conditions and treatments such as statins, anticoagulants, and beta blockers. 
    It supports analyses related to disease progression, treatment effects, rehospitalization, and public health outcomes across various cardiovascular patient populations.
Construct CDISC (Clinical Data Interchange Standards
    Consortium) compliant Analysis Results Data objects. These objects are
    used and re-used to construct summary tables, visualizations, and
    written reports. The package also exports utilities for working with
    these objects and creating new Analysis Results Data objects.
Create extra Analysis Results Data (ARD) summary objects.
    The package supplements the simple ARD functions from the 'cards'
    package, exporting functions to put statistical results in the ARD
    format. These objects are used and re-used to construct summary
    tables, visualizations, and written reports.
Implements the regression approach 
  of Zuber and Strimmer (2011) "High-dimensional regression and variable 
  selection using CAR scores" SAGMB 10: 34, <DOI:10.2202/1544-6115.1730>.
  CAR scores measure the correlation between the response and the 
  Mahalanobis-decorrelated  predictors.  The squared CAR score is a 
  natural measure of variable importance and provides a canonical 
  ordering of variables. This package provides functions for estimating 
  CAR scores, for variable selection using CAR scores, and for estimating 
  corresponding regression coefficients. Both shrinkage as well as 
  empirical estimators are available.
A simulation model and accompanying functions that support 
    assessing silvicultural concepts on the forest estate level with a focus on 
    the CO2 uptake by wood growth and CO2 emissions by forest operations. For 
    achieving this, a virtual forest estate area is split into the areas covered
    by typical phases of the silvicultural concept of interest. Given initial 
    area shares of these phases, the dynamics of these areas is simulated. The 
    typical carbon stocks and flows which are known for all phases are 
    attributed post-hoc to the areas and upscaled to the estate level. CO2 
    emissions by forest operations are estimated based on the amounts and 
    dimensions of the harvested timber. Probabilities of damage events are taken
    into account.
Provides API access to the Government of Canada Vehicle Recalls 
    Database <https://tc.api.canada.ca/en/detail?api=VRDB> used by the Defect 
    Investigations and Recalls Division for vehicles, tires, and child car 
    seats. The API wrapper provides access to recall summary information 
    searched using make, model, and year range, as well as detailed recall 
    information searched using recall number.
Given a patient-sharing network, calculate either the classic care density as
	proposed by Pollack et al. (2013) <doi:10.1007/s11606-012-2104-7> or the
	fragmented care density as proposed by Engels et al. (2024) <doi:10.1186/s12874-023-02106-0>.
	By utilizing the 'igraph' and 'data.table' packages, the provided functions scale well for
	very large graphs.
When taking online surveys, participants sometimes respond to items
    without regard to their content. These types of responses, referred to as 
    careless or insufficient effort responding, constitute significant problems 
    for data quality, leading to distortions in data analysis and hypothesis 
    testing, such as spurious correlations. The 'R' package 'careless' provides 
    solutions designed to detect such careless / insufficient effort responses 
    by allowing easy calculation of indices proposed in the literature. It 
    currently supports the calculation of longstring, even-odd consistency, 
    psychometric synonyms/antonyms, Mahalanobis distance, and intra-individual 
    response variability (also termed inter-item standard deviation). 
    For a review of these methods, see Curran (2016) <doi:10.1016/j.jesp.2015.07.006>.
Performs a Correspondence Analysis (CA) on a contingency table and creates a scatterplot of the row and column points on the selected dimensions. Optionally, the function can add segments to the plot to visualize significant associations between row and column categories on the basis of positive (unadjusted) standardized residuals larger than a given threshold.
Misc functions for training and plotting classification and
    regression models.
Functions for creating ensembles of caret models: caretList()
    and caretStack(). caretList() is a convenience function for fitting multiple
    caret::train() models to the same dataset. caretStack() will make linear or
    non-linear combinations of these models, using a caret::train() model as a
    meta-model.
Conformal time series forecasting using the caret infrastructure. 
  It provides access to state-of-the-art machine learning models for forecasting
  applications. The hyperparameter of each model is selected based on time 
  series cross-validation, and forecasting is done recursively.
Use machine learning algorithms and advanced geographic information system tools to
    build Species Distribution Modeling in a extensible and modern fashion.
We provide a toolbox to fit a continuous-time fractionally integrated ARMA process (CARFIMA) on univariate and irregularly spaced time series data via both frequentist and Bayesian machinery. A general-order CARFIMA(p, H, q) model for p>q is specified in Tsai and Chan (2005) <doi:10.1111/j.1467-9868.2005.00522.x> and it involves p+q+2 unknown model parameters, i.e., p AR parameters, q MA parameters, Hurst parameter H, and process uncertainty (standard deviation) sigma. Also, the model can account for heteroscedastic measurement errors, if the information about measurement error standard deviations is known. The package produces their maximum likelihood estimates and asymptotic uncertainties using a global optimizer called the differential evolution algorithm. It also produces posterior samples of the model parameters via Metropolis-Hastings within a Gibbs sampler equipped with adaptive Markov chain Monte Carlo. These fitting procedures, however, may produce numerical errors if p>2. The toolbox also contains a function to simulate discrete time series data from CARFIMA(p, H, q) process given the model parameters and observation times.
A framework is provided to develop R packages using 'Rust' <https://www.rust-lang.org/> with
 minimal overhead, and more wrappers are easily added. Help is provided to use 'Cargo' <https://doc.rust-lang.org/cargo/> in a manner
 consistent with CRAN policies. 'Rust' code can also be embedded directly in an R script. The package is not official, affiliated with,
 nor endorsed by the Rust project.
Estimation of population size of migratory caribou herds based on large scale 
             aggregations monitored by radio telemetry. It implements the methodology found 
             in the article by Rivest et al. (1998) about caribou abundance estimation. It 
             also includes a function based on the Lincoln-Petersen Index as applied to 
             radio telemetry data by White and Garrott (1990).
Includes commands for bootstrapping and permutation tests, a
    command for created grouped bar plots, and a demo of the
    quantile-normal plot for data drawn from different distributions.
Evaluation of the Carlson elliptic integrals and the
    incomplete elliptic integrals with complex arguments. The
    implementations use Carlson's algorithms <doi:10.1007/BF02198293>.
    Applications of elliptic integrals include probability distributions,
    geometry, physics, mechanics, electrodynamics, statistical mechanics,
    astronomy, geodesy, geodesics on conics, and magnetic field
    calculations.
In randomized controlled trial (RCT), balancing covariate is often one of the most important concern. CARM package provides functions to balance the covariates and generate allocation sequence by covariate-adjusted Adaptive Randomization via Mahalanobis-distance (ARM) for RCT. About what ARM is and how it works please see Y. Qin, Y. Li, W. Ma, H. Yang, and F. Hu (2024). "Adaptive randomization via Mahalanobis distance" Statistica Sinica. <doi:10.5705/ss.202020.0440>. In addition, the package is also suitable for the randomization process of multi-arm trials. For details, please see Yang H, Qin Y, Wang F, et al. (2023). "Balancing covariates in multi-arm trials via adaptive randomization" Computational Statistics & Data Analysis.<doi:10.1016/j.csda.2022.107642>.
'Stan' based functions to estimate CAR-MM models. These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix. The main references are Gramatica, Liverani and Congdon (2023) <doi:10.1214/23-BA1370>, Petrof, Neyens, Nuyts, Nackaerts, Nemery and Faes (2020) <doi:10.1002/sim.8697> and Gramatica, Congdon and Liverani <doi:10.1111/rssc.12480>.
Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation.  Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).
Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment".
The caroline R library contains dozens of functions useful
 for: database migration (dbWriteTable2), database style joins &
 aggregation (nerge, groupBy, & bestBy), data structure
 conversion (nv, tab2df), legend table making (sstable & leghead),
 automatic legend positioning for scatter and box plots (),  
 plot annotation (labsegs & mvlabs), data visualization 
 (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad),
 file I/O (write.delim), batch scripting, data exploration, and more.
 The package's greatest contributions lie in the database style merge, 
 aggregation and interface functions as well as in it's extensive
 use and propagation of row, column and vector names in most functions.
Mainly used to build tables that are commonly presented for
    bio-medical/health research, such as basic characteristic tables or
    descriptive statistics.
Sending functions to remote processes can be wasteful of
    resources because they carry their environments with them. With the
    carrier package, it is easy to create functions that are isolated from
    their environment. These isolated functions, also called crates, print
    at the console with their total size and can be easily tested locally
    before being sent to a remote.
Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.
Implements Competitive Adaptive Reweighted Sampling (CARS) 
    algorithm for variable selection from high-dimensional dataset using Partial 
    Least Squares (PLS) regression models. CARS algorithm iteratively applies the 
    Monte Carlo sub-sampling and exponential variable elimination techniques to 
    identify/select the most informative variables/features subjected to minimal 
    cross-validated RMSE score. The implementation of CARS algorithm is inspired 
    from the work of Li et al. (2009) <doi:10.1016/j.aca.2009.06.046>. 
    This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), 
    hyperspectral chemometrics areas, etc.
Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.
Functions to prepare and filter an origin-destination matrix for thematic flow mapping purposes.   
             This comes after Bahoken, Francoise (2016), Mapping flow matrix a contribution, PhD in Geography - Territorial sciences. See Bahoken (2017) <doi:10.4000/netcom.2565>.
Construct continuous and non-contiguous area cartograms.
Procedures for making continuous cartogram. Procedures available are:
     flow based cartogram (Gastner & Newman (2004) <doi:10.1073/pnas.0400280101>),
     fast flow based cartogram (Gastner, Seguy & More (2018) <doi:10.1073/pnas.1712674115>),
     rubber band based cartogram (Dougenik et al. (1985)
                        <doi:10.1111/j.0033-0124.1985.00075.x>).
A tool for easily matching spatial data when you have a list of
    place/region names. You might have a data frame that came from a
    spreadsheet tracking some data by suburb or state. This package can
    convert it into a spatial data frame ready for plotting. The actual map
    data is provided by other packages (or your own code).
Simplifying the creation of print-ready maps, this package offers a user-friendly interface derived from 'ggplot2' for handling OpenStreetMap data. It streamlines the map-making process, allowing users to focus on the story their maps tell. Transforming raw geospatial data into informative visualizations is made easy with simple features 'sf' geometries. Whether for urban planning, environmental studies, or impactful public presentations, this tool facilitates straightforward and effective map creation. Enhance the dissemination of spatial information with high-quality, narrative-driven visualizations!
Create and integrate maps in your R workflow. This package helps 
    to design cartographic representations such as proportional symbols, 
    choropleth, typology, flows or discontinuities maps. It also offers several 
    features that improve the graphic presentation of maps, for instance, map 
    palettes, layout elements (scale, north arrow, title...), labels or legends. 
    See Giraud and Lambert (2017) <doi:10.1007/978-3-319-57336-6_13>.
Monte Carlo simulation framework for different randomized clinical
    trial designs with a special emphasis on estimators based on covariate
    adjustment. The package implements regression-based covariate adjustment
    (Rosenblum & van der Laan (2010) <doi:10.2202/1557-4679.1138>) and a
    one-step estimator (Van Lancker et al (2024)
    <doi:10.48550/arXiv.2404.11150>) for trials with continuous, binary and
    count outcomes. The estimation of the minimum sample-size required to reach
    a specified statistical power for a given estimator uses bisection to find
    an initial rough estimate, followed by stochastic approximation
    (Robbins-Monro (1951) <doi:10.1214/aoms/1177729586>) to improve the
    estimate, and finally, a grid search to refine the estimate in the
    neighborhood of the current best solution.
A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.
Provides a cascade select widget for usage in 'Shiny'
    applications. This is useful for selection of hierarchical choices
    (e.g. continent, country, city). It is taken from the 'JavaScript'
    library 'PrimeReact'.
Apply styles to tag elements directly and with the .style
  pronoun. Using the pronoun, styles are created within the context of a tag
  element. Change borders, backgrounds, text, margins, layouts, and more.
Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. 
    In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.
It is an open source insurance claim simulation engine sponsored
    by the Casualty Actuarial Society. It generates individual insurance claims 
	including open claims, reopened claims, incurred but not reported claims 
	and future claims. It also includes claim data fitting functions to help set 
	simulation assumptions. It is useful for claim level reserving analysis.
	Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.
Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple 
    competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms 
    of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the 
    fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival 
    curves for a given covariate profile. This approach accommodates any log-linear hazard function of 
    prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide 
    a plot method for visualizing incidence density via population time plots. Based on the case-base sampling 
    approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, 
    and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.
Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. et al. (2002) <doi:10.1016/S0167-9473(02)00058-0>.
Cox model inference for relative hazard and covariate-specific pure risk estimated 
             from stratified and unstratified case-cohort data as described in 
             Etievant, L., Gail, M.H. (Lifetime Data Analysis, 2024) <doi:10.1007/s10985-024-09621-2>.
Allows users to identify similar cases for qualitative case studies using statistical matching methods.
Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).
Although discrete choice (choice-based conjoint) analysis has become a widely used 
        technique for the elicitation of consumer preferences and hence a foundation for product 
	design, to the best of our knowledge, there exists neither free and open-source nor 
	commercial software that covers the game-theoretic simulation of competitive reactions among 
	firms based on discrete choice models to improve decision making beyond traditional product 
	(line) optimization. The package does not only provide functions to fill this gap but 
	comprises an entire simulation pipeline including the upstream processes of discrete choice 
	analysis itself. It ranges from preference generation, choice design, design assessment, 
	error and response simulation, through hierarchical Bayesian estimation of mixed logit models 
	as well as convergence and model assessment, to Nash equilibrium computation. Doing so, it 
	partly draws from established packages concerned with discrete choice analysis. While its 
	structure generally aims towards end-to-end simulation as well as simulation of competitive 
	dynamics based on real data, all its key elements mentioned above may be of use independently 
	of each other. For implementation and application details, see Dressler et al. (2026) 
	<doi:10.48550/arXiv.2606.15593>.
Provides the datasets from Efron & Hastie (2016, ISBN: 9781108107952), "Computer Age Statistical Inference: Algorithms, Evidence, and Data Science", in an accessible R format for those who want to use them for study or to try to reproduce analyses from the book.
Perform evaluation of automatic subject 
  indexing methods. The main focus of the package is to enable efficient 
  computation of set retrieval and ranked retrieval metrics across multiple
  dimensions of a dataset, e.g. document strata or subsets of the label set. 
  The package also provides the possibility of computing bootstrap confidence
  intervals for all major metrics, with seamless integration of parallel
  computation and propensity scored variants of standard metrics.
Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.
Provides methods to deal with under sampling in ecological bipartite networks from
     Terry and Lewis (2020) Ecology <doi:10.1002/ecy.3047> Includes 
     tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely 
     missing links. Also includes simple functions to resample from observed networks to generate confidence 
     intervals for common ecological network metrics.
Computes a range of scatterplot diagnostics (scagnostics) on pairs
  of numerical variables in a data set. A range of scagnostics, including graph
  and association-based scagnostics described by Leland Wilkinson and Graham
  Wills (2008) <doi:10.1198/106186008X320465> and association-based
  scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be 
  computed. Summary and plotting functions are provided.
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 
    'caret' is a frequently used package for model training and prediction using machine learning. 
    CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. 
    It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate
    the performance of spatial prediction models and allows for spatial variable selection to selects
    suitable predictor variables in view to their contribution to the spatial model performance. 
    CAST further includes functionality to estimate the (spatial) area of applicability of prediction models.
    Methods are described in 
    Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; 
    Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; 
    Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; 
    Milà et al. (2022) <doi:10.1111/2041-210X.13851>; 
    Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; 
    Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>; 
    Schumacher et al. (2025) <doi:10.5194/gmd-18-10185-2025>. 
    The package is described in detail in Meyer et al. (2026) <doi:10.1007/978-3-031-99665-8_11>.
Estimate sample sizes needed to capture target levels of
    genetic diversity from a population (multivariate allele frequencies)
    for applications like germplasm conservation and breeding efforts.
    Compares bootstrap samples to a full population using linear
    regression, employing the R-squared value to represent the proportion
    of diversity captured. Iteratively increases sample size until a
    user-defined target R-squared is met. Offers a parallelized R
    implementation of a previously developed 'python' method. All ploidy
    levels are supported. For more details, see Sandercock et al. (2024) <doi:10.1073/pnas.2403505121>.
Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.
Performs analysis of categorical-variable with missing values. Implements methods from Schafer, JL, Analysis of Incomplete Multivariate Data, Chapman and Hall.
Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. 
    The main rule is to replicate the observation if it could be assigned to a few categories. 
    Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. 
    This procedure was invented and implemented in the paper by 'Nasinski', 'Majchrowska', and 'Broniatowska' (2020) <doi:10.24425/cejeme.2020.134747>.
Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022) <doi:10.1016/j.foodqual.2022.104564>. Consumers can also be clustered on their product-related hedonic responses; see Castura, Meyners, Pohjanheimo, Varela & Næs (2023) <doi:10.1111/joss.12860>. Permutation tests based on the L1-norm methods are provided; for details, see Chaya, Castura & Greenacre (2025) <doi:10.1016/j.foodqual.2025.105639>.
Analyzing responses to check-all-that-apply survey items often requires 
    data transformations and subjective decisions for combining categories. 'CATAcode'
    contains tools for exploring response patterns, facilitating data transformations, 
    applying a set of decision rules for coding responses, and summarizing response frequencies.
Gain access to the 'Spark Catalog' API making use of the 'sparklyr' API. 'Catalog' <https://spark.apache.org/docs/2.4.3/api/java/org/apache/spark/sql/catalog/Catalog.html> is the interface for managing a metastore (aka metadata catalog) of relational entities (e.g. database(s), tables, functions, table columns and temporary views).
To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).
Access public spatial data from the Spanish 'Catastro'
    through its 'INSPIRE' and related web services. Retrieve parcel,
    building, address and map image data, and convert between parcel
    references and coordinates.
Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.
Methods and utilities for testing, identifying, selecting and  
    mutating objects as categorical or continous types. These functions work on both 
    atomic vectors as well as recursive objects: data.frames, data.tables, 
    tibbles, lists, etc..
Categorical data analysis by AIC. The methodology is described in
 Sakamoto (1992) <ISBN 978-0-7923-1429-5>.
This R-package contains examples from the book "Regression for Categorical Data", Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.
Datasets used in the book "Categorical Data Analysis"
   by Agresti (2012, ISBN:978-0-470-46363-5) but not printed in the book.
   Datasets and help pages were automatically produced from the source
   <https://users.stat.ufl.edu/~aa/cda/data.html> by the R script foo.R,
   which can be found in the GitHub repository.
Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions, for 100 types of models of increasing complexity, and 72 likelihood models for the data.
Simple, fast, and automatic encodings for category data using 
             a data.table backend. Most of the methods are an implementation 
             of "Sufficient Representation for Categorical Variables" by
             Johannemann, Hadad, Athey, Wager (2019) <arXiv:1908.09874>,
             particularly their mean, sparse principal component analysis, 
             low rank representation, and multinomial logit encodings.
Provides a 'Shiny' application and supporting functions for the 
          design and analysis of consistency tests based on Kappa statistic with
          categorical responses. Wraps 'irr' and 'kappaSize' packages.
Contains some commonly used categorical variable encoders, such as 'LabelEncoder' and 'OneHotEncoder'. Inspired by the encoders implemented in Python 'sklearn.preprocessing' package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).
Includes wrapper functions around existing functions for the analysis of categorical data and introduces functions for calculating risk differences and matched odds ratios. R currently supports a wide variety of tools for the analysis of categorical data. However, many functions are spread across a variety of packages with differing syntax and poor compatibility with each another. prop_test() combines the functions binom.test(), prop.test() and BinomCI() into one output. prop_power() allows for power and sample size calculations for both balanced and unbalanced designs. riskdiff() is used for calculating risk differences and matched_or() is used for calculating matched odds ratios. For further information on methods used that are not documented in other packages see Nathan Mantel and William Haenszel (1959) <doi:10.1093/jnci/22.4.719> and Alan Agresti (2002) <ISBN:0-471-36093-7>.
Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models. Provides tools to analyse 'intraspecific' trait variability and its consequences for community assembly. Implements a framework using individual-level trait data to decompose variance at the population, species, and community levels. Methods are described in 'Taudiere' and 'Violle' (2016) <doi:10.1111/ecog.01433>.
Functions designed to simulate data that conform to basic
          unidimensional IRT models (for now 3-parameter binary response models
          and graded response models) along with Post-Hoc CAT simulations of
          those models given various item selection methods, ability estimation
          methods, and termination criteria. See
          Wainer (2000) <doi:10.4324/9781410605931>,
          van der Linden & Pashley (2010) <doi:10.1007/978-0-387-85461-8_1>,
          and Eggen (1999) <doi:10.1177/01466219922031365> for more details.
Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.
Methods and plotting functions for displaying categorical data on an 
             interactive heatmap using 'plotly'. Provides functionality for strictly 
             categorical heatmaps, heatmaps illustrating categorized continuous data 
             and annotated heatmaps. Also, there are various options to interact with the x-axis
             to prevent overlapping axis labels, e.g. via simple sliders or range sliders. 
             Besides the viewer pane, resulting plots can be saved as a standalone HTML file, 
             embedded in 'R Markdown' documents or in a 'Shiny' app.
Calculates equitable overload compensation for college instructors
    based on institutional policies, enrollment thresholds, and regular teaching load limits.
    Compensation is awarded only for credit hours that exceed the regular load and meet
    minimum enrollment criteria. When enrollment is below a specified threshold, pay is
    prorated accordingly. The package prioritizes compensation from high-enrollment courses,
    or optionally from low-enrollment courses for fairness, depending on user-defined strategy.
    Includes tools for flexible policy settings, instructor filtering, and produces
    clean, audit-ready summary tables suitable for payroll and administrative reporting.
Contains several basic utility functions including: moving
        (rolling, running) window statistic functions, read/write for
        GIF and ENVI binary files, fast calculation of AUC, LogitBoost
        classifier, base64 encoder/decoder, round-off-error-free sum
        and cumsum, etc.
Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio,  MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>.
Provides routines for the generation of response patterns under unidimensional dichotomous and polytomous computerized adaptive testing (CAT) framework. It holds many standard functions to estimate ability, select the first item(s) to administer and optimally select the next item, as well as several stopping rules. Options to control for item exposure and content balancing are also available (Magis and Barrada (2017) <doi:10.18637/jss.v076.c01>).
Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.
Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).
Cohort plAtform Trial Simulation whereby every cohort consists of two arms, 
             control and experimental treatment. Endpoints are co-primary binary endpoints and
             decisions are made using either Bayesian or frequentist decision rules. 
             Realistic trial trajectories are simulated and the operating characteristics of the 
             designs are calculated.
Provides the tools to produce catseye plots, principally
    by catseyesplot() function which calls R's standard plot() function internally, or alternatively
    by the catseyes() function to overlay the catseye plot onto an existing
    R plot window. Catseye plots illustrate the normal distribution of the mean (picture a 
    normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence
    interval; they are an intuitive way of illustrating and comparing normally distributed estimates,
    and are arguably a superior alternative to standard confidence intervals, since they show the full
    distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require
    pre-calculated means and standard errors (or standard deviations), provided as numeric vectors;
    this allows the flexibility of obtaining this information from a variety of sources, such as
    direct calculation or prediction from a model.  Catseye plots, as illustrations of the
    normal distribution of the means, are described in Cumming (2013 & 2014).
    Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.
Computes a structural similarity metric (after the style of
    MS-SSIM for images) for binary and categorical 2D and 3D images. Can be
    based on accuracy (simple matching), Cohen's kappa, Rand index, adjusted
    Rand index, Jaccard index, Dice index, normalized mutual information, or
    adjusted mutual information. In addition, has fast computation
    of Cohen's kappa, the Rand indices, and the two mutual informations.
    Implements the methods of Thompson and Maitra (2020) <doi:10.48550/arXiv.2004.09073>.
Provides methods of computerized adaptive testing for survey researchers.  See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, the Birnbaum three parameter model, the graded response, and the generalized partial credit model.  Additionally, includes several ability parameter estimation and item selection routines.  During item selection, all calculations are done in compiled C++ code.
This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.
Provides functions for computing the one-sided p-values of the Cochran-Armitage trend test
  statistic for the asymptotic and the exact conditional test. The computation of the p-value for the exact test
  is performed using an algorithm following an idea by Mehta, et al. (1992) <doi:10.2307/1390598>.
Tools for constructing, labeling, and visualizing Causal Assignment Trees
    (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna
    difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences
    (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs.
    not-yet-treated composition, and subgroup structure, and produces publication-quality
    diagrams and summary tables. Current functionality focuses on data-to-node mapping,
    node counts, cohort-year summaries, and high-quality tree plots suitable for empirical
    applications prior to estimation.
    Methods are based on Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>,
    Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>, and
    Kilanko (2026) <https://github.com/VictorKilanko/catviz>.
A new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.
Create, query, and modify causal graphs. 'caugi' (Causal Graph
    Interface) is a causality-first, high performance graph package that
    provides a simple interface to build, structure, and examine causal
    relationships.
The Cauchy Process can model pulsed continuous trait evolution
    on phylogenies. The likelihood is tractable, and is used for parameter
    inference and ancestral trait reconstruction.
    See Bastide and Didier (2023) <doi:10.1093/sysbio/syad053>.
Accelerate Bayesian analytics workflows in 'R' through interactive modelling,
    visualization, and inference. Define probabilistic graphical models using directed
    acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, 
    and programmers. This package relies on interfacing with the 'numpyro' python package.
We implement causal decomposition analysis using methods proposed by Park, Lee, and Qin (2022) and Park, Kang, and Lee (2023), which provide researchers with multiple-mediator imputation, single-mediator imputation, and product-of-coefficients regression approaches to estimate the initial disparity, disparity reduction, and disparity remaining (<doi:10.1177/00491241211067516>; <doi:10.1177/00811750231183711>). We also implement sensitivity analysis for causal decomposition using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023 <doi:10.1515/jci-2022-0031>). Finally, we include individualized causal decomposition and sensitivity analyses proposed by Park, Kang, and Lee (2025+) <doi:10.48550/arXiv.2506.19010>.
Software which provides numerous functionalities for detecting and removing group-level effects from high-dimensional scientific data which, when combined with additional assumptions, allow for causal conclusions, as-described in our manuscripts Bridgeford et al. (2024) <doi:10.1101/2021.09.03.458920> and Bridgeford et al. (2023) <doi:10.48550/arXiv.2307.13868>. Also provides a number of useful utilities for generating simulations and balancing covariates across multiple groups/batches of data via matching and propensity trimming for more than two groups.
Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.
Example data sets to run the example
    problems from causal inference textbooks. Currently, contains data
    sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" <https://theeffectbook.net>, first and second edition,
    Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", 
    and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" 
    <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.
Implements Le Cam deficiency theory for causal inference,
    as described in Akdemir (2026) <doi:10.5281/zenodo.18367347>. Provides
    theorem-backed bounds together with computable proxy diagnostics for
    information loss from confounding, selection bias, and distributional
    shift. Supports continuous, binary, count, survival, and competing risks
    outcomes. Key features include propensity-score total-variation deficiency
    proxies, negative control diagnostics, policy regret bounds, and
    sensitivity analysis via confounding frontiers.
Tools for causal structure learning from observational data, with emphasis on temporally
    ordered variables. The package implements the Temporal Peter–Clark (TPC) algorithm
    (Petersen, Osler & Ekstrøm, 2021; <doi:10.1093/aje/kwab087>), the Temporal Greedy
    Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen, 2025; <doi:10.48550/arXiv.2502.06232>)
    and Temporal Fast Causal Inference (TFCI). It provides a unified framework for specifying background
    knowledge, which can be incorporated into the implemented algorithms from the R packages 'bnlearn'
    (Scutari, 2010; <doi:10.18637/jss.v035.i03>) and 'pcalg' (Kalish et al., 2012; <doi:10.18637/jss.v047.i11>),
    as well as the Java library 'Tetrad' (Scheines et al., 1998; <doi:10.1207/s15327906mbr3301_3>).
    The package further includes utilities for visualization, comparison, and evaluation of graph
    structures, facilitating performance evaluation and methodological studies.
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments.  A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.
Causal Distillation Tree (CDT) is a novel machine learning method 
    for estimating interpretable subgroups with heterogeneous treatment effects. 
    CDT allows researchers to fit any machine learning model (or metalearner) to 
    estimate heterogeneous treatment effects for each individual, and then 
    "distills" these predicted heterogeneous treatment effects into 
    interpretable subgroups by fitting an ordinary decision tree to predict the
    previously-estimated heterogeneous treatment effects. This package 
    provides tools to estimate causal distillation trees (CDT), as detailed in
    Huang, Tang, and Kenney (2025) <doi:10.48550/arXiv.2502.07275>.
Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
Provides a unified workflow for running, classifying, visualizing,
    and interpreting sensitivity analyses for unmeasured confounding across
    multiple causal frameworks. Introduces the Causal Fragility Index (CFI),
    a single 0-100 composite score that integrates evidence from the partial
    R-squared robustness value approach (Cinelli and Hazlett, 2020,
    <doi:10.1111/rssb.12348>), E-value metrics (VanderWeele and Ding, 2017,
    <doi:10.7326/M16-2607>), and the Impact Threshold for a Confounding
    Variable (Frank, 2000, <doi:10.1177/0049124100029002001>) into one
    interpretable measure of robustness. The package also provides
    template-based plain-language narrative interpretation and
    publication-ready reporting, with optional integration with the
    'confoundvis' package for sensitivity plots.
Provides a framework for estimating causal effects of a continuous 
    exposure using observational data, and implementing matching and weighting 
    on the generalized propensity score.
    Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. 
    Matching on generalized propensity scores with continuous exposures. Journal 
    of the American Statistical Association, pp.1-29.
Draws causal hypergraph plots from models output by configurational comparative methods such as Coincidence Analysis (CNA) or Qualitative Comparative Analysis (QCA).
Implements a Bayesian approach to causal impact estimation in time
  series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
  See the package documentation on GitHub
  <https://google.github.io/CausalImpact/> to get started.
Infers the causal effect of an intervention on a multivariate response through the use of Multivariate 
    Bayesian Structural Time Series models (MBSTS) as described in Menchetti & Bojinov (2020) <arXiv:2006.12269>. 
    The package also includes functions for model building and forecasting.
Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2025) <doi:10.1017/rsm.2025.5> for a detailed guide on using the package.
The presence of a heavy tail is a feature of many scenarios when risk management
            involves extremely rare events. While parametric distributions may give adequate
            representation of the mode of data, they are likely to misrepresent heavy tails,
            and completely nonparametric approaches lack a rigorous mechanism for
            tail extrapolation; see Pickands (1975) <doi:10.1214/aos/1176343003>. The
            package 'CausalMixGPD' implements tools for Bayesian analysis of heavy-tailed
            outcomes by combining Dirichlet process mixture models for the body of the
            distribution with optional generalized Pareto tails. The method allows
            for unconditional and covariate-modulated mixtures, implements MCMC estimation
            using 'nimble', and extends to mixtures of different arms' outcomes with
            application to causal inference in the Rubin (1974)
            <doi:10.1037/h0037350> framework. Posterior summaries include density
            functions, quantiles, expected values, survival functions, and causal effects,
            with an emphasis on tail quantiles and functional measures sensitive to the
            tail.
Provides an array of statistical models common in causal inference such as 
  standardization, IP weighting, propensity matching, outcome regression, and doubly-robust 
  estimators. Estimates of the average treatment effects from each model are given with the 
  standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://miguelhernan.org/whatifbook/>).
Enumerate orientation-consistent directed networks from an undirected
    or partially directed skeleton, detect feedback loops, summarize topology,
    and simulate node dynamics via stochastic differential equations.
When causal quantities are not identifiable from the observed data, it still may be possible 
            to bound these quantities using the observed data. We outline a class of problems for which the 
            derivation of tight bounds is always a linear programming problem and can therefore, at least 
            theoretically, be solved using a symbolic linear optimizer. We extend and generalize the 
            approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide 
            a user friendly graphical interface for setting up such problems via directed acyclic 
            graphs (DAG), which only allow for problems within this class to be depicted. The user can 
            then define linear constraints to further refine their assumptions to meet their specific 
            problem, and then specify a causal query using a text interface. The program converts this 
            user defined DAG, query, and constraints, and returns tight bounds. The bounds can be 
            converted to R functions to evaluate them for specific datasets, and to latex code for 
            publication. The methods and proofs of tightness and validity of the bounds are described in
            a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) 
            <doi:10.1080/10618600.2022.2071905>.
Uses optimal transport distances to find probabilistic 
    matching estimators for causal inference.
    These methods are described in Dunipace, Eric (2021) <doi:10.48550/arXiv.2109.01991>.
    The package will build the weights, estimate treatment effects, and
    calculate confidence intervals via the methods described in the paper.
    The package also supports several other methods as described in the help 
    files.
Calculates population attributable fraction causal effects.
    The 'causalPAF' package contains a suite of functions for causal
    analysis calculations of population attributable fractions (PAF) given
    a causal diagram which apply both: Pathway-specific population
    attributable fractions (PS-PAFs) O’Connell and Ferguson (2022)
    <doi:10.1093/ije/dyac079> and Sequential population attributable
    fractions Ferguson, O’Connell, and O’Donnell (2020)
    <doi:10.1186/s13690-020-00442-x>.  Results are presentable in both
    table and plot format.
Creates publication-ready causal diagrams using 'ggplot2'.
  Provides simple templates for common causal diagrams (e.g., mediating
  mechanisms and parallel pathways) with customizable labels, colors,
  fonts, and export-friendly defaults.
Implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable 
    estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, 
    regression discontinuity, and difference-in-differences designs.
Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries.  Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<DOI: 10.1017/9781316718636>) and Pearl, 2009 Causality (<DOI:10.1017/CBO9780511803161>).
An implementation of methods for causal discovery in a structural causal model where the conditional distribution of the target node is described by a generalized linear model conditional on its causal parents.
The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.
Several causal effects are measured using least squares regressions and basis function approximations. Backward and forward selection methods based on different criteria are used to select the basis functions.
Estimates nonlinear causal dose-response functions for continuous
    treatments using spline-based methods under standard causal assumptions
    (unconfoundedness / ignorability). Implements three identification
    strategies: Inverse Probability Weighting (IPW) via the generalised
    propensity score (GPS), G-computation (outcome regression), and a
    doubly-robust combination. Natural cubic splines and B-splines are
    supported for both the exposure-response curve f(T) and the propensity
    nuisance model. Pointwise confidence bands are obtained via the sandwich
    estimator or nonparametric bootstrap. Also provides fragility diagnostics
    including pointwise curvature-based fragility, uncertainty-normalised
    fragility, and regional integration over user-defined treatment intervals.
    Builds on the framework of Hirano and Imbens (2004)
    <doi:10.1111/j.1468-0262.2004.00481.x> for continuous treatments and
    extends it to fully nonparametric spline estimation.
Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects based on different identification strategies (unconfoundedness, instruments, difference-in-differences, regression discontinuity designs).
Based on “Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis” (M. Even and J. Josse, 2025), this package provides implementations of three approaches - nearest neighbor matching, distributional regression forests, and efficient influence functions - to estimate the causal win ratio, win proportion, and net benefit.
This tool performs pairwise correlation analysis and estimate causality.
    Particularly, it is useful for detecting the metabolites that would be altered by the gut bacteria.
While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).
Comprehensive suite of Granger causality tests for time series
    and panel data. For time series: Toda-Yamamoto (1995)
    <doi:10.1016/0304-4076(94)01616-8>, Fourier-based tests with single
    frequency (Enders and Jones, 2016) <doi:10.1515/snde-2014-0101> and
    cumulative frequencies (Nazlioglu et al., 2019)
    <doi:10.1080/1540496X.2018.1434072>, quantile causality tests (Cai et al.,
    2023) <doi:10.1016/j.frl.2023.104327>, and Bootstrap Fourier Granger
    Causality in Quantiles (Cheng et al., 2021)
    <doi:10.1007/s12076-020-00263-0>. For panel data: Panel Fourier
    Toda-Yamamoto (Yilanci and Gorus, 2020)
    <doi:10.1007/s11356-020-10092-9> and Panel Quantile Causality tests
    (Wang and Nguyen, 2022) <doi:10.1080/1331677X.2021.1952089>, as well as
    Group-Mean and Pooled Fully Modified OLS estimators for panel
    cointegrating polynomial regressions (Wagner and Reichold, 2023)
    <doi:10.1080/07474938.2023.2178141>. All tests include bootstrap
    inference for robust p-values.
Design and use of control charts for detecting mean changes 
 based on a delayed updating of the in-control parameter estimates.
 See Capizzi and Masarotto (2019) <doi:10.1080/00224065.2019.1640096> for the
 description of the method.
Provides six variants of two-way correspondence analysis (ca):
   simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca,
   singly ordered non symmetrical ca, and doubly ordered non symmetrical
   ca.
Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is
             implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates.
             An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since 
             pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids
             clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.
Implements algorithms for analyzing Cayley graphs of permutation groups,
    with a focus on the TopSpin puzzle and similar permutation-based combinatorial
    puzzles. Provides methods for cycle detection, state space exploration,
    bidirectional BFS pathfinding, and finding optimal operation sequences
    in permutation groups generated by shift and reverse operations.
    Includes C++ implementations of core operations via 'Rcpp' for performance.
    Optional GPU acceleration via 'ggmlR' Vulkan backend for batch distance
    calculations and parallel state transformations.
Provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. 
  Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.
Implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.
Simulation of the stochastic 3D structure model for the nanoporous binder-conductive additive phase in battery cathodes introduced in P. Gräfensteiner, M. Osenberg, A. Hilger, N. Bohn, J. R. Binder, I. Manke, V. Schmidt, M. Neumann (2024) <doi:10.48550/arXiv.2409.11080>. The model is developed for a binder-conductive additive phase of consisting of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains.
A set of functions that helps you to generate descriptive statistics based on the variable types.
Fit multiclass Classification version of Bayesian Adaptive Smoothing Splines (CBASS) to data using reversible jump MCMC. The multiclass classification problem consists of a response variable that takes on unordered categorical values with at least three levels, and a set of inputs for each response variable. The CBASS model consists of a latent multivariate probit formulation, and the means of the latent Gaussian random variables are specified using adaptive regression splines. The MCMC alternates updates of the latent Gaussian variables and the spline parameters. All the spline parameters (variables, signs, knots, number of interactions), including the number of basis functions used to model each latent mean, are inferred. Functions are provided to process inputs, initialize the chain, run the chain, and make predictions. Predictions are made on a probabilistic basis, where, for a given input, the probabilities of each categorical value are produced. See Marrs and Francom (2023) "Multiclass classification using Bayesian multivariate adaptive regression splines" Under review.
Tools to process CBASS-derived PAM data efficiently. Minimal requirements are PAM-based photosynthetic efficiency data (or data from any other continuous variable that changes with temperature, e.g. relative bleaching scores) from 4 coral samples (nubbins) subjected to 4 temperature profiles of at least 2 colonies from 1 coral species from 1 site. Please refer to the following CBASS (Coral Bleaching Automated Stress System) papers for in-depth information regarding CBASS acute thermal stress assays, experimental design considerations, and ED5/ED50/ED95 thermal parameters: Nicolas R. Evensen et al. (2023) <doi:10.1002/lom3.10555> Christian R. Voolstra et al. (2020) <doi:10.1111/gcb.15148> Christian R. Voolstra et al. (2025) <doi:10.1146/annurev-marine-032223-024511>.
Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete beta-binomial with continuous size parameter and continuous support with x in [0, size + 1].
Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.
Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, frequency-based designs, and D-optimal designs, as well as "labeled" designs (also known as "alternative-specific designs"), designs with "no choice" options, and designs with dominant alternatives removed. Conveniently inspect and compare designs using a variety of metrics, including design balance, overlap, and D-error, and simulate choice data for a survey design either randomly or according to a utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Bayesian D-efficient designs using the 'cea' and 'modfed' methods are obtained using the 'idefix' package by Traets et al (2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the 'logitr' package by Helveston (2023) <doi:10.18637/jss.v105.i10>.
Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete binomial with continuous size parameter and continuous support with x in [0, size + 1], following Ilienko (2013) <arXiv:1303.5990>.
Provides R users with direct access to genomic and clinical data from the 'cBioPortal' web resource via user-friendly
    functions that wrap 'cBioPortal's' existing API endpoints <https://www.cbioportal.org/api/swagger-ui/index.html>. Users can browse and query genomic data on mutations,
    copy number alterations and fusions, as well as data on tumor mutational burden ('TMB'),
    microsatellite instability status ('MSI'), 'FACETS' and select clinical data points (depending on the study). 
    See <https://www.cbioportal.org/> and Gao et al., (2013) <doi:10.1126/scisignal.2004088> for more information on the cBioPortal web resource.
Methods for learning causal relationships among a set of
    foreground variables X based on signals from a (potentially much
    larger) set of background variables Z, which are known non-descendants
    of X. The confounder blanket learner (CBL) uses sparse regression
    techniques to simultaneously perform many conditional independence
    tests, with complementary pairs stability selection to guarantee
    finite sample error control. CBL is sound and complete with respect to
    a so-called "lazy oracle", and works with both linear and nonlinear
    systems. For details, see Watson & Silva (2022) <arXiv:2205.05715>.
A function that facilitates fitting three types of models 
    for contrast-based Bayesian Network Meta Analysis.  The first model is that which
    is described in Lu and Ades (2006) <doi:10.1198/016214505000001302>.  The other two 
    models are based on a Bayesian nonparametric methods that permit ties when comparing 
    treatment or for a treatment effect to be exactly equal to zero. In addition to the 
    model fits, the package provides a summary of the interplay  between treatment 
    effects based on the procedure described in Barrientos, Page, and Lin (2023)
    <doi:10.48550/arXiv.2207.06561>.
Provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method that incorporates correlation structures within the data. This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) <doi:10.1007/s11222-008-9088-5> and Algamal and Lee (2015) <doi:10.1016/j.eswa.2015.08.016>.
Implements the covariate balancing propensity score (CBPS) proposed
    by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is
    estimated such that it maximizes the resulting covariate balance as well as the
    prediction of treatment assignment. The method, therefore, avoids an iteration
    between model fitting and balance checking.  The package also implements optimal
    CBPS from Fan et al. (in-press) <DOI:10.1080/07350015.2021.2002159>,  
    several extensions of the CBPS beyond the cross-sectional, binary treatment setting.
    They include the CBPS for longitudinal settings so that it can be used in 
    conjunction with marginal structural models from Imai and Ratkovic (2015) 
    <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment 
    variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) 
    <DOI:10.1214/17-AOAS1101>, propensity score estimation with a large number of 
    covariates from Ning, Peng, and Imai (2020) <DOI:10.1093/biomet/asaa020>, and the situation 
    with multiple distinct binary treatments administered simultaneously. In the future 
    it will be extended to other settings including the generalization of experimental 
    and instrumental variable estimates.
Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.
The Central Bank of the Republic of Turkey (CBRT) provides one of 
  the most comprehensive time series databases on the Turkish economy. The 'CBRT' 
  package provides functions for accessing the CBRT's electronic data delivery 
  system <https://evds3.tcmb.gov.tr/>. It contains the lists of all data 
  categories and data groups for searching the available variables (data series).  
  As of February 17, 2026, there were 47,986 variables in the dataset. The lists 
  of data categories and data groups can be updated by the user at any time. A 
  specific variable, a group of variables, or all variables in a data group can 
  be downloaded at different frequencies using a variety of aggregation methods.
The data and meta data from Statistics
    Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses
    the open data API of Statistics Netherlands.
Uses monotonically constrained Cubic Bezier Splines (CBS) to approximate latent utility functions in intertemporal choice and risky choice data. For more information, see Lee, Glaze, Bradlow, and Kable <doi:10.1007/s11336-020-09723-4>.
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 for real estate and other domains 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>).
The Confidence Bound Target (CBT) algorithm is designed for infinite arms bandit problem. It is shown that CBT algorithm achieves the regret lower bound for general reward distributions. Reference: Hock Peng Chan and Shouri Hu (2018) <arXiv:1805.11793>.
A simple runner for fuzz-testing functions in an R package's
    public interface. Fuzz testing helps identify functions lacking sufficient
    argument validation, and uncovers problematic inputs that, while valid by
    function signature, may cause issues within the function body.
Tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing.  The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals.  Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology.
Provides a set of functions that extend the
        'cancor' function with new numerical and graphical outputs. It
        also include a regularized extension of the canonical
        correlation analysis to deal with datasets with more variables
        than observations.
Loads and creates spatial data, including layers and tools that are relevant
    to the activities of the Commission for the Conservation of Antarctic Marine Living 
    Resources. Provides two categories of functions: load functions and create functions.
    Load functions are used to import existing spatial layers from the online CCAMLR GIS
    such as the ASD boundaries. Create functions are used to create layers from user data
    such as polygons and grids.
Canonical correlation analysis and maximum correlation via
    projection pursuit, as well as fast implementations of correlation
    estimators, with a focus on robust and nonparametric methods.
Canonical correlation analysis (CCA) via reduced-rank regression with support for regularization and cross-validation. Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed  to be smooth on the graph. For more details see Donnat and Tuzhilina (2024)  <doi:10.48550/arXiv.2405.19539> and Wu, Tuzhilina and Donnat (2025) <doi:10.48550/arXiv.2507.11160>.
Class Cover Catch Digraphs, neighborhood graphs, and
        relatives.
Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.
Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <https://cvxopt.org> for more information).
Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) <doi:10.1002/9781118032923> and Carrasco et al. (2013) <doi:10.1016/j.cmpb.2012.09.002>.
Maximum likelihood estimation of the Cauchy-Cacoullos (discrete Cauchy) distribution. Probability mass, distribution and quantile function are also included. The reference paper is: Papadatos N. (2022). "The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. <doi:10.1007/s42519-022-00268-6>.
Access chemical, hazard, bioactivity, and exposure data from the 
   Computational Toxicology and Exposure ('CTX') APIs 
   <https://api-ccte.epa.gov/docs/>. 'ccdR' was developed to streamline the 
   process of accessing the information available through the 'CTX' APIs 
   without requiring prior knowledge of how to use APIs. Most data is also 
   available on the CompTox Chemical Dashboard ('CCD') 
   <https://comptox.epa.gov/dashboard/> and other resources found at the 
   EPA Computational Toxicology and Exposure Online Resources 
   <https://www.epa.gov/comptox-tools>.
Contains a function, also called 'cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.
Supporting the use of the Canadian Community Health Survey 
             (CCHS) by transforming variables from each cycle into harmonized, 
             consistent versions that span survey cycles (currently, 2001 to 
             2018). CCHS data used in this library is accessed and adapted in 
             accordance to the Statistics Canada Open Licence Agreement. This 
             package uses rec_with_table(), which was developed from 'sjmisc' 
             rec(). Lüdecke D (2018). "sjmisc: Data and Variable Transformation 
             Functions". Journal of Open Source Software, 3(26), 754. 
             <doi:10.21105/joss.00754>.
Tool for performing computational testing for conditional independence between variables in a dataset. 'CCI' implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) <doi:10.3390/a17080323>.
Provides efficient implementation of the Cross-Covariance
    Isolate Detect (CCID) methodology for the estimation of the number
    and location of multiple change-points in the second-order
    (cross-covariance or network) structure of multivariate, possibly
    high-dimensional time series. The method is motivated by the detection
    of change points in functional connectivity networks for functional
    magnetic resonance imaging (fMRI), electroencephalography (EEG),
    magentoencephalography (MEG) and electrocorticography (ECoG) data. The
    main routines in the package have been extensively tested on fMRI data. 
    For details on the CCID methodology, please see Anastasiou et
    al (2022), Cross-covariance isolate detect: A new change-point method for
    estimating dynamic functional connectivity. Medical Image Analysis, Volume
    75.
Implementation of case-control data analysis using likelihood ratio
    approaches and logistic regression for the classification of variants of
    uncertain significance (VUS) in breast, ovarian, or custom cancer
    susceptibility genes.
Convex Clustering methods, including K-means algorithm,
  On-line Update algorithm (Hard Competitive Learning) and Neural Gas
  algorithm (Soft Competitive Learning), and calculation of several
  indexes for finding the number of clusters in a data set.
Provides tools for performing consensus clustering on multiple 
    imputed datasets. The package supports a range of clustering algorithms 
    across imputations, including hierarchical methods (e.g., Ward, single, 
    complete, average) and partition-based approaches such as k-means, 
    k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'), 
    and methods for mixed or categorical data (k-modes and k-prototypes). 
    A co-assignment matrix is constructed to quantify agreement between 
    partitions, and consensus solutions are derived via hierarchical 
    clustering applied to the resulting dissimilarity matrix. Additional 
    functions are provided for validation and visualization of clustering 
    results, facilitating robust analysis in the presence of missing data.
    Consensus clustering framework is based on Monti et al. (2003) 
    <doi:10.1023/A:1023949509487>, rank aggregation methods follow 
    Pihur et al. (2007) <doi:10.1093/bioinformatics/btm158>, and the 
    PAC (Proportion of Ambiguous Clustering) metric is based on 
    Senbabaoglu et al. (2014) <doi:10.1038/srep06207>.
Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.
Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.
Implements the convex clustering through majorization-minimization (CCMM) algorithm described in Touw, Groenen, and Terada (2022) <doi:10.48550/arXiv.2211.01877> to perform minimization of the convex clustering loss function.
Provides an implementation of Congruence Class Models (CCMs) for 
    generating  networks. For additional details on CCMs see Goyal, Blitzstein, 
    and De Gruttola (2014) <doi:10.1017/nws.2014.2> and 
    Goyal, De Gruttola, Martin, Rennert, and Onnela <doi:10.48550/arXiv.2603.02467>.
    'ccmnet' facilitates sampling networks based on specific topological properties
    and attribute mixing patterns using a Markov Chain Monte Carlo framework. 
    The implementation builds upon code from the 'ergm' package; 
    see Handcock, Hunter, Butts, Goodreau, and Morris (2008) <doi:10.18637/jss.v024.i01>.
Provides a set of functions to perform queries against the 'CCM' API <https://mohcontacttracing.my.salesforce.com>.
Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses 
             the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The 
             user can select between replacement and without replacement, the number of controls, and several covariates 
             to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.
Implementation of estimators for inferring 
    the mean of censored cost data. Including the estimators
    BT from Bang and Tsiatis (2000) <doi:10.1093/biomet/87.2.329> and 
    ZT from Zhao and Tian (2001) <doi:10.1111/j.0006-341X.2001.01002.x>.
Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method.
With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.
Implements a method for identifying and removing
				the cell-cycle effect from scRNA-Seq data. The description of the 
				method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing 
				the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. 
				Different from previous methods, ccRemover implements a mechanism that
				formally tests whether a component is cell-cycle related or not, and thus
				while it often thoroughly removes the cell-cycle effect, it preserves
				other features/signals of interest in the data.
A set of radiative transfer models to quantitatively describe the absorption, reflectance and transmission of solar energy in vegetation, and model remotely sensed spectral signatures of vegetation at distinct spatial scales (leaf,canopy and stand). The main principle behind ccrtm is that many radiative transfer models can form a coupled chain, basically models that feed into each other in a linked chain (from leaf, to canopy, to stand, to atmosphere). It allows the simulation of spectral datasets in the solar spectrum (400-2500nm) using leaf models as PROSPECT5, 5b, and D which can be coupled with canopy models as 'FLIM', 'SAIL' and 'SAIL2'. Currently, only a simple atmospheric model ('skyl') is implemented. Jacquemoud et al 2008 provide the most comprehensive overview of these models <doi:10.1016/j.rse.2008.01.026>.
Provides a tool for matching ICD-10 codes to corresponding Clinical Classification Software Refined (CCSR) codes. The main function, CCSRfind(), identifies each CCSR code that applies to an individual given their diagnosis codes. It also provides a summary of CCSR codes that are matched to a dataset. The package contains 3 datasets: 'DXCCSR' (mapping of ICD-10 codes to CCSR codes), 'Legend' (conversion of DXCCSR to CCSRfind-usable format for CCSR codes with less than or equal to 1000 ICD-10 diagnosis codes), and 'LegendExtend' (conversion of DXCCSR to CCSRfind-usable format for CCSR codes with more than 1000 ICD-10 dx codes). The disc() function applies grepl() ('base') to multiple columns and is used in CCSRfind().
Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.
CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.
A simple interface for multivariate correlation analysis that unifies various classical
  statistical procedures including t-tests, tests in univariate and multivariate linear models,
  parametric and nonparametric tests for correlation, Kruskal-Wallis tests, common approximate
  versions of Wilcoxon rank-sum and signed rank tests, chi-squared tests of independence, score
  tests of particular hypotheses in generalized linear models, canonical correlation analysis and
  linear discriminant analysis.
Implements the uniform scaled beta distribution and
  the continuous convolution kernel density estimator.
Automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. 
 In addition, a 'shiny' App is provided, allowing users to analyze their data using an interactive graphical user interface, without any programming requirements.
Duplicated music data (pre-processed and formatted) for entity resolution. The total size of the data set is 9763. There are respective gold standard records that are labeled and can be considered as a unique identifier.
Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. 
    The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> .
    This package introduces the idea of explicit control table specification of data transforms.
    Works on in-memory data or on remote data using 'rquery' and 'SQL' database interfaces.
Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2025; <doi:10.48550/arXiv.2405.17290>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).
Calculation of sex- and age-standardized growth metrics 
    based on the 2000 CDC growth charts. Provides functions to generate 
    z-scores and percentiles for weight, height, and body mass index (BMI) 
    using the LMS method (lambda-mu-sigma). Includes extended BMI z-scores 
    for values above the 95th percentile to more accurately characterize 
    the sex- and age-standardized BMI of children with very high BMIs.
A session-based engine for cognitive diagnostic computerized
    adaptive testing (CD-CAT), the application of adaptive testing to
    cognitive diagnosis models. Three models are supported: the deterministic
    inputs, noisy "and" gate (DINA), the deterministic inputs, noisy "or"
    gate (DINO), and the generalized DINA (GDINA) model. Item selection
    criteria include Kullback-Leibler (KL) information, posterior-weighted
    Kullback-Leibler (PWKL), modified posterior-weighted Kullback-Leibler
    (MPWKL), and Shannon entropy (SHE). Latent attribute profiles are
    estimated by maximum likelihood estimation (MLE), maximum a posteriori
    (MAP), or expected a posteriori (EAP). Content balancing, item exposure
    control, and shadow testing are configurable through constraint
    functions. The implemented methods follow Cheng (2009)
    <doi:10.1007/s11336-009-9123-2> and de la Torre (2011)
    <doi:10.1007/s11336-011-9207-7>. Designed for real-time, item-by-item
    adaptive applications.
Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.
Allows users to seamlessly query several 'CDC PLACES' APIs (<https://data.cdc.gov/browse?q=PLACES%20&sortBy=relevance>)
    by geography, state, measure, and release year. This package also contains a
    function to explore the available measures for each release year.
Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <https://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.
Connect to the California Data Exchange Center (CDEC) 
    Web Service <http://cdec.water.ca.gov/>. 'CDEC' provides a centralized 
    database to store, process, and exchange real-time hydrologic information 
    gathered by various cooperators throughout California. The 'CDEC' Web Service 
    <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download 
    service for accessing historical records.
Implements the Centroid Decision Forest (CDF) as a single user-facing
  function CDF(). The method selects discriminative features via a multi-class
  class separability score (CSS), splits by nearest class centroid, and aggregates
  tree votes to produce predictions and class probabilities. Returns CSS-based
  feature importance as well. Amjad Ali, Saeed Aldahmani, Zardad Khan (2025) <doi:10.48550/arXiv.2503.19306>.
Estimation of one- and two-sided confidence intervals 
    via the numerical inversion of the cumulative distribution function
    of a statistic's sampling distribution. For more details, see
    section 9.2.3 of Casella and Berger (2002) <ISBN:0534243126>.
Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.
Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.
Estimates the causal decompositions of group disparities developed by Yu and Elwert (2025) <doi:10.1214/24-AOAS1990>. For the nuisance functions of the estimators, we provide both parametric and nonparametric options, as well as manual options in case the default models are not satisfying.
Estimates hidden Markov models from the family of Cholesky-decomposed Gaussian hidden Markov models (CDGHMM) under various missingness schemes. This family improves upon estimation of traditional Gaussian HMMs by introducing parsimony, as well as, controlling for dropped out observations and non-random missingness. See Neal, Sochaniwsky and McNicholas (2024) <DOI:10.1007/s11222-024-10462-0>.
Extends the 'did' package to improve efficiency and handling of unbalanced panel data. Bellego, Benatia, and Dortet-Bernadet (2024), "The Chained Difference-in-Differences", Journal of Econometrics, <doi:10.1016/j.jeconom.2024.105783>.
Convert MacArthur-Bates Communicative Development Inventory
    Words and Gestures scores to would-be scores on Words
    and Sentences, based on modeling from the
    Stanford Wordbank <https://wordbank.stanford.edu/>.
    See Day et al. (2025) <doi:10.1111/desc.70036>.
The concept of cause-deleted life expectancy improvement is statistic designed to quantify the increase in life expectancy if a certain cause of death is removed. See Adamic, P. (2015) (<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2689352>).
Provides tools to simulate categorical raster data, including
    objects from the 'terra' package. Functions focus on generating edge pixel
    values from reclassified raster data derived from the United States
    Department of Agriculture (USDA) Cropland Data Layer products.
Downloads USDA National Agricultural Statistics Service (NASS) 
    cropscape data for a specified state. Utilities for fips, abbreviation, 
    and name conversion are also provided. Full functionality requires an 
    internet connection, but data sets can be cached for later off-line use.
Functions for cognitive diagnosis modeling and multidimensional item response modeling 
    for dichotomous and polytomous item responses. This package enables the estimation of 
    the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>),
    the multiple group (polytomous) GDINA model (de la Torre, 2011, 
    <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, 
    <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, 
    <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, 
    <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis 
    (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). 
    See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> 
    or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>)     
    for further details on estimation and the package structure.
    For tutorials on how to use the CDM package see 
    George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as
    Ravand and Robitzsch (2015).
Provides tools for working with observational health data in the 
  Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax.
  Common data model database table references are stored in a single compound object along with metadata.
Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.
Provides tools for modeling time-to-event data with a cure fraction under correlated
    destructive negative binomial cure rate models. The models assume multiple latent competing 
    causes with possible dependence and allow for elimination (inactivation) of some initial causes.
    Estimation is performed via an Expectation-Maximization algorithm, and diagnostic tools based 
    on Cox-Snell residuals are provided.
Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).
Parallel coordinate plotting with resolutions for large data sets
 and missing values.
A wrapper for the 'CDRC' 'API' that returns data frames or 'sf' of 'CDRC' data. The 'API' web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.
In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the 'CDsampling' package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.
Provides interface to the 'Copernicus Data Space Ecosystem' API 
    <https://dataspace.copernicus.eu/analyse/apis>, mainly for searching the catalog of available
    data from Copernicus Sentinel missions and obtaining the images for just the area of interest 
    based on selected spectral bands. The package uses the 'Sentinel Hub' REST API interface
    <https://dataspace.copernicus.eu/analyse/apis/sentinel-hub> that provides access to various 
    satellite imagery archives. It allows you to access raw satellite data, rendered images, 
    statistical analysis, and other features. 
    This package is in no way officially related to or endorsed by Copernicus.
Generate synthetic station-based monthly climate time-series including
    temperature and rainfall, export to Network Common Data Form (NetCDF), 
    and provide visualization helpers for climate workflows. The approach is 
    inspired by statistical weather generator concepts described in Wilks (1999)
    <doi:10.1016/S0168-1923(99)00037-4> and Richardson (1981) <doi:10.1029/WR017i001p00182>.
Deriving skill structures from skill assignment 
    data for courses (sets of learning objects).
Cuddy-Della valle index gives the degree of instability present in the data by accommodating the effect of a trend. The adjusted R squared value of the best fitted model is chosen. The index is obtained by multiplying the coefficient of variation with square root of one minus the adjusted R-squared value. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.
Provides tools for trial-based economic evaluation of healthcare interventions. 
    Computes and visualizes incremental cost-effectiveness ratios, 
    cost-effectiveness acceptability curves, cost-effectiveness planes, 
    net monetary benefit tables, and one-way deterministic sensitivity analyses. 
    Supports cost-utility analyses using observed summaries and non-parametric 
    bootstrap uncertainty.
Measuring cellular energetics is essential to understanding a
    matrix’s (e.g. cell, tissue or biofluid) metabolic state. The Agilent
    Seahorse machine is a common method to measure real-time cellular
    energetics, but existing analysis tools are highly manual or lack
    functionality. The Cellular Energetics Analysis Software (ceas) R package
    fills this analytical gap by providing modular and automated Seahorse data
    analysis and visualization using the methods described by Mookerjee et al.
    (2017) <doi:10.1074/jbc.m116.774471>.
Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) <https://www.cebl.ca> to create a repository of basic and advanced statistics for teams and players.
Splits data into Gaussian type clusters using the Cross-Entropy 
    Clustering ('CEC') method. This method allows for the simultaneous use of 
    various types of Gaussian mixture models, for performing the reduction of 
    unnecessary clusters, and for discovering new clusters by splitting them. 
    'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) 
    <doi:10.1016/j.patcog.2014.03.006>.
Provides analytical methods for analyzing CRISPR screen data
    at different levels of gene expression. Multi-component normal mixture models
    and EM algorithms are used for modeling.
Implements the Capability-Ecological Developmental Model
    (CEDM) for longitudinal and multilevel data. The package supports
    estimation and interpretation of models examining how socioeconomic
    status (SES), health indicators, and contextual factors jointly relate
    to academic outcomes. Functionality includes: (1) classification of
    ecological capability regimes (amplifying, neutral, compensatory);
    (2) estimation of moderated multilevel models with higher-order
    interaction terms; (3) causal mediation analysis using doubly robust
    estimation; (4) random-effects within-between (REWB) decomposition;
    (5) nonlinear moderation using restricted cubic splines; (6) clustering
    of longitudinal health trajectories; and (7) sensitivity analysis using
    the impact threshold for a confounding variable (ITCV) and
    robustness-to-replacement (RIR) measures. The package is designed for
    use with general longitudinal multilevel datasets.
Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Model building, surrogate model
    based optimization and Efficient Global Optimization in combinatorial
    or mixed search spaces.
Contains a number of common astronomy utility functions for cosmology and angular coordinates.
Pool cells together before running differentially expression (DE) analysis. 
 Tell 'CellDEEP' how many cells you want to pool together (which shall be determined 
 by the overall cell number of data), then run DE analysis.
 Cheng et al. (2026) <doi:10.64898/2026.03.09.710522>.
Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology <doi:10.64898/2026.01.24.701240>.
Data from statistical agencies and other institutions often
    need to be protected before they can be published. This package 
    can be used to perturb statistical tables in a consistent way. The
    main idea is to add - at the micro data level - a record key for each
    unit. Based on these keys, for any cell in a statistical table a
    cell key is computed as a function on the record keys contributing to
    a specific cell. Values that are added to the cell in order to perturb
    it are derived from a lookup-table that maps values of cell keys to 
    specific perturbation values. The theoretical basis for the methods 
    implemented can be found in Thompson, Broadfoot and Elazar 
    (2013) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2013/Topic_1_ABS.pdf>
    which was extended and enhanced by Giessing and Tent 
    (2019) <https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S2_Germany_Giessing_Tent_AD.pdf>.
Provides functions to generate frequency tables and apply cell key
    perturbation to protect against statistical disclosure in tabular outputs.
    The implemented methods are described in "Cell Key Perturbation User Guide"
    <https://github.com/ONSdigital/cell-key-perturbation-R/blob/main/documentation/SML_UserDoc_CKP_R.md>.
    Developed at the UK Office for National Statistics.
Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.
Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.
Methods for analyzing (cell) motion in two or three dimensions.
	Available measures include displacement, confinement ratio, autocorrelation,
	straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks,
	steps, or subtracks with varying length. While the methodology has been developed for
	cell trajectory analysis, it is applicable to anything that moves including animals,
	people, or vehicles.
	Some of the methodology implemented in this packages was described by: 
	Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, 
	Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>,
	Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>,
	Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>,
	Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>,
	Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>,
  Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>,
  Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.
Implements a methodology for using cell volume
        distributions to estimate cell growth rates and division times
        that is described in the paper, "Cell Volume Distributions
        Reveal Cell Growth Rates and Division Times", by Michael
        Halter, John T. Elliott, Joseph B. Hubbard, Alessandro Tona
        and Anne L. Plant, which appeared in the Journal of Theoretical
        Biology. In order to reproduce the analysis used to obtain
        Table 1 in the paper, execute the command
        "example(fitVolDist)".
Provides a 'Seurat'-compatible toolkit for marker gene identification,
    expression summarization, and visualization of annotated single-cell
    transcriptomic data. 'CellWindX' identifies top cell-type-enriched markers,
    calculates marker expression percentages and average expression values across
    cell groups, and generates publication-oriented dimensional reduction plots,
    marker heatmaps, and gene-level radar plots. The package includes built-in
    aesthetic palettes and supports both exploratory analysis and downstream figure
    preparation for single-cell atlas studies. The workflow is designed to
    complement single-cell analysis frameworks such as 'Seurat' described by
    Satija et al. (2015) <doi:10.1038/nbt.3192> and Hao et al. (2021)
    <doi:10.1016/j.cell.2021.04.048>, as well as heatmap visualization methods
    implemented in 'ComplexHeatmap' described by Gu et al. (2016)
    <doi:10.1093/bioinformatics/btw313>.
Tools for detecting cellwise outliers and robust methods to analyze
    data which may contain them. Contains the implementation of the algorithms described in
    Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access)
    Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access),
    Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access),
    Raymaekers and Rousseeuw (2022) <doi:10.1080/01621459.2023.2267777> (open access)
    Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access).
    Examples can be found in the vignettes:
    "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples",
    "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples",
    and "cellwise_weights_examples".
Implementation of the Coarsened Exact Matching algorithm discussed 
	along with its properties in
  Iacus, King, Porro (2011) <DOI:10.1198/jasa.2011.tm09599>;
	Iacus, King, Porro (2012) <DOI:10.1093/pan/mpr013> and
	Iacus, King, Porro (2019) <DOI:10.1017/pan.2018.29>.
'CemCO' algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).
Broken adaptive ridge estimator for censored data is used to
  select variables and estimate their coefficients in the semi-parametric 
  accelerated failure time model for right-censored survival data.
The network analysis plays an important role in numerous application domains including biomedicine. 
             Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. 
	           The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities. 
	           Reference: 
             Ren, M., Zhang S. and Wang J. (2022). "Consistent Estimation of the Number of Communities via Regularized Network Embedding". Biometrics, <doi:10.1111/biom.13815>.
Implementation of Tobit type I and type II families for censored regression using the 'mgcv' package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.
Contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent 
             receiver operating characteristic curve and the corresponding area under the receiver 
             operating characteristic curve and the optimal cutoff point for the right and interval 
             censored survival data. See Beyene and El Ghouch (2020)<doi:10.1002/sim.8671> and Beyene 
             and El Ghouch (2022) <doi:10.1002/bimj.202000382>.
Creates a common framework for organizing, naming, and gathering 
    population, age, race, and ethnicity data from the Census Bureau. Accesses 
    the API <https://www.census.gov/data/developers/data-sets.html>. Provides 
    tools for adding information to existing data to line up with Census data.
It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.
Provides tools to extract, label, and read microdata from the
    2022 National Census of Population, Households and Dwellings of Argentina
    stored in REDATAM databases officially distributed by INDEC. Implements
    a complete province-by-province extraction pipeline with efficient memory
    management, reconstruction of hierarchical identifiers, automatic variable
    labeling from official INDEC dictionaries, and integrity verification
    against published totals. Allows working with census data directly in R
    without knowledge of REDATAM syntax, and supports export to multiple
    formats including Parquet, CSV, SPSS and SAS. Census data must be
    downloaded directly from the official INDEC portal
    (<https://www.indec.gob.ar>). This package does not distribute census data.
    Duran (2026) <doi:10.5281/zenodo.19560728>.
Easy access to data from Brazil's population censuses. The package
             provides a simple and efficient way to download and read the data 
             sets and the documentation of all the population censuses taken in 
             and after 1960 in the country. The package is built on top of the 
             'Arrow' platform <https://arrow.apache.org/docs/r/>,  which allows 
             users to work with larger-than-memory census data using 'dplyr' 
             familiar functions. <https://arrow.apache.org/docs/r/articles/arrow.html#analyzing-arrow-data-with-dplyr>.
Implement an interval censor method 
 to break ties when using data with ties to fitting a 
 bivariate copula.
Engines for survival models from the 'parsnip' package. These
    include parametric models (e.g., Jackson (2016)
    <doi:10.18637/jss.v070.i08>), semi-parametric (e.g., Simon et al
    (2011) <doi:10.18637/jss.v039.i05>), and tree-based models (e.g.,
    Buehlmann and Hothorn (2007) <doi:10.1214/07-STS242>).
Tools for estimating censored Almost Ideal (AI) and Quadratic Almost Ideal (QUAI) demand systems using Maximum Likelihood Estimation (MLE). It includes functions for calculating demand share equations and the truncated log-likelihood function for a system of equations, incorporating demographic variables. The package is designed to handle censored data, where some observations may be zero due to non-purchase of certain goods. Package also contains a procedure to approximate demand elasticities numerically and estimate standard errors via Delta Method. It is particularly useful for applied researchers analyzing household consumption data.
Maximum Likelihood estimation of censored regression (Tobit) models
   with cross-sectional and panel data.
Functions for fitting univariate linear regression models under Scale Mixtures of Skew-Normal (SMSN) distributions, considering left, right or interval censoring and missing responses. Estimation is performed via an EM-type algorithm. Includes selection criteria, sample generation and envelope. For details, see Gil, Y.A., Garay, A.M., and Lachos, V.H. (2025) 
    <doi:10.1007/s10260-025-00797-x>.
It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.
Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.
A wrapper for the U.S. Census Bureau APIs that returns data frames of 
	Census data and metadata. Available datasets include the 
	Decennial Census, American Community Survey, Small Area Health Insurance Estimates,
	Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.
Provides harmonized and non-harmonized population pyramid datasets from the Indonesian population censuses (1971–2020), along with tools for visualization and an interactive 'shiny'-based explorer application. Data are processed from IPUMS International (1971–2010) and the Population Census 2020 (BPS Indonesia).
Generates skeletons of closed 2D polygons using Voronoi diagrams. 
            It provides methods for 'sf', 'terra', and 'geos' objects to 
            compute polygon centerlines based on the generated skeletons.
            Voronoi, G. (1908) <doi:10.1515/crll.1908.134.198>.
Calculates centrality indices additional to the 'igraph' package centrality functions.
Generate mean and median weighted or unweighted spatial centers. 
    Functions are analogous to their identically named counterparts within 
    'ArcGIS Pro'. Median center methodology based off of Kuhn and Kuenne 
    (1962) <doi:10.1111/j.1467-9787.1962.tb00902.x>.
Find the numbers of test tubes that can be balanced in centrifuge
    rotors and show various ways to load them. Refer to Pham (2020)
    <doi:10.31224/osf.io/4xs38> for more information on package functionality.
Easy and convenient access to the datasets of the
  "Centre d'Estudis d'Opinio", the Catalan institution for polling and public
  opinion. The package retrieves microdata directly from the open data platform
  of the Generalitat de Catalunya and returns it in a tidy format.
Optimization solver based on the Cross-Entropy method.
It aims to find significant pathways through 
    network topology information. It has several advantages compared 
    with current pathway enrichment tools. First, pathway node instead 
    of single gene is taken as the basic unit when analysing networks 
    to meet the fact that genes must be constructed into complexes to 
    hold normal functions. Second, multiple network centrality measures are 
    applied simultaneously to measure importance of nodes from different 
    aspects to make a full view on the biological system. CePa extends 
    standard pathway enrichment methods, which include both over-representation 
    analysis procedure and gene-set analysis procedure. 
    <doi:10.1093/bioinformatics/btt008>.
Explore metadata and retrieve indicators from the statistical portal of the Economic Commission for Latin America and the Caribbean <https://statistics.cepal.org/portal/cepalstat/>.
        Provides a reproducible interface to access the 'CEPALSTAT API', organize data into tidy formats, and generate both static and interactive visualizations.
Provides data on countries and their main city or agglomeration and
    the different distance measures and dummy variables indicating whether two
    countries are contiguous, share a common language or a colonial
    relationship. The reference article for these datasets is Mayer and Zignago
    (2011) <http://www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877>.
Provides a simple algorithm to generate a continuous epidemiological week index from date variables in a dataframe. Weeks are computed as sequential 7-day intervals starting from the earliest observed date. They do not reset at calendar year boundaries and are not ISO 8601 nor MMWR calendar weeks. The approach is intended for epidemiological modeling and time-series analysis where temporal continuity is required. The generated weeks are sequential and do not reset at calendar year boundaries.
Functions and Data to support Context Driven Exploratory Projection Pursuit.
Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros 
    e tal. (Returns info of Brazilian postal codes, city names, addresses 
    and so on.)
Read Condensed Cornell Ecology Program ('CEP') and legacy
    'CANOCO' files into R data frames.
Modeling associations between covariates and power spectra of replicated time series using a cepstral-based semiparametric framework. Implements a fast two-stage estimation procedure via Whittle likelihood and multivariate regression.The methodology is based on Li and Dong (2025) <doi:10.1080/10618600.2025.2473936>.
Provides functions and data files to help CE Public-Use
    Microdata (PUMD) users calculate annual estimated expenditure means,
    standard errors, and quantiles according to the methods used by the CE with
    PUMD. For more information on the CE please visit <https://www.bls.gov/cex>.
    For further reading on CE estimate calculations please see the CE
    Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of
    Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For
    further information about CE PUMD please visit
    <https://www.bls.gov/cex/pumd.htm>.
Perform censored quantile regression of Huang (2010) <doi:10.1214/09-AOS771>, and restore monotonicity respecting via adaptive interpolation for dynamic regression of Huang (2017) <doi:10.1080/01621459.2016.1149070>. The monotonicity-respecting restoration applies to general dynamic regression models including (uncensored or censored) quantile regression model, additive hazards model, and dynamic survival models of Peng and Huang (2007) <doi:10.1093/biomet/asm058>, among others.
Fetch Australian Clean Energy Regulator data on carbon
    credits, safeguard mechanism facilities, renewable energy
    certificates, and greenhouse gas reporting. Provides tidy access
    to the Australian Carbon Credit Unit ('ACCU') Scheme project
    register, Safeguard Mechanism baselines and covered emissions,
    Large-scale Renewable Energy Target ('LRET') power station
    accreditations, Small-scale Renewable Energy Scheme ('SRES')
    installation data, the National Greenhouse and Energy Reporting
    ('NGER') scheme, and Quarterly Carbon Market Reports
    <https://cer.gov.au/markets/reports-and-data>. Includes a
    post-Chubb ACCU integrity layer (Chubb 2022 Independent Review),
    Safeguard reform handling (declining industry baselines from
    July 2023), National Greenhouse and Energy Reporting scope
    discipline (Scope 1 / Scope 2 market vs location / Climate
    Active), reconciliation against the Quarterly Carbon Market
    Report, and reproducibility helpers (snapshot pinning,
    SHA-256 cache integrity, session manifest, optional Zenodo
    deposit). Data is published by the Clean Energy Regulator
    under a Creative Commons Attribution 4.0 International licence.
Download imagery tiles to a standard cache and load the data into raster objects. 
 Facilities for 'AWS' terrain <https://registry.opendata.aws/terrain-tiles/> terrain and 'Mapbox' 
 <https://www.mapbox.com/> servers are provided.
Estimates sugar beet canopy closure with remotely sensed leaf area
    index and estimates when action might be needed to protect the crop from a 
    Leaf Spot epidemic with a negative prognosis model based on published 
    models.
The 'vctrs' package provides a concept of vector prototype
    that can be especially useful when deploying models and code.  Serialize 
    these object prototypes to 'JSON' so they can be used to check and coerce 
    data in production systems, and deserialize 'JSON' back to the correct 
    object prototypes.
Fits a Causal Effect Random Forest of Interaction Trees (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see L. Li, R. A. Levine, and J. Fan (2022) <doi:10.1002/sta4.457>.
Implements the iterated RMCD method of Cerioli (2010)
	for multivariate outlier detection via robust Mahalanobis distances. Also
	provides the finite-sample RMCD method discussed in the paper, as well as 
	the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> 
	and Green and Martin (2017) <https://christopherggreen.github.io/papers/hr05_extension.pdf>.
	See also Chapter 2 of Green (2017) <https://digital.lib.washington.edu/researchworks/handle/1773/40304>.
Provides several functions to identify and analyse miRNA sponge, including
             popular methods for identifying miRNA sponge interactions, two types 
             of global ceRNA regulation prediction methods and four types of context-specific 
             prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based 
             on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, 
             respectively. In addition, For predictive ceRNA relationship pairs, this package 
             provides several downstream analysis algorithms, including regulatory network 
             analysis and functional annotation analysis, as well as survival prognosis analysis
             based on expression of ceRNA ternary pair.
Compute the certainty equivalents and premium risks
              as tools for risk-efficiency analysis. For more technical information, please refer to: Hardaker, Richardson, Lien, & Schumann (2004) <doi:10.1111/j.1467-8489.2004.00239.x>, and Richardson, & Outlaw (2008) <doi:10.2495/RISK080231>.
Utilize the 'shiny' interface for visualizing results from a 'pyDarwin' (<https://certara.github.io/pyDarwin/>)
    machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models,
    allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and
    tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an
    R Markdown document and rendered in various output formats.
Utilize the 'shiny' interface to generate Goodness of Fit (GOF) plots and tables
      for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface,
      users can customize model diagnostics and generate the underlying R code to reproduce the 
      diagnostic plots and tables outside of the 'shiny' session. Model diagnostics can be included
      in a 'rmarkdown' document and rendered to desired output format.
Perform Nonlinear Mixed-Effects (NLME) Modeling using Certara's
    NLME-Engine. Access the same Maximum Likelihood engines used in the Phoenix
    platform, including algorithms for parametric methods, individual, and pooled
    data analysis.
    The Quasi-Random Parametric Expectation-Maximization Method (QRPEM) is also
    supported <https://www.page-meeting.org/default.asp?abstract=2338>. Execution
    is supported both locally or on remote machines. Remote execution includes
    support for Linux Sun Grid Engine (SGE), Simple Linux Utility for Resource Management
    (SLURM) grids, Linux and Windows multicore, and individual runs.
A convenient set of wrapper functions to install
    pharmacometric packages and Shiny applications developed by Certara
    PMX and Integrated Drug Development (iDD). The functions ensure the
    successful installation of packages from non-standard repositories.
Utilities that support the usage of 'pyDarwin' (<https://certara.github.io/pyDarwin/>) for ease of setup 
    and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) 
    modeling engine.
Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling
    and simulation with powerful tools for Nonlinear Mixed-Effects (NLME)
    modeling. The package provides access to the same advanced Maximum Likelihood
    algorithms used by the NLME-Engine in the Phoenix platform. These tools support
    a range of analyses, from parametric methods to individual and pooled data, and
    support integrated use within the Pirana pharmacometric workbench
    <doi:10.1002/psp4.70067>. Execution is supported both locally or on
    remote machines.
Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny'
      interface. The Pharmacometric Modeling Language (PML) code updates in real time given 
      changes to user inputs. Models can be executed using the 'Certara.RsNLME' package. 
      Additional support to generate the underlying 'Certara.RsNLME' code to recreate
      the corresponding model in R is provided in the user interface.
Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny'
      interface. Specify engine parameters and select from different run options, including 
      simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive
      check, and more. Models are executed using the 'Certara.RsNLME' package.
Utilize the 'shiny' interface to parameterize a Visual Predictive Check (VPC),
    including selecting from different binning or binless methods and performing stratification,
    censoring, and prediction correction. Generate the underlying 'tidyvpc' and 'ggplot2' code
    directly from the user interface and download R or Rmd scripts to reproduce the VPCs in R.
Facilitates the creation of 'xpose' data objects from Nonlinear Mixed
      Effects (NLME) model outputs produced by 'Certara.RsNLME' or Phoenix NLME. This
      integration enables users to utilize all 'ggplot2'-based plotting functions available
      in 'xpose' for thorough model diagnostics and data visualization. Additionally, the 
      package introduces specialized plotting functions tailored for covariate model
      evaluation, extending the analytical capabilities beyond those offered by 'xpose' alone.
Produces formal landscape PDF certificates with configurable
    color themes, optional logos, decorative borders, laurels, and corner
    ornaments. Built on 'grid' with no external 'LaTeX' or 'Quarto'
    dependency. Suitable for academic awards, professional recognition,
    and similar uses.
Provides tools to easily access and analyze Canadian Election Study data.
    The package simplifies the process of downloading, cleaning, and using 'CES' datasets
    for political science research and analysis. The Canadian Election Study ('CES')
    has been conducted during federal elections since 1965, surveying Canadians
    on their political preferences, engagement, and demographics. Data is accessed
    from multiple sources including the 'Borealis' Data repository 
    <https://borealisdata.ca/> and the official 'Canadian Election Study' website 
    <https://ces-eec.arts.ubc.ca/>. This package is not officially affiliated 
    with the Canadian Election Study, 'Borealis' Data, or the University of British 
    Columbia, and users should cite the original data sources in their work.
Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.
Collection of perceptually uniform colour maps made by Peter Kovesi
    (2015) "Good Colour Maps: How to Design Them" <arXiv:1509.03700> 
    at the Centre for Exploration Targeting (CET).
Ceteris Paribus Profiles (What-If Plots) are designed to present model 
    responses around selected points in a feature space. 
    For example around a single prediction for an interesting observation. 
    Plots are designed to work in a model-agnostic fashion, they are working 
    for any predictive Machine Learning model and allow for model comparisons.
    Ceteris Paribus Plots supplement the Break Down Plots from 'breakDown' package.
Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.
Cellular cooperation compromises the plating efficiency-based 
    analysis of clonogenic survival data. This tool provides functions that 
    enable a robust analysis of colony formation assay (CFA) data in presence 
    or absence of cellular cooperation. 
    The implemented method has been described 
    in Brix et al. (2020). (Brix, N., Samaga, D., Hennel, R. et al. 
    "The clonogenic assay: robustness of plating efficiency-based analysis is 
    strongly compromised by cellular cooperation." Radiat Oncol 15, 248 (2020).
    <doi:10.1186/s13014-020-01697-y>)
        Power regression for parameter estimation, calculation of survival
    fractions, uncertainty analysis and plotting functions are provided.
A utility to quickly obtain clean and tidy college football
    data. Serves as a wrapper around the
    <https://collegefootballdata.com/> API and provides functions to
    access live play by play and box score data from ESPN
    <https://www.espn.com> when available. It provides users the
    capability to access a plethora of endpoints, and supplement that data
    with additional information (Expected Points Added/Win Probability
    added).
Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions,
    with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for
    CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For 
    mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.
Package for the analysis of categorical functional data.
  The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>.
  It also provides functions to perform basic statistical analysis on categorical functional data.
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
A simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems,
             to break free from the complex computation of existing similarity formulas and the ability to solve big data.
This project provides a group of new functions to calculate
    the outputs of the two main components of the Canadian Forest Fire
    Danger Rating System (CFFDRS) Van Wagner and Pickett (1985)
    <https://ostrnrcan-dostrncan.canada.ca/entities/publication/29706108-2891-4e5d-a59a-a77c96bc507c>) at various time
    scales: the Fire Weather Index (FWI) System Wan Wagner (1985)
    <https://ostrnrcan-dostrncan.canada.ca/entities/publication/d96e56aa-e836-4394-ba29-3afe91c3aa6c> and the Fire Behaviour
    Prediction (FBP) System Forestry Canada Fire Danger Group (1992)
    <https://cfs.nrcan.gc.ca/pubwarehouse/pdfs/10068.pdf>. Some functions
    have two versions, table and raster based.
Citation File Format ('CFF') version 1.2.0
    <doi:10.5281/zenodo.5171937> is a human- and machine-readable file
    format for software citation metadata. Core utilities generate, read,
    write and validate Citation File Format metadata for R packages.
Facilitates the identification of counterfactual queries in
 structural causal models via the ID* and IDC* algorithms
 by Shpitser, I. and Pearl, J. (2007, 2008) <doi:10.48550/arXiv.1206.5294>, 
 <https://jmlr.org/papers/v9/shpitser08a.html>. 
 Provides a simple interface for defining causal diagrams and counterfactual 
 conjunctions. Construction of parallel worlds graphs and counterfactual graphs 
 is carried out automatically based on the counterfactual query and the causal 
 diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of 
 the package.
Implements collaborative filtering methods for recommendation systems based on user-item interaction data. Supports both explicit feedback (ratings) and implicit feedback (consumption). The package uses efficient sparse matrix representations and provides incremental updates for users, items, and similarity structures through an R6 class-based architecture. See Aggarwal (2016) <doi:10.1007/978-3-319-29659-3> for an overview.
Provides generation and estimation of censored factor models for high-dimensional data with censored errors (normal, t, logistic). Includes Sparse Orthogonal Principal Components (SOPC), and evaluation metrics.  Based on Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) <doi:10.1183/13993003.00224-2019>.
In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The subsequent phase II trials are designed to examine the potential efficacy of the drug based on the MTD obtained from the phase I trials, with the aim of identifying the optimal biological dose (OBD). The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) <doi:10.1177/09622802221079353>), randomized CFO (rCFO), precision CFO (pCFO), two-dimensional CFO (2dCFO) (Wang et al. (2023) <doi:10.3389/fonc.2023.1294258>), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) <doi:10.1002/pst.2304>), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO (Fang and Yin (2024) <doi: 10.1002/sim.10127>). It supports phase I/II trials for the CFO design and only phase I trials for the other CFO-type designs. The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization. An interactive web application for CFO is available at the provided URL.
Provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. 
    Implements a Kalman smoothing framework to generate forecasts under 
    path restrictions on selected variables. The package enables decomposition 
    of conditional forecasts into variable-specific contributions, and extraction 
    of observation weights. It also computes measures of overall and marginal variable importance to enhance 
    the economic interpretation of forecast revisions. The framework is 
    structurally agnostic and suited for policy analysis, stress testing, 
    and macro-financial applications. The methodology is described in more detail in
    Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>.
Performs forward model selection, using the C-index/concordance
  in survival analysis models.
Estimate the severity of a disease and ascertainment of
    cases, as discussed in Nishiura et al. (2009)
    <doi:10.1371/journal.pone.0006852>.
Developed as a collaboration between Earth lab and the North Central Climate Adaptation Science Center to help users gain insights from available climate data. Includes tools and instructions for downloading climate data via a 'USGS' API and then organizing those data for visualization and analysis that drive insight. Web interface for 'USGS' API can be found at <http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_CMIP5_aggregated_macav2_catalog.html>.
Support for all calendars as specified in the Climate and Forecast 
    (CF) Metadata Conventions for climate and forecasting data. The CF Metadata 
    Conventions is widely used for distributing files with climate observations 
    or projections, including the Coupled Model Intercomparison Project (CMIP) 
    data used by climate change scientists and the Intergovernmental Panel on
    Climate Change (IPCC). This package specifically allows the user to work 
    with any of the CF-compliant calendars (many of which are not compliant with 
    POSIXt). The CF time coordinate is formally defined in the CF Metadata 
    Conventions document available at <https://cf-convention.github.io/Data/cf-conventions/cf-conventions-1.13/cf-conventions.html#time-coordinate>.
Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.
Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.
'CGAL' is a C++ library that aims to provide easy access to efficient and
  reliable algorithms in computational geometry. Since its version 4, 'CGAL' can be used
  as standalone header-only library and is available under a double GPL-3|LGPL license.
  <https://www.cgal.org/>.
A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions.  It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi:10.18637/jss.v089.i05> for more details.
The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.
Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package.
Run computer experiments using the adaptive composite grid
    algorithm with a Gaussian process model.
    The algorithm works best when running an experiment that can evaluate thousands
    of points from a deterministic computer simulation.
    This package is an implementation of a forthcoming paper by Plumlee,
    Erickson, Ankenman, et al. For a preprint of the paper,
    contact the maintainer of this package.
Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi: 10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.
This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with 'Dexcom', 'iPro 2', 'Diasend', 'Libre', or 'Carelink' data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.
Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)<doi:10.1093/bioinformatics/btx826>'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'.
Tools for advanced analysis of continuous glucose monitoring (CGM)
    time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based
    algorithms for postprandial peak detection, and detection of hypoglycemic and
    hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus
    CGM metrics. Core algorithms are implemented in optimized C++ using 'Rcpp' to
    provide accurate and fast analysis on large datasets.
Imputes missing glucose values in repeated-measures continuous
    glucose monitoring (CGM) data. Workflows create time-series features from
    raw timestamps, support model selection, and return the user's original
    columns plus an imputed glucose column. Methods include multiple imputation
    by chained equations (MICE; Azur et al. (2011) <doi:10.1002/mpr.329>),
    Random Forest regression (Breiman (2001) <doi:10.1023/A:1010933404324>),
    k-nearest-neighbor regression (Zhang (2016) <doi:10.21037/atm.2016.03.37>),
    XGBoost (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>),
    LightGBM (Ke et al. (2017)
    <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>),
    and ARIMA forecasting with the forecast framework (Hyndman and Khandakar
    (2008) <doi:10.18637/jss.v027.i03>). A Python-compatible backend uses
    'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', Python
    'xgboost', and optional Python 'lightgbm'.
Continuous glucose monitoring (CGM) systems provide real-time, 
  dynamic glucose information by tracking interstitial glucose values 
  throughout the day. Glycemic variability, also known as glucose variability, 
  is an established risk factor for hypoglycemia (Kovatchev) and 
  has been shown to be a risk factor in diabetes complications. 
  Over 20 metrics of glycemic variability have been identified. 
  Here, we provide functions to calculate glucose summary metrics, 
  glucose variability metrics (as defined in clinical publications), 
  and visualizations to visualize trends in CGM data.
  Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract>
  American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc>
  Kovatchev B (2019) <doi:10.1177/1932296819826111>
  Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3>
  Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017>
  Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.
Find multiple solutions of a nonlinear least squares problem.  Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31.  Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based helper bot available at <https://chatgpt.com/g/g-684936db9e748191a2796debb00cd755-cluster-gauss-newton-method-helper-bot> .
Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.
The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.
Provides interactive 3D visualization for large-scale Cayley graphs.
    Specifically designed for analyzing state spaces of the 'TopSpin' puzzle.
    Leverages the 'Datoviz' library and Vulkan-based GPU rendering for smooth
    real-time exploration of large graphs and complex state transitions.
    Implements efficient coordinate mapping for high-dimensional permutation
    groups, allowing users to visualize the connectivity and structural
    properties of the puzzle's state space. The rendering engine provides
    high-performance visuals and interactive camera controls, making it suitable
    for mathematical analysis of group-theoretic puzzles within the R
    environment.
Functions for performing quick observations or evaluations of data, including a variety of ways to list objects by size, class, etc.  The functions 'seqle' and 'reverse.seqle' mimic the base 'rle' but can search for linear sequences. The function 'splatnd' allows the user to generate zero-argument commands without the need for 'makeActiveBinding' .  Functions provided to convert from any base to any other base, and to find the n-th greatest max or n-th least min.  In addition, functions which mimic Unix shell commands, including 'head', 'tail' ,'pushd' ,and 'popd'. Various other goodies included as well.
Implements the chain binomial model for analysis of infectious disease data.
    Contains functions for calculating probabilities of the final size of infectious
    disease outbreaks using the method from D. Ludwig (1975) 
    <doi:10.1016/0025-5564(75)90119-4> and for outbreaks that are not 
    concluded, from Lindstrøm et al. (2024) <doi:10.48550/arXiv.2403.03948>. The package
    also contains methods for estimation and regression analysis of 
    secondary attack rates.
Various statistical methods and models which are
    typically used for the estimation of outstanding claims reserves
    in general insurance, including those to estimate the claims
    development result as required under Solvency II.
Assign distinct colors to arbitrary multi-dimensional data, considering its structure.
Performs adjustments of a user-supplied independence loglikelihood 
    function using a robust sandwich estimator of the parameter covariance 
    matrix, based on the methodology in Chandler and Bate (2007) 
    <doi:10.1093/biomet/asm015>.  This can be used for cluster correlated data 
    when interest lies in the parameters of the marginal distributions or for 
    performing inferences that are robust to certain types of model 
    misspecification.  Functions for profiling the adjusted loglikelihoods are 
    also provided, as are functions for calculating and plotting confidence 
    intervals, for single model parameters, and confidence regions, for pairs 
    of model parameters.  Nested models can be compared using an adjusted 
    likelihood ratio test.
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020) <doi:10.1007/s11222-020-09940-y>.
Allows users to input their data, segmentation and function used for the segmentation (and additional arguments) and the package calculates the influence of the data on the changepoint locations, see Wilms et al. (2022) <doi:10.1080/10618600.2021.2000873>.  Currently this can only be used with the changepoint package functions to identify changes, but we plan to extend this.  There are options for different types of graphics to assess the influence.
Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) <doi:10.18637/jss.v058.i03> ).
The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, <doi:10.48550/arXiv.2410.15571>).
Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.
Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.
A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) <https://variation.com/wp-content/uploads/change-point-analyzer/change-point-analysis-a-powerful-new-tool-for-detecting-changes.pdf>. The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance.
A multiple testing procedure for clustered alternative hypotheses. 
    It is assumed that the p-values under the null hypotheses follow U(0,1) and 
    that the distributions of p-values from the alternative hypotheses are 
    stochastically smaller than U(0,1). By aggregating information, this 
    method is more sensitive to detecting signals of low magnitude than 
    standard methods. Additionally, sporadic small p-values appearing 
    within a null hypotheses sequence are avoided by averaging on the 
    neighboring p-values.
Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.
Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.
Estimation of changepoints using an "S-curve"
   approximation. Formation of confidence intervals for changepoint
   locations and magnitudes. Both abrupt and gradual changes can be
   modeled.
Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++.
Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package 'ChannelAttribution'.
Adjusts the loglikelihood of common econometric models for
    clustered data based on the estimation process suggested in 
    Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, using the 'chandwich' 
    package <https://cran.r-project.org/package=chandwich>, and provides 
    convenience functions for inference on the adjusted models.
Computes and visualize the results of the 0-1 test for chaos proposed
    by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>. The algorithm is
    available in parallel for the independent values of parameter c. Additionally,
    fast RQA is added to distinguish chaos from noise.
The main objective of the package is to enter a word of at least two letters based on which an Iterated Function System with Probabilities is constructed, and a two-dimensional fractal containing the chosen word infinitely often is generated via the Chaos Game. Additionally, the package allows to project the two-dimensional fractal on several three-dimensional surfaces and to transform the fractal into another fractal with uniform marginals.
Calculation of gas transport properties (viscosity, diffusion, thermal conductivity) using Chapman-Enskok theory (Chapman 1918, <doi:10.1098/rsta.1918.0005>) and of the second virial coefficient (Vargas et al. 2001, <doi:10.1016/s0378-4371(00)00362-9>) using the Lennard-Jones (12-6) potential. Up to the third order correction is taken into account for viscosity and thermal conductivity. It is also possible to calculate the binary diffusion coefficients of polar and non-polar gases in non-polar bath gases (Brown et al. 2011, <doi:10.1016/j.pecs.2010.12.001>). 16 collision integrals are calculated with four digit accuracy over the reduced temperature range [0.3, 400] using an interpolation function of Kim and Monroe (2014, <doi:10.1016/j.jcp.2014.05.018>).
CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association
    Study) provides a dynamically adaptive framework for genome-wide association
    studies (GWAS) that integrates chromosome-scale haplotypes with single
    nucleotide polymorphism (SNP) analysis. The method identifies and extends
    haplotype variants based on their phenotypic associations rather than
    predefined linkage blocks, enabling high-resolution detection of quantitative
    trait loci (QTL). By leveraging long-range phased haplotype information,
    CHAP-GWAS improves statistical power and offers a more comprehensive view of
    the genetic architecture underlying complex traits.
An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.
A program for reconstructing local fire histories from
    high-resolution, continuously sampled lake-sediment charcoal records.
    'CharAnalysis' decomposes a charcoal record into low- and high-frequency
    components and uses locally defined thresholds to separate fire signal
    from noise, following the approach of Higuera et al. (2009)
    <doi:10.1890/07-2019.1>, with underlying assumptions and rationale
    described in Higuera et al. (2010) <doi:10.1071/WF09134>. The package is
    designed for macroscopic charcoal records with contiguous sampling fine
    enough to resolve individual fire events, and is not appropriate for
    low-resolution or discontinuously sampled records. See the package URL
    for the User's Guide and application examples.
Creates a new chars class which looks like a string but is actually 
    a vector of individual characters, making 'strings' iterable. This class 
    enables vector operations on 'strings' such as reverse, sort, head, and set 
    operations.
Provides a standardized and reproducible framework for characterizing
    and classifying discrete color classes from digital images of biological organisms.
    The package automatically determines the presence or absence of 10 human-visible
    color categories (black, blue, brown, green, grey, orange, purple, red, white, yellow)
    using a biologically-inspired Color Look-Up Table (CLUT) that partitions HSV color space.
    Supports both fully automated and semi-automated (interactive) workflows with complete
    provenance tracking for reproducibility. Pre-processes images using the 'recolorize'
    package (Weller et al. 2024 <doi:10.1111/ele.14378>) for spatial-color binning, and
    integrates with 'pavo' (Maia et al. 2019 <doi:10.1111/2041-210X.13174>) for color
    pattern geometry statistics. Designed for high-throughput analysis and seamless
    integration with downstream evolutionary analyses.
Make fake data that looks realistic, supporting addresses, 
    person names, dates,
    times, colors, coordinates, currencies, digital object identifiers
    ('DOIs'), jobs, phone numbers, 'DNA' sequences, doubles and integers
    from distributions and within a range.
For those wishing to interact with the 'Charles Schwab Individual Trader API' (<https://developer.schwab.com/products/trader-api--individual>) with R in a simplified manner, this package offers wrapper functions around authentication and the available API calls to streamline the process.
Provides a very simple syntax for the user to generate custom plot(s) without having to remember complicated 'ggplot2' syntax. The 'chartql' package uses 'ggplot2' and manages all the syntax complexities internally. As an example, to generate a bar chart of company sales faceted by product category further faceted by season of the year, we simply write: "CHART bar X category, season Y sales".
Functionality to perform adaptive multi-wave sampling for efficient chart validation. Code allows one to define strata, adaptively sample using several types of confidence bounds for the quantity of interest (Lai's confidence bands, Bayesian credible intervals, normal confidence intervals), and sampling strategies (random sampling, stratified random sampling, Neyman's sampling, see Neyman (1934) <doi:10.2307/2342192> and Neyman (1938) <doi:10.1080/01621459.1938.10503378>).
The Large Language Model (LLM) represents a groundbreaking advancement 
    in data science and programming, and also allows us to extend the world of R. 
    A seamless interface for integrating the 'OpenAI' Web APIs into R is provided in this package. 
    This package leverages LLM-based AI techniques, enabling efficient knowledge discovery and data analysis.
    The previous functions such as seamless translation and image generation have been moved 
    to other packages 'deepRstudio' and 'stableDiffusion4R'.
'OpenAI's 'ChatGPT' <https://chat.openai.com/> coding assistant for 'RStudio'. A set 
  of functions and 'RStudio' addins that aim to help the R developer in tedious coding tasks.
Provides a flexible interface for interacting with Large Language Model ('LLM') 
    providers including 'OpenAI', 'Azure OpenAI', 'Azure AI Foundry', 'Groq', 'Anthropic', 
    'DeepSeek', 'DashScope', 'Gemini', 'Grok', 'GitHub Models', and AWS Bedrock. Supports both synchronous and asynchronous 
    chat-completion APIs, with
    features such as retry logic, dynamic model selection, customizable parameters, and
    multi-message conversation handling. Designed to streamline integration with
    state-of-the-art LLM services across multiple platforms.
Evaluates stimuli using Large Language Models.
  Supports multiple LLM providers: 'OpenAI', 'Anthropic', 'Ollama', 'LM Studio',
  'DeepSeek', 'Groq', 'Mistral', and 'OpenAI-compatible' endpoints.
  Stimuli: plain text, local image/audio files, or image URLs.
  Audio is transcribed via 'OpenAI Whisper' before rating.
  Supports numeric, text, and raw return types.
A native R 'torch' port of the 'Chatterbox' text-to-speech
    engine <https://github.com/resemble-ai/chatterbox>. Provides speech
    synthesis with voice cloning; model weights are downloaded from
    'HuggingFace' <https://huggingface.co/> via the 'hfhub' package.
Enables user interactivity with large-language models ('LLM') inside
    the 'RStudio' integrated development environment (IDE). The user can
    interact with the model using the 'shiny' app included in this package, or
    directly in the 'R' console. It comes with back-ends for 'OpenAI', 'GitHub'
    'Copilot', and 'LlamaGPT'.
Provides a modified boxplot with a new fence coefficient determined by Lin et al. (2025). The traditional fence coefficient k=1.5 in Tukey's boxplot is replaced by a coefficient based on Chauvenet's criterion, as described in their formula (9). The new boxplot can be implemented in 'base R' with function chau_boxplot(), and in 'ggplot2' with function geom_chau_boxplot().
Provides easy access to historical climate data in Canada from R.
	     Search for weather stations and download raw hourly, daily or
	     monthly weather data across Canada from 1840 to present. Implements
	     public API access as detailed at <https://climate.weather.gc.ca>.
Fast and memory-efficient (or 'cheap') tools to facilitate
    efficient programming, saving time and memory. It aims to provide
    'cheaper' alternatives to common base R functions, as well as some
    additional functions.
A simple package to grab cheat sheets and save them to your local computer.
Utility functions that allow checking the basic validity of a function argument or any other value, 
             including generating an error and assigning a default in a single line of code. The main purpose of
             the package is to provide simple and easily readable argument checking to improve code robustness.
Providing more beautiful and more meaningful return messages for checkmate assertions
    and checks helping users to better understand errors.
Check digits are used like file hashes to verify that a
    number has been transcribed accurately. The functions provided by this
    package help to calculate and verify check digits according to various
    algorithms.
Creates auto-grading check-fields and check-boxes for 'rmarkdown' 
    or 'quarto' HTML. It can be used in class, when teacher share materials 
    and tasks, so students can solve some problems and check their work. In 
    contrast to the 'learnr' package, the 'checkdown' package works serverlessly
    without 'shiny'.
Systematically Run R checks against multiple packages. Checks are run in 
    parallel with strategies to minimize dependency installation. Provides
    out of the box interface for running reverse dependency check.
Checks that students have the correct version of 'R', 'R' packages,
    'RStudio' and other dependencies installed, and that the recommended 'RStudio' 
    configuration has been applied.
A minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis.
Deal with packages 'check' outputs and reduce the risk of
    rejection by 'CRAN' by following policies.
Confirms if the number is Luhn compliant.
    Can check if credit card, IMEI number or any other Luhn based number is correct. 
    For more info see: <https://en.wikipedia.org/wiki/Luhn_algorithm>.
Tests and assertions to perform frequent argument checks. A
    substantial part of the package was written in C to minimize any worries
    about execution time overhead.
Validates common string formats including financial
    identifiers (ISIN, CUSIP, SEDOL, FIGI, IBAN, LEI), publication
    identifiers (ISBN, ISSN, DOI, ORCID), and general formats (email,
    UUID, URL, semver), with check digit verification where applicable.
Provides a lightweight data validation and testing toolkit for R. 
    Its guiding philosophy is that adding code-based data checks to users' 
    existing workflow should be both quick and intuitive. The suite of 
    functions included therefore mirror the common data checks many users 
    already perform by hand or by eye. Additionally, the 'checkthat' package is 
    optimized to work within 'tidyverse' data manipulation pipelines.
Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients.
Given a non-linear model, calculate the local explanation. 
    We purpose view the data space, explanation 
    space, and model residuals as ensemble graphic interactive on a shiny 
    application. After an observation of interest is identified, the normalized 
    variable importance of the local explanation is used as a 1D projection
    basis. The support of the local explanation is then explored by changing
    the basis with the use of the radial tour <doi:10.32614/RJ-2020-027>; 
    <doi:10.1080/10618600.1997.10474754>.
Contains tools for working with data during statistical analysis, promoting flexible, intuitive, and reproducible workflows. There are functions designated for specific statistical tasks such building a custom univariate descriptive table, computing pairwise association statistics, etc. These are built on a collection of data manipulation tools designed for general use that are motivated by the functional programming concept.
An R interface to 'Cheetah Grid', a high-performance JavaScript table widget. 
    'cheetahR' allows users to render millions of rows in just a few milliseconds, 
    making it an excellent alternative to other R table widgets. The package wraps 
    the 'Cheetah Grid' JavaScript functions and makes them readily available for R users.
    The underlying grid implementation is based on 'Cheetah Grid'
    <https://github.com/future-architect/cheetah-grid>.
Contains the Multi-Species Acute Toxicity Database (CAS & SMILES
    columns only) [United States (US) Department of Health and Human Services
    (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI),
    "Multi-Species Acute Toxicity Database",
    <https://cactus.nci.nih.gov/download/acute-toxicity-db/>] combined with the
    Toxic Substances Control Act (TSCA) Inventory [United States Environmental
    Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical
    Substance Inventory",
    <https://www.epa.gov/tsca-inventory/how-access-tsca-inventory} and
    <https://cdxapps.epa.gov/oms-substance-registry-services/substance-list-details/169>]
    and the Agency for Toxic Substances and Disease Registry (ATSDR) Database
    [United States (US) Department of Health and Human Services (DHHS) Centers
    for Disease Control and Prevention (CDC)/Agency for Toxic Substances and
    Disease Registry (ATSDR), "Agency for Toxic Substances and Disease Registry
    (ATSDR) Database",
    <https://cdxapps.epa.gov/oms-substance-registry-services/substance-list-details/105>]
    in 2 data sets. One data set has a focus on the latter 2 databases and one
    data set focuses on the former database. Also contains the collection of
    chemical data from Wikipedia compiled in the US EPA CompTox Chemicals
    Dashboard [United States Environmental Protection Agency (US EPA) /
    Wikimedia Foundation, Inc. "CompTox Chemicals Dashboard v2.2.1",
    <https://comptox.epa.gov/dashboard/chemical-lists/WIKIPEDIA>].
Combines taxonomic classifications of high-throughput 16S rRNA
  gene sequences with reference proteomes of archaeal and bacterial taxa to
  generate amino acid compositions of community reference proteomes. Calculates
  chemical metrics including carbon oxidation state ('Zc'), stoichiometric
  oxidation and hydration state ('nO2' and 'nH2O'), H/C, N/C, O/C, and S/C
  ratios, grand average of hydropathicity ('GRAVY'), isoelectric point ('pI'),
  protein length, and average molecular weight of amino acid residues. Uses
  precomputed reference proteomes for archaea and bacteria derived from the
  Genome Taxonomy Database ('GTDB'). Also includes reference proteomes derived
  from the NCBI Reference Sequence ('RefSeq') database and manual mapping from
  the 'RDP Classifier' training set to 'RefSeq' taxonomy as described by Dick and
  Tan (2023) <doi:10.1007/s00248-022-01988-9>. Processes taxonomic
  classifications in 'RDP Classifier' format or OTU tables in 'phyloseq-class'
  objects from the Bioconductor package 'phyloseq'.
Simple functions for plotting linear
	calibration functions and estimating standard errors for measurements
	according to the Handbook of Chemometrics and Qualimetrics: Part A
	by Massart et al. (1997) There are also functions estimating the limit
	of detection (LOD) and limit of quantification (LOQ).
	The functions work on model objects from - optionally weighted - linear
	regression (lm) or robust linear regression ('rlm' from the 'MASS' package).
A collection of functions that have been developed to assist
    experimenter in modeling chemical degradation kinetic data. The
    selection of the appropriate degradation model and parameter
    estimation is carried out automatically as far as possible and is
    driven by a rigorous statistical interpretation of the results.  The
    package integrates already available goodness-of-fit statistics for
    nonlinear models. In addition it allows data fitting with the
    nonlinear first-order multi-target (FOMT) model.
We aim to deal with the average treatment effect (ATE), where the data are
    subject to high-dimensionality and measurement error. This package primarily contains two 
    functions, which are used to generate artificial data and estimate ATE with high-dimensional 
    and error-prone data accommodated.
Quantify and visualise various measures of chemical diversity and
    dissimilarity, for phytochemical compounds and other sets of chemical
    composition data. Importantly, these measures can incorporate biosynthetic
    and/or structural properties of the chemical compounds, resulting in a more
    comprehensive quantification of diversity and dissimilarity. For details,
    see Petrén, Köllner and Junker (2023) <doi:10.1111/nph.18685>.
R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).
A collection of functions for top-down exploratory data analysis
    of spectral data including nuclear magnetic resonance (NMR), infrared (IR),
    Raman, X-ray fluorescence (XRF) and other similar types of spectroscopy.
    Includes functions for plotting and inspecting spectra, peak alignment,
    hierarchical cluster analysis (HCA), principal components analysis (PCA) and
    model-based clustering. Robust methods appropriate for this type of
    high-dimensional data are available. ChemoSpec is designed for structured
    experiments, such as metabolomics investigations, where the samples fall into
    treatment and control groups. Graphical output is formatted consistently for
    publication quality plots. ChemoSpec is intended to be very user friendly and
    to help you get usable results quickly. A vignette covering typical operations
    is available.
A collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. 'ChemoSpec2D' deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the  sample from a statistical perspective.  In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. 'ChemoSpec2D' takes many of its cues from 'ChemoSpec' and tries to create consistent graphical output and to be very user friendly.
Functions supporting the common needs of packages 'ChemoSpec' and 'ChemoSpec2D'.
Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, <doi:10.1198/10618600152627997>.    Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2).
Provides an alternative approach to multiple testing
        by calculating a simultaneous upper confidence bounds for the
        number of true null hypotheses among any subset of the hypotheses of interest, 
        using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.
Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC.
This is an opinionated wrapper around the
    python-chess package. It allows users to read and write PGN files as
    well as create and explore game trees such as the ones seen in chess
    books.
A chess program which allows the user to create a game, add moves,
             check for legal moves and game result, plot the board, take back, read and write FEN (Forsyth–Edwards Notation).
             A basic chess engine based on minimax is implemented.
Provides functions to work with directed (asymmetric) and 
    undirected (symmetric) spatial networks. It makes the creation of 
    connectivity matrices easier, i.e. a binary matrix of dimension n x n, where 
    n is the number of nodes (sampling units) indicating the presence (1) or  
    the absence (0) of an edge (link) between pairs of nodes. Different network
    objects can be produced by 'chessboard': node list, neighbor list, edge 
    list, connectivity matrix. It can also produce objects that will be used 
    later in Moran's Eigenvector Maps (Dray et al. (2006) <doi:10.1016/j.ecolmodel.2006.02.015>)
    and Asymetric Eigenvector Maps (Blanchet et al. (2008) <doi:10.1016/j.ecolmodel.2008.04.001>), 
    methods available in the package 'adespatial' (Dray et al. (2023) 
    <https://CRAN.R-project.org/package=adespatial>). This work is part of the 
    FRB-CESAB working group Bridge 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/bridge/>.
Datasets of the International Chess Federation's player ratings and country information analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".
Applies the change-in-effect estimate method to assess confounding 
    effects in medical and epidemiological research (Greenland & Pearce (2016) 
    <doi:10.1146/annurev-publhealth-031914-122559> ). It starts with a crude model 
    including only the outcome and exposure variables. At each of the subsequent 
    steps, one variable which creates the largest change among the remaining variables 
    is selected. This process is repeated until all variables have been entered into 
    the model (Wang Z. Stata Journal 2007; 7, Number 2, pp. 183–196). Currently, the 'chest' 
    package has functions for linear regression, logistic regression, negative 
    binomial regression, Cox proportional hazards model and conditional logistic 
    regression.
Provides tools to process and analyze chest expansion using 3D marker data from motion capture systems. Includes functions for data processing, marker position adjustment, volume calculation using convex hulls, and visualization in 2D and 3D. Barber et al. (1996) <doi:10.1145/235815.235821>. TAMIYA Hiroyuki et al. (2021) <doi:10.1038/s41598-021-01033-8>.
Provide standard tables, listings, and graphs (TLGs)
    libraries used in clinical trials. This package implements a structure
    to reformat the data with 'dunlin', create reporting tables using
    'rtables' and 'tern' with standardized input arguments to enable quick
    generation of standard outputs.  In addition, it also provides
    comprehensive data checks and script generation functionality.
Light weight implementation of the standard distribution 
  functions for the chi distribution, wrapping those for the chi-squared 
  distribution in the stats package.
Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model.
Measuring child development starts by collecting responses to 
    developmental milestones, such as "able to sit" or "says two words". 
    There are many ways to combine such responses into summaries. 
    The package bundles publicly available datasets with individual milestone 
    data for children aged 0-5 years, with the aim of supporting the construction, 
    evaluation, validation and interpretation of methodologies that aggregate 
    milestone data into informative measures of child development.
Tools for connecting to 'CHILDES', an open repository for
    transcripts of parent-child interaction. For more information on the 
    underlying data, see <https://langcog.github.io/childes-db-website/>.
Tools for extracting word and phrase frequencies from the Child Language Data Exchange System (CHILDES) 
    database via the 'childesr' API. Supports type-level word counts, 
    token-mode searches with simple wildcard patterns and part-of-speech 
    filters, optional stemming, and Zipf-scaled frequencies. Provides 
    normalization per number of tokens or utterances, speaker-role 
    breakdowns, dataset summaries, and export to Excel workbooks for 
    reproducible child language research. 
    The CHILDES database is maintained at <https://talkbank.org/childes/>.
Reads demographic data from a variety of public data sources, extracting and harmonizing variables useful for the study of childfree individuals. The identification of childfree individuals and those with other family statuses uses Neal & Neal's (2024) "A Framework for Studying Adults who Neither have Nor Want Children" <doi:10.1177/10664807231198869>; A pre-print is available at <doi:10.31234/osf.io/fa89m>.
Tools to simulate child-penalty data and estimate DID, TD, and NTD
    identification frameworks from Leventer (2025), "Identification of Child
    Penalties" <doi:10.48550/arXiv.2602.07486>.
Calculation of standard deviation scores and percentiles adduced from different
    standards (WHO, UK, Germany, Italy, China, etc). Also, references for laboratory values in
    children and adults are available, e.g., serum lipids, iron-related blood parameters, IGF, liver enzymes. See package documentation for full list.
Provides functions to access data from public RESTful APIs including
    'FINDIC API', 'REST Countries API', 'World Bank API', and 'Nager.Date', 
    retrieving real-time or historical data related to Chile such as financial indicators, 
    holidays, international demographic and geopolitical indicators, and more. 
    Additionally, the package includes curated datasets related to Chile, covering topics 
    such as human rights violations during the Pinochet regime, electoral data, census samples, 
    health surveys, seismic events, territorial codes, and environmental measurements. 
    The package supports research and analysis focused on Chile by integrating open APIs with 
    high-quality datasets from multiple domains. For more information on the APIs, see: 
    'FINDIC' <https://findic.cl/>, 
    'REST Countries' <https://restcountries.com/>, 
    'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, 
    and 'Nager.Date' <https://date.nager.at/Api>.
Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple "Gala" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the 'chillR' package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model.
The phenology of plants (i.e. the timing of their annual life
    phases) depends on climatic cues. For temperate trees and many other plants,
    spring phases, such as leaf emergence and flowering, have been found to result
    from the effects of both cool (chilling) conditions and heat. Fruit tree
    scientists (pomologists) have developed some metrics to quantify chilling
    and heat (e.g. see Luedeling (2012) <doi:10.1016/j.scienta.2012.07.011>).
    'chillR' contains functions for processing temperature records into
    chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units
    (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often
    considered the most promising, but they are difficult to calculate. This package
    makes it easy. 'chillR' also contains procedures for conducting a PLS analysis
    relating phenological dates (e.g. bloom dates) to either mean temperatures or
    mean chill and heat accumulation rates, based on long-term weather and phenology
    records (Luedeling and Gassner (2012) <doi:10.1016/j.agrformet.2011.10.020>).
    As of version 0.65, it also includes functions for generating weather
    scenarios with a weather generator, for conducting climate change analyses
    for temperature-based climatic metrics and for plotting results from such
    analyses. Since version 0.70, 'chillR' contains a function for interpolating
    hourly temperature records.
Provides functions to access data from public RESTful APIs including 'Nager.Date', 
    'World Bank API', and 'REST Countries API', retrieving real-time or historical data 
    related to China, such as holidays, economic indicators, and international demographic 
    and geopolitical indicators. Additionally, the package includes one of the largest 
    curated collections of open datasets focused on China and Hong Kong, covering topics 
    such as air quality, demographics, input-output tables, epidemiology, political 
    structure, names, and social indicators. The package supports reproducible research 
    and teaching by integrating reliable international APIs and structured datasets from 
    public, academic, and government sources.
    For more information on the APIs, see: 
    'Nager.Date' <https://date.nager.at/Api>, 
    'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, 
    and 'REST Countries API' <https://restcountries.com/>.
A database of Chinese surnames and given names (1930-2008).
    This database contains nationwide frequency statistics of
    1,806 Chinese surnames and 2,614 Chinese characters used in given names,
    covering about 1.2 billion Han Chinese population
    (96.8 percent of the Han Chinese household-registered population
    born from 1930 to 2008 and still alive in 2008).
    This package also contains a function for computing multiple indices of
    Chinese surnames and given names for social science research (e.g.,
    name uniqueness, name gender, name valence, and name warmth/competence).
    Details are provided at
    <https://psychbruce.github.io/ChineseNames/>.
Provides simple, reproducible access to datasets from the
    Chicago Open Data portal <https://data.cityofchicago.org/>. Functions return
    results as tidy tibbles and support optional filtering, sorting,
    and row limits via the Socrata API.
A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) <doi:10.1093/bioinformatics/btv205>.
API Client for the Climate Hazards Center 'CHIRPS' and
    'CHIRTS'.  The 'CHIRPS' data is a quasi-global (50°S – 50°N)
    high-resolution (0.05 arc-degrees) rainfall data set, which
    incorporates satellite imagery and in-situ station data to create
    gridded rainfall time series for trend analysis and seasonal drought
    monitoring. 'CHIRTS' is a quasi-global (60°S – 70°N), high-resolution
    data set of daily maximum and minimum temperatures. For more details
    on 'CHIRPS' and 'CHIRTS' data please visit its official home page
    <https://www.chc.ucsb.edu/data>.
Provides the facility to perform the chi-square and G-square test of independence, calculates the retrospective power of the traditional chi-square test, compute permutation and Monte Carlo p-value, and provides measures of association for tables of any size such as Phi, Phi corrected, odds ratio with 95 percent CI and p-value, Yule' Q and Y, adjusted contingency coefficient, Cramer's V, V corrected, V standardised, bias-corrected V, W, Cohen's w, Goodman-Kruskal's lambda, and tau. It also calculates standardised, moment-corrected standardised, and adjusted standardised residuals, and their significance, as well as the Quetelet Index, IJ association factor, and adjusted standardised counts. It also computes the chi-square-maximising version of the input table. Different outputs are returned in nicely formatted tables.
For developers to check user-supplied function arguments.  It
    is designed to be simple, fast and customizable.  Error messages
    follow the tidyverse style guide.
As different antipsychotic medications have different potencies,
    the doses of different medications cannot be directly compared. Various
    strategies are used to convert doses into a common reference so that
    comparison is meaningful. Chlorpromazine (CPZ) has historically been used
    as a reference medication into which other antipsychotic doses can be
    converted, as "chlorpromazine-equivalent doses". Using conversion keys
    generated from widely-cited scientific papers, e.g. Gardner et. al 2010
    <doi:10.1176/appi.ajp.2009.09060802> and Leucht et al. 2016
    <doi:10.1093/schbul/sbv167>, antipsychotic doses are converted
    to CPZ (or any specified antipsychotic) equivalents. The use of the package
    is described in the included vignette. Not for clinical use.