The geographic dimension plays a fundamental role in
    multidimensional systems. To define a geographic dimension in a star
    schema, we need a table with attributes corresponding to the levels of
    the dimension. Additionally, we will also need one or more geographic
    layers to represent the data using this dimension. The goal of this
    package is to support the definition of geographic dimensions from
    layers of geographic information related to each other. It makes it
    easy to define relationships between layers and obtain the necessary
    data from them.
Dependency-free, ultra fast calculation of geodesic
    distances.  Includes the reference nanometre-accuracy geodesic
    distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by
    the 'sf' package, as well as Haversine and Vincenty distances. Default
    distance measure is the "Mapbox cheap ruler" which is generally more
    accurate than Haversine or Vincenty for distances out to a few hundred
    kilometres, and is considerably faster. The main function accepts one
    or two inputs in almost any generic rectangular form, and returns
    either matrices of pairwise distances, or vectors of sequential
    distances.
Methods for calculating gradient surface metrics for
    continuous analysis of landscape features.
Draw geospatial objects by clicks on the map.
    This packages can help data analyst who want to check
    their own geospatial hypothesis but has no ready-made geospatial objects.
Provides a gradient descent algorithm to find a geodesic relationship between real-valued independent variables and a manifold-valued dependent variable (i.e. geodesic regression). Available manifolds are Euclidean space, the sphere, hyperbolic space, and Kendall's 2-dimensional shape space. Besides the standard least-squares loss, the least absolute deviations, Huber, and Tukey biweight loss functions can also be used to perform robust geodesic regression. Functions to help choose appropriate cutoff parameters to maintain high efficiency for the Huber and Tukey biweight estimators are included, as are functions for generating random tangent vectors from the Riemannian normal distributions on the sphere and hyperbolic space. The n-sphere is a n-dimensional manifold: we represent it as a sphere of radius 1 and center 0 embedded in (n+1)-dimensional space. Using the hyperboloid model of hyperbolic space, n-dimensional hyperbolic space is embedded in (n+1)-dimensional Minkowski space as the upper sheet of a hyperboloid of two sheets. Kendall's 2D shape space with K landmarks is of real dimension 2K-4; preshapes are represented as complex K-vectors with mean 0 and magnitude 1. Details are described in Shin, H.-Y. and Oh, H.-S. (2020) <arXiv:2007.04518>. Also see Fletcher, P. T. (2013) <doi:10.1007/s11263-012-0591-y>.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Provides geographical faceting functionality for 'ggplot2'.
    Geographical faceting arranges a sequence of plots of data for different
    geographical entities into a grid that preserves some of the geographical
    orientation.
Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.
Methods for processing spatial data for decision-making. 
  This package is an R implementation of methods provided by the open source software GeoFIS <https://www.geofis.org> (Leroux et al. 2018) <doi:10.3390/agriculture8060073>. 
  The main functionalities are the management zone delineation (Pedroso et al. 2010) <doi:10.1016/j.compag.2009.10.007> and data aggregation (Mora-Herrera et al. 2020) <doi:10.1016/j.compag.2020.105624>.
A model building procedure to build parsimonious geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects, smoothing splines and a smooth spatial surface to model spatial autocorrelation. The resulting covariate set after gradient boosting is further reduced through backward elimination and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study in Berne (Switzerland) is provided. Nussbaum, M., Walthert, L., Fraefel, M., Greiner, L., and Papritz, A. (2017) <doi:10.5194/soil-3-191-2017>.
The American Community Survey (ACS)
    <https://www.census.gov/programs-surveys/acs> offers geodatabases with
    geographic information and associated data of interest to researchers
    in the area. The goal of this package is to generate objects that
    allow us to access and consult the information available in various
    formats, such as in 'GeoPackage' format or in multidimensional 'ROLAP'
    (Relational On-Line Analytical Processing) star format.
Bindings to the 'GeographicLib' C++ library
    <https://geographiclib.sourceforge.io/> for precise geodetic calculations
    including geodesic computations (distance, bearing, paths, intersections),
    map projections (UTM/UPS, Transverse Mercator, Lambert Conformal Conic,
    and more), grid reference systems (MGRS, Geohash, GARS, Georef),
    coordinate conversions (geocentric, local Cartesian), and polygon area
    on the WGS84 ellipsoid. All functions are fully vectorized.
Turn irregular polygons (such as geographical regions) into regular or hexagonal grids.
    This package enables the generation of regular (square) and hexagonal grids through the package 
    'sp' and then assigns the content of the existing polygons to the new grid using 
    the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). 
    This prevents the need for manual generation of hexagonal grids or regular grids 
    that are supposed to reflect existing geography.
Tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.
The functionality provided by this package is an expansion of the code 
    of the 'statebins' package, created by B. Rudis (2022), <doi:10.32614/CRAN.package.statebins>. It allows for the creation of square choropleths 
    for the entire world, provided an appropriate specified grid is supplied.
Classes for 'GeoJSON' to make working with 'GeoJSON' easier.
    Includes S3 classes for 'GeoJSON' classes with brief summary output,
    and a few methods such as extracting and adding bounding boxes,
    properties, and coordinate reference systems; working with 
    newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' 
    format.
Convert data to 'GeoJSON' or 'TopoJSON' from various R
    classes, including vectors, lists, data frames, shape files, and
    spatial classes.  'geojsonio' does not aim to replace packages like
    'sp', 'rgdal', 'rgeos', but rather aims to be a high level client to
    simplify conversions of data from and to 'GeoJSON' and 'TopoJSON'.
Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://datatracker.ietf.org/doc/html/rfc7946>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
Converts Between GeoJSON and simple feature objects.
A compilation of tools to complete common tasks for studying gerrymandering. 
    This focuses on the geographic tool side of common problems, such as linking 
    different levels of spatial units or estimating how to break up units. Functions 
    exist for creating redistricting-focused data for the US.
Set of routines for making map projections (forward and inverse), topographic maps, perspective plots, geological maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Data sets included here are for use with package GEOmap. These include world map, USA map, Coso map, Japan Map.
Performs Geometrical Archetypal Analysis after creating Grid Archetypes 
    which are the Cartesian Product of all minimum, maximum variable values. Since the archetypes 
    are fixed now, we have the ability to compute the convex composition coefficients for all 
    our available data points much faster by using the half part of Principal Convex Hull Archetypal method. 
    Additionally we can decide to keep as archetypes the closer to the Grid Archetypes ones. Finally 
    the number of archetypes is always 2 to the power of the dimension of our data points 
    if we consider them as a vector space. 
    Cutler, A., Breiman, L. (1994)      <doi:10.1080/00401706.1994.10485840>.
    Morup, M., Hansen, LK. (2012) <doi:10.1016/j.neucom.2011.06.033>.
    Christopoulos, DT. (2024) <doi:10.13140/RG.2.2.14030.88642>.
Tools to easily visualize geographic data of Morocco.
    This package interacts with data available through the
    'geomarocdata' package, which is available in a 'drat' 
    repository.   The size of the 'geomarocdata' package is
    approximately 12 MB.
Geospatial data integration framework that merges raster, spatial polygon, and (dynamic) spatial points data into a spatial (panel) data frame at any geographical resolution.
Provides facilities to read, write and validate geographic metadata 
 defined with ISO TC211 / OGC ISO geographic information metadata standards, and 
 encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. 
 This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) 
 and 19136 (GML). Other interoperable schemas from the OGC are progressively supported 
 as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML 
 Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID).
Tools for geometric morphometric analyses and multidimensional
    data. Implements methods for morphological disparity analysis using
    bootstrap and rarefaction,
    as reviewed in Foote (1997) <doi:10.1146/annurev.ecolsys.28.1.129>.
    Includes integration and modularity testing,
    following Fruciano et al. (2013) <doi:10.1371/journal.pone.0069376>,
    using Escoufier's RV coefficient as test statistic as well as
    two-block partial least squares -
    PLS, Rohlf and Corti (2000) <doi:10.1080/106351500750049806>.
    Also includes vector angle comparisons,
    orthogonal projection for data correction
    (Burnaby (1966) <doi:10.2307/2528217>; Fruciano (2016) <doi:10.1007/s00427-016-0537-4>),
    and parallel analysis for dimensionality reduction (Buja and Eyuboglu (1992)
    <doi:10.1207/s15327906mbr2704_2>).
Geometry shapes in 'R' are typically represented by matrices (points, lines), with more complex 
  shapes being lists of matrices (polygons). 'Geometries' will convert various 'R' objects into these shapes. 
  Conversion functions are available at both the 'R' level, and through 'Rcpp'.
Makes the 'Qhull' library <http://www.qhull.org>
    available in R, in a similar manner as in Octave and MATLAB. Qhull
    computes convex hulls, Delaunay triangulations, halfspace
    intersections about a point, Voronoi diagrams, furthest-site
    Delaunay triangulations, and furthest-site Voronoi diagrams. It
    runs in 2D, 3D, 4D, and higher dimensions. It implements the
    Quickhull algorithm for computing the convex hull. Qhull does not
    support constrained Delaunay triangulations, or mesh generation of
    non-convex objects, but the package does include some R functions
    that allow for this.
Simulates from discrete and continuous target distributions using geometric
 Metropolis-Hastings (MH) algorithms. Users specify the target distribution by an R
 function that evaluates the log un-normalized pdf or pmf. The package also contains a
 function implementing a specific geometric MH algorithm for performing high-dimensional
 Bayesian variable selection.
The 'geomod' does spatial prediction of the Geotechnical soil properties. 
    It predicts the spatial distribution of Geotechnical properties of soil e.g. shear strength, 
    permeability, plasticity index, Standard Penetration Test (SPT) counts, etc. The output of the prediction takes the form of a 
    map or a series of maps. It uses the interpolation technique where a single or statistically “best” 
    estimate of spatial occurrence soil property is determined. The interpolation is based on both the 
    sampled data and a variogram model for the spatial correlation of the sampled data. 
    The single estimate is produced by a Kriging technique.
Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields,  b) inference  for random fields using standard likelihood and a likelihood approximation  method called  weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial  and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.
Utilizes methods of the 'PyMongo' 'Python' library to initialize, insert and query 'GeoJson' data (see <https://github.com/mongodb/mongo-python-driver> for more information on 'PyMongo'). Furthermore, it allows the user to validate 'GeoJson' objects and to use the console for 'MongoDB' (bulk) commands. The 'reticulate' package provides the 'R' interface to 'Python' modules, classes and functions.
Read and manipulate landmark data, generate shape
    variables via Procrustes analysis for points, curves and surfaces, perform
    shape analyses, and provide graphical depictions of shapes and patterns of
    shape variation.
A 'ggplot2' extension that allows text to follow curved paths.
    Curved text makes it easier to directly label paths or neatly annotate in 
    polar co-ordinates.
Multidimensional systems allow complex queries to be carried
    out in an easy way. The geographical dimension, together with the
    temporal dimension, plays a fundamental role in multidimensional
    systems. Through this package, vector geographic data layers can be
    associated to the attributes of geographic dimensions, so that the
    results of multidimensional queries can be obtained directly as vector
    layers.  The multidimensional structures on which we can define the
    queries can be created from a flat table or imported directly using
    functions from this package.
The web service at <https://www.geonames.org/> provides a number of spatial data queries, including 
    administrative area hierarchies, city locations and some country postal code queries. A (free) username
    is required and rate limits exist.
Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and expose it to OGC CSW.
Provides classes and methods for handling networks or 
  graphs whose nodes are geographical (i.e. locations in the globe).
  The functionality includes the creation of objects of class geonetwork
  as a graph with node coordinates, the computation of network measures,
  the support of spatial operations (projection to different Coordinate
  Reference Systems, handling of bounding boxes, etc.) and the plotting of
  the geonetwork object combined with supplementary cartography for spatial 
  representation.
Provides an interface to the 'GeoNode' API, allowing to upload and publish metadata and data in 'GeoNode'. 
  For more information about the 'GeoNode' API, see <https://geonode.org/>.
Provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. Includes map-based visualisation of the matched regions for validation and exploration. Designed for regional data analysis, reproducible workflows, and integration with common geospatial R packages.
Provides convenient access to the official spatial datasets of Peru as 'sf' objects in R. This package includes a wide range of geospatial data covering various aspects of Peruvian geography, such as: administrative divisions (Source: INEI <https://ide.inei.gob.pe/>), protected natural areas  (Source: GEO ANP - SERNANP <https://geo.sernanp.gob.pe/visorsernanp/>). All datasets are harmonized in terms of attributes, projection, and topology, ensuring consistency and ease of use for spatial analysis and visualization.
Toolset to create perpendicular profile graphs and swath
    profiles. Method are based on coordinate rotation algorithm by
    Schaeben et al. (2024) <doi:10.1002/mma.9823>.
Integrating applied psychological and psychometric methods into geographical analysis. With the emergence of geo-referenced questionnaires, spatially explicit psychological and psychometric methods can offer a geographically contextualised approach that reflects latent traits and processes at a more local scale, leading to more tailored research and decision-making processes. The implemented methods include Geographically Weighted Cronbach's alpha and its bandwidth selection. See Zhang & Li (2025) <doi:10.1111/gean.70021>.
Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.
Calculates and analyzes six measures of geographic range from a set of longitudinal and latitudinal occurrence data. Measures included are minimum convex hull area, minimum spanning tree distance, longitudinal range, latitudinal range, maximum pairwise great circle distance, and number of X by X degree cells occupied.
Reference datasets commonly used in the geosciences. These include 
  standard atomic weights of the elements, a periodic table, a list of minerals 
  including their abbreviations and chemistry, geochemical data of reservoirs 
  (primitive mantle, continental crust, mantle, basalts, etc.), decay constants 
  and isotopic ratios frequently used in geochronology, color codes of the 
  chronostratigraphic chart. In addition, the package provides functions for 
  basic queries of atomic weights, the list of minerals, and chronostratigraphic
  chart colors. All datasets are fully referenced, and a BibTeX file containing 
  the references is included.
Provides functions for efficiently fitting linear models with spatially correlated errors by robust (Kuensch et al. (2011) <doi:10.3929/ethz-a-009900710>) and Gaussian (Harville (1977) <doi:10.1080/01621459.1977.10480998>) (Restricted) Maximum Likelihood and for computing robust and customary point and block external-drift Kriging predictions (Cressie (1993) <doi:10.1002/9781119115151>), along with utility functions for variogram modelling in ad hoc geostatistical analyses, model building, model evaluation by cross-validation, (conditional) simulation of Gaussian processes (Davies and Bryant (2013) <doi:10.18637/jss.v055.i09>), unbiased back-transformation of Kriging predictions of log-transformed data (Cressie (2006) <doi:10.1007/s11004-005-9022-8>).
Provides an R API to the Open Source Geometry Engine
  ('GEOS') library (<https://libgeos.org/>) and a vector format 
  with which to efficiently store 'GEOS' geometries. High-performance functions 
  to extract information from, calculate relationships between, and
  transform geometries are provided. Finally, facilities to import 
  and export geometry vectors to other spatial formats are provided.
This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.
Provides an R interface to the GeoServer REST API, allowing to upload 
 and publish data in a GeoServer web-application and expose data to OGC Web-Services. 
 The package currently supports all CRUD (Create,Read,Update,Delete) operations
 on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes,
 layers, styles, as well as vector data upload operations. For more information about 
 the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.
Functionality for adding the geological timescale to bivariate plots.
Understanding spatial association is essential for spatial 
             statistical inference, including factor exploration and spatial prediction. 
             Geographically optimal similarity (GOS) model is an effective method 
             for spatial prediction, as described in Yongze Song (2022) 
             <doi:10.1007/s11004-022-10036-8>. GOS was developed based on 
             the geographical similarity principle, as described in Axing Zhu (2018) 
             <doi:10.1080/19475683.2018.1534890>. GOS has advantages in 
             more accurate spatial prediction using fewer samples and 
             critically reduced prediction uncertainty.
R binds 'GeoSpark' <http://geospark.datasyslab.org/> extending 'sparklyr' 
    <https://spark.rstudio.com/> R package to make distributed 'geocomputing' easier. Sf is a
    package that provides [simple features] <https://en.wikipedia.org/wiki/Simple_Features> access
    for R and which is a leading 'geospatial' data processing tool. 'Geospark' R package bring 
    the same simple features access like sf but running on Spark distributed system.
A comprehensive toolkit for geospatiotemporal analysis
    featuring 60+ vegetation indices, advanced raster visualization,
    universal spatial mapping, water quality analysis, CDL crop analysis,
    spatial interpolation, temporal analysis, and terrain analysis.
    Designed for agricultural research, environmental monitoring, remote
    sensing applications, and publication-quality mapping with support for
    any geographic region and robust error handling. Methods include
    vegetation indices calculations (Rouse et al. 1974), NDVI and enhanced
    vegetation indices (Huete et al. 1997)
    <doi:10.1016/S0034-4257(97)00104-1>, (Akanbi et al. 2024) 
    <doi:10.1007/s41651-023-00164-y>, spatial interpolation techniques
    (Cressie 1993, ISBN:9780471002556), water quality indices (McFeeters
    1996) <doi:10.1080/01431169608948714>, and crop data layer analysis
    (USDA NASS 2024)
    <https://www.nass.usda.gov/Research_and_Science/Cropland/>.  Funding:
    This material is based upon financial support by the National Science
    Foundation, EEC Division of Engineering Education and Centers, NSF
    Engineering Research Center for Advancing Sustainable and Distributed
    Fertilizer production (CASFER), NSF 20-553 Gen-4 Engineering Research
    Centers award 2133576.
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis 
        functions (optimization, prediction and cross-validation), summary
        statistics from cross-validation, pocket plot, and design of
        optimal sampling networks through sequential and simultaneous
        points methods.
Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
A collection of datasets and simplified functions for an introductory (geo)statistics module at University College London. Provides functionality for compositional, directional and spatial data, including ternary diagrams, Wulff and Schmidt stereonets, and ordinary kriging interpolation. Implements logistic and (additive and centred) logratio transformations. Computes vector averages and concentration parameters for the von-Mises distribution. Includes a collection of natural and synthetic fractals, and a simulator for deterministic chaos using a magnetic pendulum example. The main purpose of these functions is pedagogical. Researchers can find more complete alternatives for these tools in other packages such as 'compositions', 'robCompositions', 'sp', 'gstat' and 'RFOC'. All the functions are written in plain R, with no compiled code and a minimal number of dependencies. Theoretical background and worked examples are available at <https://tinyurl.com/UCLgeostats/>.
Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector'
    objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian
    geostatistical models use Maximum Likelihood Estimation.  For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The 'RandomFields' package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.
Provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the 'terra' geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions.
Provides efficient geospatial thinning algorithms to reduce
    the density of coordinate data while maintaining spatial
    relationships. Implements K-D Tree and brute-force distance-based
    thinning, as well as grid-based and precision-based thinning methods.
    For more information on the methods, see Elseberg et al. (2012)
    <https://hdl.handle.net/10446/86202>.
The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.
It analyzes raster maps and other information as input/output
    files from the Hydrological Distributed Model GEOtop. It contains functions
    and methods to import maps and other keywords from geotop.inpts file. Some
    examples with simulation cases of GEOtop 2.x/3.x are presented in the package.
    Any information about the GEOtop Distributed Hydrological Model can be found in the provided documentation.
Connecting spatiotemporal exposure to individual and
    population-level risk via source-to-outcome continuum modeling. The
    package, methods, and case-studies are described in Messier, Reif, and
    Marvel (2025) <doi:10.1186/s40246-024-00711-8> and Eccles et al.
    (2023) <doi:10.1016/j.scitotenv.2022.158905>.
Provides functions and methods for: splitting large raster objects
             into smaller chunks, transferring images from a binary format into raster 
             layers, transferring raster layers into an 'RData' file, calculating the 
             maximum gap (amount of consecutive missing values) of a numeric vector, 
             and fitting harmonic regression models to periodic time series. The homoscedastic
             harmonic regression model is based on G. Roerink, M. Menenti and W. Verhoef (2000) <doi:10.1080/014311600209814>.
Simulation, estimation and testing for geopolitical volatility (GEOVOL) based on the global common volatility model of Engle and Campos-Martins (2023) <doi:10.1016/j.jfineco.2022.09.009>. GEOVOL is modelled as a latent multiplicative volatility factor with heterogeneous factor loadings. Estimation is carried out as a maximization-maximization procedure, where GEOVOL and the GEOVOL loadings are estimated iteratively until convergence.
Contains the development of a tool that provides a web-based
    graphical user interface (GUI) to perform Techniques from a subset of
    spatial statistics known as geographically weighted (GW) models.
    Contains methods described by Brunsdon et al., 1996
    <doi:10.1111/j.1538-4632.1996.tb00936.x>, Brunsdon et al., 2002
    <doi:10.1016/s0198-9715(01)00009-6>, Harris et al., 2011
    <doi:10.1080/13658816.2011.554838>, Brunsdon et al., 2007
    <doi:10.1111/j.1538-4632.2007.00709.x>.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format.
    See <https://developers.google.com/maps/documentation/utilities/polylinealgorithm>
    for more information.
Implements key features of 'Gephi' for network visualization, including 'ForceAtlas2' (with LinLog mode), network scaling, and network rotations. It also includes easy network visualization tools such as edge and node color assignment for recreating 'Gephi'-style graphs in R. The package references layout algorithms developed by Jacomy, M., Venturini T., Heymann S., and Bastian M. (2014) <doi:10.1371/journal.pone.0098679> and Noack, A. (2009) <doi:10.48550/arXiv.0807.4052>.
Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. <doi:10.1080/02664763.2016.1268571>. Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. <doi:10.1080/03610918.2018.1491988>. Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.
Implements a new multiple imputation method that draws
    imputations from a latent joint multivariate normal model which
    underpins generally structured data. This model is constructed using a
    sequence of flexible conditional linear models that enables the
    resulting procedure to be efficiently implemented on high dimensional
    datasets in practice. See Robbins (2021) <arXiv:2008.02243>.
Provides tools to download datasets of German elections
    covering local, state, federal, mayoral, European Parliament, and county
    (Kreistag) elections, with federal county-level coverage from 1953 and
    other families extending through 2025. The package supplies turnout, vote
    shares, and derived indicators at the municipal and county level, including
    geographically harmonized datasets that account for changes in municipal
    boundaries over time and incorporate mail-in voting districts. Bundled data
    includes county-level INKAR covariates (1995-2022) and municipality-level
    Zensus 2022 indicators. Data is sourced from
    <https://github.com/awiedem/german_election_data>.
Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. 
    The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package 'bibliorefer'. 
    The generated file contains the list of metadata of the principal references in 'BibTex' format. 
    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>.
    Hamid Derviş. (2019) <doi:10.5530/jscires.8.3.32>.
A collection of different indices and visualization techniques for evaluate the seed germination process in ecophysiological studies (Lozano-Isla et al. 2019) <doi:10.1111/1440-1703.1275>.
Provides functions to compute various germination indices
    such as germinability, median germination time, mean germination time,
    mean germination rate, speed of germination, Timson's index,
    germination value, coefficient of uniformity of germination,
    uncertainty of germination process, synchrony of germination etc. from
    germination count data. Includes functions for fitting cumulative seed
    germination curves using four-parameter hill function and computation
    of associated parameters. See the vignette for more, including full
    list of citations for the methods implemented.
Simulating, visualizing and comparing tumor clonal data by using simple commands. This aims at providing a tool to help researchers to easily simulate tumor data and analyze the results of their approaches for studying the composition and the evolutionary history of tumors.
Simple git client for R based on 'libgit2' <https://libgit2.org> with
    support for SSH and HTTPS remotes. All functions in 'gert' use basic R data 
    types (such as vectors and data-frames) for their arguments and return values.
    User credentials are shared with command line 'git' through the git-credential
    store and ssh keys stored on disk or ssh-agent.
Implementing generalized structured component analysis (GSCA) and
    its basic extensions, including constrained single and multiple group
    analysis, and second order latent variable modeling. For a comprehensive
    overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754).
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Reproducible, programmatic retrieval of datasets from the
    GESIS Data Archive.  The GESIS Data Archive <https://search.gesis.org>  
    makes available thousands of invaluable datasets, but researchers using
    these datasets are caught in a bind.  The archive's terms and conditions
    bar dissemination of downloaded datasets to third parties, but to ensure 
    that one's work can be reproduced, assessed, and built upon by others, one
    must provide access to the raw data one has employed.  The 'gesisdata'
    package cuts this knot by providing registered users with programmatic,
    reproducible access to GESIS datasets from within 'R'.
Promote access to the GESLA
    <https://gesla787883612.wordpress.com> (Global Extreme Sea Level
    Analysis) dataset, a higher-frequency sea-level record data from all
    over the world. It provides functions to download it entirely, or
    query subsets directly into R, without the need of downloading the
    full dataset. Also, it provides a built-in web-application, so that
    users can apply basic filters to select the data of interest,
    generating informative plots, and showing the selected sites.
Provides a suite of function-building tools centered around a
  (forward) composition operator, %>>>%, which extends the semantics of the
  'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables
  you to construct composite functions that can be inspected and transformed as
  list-like objects. In conjunction with %>>>%, a compact function constructor,
  fn(), and a partial-application constructor, partial(), are also provided;
  both support quasiquotation.
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
Implementation of global envelopes for a set of general d-dimensional vectors T
    in various applications. A 100(1-alpha)% global envelope is a band bounded by two
    vectors such that the probability that T falls outside this envelope in any of the d
    points is equal to alpha. Global means that the probability is controlled simultaneously
    for all the d elements of the vectors. The global envelopes can be used for graphical
    Monte Carlo and permutation tests where the test statistic is a multivariate vector or
    function (e.g. goodness-of-fit testing for point patterns and random sets, functional
    analysis of variance, functional general linear model, n-sample test of correspondence
    of distribution functions), for central regions of functional or multivariate data (e.g.
    outlier detection, functional boxplot) and for global confidence and prediction bands
    (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See
    Myllymäki and Mrkvička (2024) <doi:10.18637/jss.v111.i03>,
    Myllymäki et al. (2017) <doi:10.1111/rssb.12172>,
    Mrkvička and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>,
    Mrkvička et al. (2016) <doi:10.1016/j.spasta.2016.04.005>,
    Mrkvička et al. (2017) <doi:10.1007/s11222-016-9683-9>,
    Mrkvička et al. (2020) <doi:10.14736/kyb-2020-3-0432>,
    Mrkvička et al. (2021) <doi:10.1007/s11009-019-09756-y>,
    Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>,
    Mrkvička et al. (2022) <doi:10.1002/sim.9236>,
    Dai et al. (2022) <doi:10.5772/intechopen.100124>,
    Dvořák and Mrkvička (2022) <doi:10.1007/s00180-021-01134-y>,
    Mrkvička et al. (2023) <doi:10.48550/arXiv.2309.04746>, and
    Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.
Dynamically retrieve data from the web to render HTML tables
    on inspection in R Markdown HTML documents.
Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the 'memoise' package and
    , multicore/multisession with 'furrr' and format of output data (long/wide).
Provides functions that automate downloading and importing
    University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0
    climatology data, facilitates the calculation of minimum temperature
    and maximum temperature and formats the data into a data.table object
    or a 'terra' 'SpatRaster' object.  'CRU' 'CL' v. 2.0 data are a
    gridded climatology of 1961-1990 monthly means released in 2002 and
    cover all land areas (excluding Antarctica) at 10 arc minutes
    (0.1666667 degree) resolution.  For more information see the
    description of the data provided by the University of East Anglia
    Climate Research Unit,
    <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,…,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.
Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange 
            <https://www.b3.com.br/>. 
            All data is downloaded and imported from CVM's public ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/>.
The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design.
    The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.
Reads corporate data such as board composition and compensation for companies traded at B3, 
             the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from the ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/DOC/FRE/>.
A micro-package for getting your 'IP' address, either the 
    local/internal or the public/external one. Currently only 'IPv4' addresses
    are supported.
Tool for import and process data from 'Lattes' curriculum platform (<http://lattes.cnpq.br/>). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in 'Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. 'getLattes' package allows work with 'Lattes' data exported to XML format.
A simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.
Quantifying systematic heterogeneity in meta-analysis using R.
    The M statistic aggregates heterogeneity information across multiple
    variants to, identify systematic heterogeneity patterns and their direction
    of effect in meta-analysis. It's primary use is to identify outlier studies,
    which either show "null" effects or consistently show stronger or weaker
    genetic effects than average across, the panel of variants examined in a
    GWAS meta-analysis. In contrast to conventional heterogeneity metrics
    (Q-statistic, I-squared and tau-squared) which measure random heterogeneity
    at individual variants, M measures systematic (non-random)
    heterogeneity across multiple independently associated variants. Systematic
    heterogeneity can arise in a meta-analysis due to differences in the study
    characteristics of participating studies. Some of the differences may
    include: ancestry, allele frequencies, phenotype definition, age-of-disease
    onset, family-history, gender, linkage disequilibrium and quality control
    thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
    statistical theory, documentation and examples.
Package designed to be used with Rscript to write
    '#!' shebang scripts that accept short and long flags/options.
    Many users will prefer using instead the packages optparse or argparse
    which add extra features like automatically generated help option and usage,
    support for default values, positional argument support, etc.
This is a command-line argument parser which wraps the 
    powerful Perl module Getopt::Long and with some adaptations for easier use
	in R.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
Allows get address and port 
	of the free proxy server, from one of two services
	<http://gimmeproxy.com/> or <https://getproxylist.com/>. 
	And it's easy to redirect your Internet connection through
	a proxy server.
Imports time series data from the 'Quandl' database <https://data.nasdaq.com/>. The package uses the  'json api' at <https://data.nasdaq.com/search>, local caching ('memoise' package) and the tidy format by default. 
   Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package 'Quandl', with faster data importation in the tidy/long format.
Load polar volume and vertical profile data for
    aeroecological research directly into R. With 'getRad' you can access
    data from several sources in Europe and the US and standardize it to
    facilitate further exploration in tools such as 'bioRad'.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.
Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the 'gets' package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.
An implementation of SPRE (standardised predicted random-effects)
    statistics in R to explore heterogeneity in genetic association meta-
    analyses, as described by Magosi et al. (2019) 
    <doi:10.1093/bioinformatics/btz590>. SPRE statistics are precision 
    weighted residuals that indicate the direction and extent with which 
    individual study-effects in a meta-analysis deviate from the average 
    genetic effect. Overly influential positive outliers have the potential 
    to inflate average genetic effects in a meta-analysis whilst negative 
    outliers might lower or change the direction of effect. See the 'getspres' 
    website for documentation and examples 
    <https://magosil86.github.io/getspres/>.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <https://www.tesourodireto.com.br/>.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
A simple wrapper for 'Wikipedia' data. Specifically, this 
    package looks to fill a gap in retrieving text data in a tidy format that can be used for Natural Language Processing.
A novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.
Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.
Generates experiments - simulating structured or experimental data as: 
             completely randomized design, randomized block design, latin square design, 
             factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; 
             Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; 
             Montgomery, 2001, ISBN:0471316490).
Download geyser eruption and observation data from the GeyserTimes
  site (<https://geysertimes.org>) and optionally store it locally. The vignette
  shows a simple analysis of downloading, accessing, and summarizing the data.
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.
Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL '<https://oops.uni-oldenburg.de/id/eprint/4290>').
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
    Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.
Performs test procedures for general hypothesis testing problems for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023 <arXiv:2301.12009>). We can verify the global hypothesis about equality as well as the particular hypotheses defined by contrasts, e.g., we can conduct post hoc tests. We also provide the simultaneous confidence intervals for contrasts.
We implemented multiple tests based on the restricted mean survival time (RMST) for general factorial designs as described in Munko et al. (2024) <doi:10.1002/sim.10017>. Therefore, an asymptotic test, a groupwise bootstrap test, and a permutation test are incorporated with a Wald-type test statistic. The asymptotic and groupwise bootstrap test take the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMST contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.
We implemented multiple tests based on the restricted mean time lost (RMTL) for general factorial designs as described in Munko et al. (2024) <doi:10.48550/arXiv.2409.07917>. Therefore, an asymptotic test and a permutation test are incorporated with a Wald-type test statistic. The asymptotic test takes the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMTL contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.
Implemented are three Wald-type statistic and respective
  permuted versions for null hypotheses formulated in terms of cumulative hazard rate functions, medians and the concordance measure, respectively, in the general framework of survival factorial designs with possibly heterogeneous survival and/or censoring distributions, for crossed designs with an arbitrary number of factors and nested designs with up to three factors.
	Ditzhaus, Dobler and Pauly (2020) <doi:10.1177/0962280220980784> 
	Ditzhaus, Genuneit, Janssen, Pauly (2023) <doi:10.1111/biom.13575>
	Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.
The philosophy in the package is described in Stasny (1988) <doi:10.2307/1391558> and Guti?rrez, A., Trujillo, L. & Silva, N. (2014), <ISSN:1492-0921> to estimate the gross flows under complex surveys using a Markov chain approach with non response.
Focuses on data collecting, analyzing and visualization in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites such as MEP (Ministry of Environmental Protection of China, <https://www.mee.gov.cn>), water 
  related projects identification and environmental data visualization.
Facilitates the extraction and organization of strand-specific genomic features from GFF3 files. In many species and variants, high quality genome annotations are not always available, necessitating de novo annotation using tools such as AUGUSTUS (Stanke et al., 2006; <doi:10.1093/nar/gkl200>). However, downstream processing of such annotations to obtain structured information, such as strand-wise gene locations, transcript regions, and associated protein identifiers—can be computationally intensive and complex.
    'GFFStrandLoc' provides a streamlined framework to parse GFF3 files and generate structured outputs containing strand-wise and region-wise genomic coordinates for each transcript, along with their associated protein information. Additionally, it enables users to define custom promoter lengths and extract corresponding promoter region coordinates for genes in a strand-aware manner. By simplifying post-annotation processing, it enhances the usability of de novo annotated genomic datasets for downstream analysis and interpretation.
Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2018) <doi:10.1007/s00180-018-0804-0> and Shih and Emura (2019) <doi:10.1007/s00362-016-0865-5> for details.
Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.
Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.
Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables.
    Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model,
    respectively. The factor matrix and loading matrix together with the number of factors can be well estimated. 
    This model can be employed in social and behavioral sciences, economy and finance, and  genomics, 
    to extract interpretable nonlinear factors. More details can be referred to 
    Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2023) <doi:10.1080/01621459.2021.1999818>.
Download 'Google' fonts and generate 'CSS' to use in 'rmarkdown' documents and 
  'shiny' applications. Some popular fonts are included and ready to use.
Implements the non-iterative conditional expectation (NICE) 
    algorithm of the g-formula algorithm (Robins (1986) 
    <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). 
    The g-formula can estimate an outcome's counterfactual mean or risk under 
    hypothetical treatment strategies (interventions) when there is sufficient 
    information on time-varying treatments and confounders. 
    This package can be used for discrete or continuous time-varying treatments 
    and for failure time outcomes or continuous/binary end of follow-up 
    outcomes. The package can handle a random measurement/visit process and a 
    priori knowledge of the data structure, as well as censoring (e.g., by loss 
    to follow-up) and two options for handling competing events for failure time
    outcomes. Interventions can be flexibly specified, both as interventions on 
    a single treatment or as joint interventions on multiple treatments.
    See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on 
    how to use the package.
Implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi: 10.1111/biom.13321>). 
    Both singly robust and doubly robust ICE estimators based on parametric models are available. 
    The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events.
    The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.
Implements the G-Formula method for causal inference with time-varying treatments and
  confounders using Bayesian multiple imputation methods, as described by
  Bartlett et al (2025) <doi:10.1177/09622802251316971>. It creates multiple synthetic imputed
  datasets under treatment regimes of interest using the 'mice' package. These can then be analysed
  using rules developed for analysing multiple synthetic datasets.
Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix 'g' is a reminder of who to blame if things do not work as they should.
It is not very easy to define segments for y-axis in a 'ggplot2' plot. gg.gap() function in this package can carry it out.
Streamlines exploratory data analysis by providing a turnkey approach 
  to visualising n-dimensional data which graphically reveals correlative or associative relationships 
  between 2 or more features. Represents all dataset features as distinct, vertically 
  aligned bar or tile plots, with plot types auto-selected based on whether variables are categorical or numeric.
A 'ggplot2' extension providing an integrative framework for
    composable visualization, enabling the creation of complex multi-plot
    layouts such as insets, circular arrangements, and multi-panel compositions.
    Built on the grammar of graphics, it offers tools to align, stack, and nest
    plots, simplifying the construction of richly annotated figures for
    high-dimensional data contexts—such as genomics, transcriptomics, and
    microbiome studies—by making it easy to link related plots, overlay
    clustering results, or highlight shared patterns.
Providing publication-ready graphs for Multiple sequence alignment. Moreover, it provides a unique solution for visualizing the multiple sequence alignment without the need to do the alignment in each run which is a big limitation in other available packages.
'D&D' alignment charts show 9 boxes with values for good
    through evil and values for chaotic through lawful. This package
    easily creates these alignment charts from user-provided image paths
    and alignment values.
Extra geoms and scales for 'ggplot2', including geom_cloud(),
  a Normal density cloud replacement for errorbars;
  transforms ssqrt_trans and pseudolog10_trans, which are loglike but 
  appropriate for negative data; interp_trans() and warp_trans() which
  provide scale transforms based on interpolation;
  and an infix compose operator for scale transforms.
Alluvial plots use variable-width ribbons and stacked bar plots to
    represent multi-dimensional or repeated-measures data with categorical or
    ordinal variables; see Riehmann, Hanfler, and Froehlich (2005)
    <doi:10.1109/INFVIS.2005.1532152> and Rosvall and Bergstrom (2010)
    <doi:10.1371/journal.pone.0008694>.
    Alluvial plots are statistical graphics in the sense of Wilkinson (2006)
    <doi:10.1007/0-387-28695-0>; they share elements with Sankey diagrams and
    parallel sets plots but are uniquely determined from the data and a small
    set of parameters. This package extends Wickham's (2010)
    <doi:10.1198/jcgs.2009.07098> layered grammar of graphics to generate
    alluvial plots from tidy data.
The R package 'ggplot2' is a plotting system based on the
    grammar of graphics.  'GGally' extends 'ggplot2' by adding several
    functions to reduce the complexity of combining geometric objects with
    transformed data.  Some of these functions include a pairwise plot
    matrix, a two group pairwise plot matrix, a parallel coordinates plot,
    a survival plot, and several functions to plot networks.
Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.
The grammar of graphics as implemented in the 'ggplot2'
    package has been successful in providing a powerful API for creating
    static visualisation. In order to extend the API for animated graphics
    this package provides a completely new set of grammar, fully
    compatible with 'ggplot2' for specifying transitions and animations in
    a flexible and extensible way.
Geoms for placing arrowheads at multiple points along a segment, not just at the end; position function to shift starts and ends of arrows to avoid exactly intersecting points.
A 'ggplot2' extension that adds specialised arrow geometry layers. 
    It offers more arrow options than the standard 'grid' arrows that 
    are built-in many line-based geom layers.
Automatically choose an appropriate chart type based on the
    types and values in the data. Apply more accessible default styling and colours to 'ggplot2' charts.
Mapping tools that convert place names to coordinates on the fly.
    These 'ggplot2' extensions make maps from a data frame where one of the
    columns contains place names, without having to directly work with the
    underlying geospatial data and tools. The corresponding map data must be
    registered with 'cartographer' either by the user or by another package.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
A 'ggplot2' based implementation of biplots, giving a representation of a dataset in
    a two dimensional space accounting for the greatest variance, together with variable vectors
    showing how the data variables relate to this space. It provides a 
    replacement for stats::biplot(), but with many enhancements to control the analysis and
    graphical display. It implements 
    biplot and scree plot methods which can be used with the results of prcomp(), princomp(),
    FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using 'ggplot2' techniques.
Wrapper 'ggplot2' functions for publication-quality visualisation. 
    Aligned with 'ggplot2' and 'tidyverse'. Covers much of what 'ggplot2' does.
Algebra of operations for blending, copying, adjusting, and 
    compositing layers in 'ggplot2'. Supports copying and adjusting the 
    aesthetics or parameters of an existing layer, partitioning a layer 
    into multiple pieces for re-composition, applying affine transformations
    to layers, and combining layers (or partitions of layers) using blend modes
    (including commutative blend modes, like multiply and darken). Blend 
    mode support is particularly useful for creating plots with overlapping 
    groups where the layer drawing order does not change the output; 
    see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.
A set of geometries to make line plots a little bit nicer. Use
    along with 'ggplot2' to:
    - Improve the clarity of line plots with many overlapping lines
    - Draw more realistic worms.
Provides curly braces and square brackets in 'ggplot2' plus matching text.
    stat_brace() plots braces/brackets to embrace data.
    stat_bracetext() plots corresponding text, fitting to the braces from stat_brace().
A 'ggplot2'-consistent approach to generating 2D displays of  volumetric brain imaging data.
  Display data from multiple NIfTI images using standard 'ggplot2' conventions such scales, limits, and
  themes to control the appearance of displays. The resulting plots are returned as 'patchwork' objects,
  inheriting from 'ggplot', allowing for any standard modifications of display aesthetics supported by 'ggplot2'.
An implementation of scale functions for setting axis breaks of a 'gg' plot (S Xu (2021) <doi:10.3389/fgene.2021.774846>).
A new take on the bar chart. Similar to a waffle style chart but
  instead of squares the layout resembles a brick wall.
When comparing discrete data mini bubble plots allow displaying 
   more information than traditional bubble plots via colour, shape or labels. 
   Exact overlapping coordinates will be transformed so they surround the
   original point circularly without overlapping. This is implemented as a 
   position_surround() function for 'ggplot2'.
Saves a 'ggplot' object into multiple files, each with a layer 
    added incrementally. Generally to be used in presentation slides.
    Flexible enough to allow different file types for the final complete plot,
    and intermediate builds.
R provides fantastic tools for changepoint
    analysis, but plots generated by the tools do 
    not have the 'ggplot2' style. This tool, however, 
    combines 'changepoint', 'changepoint.np' and 'ecp'
    together, and uses 'ggplot2' to visualize changepoints.
Streamline the creation of common charts by taking care of a lot of
    data preprocessing and plot customization for the user. Provides a
    high-level interface to create plots using 'ggplot2'.
Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.
A function built on 'ggplot2' that visualizes pairwise 'BLAST' alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences.
Provides a 'ggplot2' grammar for circular, axial and directional data, including rose diagrams, circular densities, mean directions, confidence arcs, theoretical circular distributions and movement data visualizations.
Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.
William S. Cleveland's book 'Visualizing Data' is a classic piece 
	of literature on Exploratory Data Analysis. Although it was written 
	several decades ago, its content is still relevant as it proposes several 
	tools which are useful to discover patterns and relationships among the data 
	under study, and also to assess the goodness of fit o a model.  This package 
	provides functions to produce the 'ggplot2' versions of the visualization tools 
	described in this book and is thought to be used in the context of courses on 
	Exploratory Data Analysis.
Add mean comparison annotations to a 'ggplot'. 
             This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. 
             Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. 
             For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).
Create correlation heatmaps with 'ggplot2' and customise them
    with flexible annotation and clustering. Symmetric heatmaps can use
    triangular or mixed layouts, removing redundant information or displaying
    complementary information in the two halves. There is also support for
    general heatmaps not displaying correlations.
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for
    reordering the correlation matrix and displays the significance level
    on the plot. It also includes a function for computing a matrix of
    correlation p-values.
A 'ggplot2' extension for creating 3D figures.
    Provides 3D geoms, stats, and a coord_3d() coordinate
    system supporting rotation, perspective, and lighting.
Tidy, analyze, and plot directed acyclic graphs (DAGs).
    'ggdag' is built on top of 'dagitty', an R package that uses the
    'DAGitty' web tool (<https://dagitty.net/>) for creating and analyzing
    DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using
    'ggplot2' and 'ggraph', as well as common analytic and graphical
    functions, such as determining adjustment sets and node relationships.
It provides a custom 'ggplot2' geom to add day/night patterns to plots.
    It visually distinguishes daytime and nighttime periods. It is useful for 
    visualizing data that spans multiple days and for highlighting diurnal patterns.
Provides 'ggplot2' functions to return the results of seasonal and trading day adjustment 
    made by 'RJDemetra'. 'RJDemetra' is an 'R' interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>),
    the seasonal adjustment software officially recommended to the members of the European Statistical System and
    the European System of Central Banks.
This is a set of tools for dendrograms and
    tree plots using 'ggplot2'.  The 'ggplot2' philosophy is to
    clearly separate data from the presentation.
    Unfortunately the plot method for dendrograms plots
    directly to a plot device without exposing the data.
    The 'ggdendro' package resolves this by making available
    functions that extract the dendrogram plot data. The package
    provides implementations for 'tree', 'rpart', as well as diana and agnes
    (from 'cluster') diagrams.
The 'ggplot2' package provides simple functions for visualizing contours
  of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators 
  as well as more interpretable visualizations based on highest density regions instead of
  the traditional height of the estimated density surface.
Creates diagrams with an object-oriented approach. Geometric
    objects have computed properties with information about themselves
    (e.g., their area) or about their relationships with other objects
    (e.g, the distance between their edges). The objects have methods to
    convert them to geoms that can be plotted in 'ggplot2'.
A 'ggplot2' extension for visualising uncertainty with the goal
    of signal suppression. Usually, uncertainty visualisation focuses on
    expressing uncertainty as a distribution or probability, whereas
    'ggdibbler' differentiates itself by viewing an uncertainty
    visualisation as an adjustment to an existing graphic that
    incorporates the inherent uncertainty in the estimates.  You provide
    the code for an existing plot, but replace any of the variables with a
    vector of distributions, and it will convert the visualisation into
    it's signal suppression counterpart.
Provides 'ggplot2' extensions for creating dice-based visualizations
    where each dot position represents a specific categorical variable. The package
    includes 'geom_dice()' for displaying presence/absence of categorical variables
    using traditional dice patterns. Each dice position (1-6) represents a different
    category, with dots shown only when that category is present. This allows
    intuitive visualization of up to 6 categorical variables simultaneously.
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for
    visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as 
    frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as 
    bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include
    but are not limited to: points with multiple uncertainty intervals, 
    eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>,
    density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>,
    quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>,
    complementary cumulative distribution function 
    barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>,
    and fit curves with multiple uncertainty ribbons.
Hierarchical Bayesian models. The package provides tools to fit two response time models, using the population-based Markov Chain Monte Carlo.
A fast 'C++' implementation of the design-based, Diffusion Decision Model (DDM) and the Linear Ballistic Accumulation (LBA) model. It enables the user to optimise the choice response time model by connecting with the Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampler implemented in the 'ggdmc' package. The package fuses the hierarchical modelling, Bayesian inference, choice response time models and factorial designs, allowing users to build their own design-based models. For more information on the underlying models, see the works by Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>, and Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.
Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.
A suite of tools for specifying and examining experimental designs related to choice response time models (e.g., the Diffusion Decision Model). This package allows users to define how experimental factors influence one or more model parameters using R-style formula syntax, while also checking the logical consistency of these associations. Additionally, it integrates with the 'ggdmc' package, which employs Differential Evolution Markov Chain Monte Carlo (DE-MCMC) sampling to optimise model parameters. For further details on the model-building approach, see Heathcote, Lin, Reynolds, Strickland, Gretton, and Matzke (2019) <doi:10.3758/s13428-018-1067-y>.
Provides tools for specifying and evaluating standard and truncated probability distributions, with support for log-space computation and joint distribution specification. It enables Bayesian computation for cognition models and includes utilities for density calculation, sampling, and visualisation, facilitating prior distribution specification and model assessment in hierarchical Bayesian frameworks.
Uses 'ggplot2' to visualise either (a) a single DNA/RNA sequence split across multiple lines, (b) multiple DNA/RNA sequences, each occupying a whole line, or (c) base modifications such as DNA methylation called by modified bases models in Dorado or Guppy. Functions starting with visualise_<>() are the main plotting functions, and functions starting with extract_and_sort_<>() are key helper functions for reading files and reformatting data. Source code is available at <https://github.com/ejade42/ggDNAvis>, a full non-expert user guide is available at <https://ejade42.github.io/ggDNAvis/>, and an interactive web-app version of the software is available at <https://ejade42.github.io/ggDNAvis/articles/interactive_app.html>.
Generate commonly used plots in the field of design of experiments using 'ggplot2'.
  'ggDoE' currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot,
  regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs,
  contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.
A data visualization design that provides comparison between 
    two (Double) data sources (usually on a par with each other) on one 
    reformed heatmap, while inheriting 'ggplot2' features.
Create biplots for GGE (genotype plus
    genotype-by-environment) and GGB (genotype plus
    genotype-by-block-of-environments) models.  See Laffont et al. (2013)
    <doi:10.2135/cropsci2013.03.0178>.
Provides a series of aliases to commonly used but difficult 
    to remember 'ggplot2' sequences.
Genotype plus genotype-by-environment (GGE) biplots rendered using 'ggplot2'. Provides a command line interface to all of the functionality contained within the archived package 'GGEBiplotGUI'.
Provides interactive visualisations for exploratory data analysis of high-dimensional datasets.
  Includes parallel coordinate plots for exploring large datasets with mostly quantitative features, 
  but also stacked one-dimensional visualisations that more effectively show missingness and complex categorical relationships in smaller datasets.
Compute marginal effects and adjusted predictions from statistical
    models and returns the result as tidy data frames. These data frames are
    ready to use with the 'ggplot2'-package. Effects and predictions can be
    calculated for many different models. Interaction terms, splines and
    polynomial terms are also supported. The main functions are ggpredict(),
    ggemmeans() and ggeffect(). There is a generic plot()-method to plot the
    results using 'ggplot2'.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. Uses the Grammar of Graphics.
Audits 'ggplot2' visualizations for accessibility issues, misleading 
    practices, and readability problems. Checks for color accessibility concerns 
    including colorblind-unfriendly palettes, misleading scale manipulations such 
    as truncated axes and dual y-axes, text readability issues like small fonts 
    and overlapping labels, and general accessibility barriers. Provides 
    comprehensive audit reports with actionable suggestions for improvement.
    Color vision deficiency simulation uses methods from the 'colorspace' 
    package Zeileis et al. (2020) <doi:10.18637/jss.v096.i01>. Contrast 
    calculations follow WCAG 2.1 guidelines (W3C 2018 
    <https://www.w3.org/WAI/WCAG21/Understanding/contrast-minimum>).
Extends the 'ggplot2' error geoms.
    geom_error() accepts an error aesthetic with auto-inference of the orientation.
    It also supports `error_neg` and `error_pos` for asymmetric cases, with full control over aesthetics per side, such as color, width etc...
Extension of 'ggplot2' providing layers, scales and preprocessing functions
    useful to represent behavioural variables that are recorded over multiple animals and days.
    This package is part of the 'rethomics' framework <https://rethomics.github.io/>.
Provides 'ggplot2'-based visualization functions for output objects
    from the 'exametrika' package, which implements test data engineering methods
    described in Shojima (2022, ISBN:978-981-16-9547-1). Supports a wide range of
    psychometric models including Item Response Theory, Latent Class Analysis,
    Latent Rank Analysis, Biclustering (binary, ordinal, and nominal),
    Bayesian Network Models, and related network models. All plot functions
    return 'ggplot2' objects that can be further customized by the user.
Collection of functions and layers to enhance 'ggplot2'. The 
    flagship function is 'ggMarginal()', which can be used to add marginal
    histograms/boxplots/density plots to 'ggplot2' scatterplots.
Readable, complete and pretty graphs for correspondence analysis made 
    with 'FactoMineR'. They can be rendered as interactive 'HTML' plots, showing useful
    informations at mouse hover. The interest is not mainly visual but statistical:
    it helps the reader to keep in mind the data contained in the cross-table or Burt
    table while reading the correspondence analysis, thus preventing over-interpretation.
    Most graphs are made with 'ggplot2', which means that you can use the + syntax to 
    manually add as many graphical pieces you want, or change theme elements. 3D 
    graphs are made with 'plotly'.
Add vector field layers to ggplots. Ideal for visualising
    wind speeds, water currents, electric/magnetic fields, etc.
    Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars)
    objects as input. Vector fields are depicted as arrows starting at specified
    locations, and with specified angles and radii.
When you prepare a presentation or a report, you often need to manage a large number of 'ggplot' figures. You need to change the figure size, modify the title, label, themes, etc. It is inconvenient to go back to the original code to make these changes. This package provides a simple way to manage 'ggplot' figures. You can easily add the figure to the database and update them later using CLI (command line interface) or GUI (graphical user interface).
Contains functions to create life history parameter plots from raw data. 
	The plots are created using 'ggplot2', and calculations done using the 'tidyverse'
	collection of packages. The package contains references to FishBase 
	(Froese R., Pauly D., 2023) <https://www.fishbase.se/>.
A 'ggplot2' extension to fit text into a box by growing, shrinking or wrapping the text.
Provides 'ggplot2' equivalents of fixest::coefplot() and fixest::iplot(),
    for producing nice coefficient plots and interaction plots. Enables some
    additional functionality and convenience features, including grouped
    multi-'fixest' object faceting and programmatic updates to existing plots
    (e.g., themes and aesthetics).
Flowcharts can be a useful way to visualise complex processes. This package 
    uses the layered grammar of graphics of 'ggplot2' to create simple flowcharts.
A 'ggplot2' extension that provides tools for automatically
    creating scales to focus on subgroups of the data plotted 
    without losing other information.
Scrapes football match shots data from 'Understat' <https://understat.com/> and visualizes it using interactive plots:
    - A detailed shot map displaying the location, type, and xG value of shots taken by both teams.
    - An xG timeline chart showing the cumulative xG for each team over time, annotated with the details of scored goals.
The aim of 'ggplot2' is to aid in visual data investigations. This
    focus has led to a lack of facilities for composing specialised plots.
    'ggforce' aims to be a collection of mainly new stats and geoms that fills
    this gap. All additional functionality is aimed to come through the official
    extension system so using 'ggforce' should be a stable experience.
Transform model coefficients into flexible forest
    plots using 'ggplot2'. Provides helpers to standardize
    coefficient data from a range of modelling workflows and render
    publication-ready forest plots with a consistent interface.
Provides a formula interface to 'ggplot2' graphics.
Unified plotting tools for statistics commonly used, such as GLM,
    time series, PCA families, clustering and survival analysis. The package offers
    a single plotting interface for these analysis results and plots in a unified
    style using 'ggplot2'.
A 'ggplot2' extension that supports arbitrary 
    hand-crafted colourable & fillable shapes. New shapes may be feature 
    requested via a Github issue.
Useful functions and utilities for 'ggplot' object (e.g., geometric layers, themes, and utilities to edit the object).
Provides a range of filters that can be applied to layers from the
    'ggplot2' package and its extensions, along with other graphic elements such
    as guides and theme elements. The filters are applied at render time and
    thus uses the exact pixel dimensions needed.
The function gggap() streamlines the creation of segments on
    the y-axis of 'ggplot2' plots which is otherwise not a trivial task to
    accomplish.
A variety of multivariable data summary statistics and 
    constructions have been proposed, either to generalize univariable analogs 
    or to exploit multivariable properties.
    Notable among these are the bivariate peelings surveyed by Green 
    (1981, ISBN:978-0-471-28039-2),
    the bag-and-bolster plots proposed by Rousseeuw &al (1999) 
    <doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by
    Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional 
    relationships among data in a low-dimensional plot.
    Additionally, biplots of singular value--decomposed tabular data, such as
    from principal components analysis, make use of vectors, calibrated axes,
    and other representations of variable elements to complement point markers
    for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and
    Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals.
    Because they treat the abscissa and ordinate as commensurate or the data
    elements themselves as point masses or unit vectors, these multivariable
    tools can be thought of as belonging to geometric data analysis; see Podani
    (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux &
    Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. 'gggda' extends
    Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics
    with statistical transformation ("stat") and geometric construction ("geom")
    layers for many of these tools, as well as convenience coordinate systems
    to emphasize intrinsic geometry of the data.
A 'ggplot2' extension for drawing gene arrow maps.
An extension of 'ggplot2' for creating complex genomic
 maps. It builds on the power of 'ggplot2' and 'tidyverse' adding new 'ggplot2'-style
 geoms & positions and 'dplyr'-style verbs to manipulate the underlying data. It
 implements a layout concept inspired by 'ggraph' and introduces tracks to bring
 tidiness to the mess that is genomics data.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Moon charts are like pie charts except that the proportions are
    shown as crescent or gibbous portions of a circle, like the lit and unlit
    portions of the moon. As such, they work best with only one or two groups.
    'gggibbous' extends 'ggplot2' to allow for plotting multiple moon charts in
    a single panel and does not require a square coordinate system.
An extension of 'ggplot2' that makes it easy to add
             raw 'grid' output, such as customised annotations, to a 
             'ggplot2' plot.
Provides one-liner functions for common legend and guide operations
    in 'ggplot2'. Simplifies legend positioning, styling, wrapping, and
    collection across multi-panel plots created with 'patchwork' or 'cowplot'.
A 'ggplot2' extension that does a variety of little
    helpful things.  The package extends 'ggplot2' facets through
    customisation, by setting individual scales per panel, resizing panels
    and providing nested facets.  Also allows multiple colour and fill
    scales per plot. Also hosts a smaller collection of stats, geoms and axis 
    guides.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
A Humanitarian Data Exchange (HDX) theme, color palettes, and
    scales for 'ggplot2' to allow users to easily follow the HDX visual design
    guide, including convenience functions for for loading and using the
    Source Sans 3 font.
Create hexagonal heatmaps with 'ggplot2',
    using the 'size' aesthetic to variably size each hexagon.
Make it easier to explore data with highlights.
Provides a 'ggplot2' extension for drawing Hinton diagrams,
    a visualisation technique for numerical matrices in which the area of
    each square is proportional to the magnitude of the corresponding entry.
    For signed data, white squares indicate positive values and black squares
    indicate negative values on a grey background. Hinton diagrams are
    especially useful for visualising PCA weight matrices, correlation matrices, 
    and transition matrices.
Wrapper around geom_histogram() of 'ggplot2' to plot the histogram of a numeric vector. This is especially useful, since qplot() was deprecated in 'ggplot2' 3.4.0.
A user-friendly, highly customizable R package for building
    horizon plots in the 'ggplot2' environment.
Splits date and time of day components from
    continuous 'datetime' objects, then plots them using grammar
    of graphics ('ggplot2'). Plots can also be decorated with
    solar cycle information (e.g., sunset, sunrise, etc.).
    This is useful for visualising data that are associated with
    the solar cycle.
Supports image files and graphic objects to be visualized in
    'ggplot2' graphic system.
Provides two new layer types for displaying image data as layers
  within the Grammar of Graphics framework. Displays images using either a
  rectangle interface, with a fixed bounding box, or a point interface using a
  central point and general size parameter. Images can be given as local
  JPEG or PNG files, external resources, or as a list column containing
  raster image data.
Provide specialized 'ggplot2' layers and scales for spatial
    uncertainty visualization, including bivariate choropleth maps, pixel maps,
    glyph maps, and exceedance probability maps.
Visualise the results of F test to compare two variances, Student's t-test, test of equal or given proportions, Pearson's chi-squared test for count data and test for association/correlation between paired samples.
Extensions to 'ggplot2' providing low-level debug tools: statistics
    and geometries echoing their data argument. Layer manipulation: deletion,
    insertion, extraction and reordering of layers. Deletion of unused variables
    from the data object embedded in "ggplot" objects.
Extends 'ggplot2' for visualizing interval-valued data with
    scatter plots, histograms, index plots, boxplots, radar plots, PCA
    displays, and correlation heatmaps. The package also converts
    classical data tables into interval-valued data using clustering
    algorithms or user-defined groupings.
A 'ggplot2' extension that enables visualization of IP
    (Internet Protocol) addresses and networks. The address space is
    mapped onto the Cartesian coordinate system using a space-filling
    curve. Offers full support for both IPv4 and IPv6 (Internet Protocol
    versions 4 and 6) address spaces.
A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://activinsights.com/>, binary (.gt3x) and .csv-export data from  'Actigraph' <https://ametris.com/> devices, and binary (.cwa) and .csv-export data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format. Also the package allows for external function embedding.
Create interactive 'ggplot2' graphics using 'htmlwidgets'.
Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots.
    All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.
Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from 'GENEActiv' <https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from 'Axivity' <https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from 'Actical', 'Fitbit', 'Actiwatch', 'ActiGraph', and 'PhilipsHealthBand'. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.
Provides stat_isotonic() to add weighted univariate isotonic regression curves.
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) <doi:10.1007/s11222-014-9498-5>.
Provides multiple palettes based on pride flags with tailored themes.
Visualise overlapping time series lines as a heatmap of line
    density. Provides a 'ggplot2' statistic implementing the DenseLines
    algorithm, which "normalizes time series by the arc length to compute
    accurate densities"
    (Moritz and Fisher, 2018) <doi:10.48550/arXiv.1808.06019>.
Allows for easy creation of diagnostic plots for a variety of model objects using the Grammar of Graphics.  
  Provides functionality for both individual diagnostic plots and an array of four standard diagnostic plots.
Provides a way to log 'ggplot' component calls, which can be
    useful for debugging and understanding how 'ggplot' objects are created.
    The logged calls can be printed, saved, and re-executed to reproduce the
    original 'ggplot' object.
Provides statistical transformations for plotting empirical 
    ordinary Lorenz curve (Lorenz 1905) <doi:10.2307/2276207> and 
    generalized Lorenz curve (Shorrocks 1983) <doi:10.2307/2554117>.
Plot glycans following the Symbol Nomenclature for Glycans (SNFG) 
    using 'ggplot2'. SNFG provides a standardized visual representation of 
    glycan structures.
Create network-style visualizations of pairwise relationships
    using custom edge glyphs built on top of 'ggplot2'. The package
    supports both statistical and non-statistical data and allows users to
    represent directed relationships. This enables clear,
    publication-ready graphics for exploring and communicating relational
    structures in a wide range of domains. The method was first used in
    Abu-Akel et al. (2021) <doi:10.1371/journal.pone.0245100>. Code is
    released under the MIT License; included datasets are licensed under
    the Creative Commons Attribution 4.0 International (CC BY 4.0).
Provides functions for defining 
    mixed graphs containing three types of edges, directed, 
    undirected and bi-directed, with possibly multiple edges.
    These graphs are useful because they capture fundamental
    independence structures in multivariate distributions
    and in the induced distributions after marginalization 
    and conditioning.
    The package is especially concerned with Gaussian graphical
    models for
    (i) ML estimation for directed acyclic graphs, undirected and 
    bi-directed graphs and ancestral graph models
    (ii) testing several conditional independencies
    (iii) checking global identification of DAG Gaussian models
    with one latent variable
    (iv) testing Markov equivalences and generating Markov 
    equivalent graphs of specific types.
A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.
A 'ggplot2' extension centered on map visualization of China
    and the globe. Provides customizable projections, boundary styles,
    coordinate grids, scale bars, and buffer zones for thematic maps,
    suitable for spatial data analysis and cartographic visualization.
Helper to add insets based on geom_sf() from 'ggplot2'.
    This package gives you a drop-in replacement for geom_sf() that supports
    adding a zoomed inset map without having to create and embed a separate plot.
A quick and easy way of plotting the columns of two matrices or 
    data frames against each other using 'ggplot2'. Although 'ggmatplot' doesn't 
    provide the same flexibility as 'ggplot2', it can be used as a workaround for 
    having to wrangle wide format data into long format for plotting with
    'ggplot2'.
Tools for assessing and diagnosing convergence of
    Markov Chain Monte Carlo simulations, as well as for graphically display
    results from full MCMC analysis. The package also facilitates the graphical
    interpretation of models by providing flexible functions to plot the
    results against observed variables, and functions to work with
    hierarchical/multilevel batches of parameters
    (Fernández-i-Marín, 2016 <doi:10.18637/jss.v070.i09>).
Enhance a 'mice' imputation workflow with visualizations for
    incomplete and/or imputed data. The plotting functions produce
    'ggplot' objects which may be easily manipulated or extended. Use
    'ggmice' to inspect missing data, develop imputation models, evaluate
    algorithmic convergence, or compare observed versus imputed data.
Fit penalized multivariable linear mixed models with a single 
    random effect to control for population structure in genetic association 
    studies. The goal is to simultaneously fit many genetic variants at the 
    same time, in order to select markers that are independently associated 
    with the response. Can also handle prior annotation information, 
    for example, rare variants, in the form of variable weights. For more 
    information, see the website below and the accompanying paper: 
    Bhatnagar et al., "Simultaneous SNP selection and adjustment for 
    population structure in high dimensional prediction models", 2020, 
    <DOI:10.1371/journal.pgen.1008766>.
Provides 'R' bindings to the 'GGML' tensor library for machine
    learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU
    fallback. The package features a 'Keras'-like sequential API and a
    'PyTorch'-style 'autograd' engine for building, training, and deploying
    neural networks. Key capabilities include high-performance 5D tensor
    operations, 'f16' precision, and efficient quantization. It supports
    native 'ONNX' model import (50+ operators) and 'GGUF' weight loading
    from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for
    zero-overhead inference via dedicated weight buffering, it integrates
    seamlessly as a 'parsnip' engine for 'tidymodels' and provides
    first-class learners for the 'mlr3' framework.
    See <https://github.com/ggml-org/ggml> for more information about the
    underlying library.
Estimate Gaussian graphical models with nonconvex penalties,
  including methods described by Williams (2020) <doi:10.31234/osf.io/ad57p>.
  Penalties include atan (Wang and Zhu, 2016) <doi:10.1155/2016/6495417>,
  seamless L0 (Dicker, Huang and Lin, 2013) <doi:10.5705/ss.2011.074>,
  exponential (Wang, Fan and Zhu, 2018) <doi:10.1007/s10463-016-0588-3>,
  smooth integration of counting and absolute deviation (Lv and Fan, 2009)
  <doi:10.1214/09-AOS683>, logarithm (Mazumder, Friedman and Hastie, 2011)
  <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation
  (Fan and Li, 2001) <doi:10.1198/016214501753382273>, and minimax concave
  penalty (Zhang, 2010) <doi:10.1214/09-AOS729>. The package also provides
  extensions for variable inclusion probabilities, multiple regression
  coefficients, and statistical inference (Janková and van de Geer, 2015)
  <doi:10.1214/15-EJS1031>.
Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://www.iso.org/standard/44473.html>.
Estimation of partial correlation matrix using ridge penalty 
    followed by thresholding and reestimation. Under multivariate Gaussian 
    assumption, the matrix constitutes an Gaussian graphical model (GGM).
Facilitates efficient visualization of Relative Synonymous Codon Usage patterns across species. Based on analytical outputs from 'codonW', 'MEGA', and 'Phylosuite', it supports multi-species 'RSCU' comparisons and allows users to explore visual analysis of structurally similar datasets.
A 'grammar of graphics' approach for visualizing summary statistics from multiple Genome-wide Association Studies (GWAS). It offers geneticists, bioinformaticians, and researchers a powerful yet flexible tool for illustrating complex genetic associations using data from various GWAS datasets. The visualizations can be extensively customized, facilitating detailed comparative analysis across different genetic studies. Reference: Uffelmann, E. et al. (2021) <doi:10.1038/s43586-021-00056-9>.
Create plots that combine a phylogeny and frequency dynamics.
    Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo".
    Inspired by similar plots in publications of the labs of RE Lenski and JE
    Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose
    code this package exploits).
It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Geometries to plot network objects with 'ggplot2'.
Use multiple fill and colour scales in 'ggplot2'.
Uses 'ggplot2' to create normally distributed violin plots
    with specified means and standard deviations. This function can be
    useful in showing hypothetically normal distributions and confidence
    intervals.
Allows plotting data on bathymetric maps using 'ggplot2'. Plotting
  oceanographic spatial data is made as simple as feasible, but also flexible
  for custom modifications. Data that contain geographic information from 
  anywhere around the globe can be plotted on maps generated by the basemap()
  or qmap() functions using 'ggplot2' layers separated by the '+' operator. The 
  package uses spatial shape- ('sf') and raster ('stars') files, geospatial 
  packages for R to manipulate, and the 'ggplot2' package to plot these 
  files. The package ships with low-resolution spatial data files and 
  higher resolution files for detailed maps are stored in the 
  'ggOceanMapsLargeData' repository on GitHub and downloaded automatically 
  when needed.
Discrete scales for the colorblind-friendly 'Okabe-Ito'
    palette, including 'color', 'fill', and 'edge_colour'. 'ggokabeito'
    provides 'ggplot2' and 'ggraph' scales to easily use the 'Okabe-Ito'
    palette in your data visualizations.
The 'vegan' package includes several functions for adding features
  to ordination plots: ordiarrows(),  ordiellipse(), ordihull(),
  ordispider() and ordisurf(). This package adds these same   features to 
  ordination plots made with 'ggplot2'. In addition, gg_ordibubble() sizes
  points relative to the value of an environmental variable.
Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.
Create groups of 'ggplot2' layers that can be easily migrated from one plot
    to another, reducing redundant code and improving the ability to format many
    plots that draw from the same source 'ggpacket' layers.
Facilitates the creation of page layout
    visualizations in which words are represented as rectangles with sizes
    relating to the length of the words. Which then is divided in lines
    and pages for easy overview of up to quite large texts.
Extends 'ggplot2' functionality to the 'partykit' package. 'ggparty' provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'.
A 'ggplot2' extension that enables robust image grobs in
    panels and theme elements.
Provides 'ggplot2' geoms filled with various patterns.  Includes a patterned version of every 'ggplot2' geom that has a region that can be filled with a pattern.  Provides a suite of 'ggplot2' aesthetics and scales for controlling pattern appearances.  Supports over a dozen builtin patterns (every pattern implemented by 'gridpattern') as well as allowing custom user-defined patterns.
Provides tools for creating publication-ready dimensionality reduction plots, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). This package helps visualize high-dimensional data with options for custom labels, density plots, and faceting, using the 'ggplot2' framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.
Modern Parallel Coordinate Plots have been introduced in the 1980s as 
    a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation
    also incorporates categorical variables into the plots in a principled manner. 
    By separating the data managing part from the visual rendering, we give full access
    to the users while keeping the number of parameters manageably low.
Provides plotting functions for visualizing pedigrees and family trees. The package complements a behavior genetics package 'BGmisc' [Garrison et al. (2024) <doi:10.21105/joss.06203>] by rendering pedigrees using the 'ggplot2' framework. Features include support for duplicated individuals, complex mating structures, integration with simulated pedigrees, and layout customization. Due to the impending deprecation of kinship2, version 1.0 incorporates the layout helper functions from kinship2. The pedigree alignment algorithms are adapted from 'kinship2' [Sinnwell et al. (2014) <doi:10.1159/000363105>]. We gratefully acknowledge the original authors: Jason Sinnwell, Terry Therneau, Daniel Schaid, and Elizabeth Atkinson for their foundational work.
Implements methods to plot periodic data in any arbitrary range on the fly.
Provides a convenient way to analyze and visualize 'PICRUSt2' output with pre-defined plots and functions. Allows for generating statistical plots about microbiome functional predictions and offers customization options. Features a one-click option for creating publication-level plots, saving time and effort in producing professional-grade figures. Streamlines the 'PICRUSt2' analysis and visualization process. For more details, see Yang et al. (2023) <doi:10.1093/bioinformatics/btad470>.
Enables users to create simple plots of biological culture plates as well as microplates. Both continuous and discrete values can be plotted onto the plate layout.
Currently provides geom_balance_of_trade(), a 'ggplot2' layer that fills the area  
  between exports and imports series (with automatic crossing detection  
  and conditional coloring for surplus vs. deficit), and overlays lines  
  and points by default.
Provides the ggplot binning layer stat_summaries_hex(),
    which functions similar to its singular form,
    but allows the use of multiple statistics per bin.
    Those statistics can be mapped to multiple bin aesthetics.
A system for 'declaratively' creating graphics, based on "The
    Grammar of Graphics". You provide the data, tell 'ggplot2' how to map
    variables to aesthetics, what graphical primitives to use, and it
    takes care of the details.
Selected utilities, in particular 'geoms' and 'stats'
    functions, extending the 'ggplot2' package. This package imports
    functions from 'EnvStats' <doi:10.1007/978-1-4614-8456-1> by Millard
    (2013), 'ggpp' <https://CRAN.R-project.org/package=ggpp> by Aphalo et
    al. (2023) and 'ggstats' <doi:10.5281/zenodo.10183964> by Larmarange
    (2023), and then exports them. This package also contains modified
    code from 'ggquickeda' <https://CRAN.R-project.org/package=ggquickeda>
    by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions
    to plots.  All functions are tested to make sure that they work
    reliably.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
An 'RStudio' addin for teaching and learning making plot using the 'ggplot2' package.
    You can learn each steps of making plot by clicking your mouse without coding.
    You can get resultant code for the plot.
Easily explore data by creating ggplots through a (shiny-)GUI. R-code to recreate graph provided.
Convert plot function call (using expression or formula) to 'grob' or 'ggplot' object that compatible to the 'grid' and 'ggplot2' ecosystem. With this package, we are able to e.g. using 'cowplot' to align plots produced by 'base' graphics, 'ComplexHeatmap', 'eulerr', 'grid', 'lattice', 'magick', 'pheatmap', 'vcd' etc. by converting them to 'ggplot' objects.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Statistics to locate and tag peaks and valleys and to label plots
    with the equation of a fitted polynomial model by ordinary least squares,
    major axis, quantile and robust and resistant regression approaches. Line 
    and model equation for Normal mixture models. Labels for P-value, 
    R^2 or adjusted R^2 or information criteria for fitted models; parametric
    and non-parametric correlation; ANOVA table or summary table for fitted 
    models as plot insets; annotations for multiple pairwise
    comparisons with adjusted P-values. Model fit classes for which suitable 
    methods are provided by package 'broom' and 'broom.mixed' are supported as 
    well as user-defined wrappers on model fit functions, allowing model
    selection and conditional labelling. Scales and stats to 
    build volcano and quadrant plots based on
    outcomes, fold changes, p-values and false discovery rates.
At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling
  activities in order to reduce the overall effort required for generating such plots.
  For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox,
  called 'ggPMX' to execute them. 'ggPMX' is a toolbox that can generate all diagnostic plots at a quality sufficient
  for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP
  <doi:10.1002/psp4.12161>. While not required, you can get/install the 'R' '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, 'R' can use 'Monolix' directly to create the required
    Chart Data instead of exporting it from the 'Monolix' gui.
A cross between a 2D density plot and a scatter plot,
    implemented as a 'ggplot2' geom. Points in the scatter plot are 
    colored by the number of neighboring points. This is useful to
    visualize the 2D-distribution of points in case of overplotting.
A collection of layers for 'ggplot2'. Provides geoms built on
    linear and radial gradients from the 'grid' package, giving areas, bars,
    paths, rectangles, and ridgelines a fading or glowing visual effect.
    Also includes mathematically driven layers — catenary curves, 
    Chaikin's corner-cutting smoothing
    (Chaikin, 1974, <doi:10.1016/0146-664X(74)90028-8>), and Fourier-series
    reconstruction — plus Lexis diagrams, isotype bar charts.
Provides basic graphing functions to fully demonstrate
    point-to-point connections in a polar coordinate space.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
Create engaging population and point plots charts in R. 'ggpop' allows users to represent population data and points proportionally using customizable icons, facilitating the creation of circular representative population charts as well as any point-plots.
Extensions to 'ggplot2' respecting the grammar of graphics 
    paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to 
    plots using native data coordinates, while geom_table_npc(), geom_plot_npc()
    and geom_grob_npc() do the same using "npc" coordinates through new 
    aesthetics "npcx" and "npcy". Statistics: select observations based on 2D 
    density. Positions: radial nudging away from a center point and nudging away
    from a line or curve; combined stacking and nudging; combined dodging and
    nudging.
Provides various themes, palettes, and other functions
    that are used to customise ggplots to look like they were made in 'GraphPad 
    Prism'. The 'Prism'-look is achieved with theme_prism() and 
    scale_fill|colour_prism(), axes can be changed with custom guides like 
    guide_prism_minor(), and significance indicators added with add_pvalue().
The 'ggplot2' package is excellent and flexible for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, 
    and adds the resulting p-values to the plot with an annotation bar.
    Visualizing group differences are frequently performed by boxplots, bar plots, etc.
    Statistical test results are often needed to be annotated on these plots. 
    This package provides a convenient function that works on 'ggplot2' objects, 
    performs the desired statistical test between groups of interest and annotates the test results on the plot.
A quantile-quantile plot can be used to compare a sample of p-values 
  to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
  plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
  calculate the quantiles, but thins it out in a way that focuses on points near zero 
  before plotting to speed up plotting and decrease file size, when vector graphics are stored.
Quickly and easily perform exploratory data analysis by uploading your
     data as a 'csv' file. Start generating insights using 'ggplot2' plots and
     'table1' tables with descriptive stats, all using an easy-to-use point and click 
     'Shiny' interface.
An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.
Extend 'ggplot2' facets to panel layouts arranged in a grid
    with ragged edges. facet_ragged_rows() groups panels into rows that
    can vary in length, facet_ragged_cols() does the same but for columns.
    These can be useful, for example, to represent nested or partially
    crossed relationships between faceting variables.
The 'geom_rain()' function adds different geoms together using 'ggplot2' to create raincloud plots.
Graphic elements for exploring Random Forests using the
    'randomForest' or 'randomForestSRC' package for survival, regression
    and classification forests and 'ggplot2' package plotting. Implements
    visualisations of the methods described in Breiman (2001)
    <doi:10.1023/A:1010933404324> and Ishwaran, Kogalur, Blackstone, and
    Lauer (2008) <doi:10.1214/08-AOAS169>.
The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Rasterize only specific layers of a 'ggplot2' plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without loosing vector properties of the scale-sensitive information.
You can use this function to easily draw a combined histogram and restricted cubic spline. 
    The function draws the graph through 'ggplot2'. RCS fitting requires the use of the rcs() function of the 'rms' package. 
    Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.
A collection of custom 'ggplot2'-based visualizations for 
    data exploration and analysis. Each function handles data 
    preprocessing and returns a object that can be further customized
    using standard 'ggplot2' syntax.
Provides 'ggplot2' extensions for political map making. Implements
    new geometries for groups of simple feature geometries. Adds palettes and scales
    for red to blue color mapping and for discrete maps. Implements tools for easy
    label generation and placement, automatic map coloring, and themes.
Complete themes for publication-quality 'ggplot2' visualisation. 
    Also provides functions to modify these based on the positional axis scales 
    and focus of a particular plot.
Provides text and label geoms for 'ggplot2' that help to avoid
    overlapping text labels. Labels repel away from each other and away from the
    data points.
Provides functions that make it easy to reveal 'ggplot2' graphs incrementally. The functions take a plot produced with 'ggplot2' and return a list of plots showing data incrementally by panels, layers, groups, the values in an axis or any arbitrary aesthetic.
The Graphical Group Ridge 'GGRidge' package package classifies ridge regression predictors in disjoint groups of conditionally correlated variables and derives different penalties (shrinkage parameters) for these groups of predictors. It combines the ridge regression method with the graphical model for high-dimensional data (i.e. the number of predictors exceeds the number of cases) or ill-conditioned data (e.g. in the presence of multicollinearity among predictors). The package reduces the mean square errors and the extent of over-shrinking of predictors as compared to the ridge method.Aldahmani, S. and Zoubeidi, T. (2020) <DOI:10.1080/00949655.2020.1803320>.
Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
The risk plot may be one of the most commonly used figures in 
    tumor genetic data analysis. We can conclude the following two points: 
    Comparing the prediction results of the model with the real survival situation 
    to see whether the survival rate of the high-risk group is lower than that of the 
    low-level group, and whether the survival time of the high-risk group is 
    shorter than that of the low-risk group. The other is to compare the heat 
    map and scatter plot to see the correlation between the predictors and the 
    outcome.
Creates bar plots with rounded corners using 'ggplot2'.
    The code in this package was adapted from a solution provided by 
    Stack Overflow user 'sthoch' in the following post
    <https://stackoverflow.com/questions/62176038/r-ggplot2-bar-chart-with-round-corners-on-top-of-bar>.
Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
Works with 'ggplot2' to add a Van Gogh color palette to the user’s repertoire.
 It also has a function that work alongside 'ggplot2' to create more interesting data visualizations and add contextual information to the user’s plots.
Sankey and alluvial diagrams visualise flows of quantities across
    stages in stacked bars. This package makes it easy to create such
    diagrams using 'ggplot2'.
The function combines a scatter plot with ridgelines to better visualise the distribution between sample groups. The plot is created with 'ggplot2'.
Provides specialized visualization tools for Single-Case Experimental Design (SCED) 
    research using 'ggplot2'. SCED studies are a crucial methodology in behavioral and 
    educational research where individual participants serve as their own controls through 
    carefully designed experimental phases. This package extends 'ggplot2' to create 
    publication-ready graphics with professional phase change lines, support for multiple 
    baseline designs, and styling functions that follow SCED visualization conventions. 
    Key functions include adding phase change demarcation lines to existing plots and 
    formatting axes with broken axis appearance commonly used in single-case research.
A collection of 'ggplot2' color palettes inspired by
    plots in scientific journals, data visualization libraries,
    science fiction movies, and TV shows.
Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The 'ggscidca' package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) <doi:10.1016/S1470-2045(14)71116-7>.
Annotation helper functions for publication-quality 'ggplot2' 
    visualisation. These functions make it easier to annotate plots in 
    a way that stays consistent with the set theme.
Provides a 'ggplot2' geom and position for visualizing brain
    region data on cortical, subcortical, and white matter tract atlases.
    Brain atlas geometries are stored as simple features ('sf'), enabling
    seamless integration with the 'ggplot2' ecosystem including faceting,
    custom scales, and themes.  Mowinckel & Vidal-Piñeiro (2020)
    <doi:10.1177/2515245920928009>.
Provides the 'ggseg_atlas' S3 class used across the 'ggseg'
    ecosystem for 2D and 3D brain visualisation. Ships three bundled atlases
    ('Desikan-Killiany', 'FreeSurfer' 'aseg', 'TRACULA') and functions for
    querying, subsetting, renaming, and enriching atlas objects. Also includes
    readers for 'FreeSurfer' statistics files.
Provides additional brain surface meshes for cortical and
    cerebellar visualisation in the 'ggsegverse' ecosystem. Cortical surfaces
    include pial, white, midthickness, semi-inflated, sphere, smoothwm, and
    orig at fsaverage5 resolution. Cerebellar surfaces include the Spatially
    Unbiased Infratentorial Template (SUIT) flatmap. All meshes follow the
    same vertices/faces data frame format used by 'ggseg.formats' and
    'ggseg3d'.
Plot brain atlases as interactive 3D meshes using 'Three.js'
    via 'htmlwidgets', or render publication-quality static images through
    'rgl' and 'rayshader'. A pipe-friendly API lets you map data onto
    brain regions, control camera angles, toggle region edges, overlay
    glass brains, and snapshot or ray-trace the result. Additional
    atlases are available through the 'ggsegverse' r-universe. Mowinckel
    & Vidal-Piñeiro (2020) <doi:10.1177/2515245920928009>.
It provides a better alternative for stacked bar plot by creating a segmented total bar plot with custom annotations and labels. It is useful for visualizing the total of a variable and its segments in a single bar, making it easier to compare the segments and their contributions to the total.
Provides an interactive workflow for visualizing structural equation modeling (SEM), multi-group path diagrams, and network diagrams in R. Users can directly manipulate nodes and edges to create publication-quality figures while maintaining statistical model integrity. Supports integration with 'lavaan', 'OpenMx', 'tidySEM', and 'blavaan' etc. Features include parameter-based aesthetic mapping, generative AI assistance, and complete reproducibility by exporting metadata for script-based workflows.
The extensive range of functions provided by this package makes it possible to draw highly versatile sequence logos. Features include, but not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other plots 'ggplot2' plots.
A set of wrapper functions that mainly re-produces most of the 
  sequence plots rendered with TraMineR::seqplot(). Whereas 'TraMineR' uses base 
  R to produce the plots this library draws on 'ggplot2'. 
  The plots are produced on the basis of a sequence object defined  
  with TraMineR::seqdef(). The package automates the reshaping and plotting 
  of sequence data. Resulting plots are of class 'ggplot', i.e. components 
  can be added and tweaked using '+' and regular 'ggplot2' functions.
A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), 
    geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more 
    aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a 
    neon glow around lines to get a steampunk style.
The grammar of graphics as shown in 'ggplot2' has provided
  an expressive API for users to build plots. 'ggside' extends 'ggplot2'
  by allowing users to add graphical information about one of the main panel's
  axis using a familiar 'ggplot2' style API with tidy data. This package is
  particularly useful for visualizing metadata on a discrete axis, or summary
  graphics on a continuous axis such as a boxplot or a density distribution.
Enrich your 'ggplots' with group-wise comparisons.
    This package provides an easy way to indicate if two groups are
    significantly different. Commonly this is shown by a bracket on top
    connecting the groups of interest which itself is annotated with the
    level of significance (NS, *, **, ***).  The package provides a single
    layer (geom_signif()) that takes the groups for comparison and the
    test (t.test(), wilcox.text() etc.) as arguments and adds the
    annotation to the plot.
Provides 'ggplot2' extensions for creating skewed boxplots
    using several statistical methods (Kimber, 1990 <doi:10.2307/2347808>; Hubert and Vandervieren, 2008 <doi:10.1016/j.csda.2007.11.008>; Adil et al., 2015 <doi:10.18187/pjsor.v11i1.500>; Babura et al., 2017 <doi:10.1063/1.4982872>; Walker et al., 2018 <doi:10.1080/00031305.2018.1448891>). The package implements custom
    statistical transformations and geometries to visualize data
    distributions with an emphasis on skewness.
Simple tools to draw sky maps in 'ggplot2' using galactic or
    equatorial coordinates. Includes custom coordinate systems, grid
    labels, and helpers for sky map breaks.
Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.
The 'ggplot2' package provides a powerful set of tools
  for visualising and investigating data. The 'ggsoccer' package provides a
  set of functions for elegantly displaying and exploring soccer event data
  with 'ggplot2'. Providing extensible layers and themes, it is designed to
  work smoothly with a variety of popular sports data providers.
An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. 
 Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014.
 <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>. 
 Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided.
 The structure files can be adopted for localization or for insurance companies who use Internal Models (IM).
 Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts.
 With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment).
The aim of this package is to offer more variability of graphics based on the self-organizing maps.
Functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in 'ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.
Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of spatial objects.
Inspects 'ggplot' objects by extracting their
    full declarative specification - layers, aesthetic mappings, scales,
    facets, coordinate systems, and labels - as tidy data frames. A second
    tier of functions enables structural comparison of two 'ggplot' objects,
    supporting automated plot testing, auditing, and framework-agnostic
    grading workflows. Unlike 'ggcheck', which is designed exclusively for
    'learnr'/'gradethis' pipelines and returns ad-hoc objects, 'ggspec'
    returns rectangular, pipeable output and does not require any grading
    framework as a dependency.
Additional annotations, stats, geoms and scales for plotting 
    "light" spectra with 'ggplot2', together with specializations of ggplot()
    and autoplot() methods for spectral data and waveband definitions
    stored in objects of classes defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Allows you to write queries that combine SQL (Structured Query
  Language) data retrieval with visualization specifications in a single,
  composable syntax. The 'ggsql' package binds directly with the 'ggsql' 'Rust'
  library and allows you to set up readers and writers and execute queries
  against it. The package also offers 'knitr' and 'shiny' integration allowing
  the user to use 'ggsql' in both frameworks.
Easily create overlapping grammar of graphics plots for scientific data visualization. 
  This style of plotting is particularly common in climatology and oceanography research communities.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'. This package is now superseded by 'ggplot2' itself
    which now has full native support for horizontal layouts. It
    remains available for backward compatibility.
To create the multiple polygonal point layer for easily discernible shapes, 
             we developed the package, it is like the 'geom_point' of 'ggplot2'.
             It can be used to draw the scatter plot.
Provides new statistics, new geometries and new positions for 
    'ggplot2' and a suite of functions to facilitate the creation of 
    statistical plots.
Extension of 'ggplot2', 'ggstatsplot' creates graphics with
    details from statistical tests included in the plots themselves. It
    provides an easier syntax to generate information-rich plots for
    statistical analysis of continuous (violin plots, scatterplots,
    histograms, dot plots, dot-and-whisker plots) or categorical (pie and
    bar charts) data. Currently, it supports the most common types of
    statistical approaches and tests: parametric, nonparametric, robust,
    and Bayesian versions of t-test/ANOVA, correlation analyses,
    contingency table analysis, meta-analysis, and regression analyses.
    References: Patil (2021) <doi:10.21105/joss.03236>.
Provides an extension to 'ggplot2'
  (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two types of
  continuous confidence interval plots (Violin CI and Gradient CI plots),
  typically for the sample mean. These plots contain multiple user-defined
  confidence areas with varying colours, defined by the underlying
  t-distribution used to compute standard confidence intervals for the mean of
  the normal distribution when the variance is unknown. Two types of plots are
  available, a gradient plot with rectangular areas, and a violin plot where
  the shape (horizontal width) is defined by the probability density function
  of the t-distribution. These visualizations are studied in
  (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) 
  <doi:10.1109/TVCG.2021.3073466>.
Create epicurves, epigantt charts, and diverging bar charts
    using 'ggplot2'. Prepare data for visualisation or other reporting for
    infectious disease surveillance and outbreak investigation (time
    series data).  Includes tidy functions to solve date based
    transformations for common reporting tasks, like (A) seasonal date
    alignment for respiratory disease surveillance, (B) date-based case
    binning based on specified time intervals like isoweek, epiweek, month
    and more, (C) automated detection and marking of the new year based on
    the date/datetime axis of the 'ggplot2', (D) labelling of the last
    value of a time-series.  An introduction on how to use epicurves can
    be found on the US CDC website (2012,
    <https://www.cdc.gov/training/quicklearns/epimode/index.html>).
Functions for survey data including svydesign objects from the 'survey' package that call 'ggplot2' to make bar charts, histograms, boxplots, and hexplots of survey data.
Ease the creation of time-to-event (i.e. survival) endpoint
    figures. The modular functions create figures ready for publication.
    Each of the functions that add to or modify the figure are written as
    proper 'ggplot2' geoms or stat methods, allowing the functions from
    this package to be combined with any function or customization from
    'ggplot2' and other 'ggplot2' extension packages.
Offers various swiss maps as data frames and 'ggplot2' objects and gives the
    possibility to add layers of data on the maps. Data are publicly available
    from the swiss federal statistical office. In addition to the \code{maps2} 
    object (a list of 8 swiss maps, at various levels), there are the data 
    frames with the boundaries used to produce these maps 
    (\code{shp_df}, a list with 8 data frames).
Extends the 'ggplot2' plotting system to support network visualization. Inspired by the 'Method 1' in 'ggtree' (G Yu (2018) <doi:10.1093/molbev/msy194>), 'ggtangle' is designed to work with network associated data.
A collection of palettes and themes for 'ggplot2', offering a light, pastel aesthetic. Syntax follows the 'viridis' package.
Extends the functionality of 'ggplot2', providing the capability
    to plot ternary diagrams for (subset of) the 'ggplot2' geometries. Additionally,
    'ggtern' has implemented several NEW geometries which are unavailable to the
    standard 'ggplot2' release.
A 'ggplot2' extension that enables the rendering of
    complex formatted plot labels (titles, subtitles, facet labels,
    axis labels, etc.). Text boxes with automatic word wrap are also
    supported.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
Designed to customize 'ggplot' graphics according to the institutional identity of the University of Ljubljana.
Create tibbles and lists of 'ggplot' figures that can be modified as easily as
  regular 'ggplot' figures.  Typical use cases are for creating reports or web
  pages where many figures are needed with different data and similar
  formatting.
Annotation of 'ggplot2' plots with arbitrary 'TikZ' code, using absolute data or relative plot coordinates.
Extends the capabilities of 'ggplot2' by providing grammatical
    elements and plot helpers designed for visualizing temporal patterns. The 
    package implements a grammar of temporal graphics, which leverages calendar
    structures to highlight changes over time. The package also provides plot 
    helper functions to quickly produce commonly used time series graphics,
    including time plots, season plots, and seasonal sub-series plots.
Provides 'ggplot2' geoms that allow groups of data points to be
    outlined or highlighted for emphasis. This is particularly useful when
    working with dense datasets that are prone to overplotting.
Provides a simple way to translate text elements in 'ggplot2' plots using a dictionary-based approach.
Provides 'ggplot2' geoms analogous to 'geom_col()' and 'geom_bar()'
    that allow for treemaps using 'treemapify' nested within each bar segment.
    Also provides geometries for subgroup bordering and text annotation.
Add trendline and confidence interval of linear or nonlinear regression model and
    show equation to 'ggplot' as simple as possible. For a general overview of the methods used in
	  this package, see Ritz and Streibig (2008) <doi:10.1007/978-0-387-09616-2> and 
	  Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.
A collection of several geoms to create graphics, using
    'ggplot2' and the Cartesian coordinate system. You use the familiar
    mapping 'Grammar of Graphics' without the need to do another
    transformation into polar coordinates.
An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.
Replace the standard x-axis in 'ggplots' with a combination matrix
  to visualize complex set overlaps. 'UpSet' has introduced a new way to visualize
  the overlap of sets as an alternative to Venn diagrams. 
  This package provides a simple way to produce such plots using 'ggplot2'. 
  In addition it can convert any categorical axis into a combination
  matrix axis.
A simple, opinionated toolkit for visualizing genomic variant data
    using a 'ggplot2'-native grammar. Accepts VCF files or plain data frames and
    produces publication-ready lollipop plots, consequence summaries, mutational
    spectrum charts, and cohort-level comparisons with minimal code. Designed for
    both wet-lab biologists and experienced bioinformaticians.
Functions to produce 'ggplot2'-based plots of objects produced by
    functions in the 'vegan' package. Provides 'fortify()', 'autoplot()', and
    'tidy()' methods for many of 'vegan''s functions. The aim of 'ggvegan' is to
    make it easier to work within the 'tidyverse' with 'vegan'.
An easy-to-use way to draw pretty Venn diagrams using 'ggplot2'.
    This package provides functions to create Venn diagrams with customizable
    colors, labels, and styling options.
Easy-to-use functions to generate 2-7 sets Venn or upset plot in publication quality. 
  'ggVennDiagram' plot Venn or upset using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets 
  Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which 
  are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to 
  integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections 
  of them, then separately plot Venn in four components, set edges/labels, and region edges/labels.
  From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar.
  From version 1.4.4, it supports unlimited number of sets, as it can draw a plain upset plot automatically when
  number of sets is more than 7.
Una coleccion de conjuntos de datos para el libro
    "Graficas versatiles con ggplot: Analisis visuales de datos",
    por Raymond L. Tremblay y Julian Hernandez-Serrano. Incluye datos
    de ecologia, salud publica, educacion, economia y biodiversidad
    para la ensenanza de visualizacion de datos con 'ggplot2'.
A 'ggplot2' extension for visualizing vector fields in two-dimensional 
           space. Provides flexible tools for creating vector and stream field layers, 
           visualizing gradients and potential fields, and smoothing vector and 
           scalar data to estimate underlying patterns.
Preview what a 'ggplot2' plot would look like if you save it to a file. 
    Attach picture dimensions as a canvas() element and get an instant preview. 
    These dimensions will then be used when you save the plot.
An implementation of an interactive grammar of graphics,
    taking the best parts of 'ggplot2', combining them with the reactive
    framework of 'shiny' and drawing web graphics using 'vega'.
Provides functionality to create customizable volcano plots for
    visualizing differential gene expression analysis results. The package
    offers options to highlight genes of interest, adjust significance
    thresholds, customize colors, and add informative labels. Designed
    specifically for RNA-seq data analysis workflows.
Provides publication-ready volcano plots for visualizing differential expression results, commonly used in RNA-seq and similar analyses. This tool helps create high-quality visual representations of data using the 'ggplot2' framework Wickham (2016) <doi:10.1007/978-3-319-24277-4>.
Provides browser-native 'WebGL' rendering for R graphics through
    'htmlwidgets'. The package supports grammar-style graphics workflows and
    renderer-ready specifications for dense analytical and scientific scenes,
    including point, line, trajectory, raster, vector, mesh, and surface
    layers, shader-driven display modes, timeline controls, structured views,
    selection metadata, and publication-oriented static export helpers.
    Rendering stays in the browser, and the core package remains
    cross-platform without requiring 'CUDA', 'Metal', or 'OpenCL' toolchains.
Width helper functions for publication-quality 'ggplot2' 
    visualisation. These functions make it easier to create geoms such 
    as bars that have a consistent width appearance across plots.
Provides a word cloud text geom for 'ggplot2'. Texts
    are placed so that they do not overlap as in 'ggrepel'.  The algorithm
    used is a variation around the one of 'wordcloud2.js'.
The 'ggplot2' package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid 'ggplot2' commands.
Minimal client to access the 'GitHub' 'API'.
'GitHub' apps provide a powerful way to manage fine grained
    programmatic access to specific 'git' repositories, without having to
    create dummy users, and which are safer than a personal access token
    for automated tasks. This package extends the 'gh' package to let you
    authenticate and interact with 'GitHub' <https://docs.github.com/en/rest/overview>
    in 'R' as an app.
Functions are provided for quantifying evolution and selection on complex traits. 
              The package implements effective handling and analysis algorithms scaled for 
              genome-wide data and calculates a composite statistic, denoted Ghat, which is used 
              to test for selection on a trait. The package provides a number of simple examples 
              for handling and analysing the genome data and visualising the output and results. 
              Beissinger et al., (2018) <doi:10.1534/genetics.118.300857>.
Interface for the GitHub API that enables efficient 
    management of courses on GitHub. It has a functionality for 
    managing organizations, teams, repositories, and users on GitHub 
    and helps automate most of the tedious and repetitive tasks 
    around creating and distributing assignments.
A statistical hypothesis test for conditional independence.
    Given residuals from a sufficiently powerful regression, it tests whether 
    the covariance of the residuals is vanishing. It can be applied to both
    discretely-observed functional data and multivariate data. 
    Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas
    Peters (2022) <doi:10.1111/rssb.12544>.
The goal of 'GHCNr' is to provide a fast and friendly interface with the Global Historical Climatology Network daily (GHCNd) database, which contains daily summaries of weather station data worldwide (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>). GHCNd is accessed through the web API <https://www.ncei.noaa.gov/access/services/data/v1>. 'GHCNr' main functionalities consist of downloading data from GHCNd, filter it, and to aggregate it at monthly and annual scales.
Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> 
    films, ported to R for your enjoyment.
Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.
A 'GraphQL' client, with an R6 interface for initializing a
    connection to a 'GraphQL' instance, and methods for constructing
    queries, including fragments and parameterized queries. Queries are
    checked with the 'libgraphqlparser' C++ parser via the 'graphql'
    package.
A collection of commonly used visualizations of temporal and
    spatio-temporal health data including case counts, incidence rates, and
    covariates. The available plot types include time series, heatmaps,
    seasonality plots, maps and more. The package supports standard data
    transformations such as temporal and spatial aggregations, while
    offering extensive customization options for the resulting figures.
Supports modeling health outcomes using Bayesian hierarchical 
  spatio-temporal models with complex covariate effects (e.g., linear, 
  non-linear, interactions, distributed lag linear and non-linear 
  models) in the 'INLA' framework. It is designed to help users identify key 
  drivers and  predictors of disease risk by enabling streamlined model 
  exploration, comparison, and visualization of complex covariate effects. 
  See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021)
  <doi:10.1016/S2542-5196(20)30292-8>.
Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. 
        Quantitative risk management: Concepts, techniques and tools. 
        Princeton University Press, Princeton (2005).
Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG).
    To learn how to use it, check the vignettes for a quick tutorial.
    Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300>
    together with those relevant references from the one listed below.
    The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <doi:10.48550/arXiv.1607.02441>.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
    Casiraghi, G., (2017) <doi:10.48550/arXiv.1702.02048>.
    Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>.
    Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
    Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>.
    Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.
Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>.
A plain 'Rcpp' wrapper for 'MeCab' that can segment Chinese,
    Japanese, and Korean text into tokens. The main goal of this package
    is to provide an alternative to 'tidytext' using morphological
    analysis.
Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages.
An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.
Penalised likelihood estimation of a covariance matrix via the ridge-regularised covglasso estimator described in Cibinel et al. (2024) <doi:10.48550/arXiv.2410.02403>. Based on the 'C++' code of the 'R' package 'covglasso' (by Michael Fop, <https://orcid.org/0000-0003-3936-2757>) and the 'R' code of 'icf' (by Mathias Drton, <https://orcid.org/0000-0001-5614-3025>) within the 'R' package 'ggm'.
Providing access to the API for Gas Infrastructure Europe's
    natural gas transparency platforms <https://agsi.gie.eu/> and <https://alsi.gie.eu/>. Lets the user
    easily download metadata on companies and gas storage units covered by
    the API as well as the respective data on regional, country, company
    or facility level.
Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.
Implements various Gifi methods in a user-friendly way: categorical principal component analysis (princals), multiple correspondence analysis (homals), monotone regression analysis (morals).
Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. 
    Converts images to GIF animations using pngquant's efficient cross-frame 
    palettes and temporal dithering with thousands of colors per frame.
Retrieving regional plant checklists, species traits and
  distributions, and environmental data from the Global Inventory of Floras and
  Traits (GIFT). More information about the GIFT database can be found at
  <https://gift.uni-goettingen.de/about> and the map of available floras can be
  visualized at <https://gift.uni-goettingen.de/map>. The API and associated
  queries can be accessed according the following scheme:
  <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
A Gibbs sampler corresponding to a Group 
    Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. 
    Hyperparameters in the GIGG prior specification can either be fixed by the 
    user or can be estimated via Marginal Maximum Likelihood Estimation.
    Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar 
    Mukherjee (2021) <arXiv:2102.10670>.
Generator and density function for the
  Generalized Inverse Gaussian (GIG) distribution.
Provides a simple to use, intuitive, and
  extensible interface to several stochastic simulation
  algorithms for generating simulated trajectories of finite
  population continuous-time model. Currently it implements
  Gillespie's exact stochastic simulation algorithm (Direct
  method) and several approximate methods (Explicit tau-leap,
  Binomial tau-leap, and Optimized tau-leap). The package also
  contains a library of template models that can be run as demo
  models and can easily be customized and extended. Currently the
  following models are included, 'Decaying-Dimerization' reaction
  set, linear chain system, logistic growth model, 'Lotka'
  predator-prey model, Rosenzweig-MacArthur predator-prey model,
  'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model.
  Pineda-Krch et al. (2008) <doi:10.18637/jss.v025.i12>.
A fast, scalable, and versatile framework for
    simulating large systems with Gillespie's Stochastic Simulation
    Algorithm ('SSA').  This package is the spiritual successor to the
    'GillespieSSA' package originally written by Mario Pineda-Krch.
    Benefits of this package include major speed improvements (>100x),
    easier to understand documentation, and many unit tests that try to
    ensure the package works as intended. Cannoodt and Saelens et al. (2021) 
    <doi:10.1038/s41467-021-24152-2>.
Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression.
        One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study.
        Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated.
        To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper.
        For more details see the help of the function final_model() and the original study (1996) <doi:10.2307/2348411>.
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models. For binary outcome, data can be sampled in prospective cohort studies or case-control studies. Described in Zhang et al. (2020)<doi:10.1093/biomet/asaa014>.
Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the 'gimap' package is genetic interaction scores which are  the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <doi:10.1016/j.celrep.2021.109597>.
Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.
Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the 'graphicalVAR' approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
Simplifies the process of creating essential visualizations
    in R, offering a range of plotting functions for common chart types
    like violin plots, pie charts, and histograms.  With an intuitive
    interface, users can effortlessly customize colors, labels, and
    styles, making it an ideal tool for both beginners and experienced
    data analysts. Whether exploring datasets or producing quick visual
    summaries, this package provides a streamlined solution for
    fundamental graphics in R.
Facilitate reporting for regression and correlation modeling,
    hypothesis testing, variance analysis, outlier detection, and detailed
    descriptive statistics.
This is a set of functions to retrieve information about GIMMS
    NDVI3g files currently available online; download (and re-arrange, in the 
    case of NDVI3g.v0) the half-monthly data sets; import downloaded files from 
    ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based 
    on the widespread 'raster' package; conduct quality control; and generate 
    monthly composites (e.g., maximum values) from the half-monthly input data. 
    As a special gimmick, a method is included to conveniently apply the 
    Mann-Kendall trend test upon 'Raster*' images, optionally featuring 
    trend-free pre-whitening to account for lag-1 autocorrelation.
Implements GINA-X, a genome-wide iterative fine-mapping method designed for non-Gaussian traits. It supports the identification of credible sets of genetic variants.
Providing various equations to calculate Gini coefficients.
    The methods used in this package can be referenced from Brown MC (1994) <doi: 10.1016/0277-9536(94)90189-9>.
An implementation of Gini-based weighting approaches in constructing composite indicators, providing functionalities for normalization, aggregation, and ranking comparison.
Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.
An implementation of a new Gini covariance and correlation to measure dependence between a categorical and numerical variables. Dang, X., Nguyen, D., Chen, Y. and Zhang, J., (2018) <arXiv:1809.09793>.
Estimates the Gini index and computes variances and confidence intervals for finite and infinite populations, using different methods; also computes Gini index for continuous probability distributions, draws samples from continuous probability distributions with Gini indices set by the user; uses 'Rcpp'.
    References: 
    Muñoz et al. (2023) <doi:10.1177/00491241231176847>.
    Álvarez et al. (2021) <doi:10.3390/math9243252>.
    Giorgi and Gigliarano (2017) <doi:10.1111/joes.12185>.
    Langel and Tillé (2013) <doi:10.1111/j.1467-985X.2012.01048.x>.
Density function and generation of random variables from the Generalized Inverse Normal (GIN) distribution from Robert (1991) <doi:10.1016/0167-7152(91)90174-P>. Also provides density functions and generation from the GIN distribution truncated to positive or negative reals. Theoretical guarantees supporting the sampling algorithms and an application to Bayesian estimation of network formation models can be found in the working paper Ding, Estrada and Montoya-Blandón (2023) <https://www.smontoyablandon.com/publication/networks/network_externalities.pdf>.
A workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework.
    References: 
            Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>.
            Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.
Maximum likelihood estimation under relational models, with or without the overall effect.
The Gaussian Interval Plot (GIplot) is a pictorial representation of the mean
    and the standard deviation of a quantitative variable. It also flags 
    potential outliers (together with their frequencies) that are c standard deviations away from the mean.
Find the permutation symmetry group such that the covariance
    matrix of the given data is approximately invariant under it.
    Discovering such a permutation decreases the number of observations
    needed to fit a Gaussian model, which is of great use when it is
    smaller than the number of variables. Even if that is not the case,
    the covariance matrix found with 'gips' approximates the actual
    covariance with less statistical error. The methods implemented in
    this package are described in Graczyk et al. (2022)
    <doi:10.1214/22-AOS2174>. Documentation about 'gips' is
    provided via its website at <https://przechoj.github.io/gips/> and
    the paper by Chojecki, Morgen, Kołodziejek
    (2025, <doi:10.18637/jss.v112.i07>).
Extends classical linear and quadratic discriminant analysis
    by incorporating permutation group symmetries into covariance matrix
    estimation. The package leverages methodology from the 'gips'
    framework to identify and impose permutation structures that act as a
    form of regularization, improving stability and interpretability in
    settings with symmetric or exchangeable features. Several discriminant
    analysis variants are provided, including pooled and class-specific
    covariance models, as well as multi-class extensions with shared or
    independent symmetry structures. For more details about 'gips' methodology see
    and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen,
    Kołodziejek (2025, <doi:10.18637/jss.v112.i07>).
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices.
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the
 likelihood using MCMC samplers for large lattices.
Tools to download data from the GISCO (Geographic Information
    System of the Commission) Eurostat database
    <https://ec.europa.eu/eurostat/web/gisco>. Global and European map
    data available.  This package is in no way officially related to or
    endorsed by Eurostat.
Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases
  <https://www.sciencedirect.com/topics/computer-science/gis-database>,
  the R package 'GISINTEGRATION' offers a robust solution for efficiently preparing  GIS data for advanced 
  spatial analyses. This package excels in simplifying intrica  procedures like data cleaning, normalization, 
  and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.
A collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.
Mapping and spatial data manipulation tools - in particular
    drawing thematic maps with nice looking legends,  and aggregation of point
    data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g., 
    <https://en.wikipedia.org/wiki/GitHub#Gist>, 
    <https://docs.github.com/en/github/writing-on-github/creating-gists/>). A 'gist'
    is simply one or more files with code/text/images/etc. This package allows
    the user to create new 'gists', update 'gists' with new files, rename files,
    delete files, get and delete 'gists', star and 'un-star' 'gists', fork 'gists',
    open a 'gist' in your default browser, get embed code for a 'gist', list
    'gist' 'commits', and get rate limit information when 'authenticated'. Some
    requests require authentication and some do not. 'Gists' website: 
    <https://gist.github.com/>.
Interface to the 'libgit2' library, which is a pure C
    implementation of the 'Git' core methods. Provides access to 'Git'
    repositories to extract data and running some basic 'Git'
    commands.
The git2rdata package is an R package for writing and reading
    dataframes as plain text files.  A metadata file stores important
    information.  1) Storing metadata allows to maintain the classes of
    variables.  By default, git2rdata optimizes the data for file storage.
    The optimization is most effective on data containing factors.  The
    optimization makes the data less human readable.  The user can turn
    this off when they prefer a human readable format over smaller files.
    Details on the implementation are available in vignette("plain_text",
    package = "git2rdata").  2) Storing metadata also allows smaller row
    based diffs between two consecutive commits.  This is a useful feature
    when storing data as plain text files under version control.  Details
    on this part of the implementation are available in
    vignette("version_control", package = "git2rdata").  Although we
    envisioned git2rdata with a git workflow in mind, you can use it in
    combination with other version control systems like subversion or
    mercurial.  3) git2rdata is a useful tool in a reproducible and
    traceable workflow.  vignette("workflow", package = "git2rdata") gives
    a toy example.  4) vignette("efficiency", package = "git2rdata")
    provides some insight into the efficiency of file storage, git
    repository size and speed for writing and reading.
An interactive git user interface from the R command line. Intuitive
         tools to make commits, branches, remotes, and diffs an integrated part
         of R coding. Built on git2r, a system installation of git is not required
         and has default on-premises remote option.
Scan multiple 'Git' repositories, pull specified files content and process it with large language models. You can summarize the content in specific way, extract information and data, or find answers to your questions about the repositories. The output can be stored in vector database and used for semantic search or as a part of a RAG (Retrieval Augmented Generation) prompt.
Query, set, delete credentials from the 'git' credential
    store. Manage 'GitHub' tokens and other 'git' credentials. This
    package is to be used by other packages that need to authenticate to
    'GitHub' and/or other 'git' repositories.
Read all commit messages of your local git repository and
    sort them according to tags or specific text pattern into chapters of
    a HTML book using 'bookdown'. The git history book presentation helps
    organisms required to testify for every changes in their source code,
    in relation to features requests.
'Gitea' is a community managed, lightweight code hosting solution 
  were projects and their respective git repositories can be managed 
  <https://gitea.io>. This package gives an interface to the 'Gitea' API to 
  access and manage repositories, issues and organizations directly in R.
This is a 'GitHub' API wrapper for R. <https://docs.github.com/en/rest> It uses the 'gh' package but has things wrapped up for convenient use cases.
Simple interface to query gitignore.io to fetch
    gitignore templates that can be included in the .gitignore file. More
    than 450 templates are currently available.
Provides R functions to access the API of the project and
    repository management web application 'GitLab'. For many common tasks
    (repository file access, issue assignment and status, commenting)
    convenience wrappers are provided, and in addition the full API can be
    used by specifying request locations. 'GitLab' is open-source software
    and can be self-hosted or used on <https://about.gitlab.com>.
A light-weight, dependency-free, application programming interface
    (API) to access system-level 'Git' <https://git-scm.com/downloads> commands
    from within 'R'. Contains wrappers and defaults for common data science
    workflows as well as 'Zsh' <https://github.com/ohmyzsh/ohmyzsh> plugin aliases.
    A generalized API syntax is also available.
Obtain standardized data from multiple 'Git' services, including 'GitHub' and 'GitLab'. 
    Designed to be 'Git' service-agnostic, this package assists teams with activities spread across various 
    'Git' platforms by providing a unified way to access repository data.
In computationally demanding data analysis pipelines,
  the 'targets' R package (2021, <doi:10.21105/joss.02959>) maintains
  an up-to-date set of results while skipping tasks that do not need to rerun.
  This process increases speed and increases trust in the final end product.
  However, it also overwrites old output with new output, and past
  results disappear by default. To preserve historical output, the 'gittargets'
  package captures version-controlled snapshots of the data store,
  and each snapshot links to the underlying commit of the source code.
  That way, when the user rolls back the code to a previous branch or commit,
  'gittargets' can recover the data contemporaneous with that commit so that
  all targets remain up to date.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.  Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.
An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association.
Data sets used in the book Marra and Radice (2025, ISBN:9781032973111) "Copula Additive Distributional Regression Using R", for illustrating the fitting of various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association.
Functions for the g-and-k and generalised g-and-h distributions.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.
Imports the 'gkmSVM' v2.0 functionalities into R <https://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
    Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 
    'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages <https://bioconductor.org>.
Kernel regularized least squares, also known as kernel ridge regression, 
    is a flexible machine learning method. This package implements this method by 
    providing a smooth term for use with 'mgcv' and uses random sketching to 
    facilitate scalable estimation on large datasets. It provides additional 
    functions for calculating marginal effects after estimation and for use with 
    ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), 
    and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024)
    <doi:10.1017/pan.2023.27> provide further details.
Implements the five-parameter Generalized Kumaraswamy ('gkw') 
    distribution proposed by 'Carrasco, Ferrari and Cordeiro (2010)' 
    <doi:10.48550/arXiv.1004.0911> and its seven nested sub-families for 
    modeling bounded continuous data on the unit interval (0,1). The 'gkw' 
    distribution extends the Kumaraswamy distribution described by Jones (2009) 
    <doi:10.1016/j.stamet.2008.04.001>. Provides density, distribution, 
    quantile, and random generation functions, along with analytical 
    log-likelihood, gradient, and Hessian functions implemented in 'C++' via 
    'RcppArmadillo' for maximum computational efficiency. Suitable for modeling 
    proportions, rates, percentages, and indices exhibiting complex features 
    such as asymmetry, or heavy tails and other shapes not adequately captured by 
    standard distributions like simple Beta or Kumaraswamy.
Implements regression models for bounded continuous data in the open interval (0,1) 
    using the five-parameter Generalized 'Kumaraswamy' distribution. Supports modeling 
    all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of 
    predictors through various link functions. Provides efficient maximum likelihood 
    estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics,
    model comparison tools, and simulation methods. Particularly useful for analyzing 
    proportions, rates, indices, and other bounded response data with complex 
    distributional features not adequately captured by simpler models.
Contains methods for fitting Generalized Linear Models (GLMs) 
  and Generalized Additive Models (GAMs). Generalized regression models are
  common methods for handling data for which assuming Gaussian-distributed
  errors is not appropriate. For instance, if the response of interest is
  binary, count, or proportion data, one can instead model the expectation of
  the response based on an appropriate data-generating distribution.
  This package provides methods for fitting GLMs and GAMs under
  Beta regression, Poisson regression, Gamma regression, and Binomial regression
  (currently GLM only) settings. Models are fit using local scoring algorithms
  described in Hastie and Tibshirani (1990) <doi:10.1214/ss/1177013604>.
Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Efficiently implements the Graphical Lasso algorithm,
            utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm 
            introduces an L1 penalty to derive sparse inverse covariance matrices from 
            observations of multivariate normal distributions. Features include the 
            generation of random and structured sparse covariance matrices, beneficial 
            for simulations, statistical method testing, and educational purposes in 
            graphical modeling. A unique function for regularization parameter selection 
            based on predefined sparsity levels is also offered, catering to users with
            specific sparsity requirements in their models. The methodology for sparse 
            inverse covariance estimation implemented in this package is based on the 
            work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.
Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.
Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.
Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.
A fast and improved implementation of the graphical LASSO.
Tools for creating animated glassmorphism-style tab
    navigation and select filter widgets in 'Shiny' applications.
    Provides a tab navigation component with a sliding glass halo
    animation, a searchable multi-select dropdown, and a single-select
    dropdown — all with multiple colour themes and server-side update
    helpers. Tabs support icons, numeric badges, disable/enable toggling,
    runtime append/remove, reactive rendering via 'renderGlassTabs()',
    URL bookmarking, and compact mode for dashboard card layouts.
    'glassTabCondition()' generates 'conditionalPanel()' condition strings
    without needing to recall the internal input key pattern.
    'glasstabs_news()' displays the release notes from the R console.
    Built-in example apps can be launched with 'runGlassExample()'.
    All widgets are compatible with standard 'Shiny' layouts and
    'bs4Dash' dashboards.
    For full documentation and examples see Arthur (2026)
    <https://prigasg.github.io/glasstabs/>.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Fits multiple-group latent class analysis (LCA) for exploring 
    differences between populations in the data with a multilevel structure. 
    There are two approaches to reflect group differences in glca: 
    fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>;
    Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA 
    (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).
Enables calculation of image textures (Haralick 1973) 
    <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices 
    (GLCMs). Supports processing images that cannot fit in memory.
Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.
The generalised lambda distribution, or Tukey lambda distribution, 
  provides a wide variety of shapes with one functional form.   
  This package provides random numbers, quantiles, probabilities, 
  densities and density quantiles for four different types of the distribution,
  the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden
  and Loots 2009) and FM5 - see documentation for details.
  It provides the density function, distribution function, and Quantile-Quantile 
  plots.  
  It implements a variety of estimation methods for the distribution, 
  including diagnostic plots. 
  Estimation methods include the starship (all 4 types), 
  method of L-Moments for the GPD and FKML types, and a 
  number of methods for only the FKML type.  
  These include maximum likelihood, maximum product of spacings, 
  Titterington's method, Moments, Trimmed L-Moments and 
  Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" <doi:10.1016/j.csda.2007.06.021>, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" <doi:10.1111/1467-842X.00089>, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" <doi:10.22237/jmasm/1130803560>, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" <doi:10.1016/j.csda.2006.06.008>, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" <doi:10.18637/jss.v021.i09>, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" <doi:10.1016/j.csda.2009.02.014>, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" <doi:10.1201/b10159>, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" <doi:10.1201/b10159>, Su (2015)   "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.
Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of  the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.  References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
The official implementation of the Global Livestock Environmental Assessment
  Model (GLEAM) of the Food and Agriculture Organization of the United Nations (FAO) in R. 
  GLEAM-X provides a modular, transparent framework for simulating livestock production 
  systems and quantifying their environmental impacts.
  Methodological background: MacLeod et al. (2017) "Invited review: A position on the Global
  Livestock Environmental Assessment Model (GLEAM)" <doi:10.1017/S1751731117001847>. Further
  information: <https://www.fao.org/gleam/en/>.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.
Provides functions to load and analyze three open Electronic
    Health Records (EHRs) datasets of patients diagnosed with
    glioblastoma, previously released under the Creative Common
    Attribution 4.0 International (CC BY 4.0) license.  Users can generate
    basic descriptive statistics, frequency tables and save descriptive
    summary tables, as well as create and export univariate or bivariate
    plots.  The package is designed to work with the included datasets and
    to facilitate quick exploratory data analysis and reporting.  More
    information about these three datasets of EHRs of patients with
    glioblastoma can be found in this article: Gabriel Cerono, Ombretta
    Melaiu, and Davide Chicco, 'Clinical feature ranking based on ensemble
    machine learning reveals top survival factors for glioblastoma
    multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March
    2024).  <doi:10.1007/s41666-023-00138-1>.
Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches. Allows bootstrapping. See Espeland and Hui (1987)
             <doi:10.2307/2531553> for general approach.
Analysis of multivariate data using generalized linear latent variable models (gllvm). 
      Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Provides two functions that generate source code implementing the predict 
  function of fitted glm objects. In this version, code can be generated for either 'C' 
  or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm 
  predictive models into production. The source code generated by this package implements 
  two function/methods. One of such functions implements the equivalent to 
  predict(type="response"), while the second implements predict(type="link"). Source code 
  is written to disk as a .c or .java file in the specified path. In the case of c, an .h 
  file is also generated.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model],
	polr() [ordinal logistic model], vglm() [generalized ordinal logistic model],	multinom() [multinomial model], tobit() [tobit model],
	svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Fits Generalised Linear Models (GLMs) with sparse and dense 'Matrix' matrices for memory efficiency. Acts as a wrapper for the glm4() function in the 'MatrixModels' package <doi:10.32614/CRAN.package.MatrixModels>, but adds convenient model methods and functions designed to mimic those associated with the glm() function from the 'stats' package.
Provides functions for estimating Rasch model parameters 
    using the Generalized Linear Model (GLM) framework. The methods 
    implemented are based on Brown (2018, ISBN:978-3-319-93547-8) 
    <doi:10.1007/978-3-319-93549-2> and Debelak et al. (2022, 
    ISBN:978-1-138-71046-7) <doi:10.1201/9781315200620>.
Provides Bayesian linear and generalized linear model fitting
    with independent and identically distributed (iid) posterior samples. The
    main functions mirror R's lm() and glm() interfaces while adding prior
    family specifications for Gaussian, Poisson, binomial, and Gamma models
    with log-concave likelihoods. Sampling for supported non-conjugate models
    uses accept-reject methods based on likelihood subgradients as in Nygren
    and Nygren (2006) <doi:10.1198/016214506000000357>. The package also
    includes tools for prior setup, posterior summaries, prediction,
    diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for
    larger models.
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.
In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) <doi:10.1093/biomet/asv042>, we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix. The package accompanies the paper "GLMcat: An R Package for Generalized Linear Models for Categorical Responses" in the Journal of Statistical Software, Volume 114, Issue 9 (see <doi:10.18637/jss.v114.i09>).
Provides Generalized Inferences based on exact distributions and exact probability statements for mixed effect models, provided by such papers as Weerahandi and Yu (2020) <doi:10.1186/s40488-020-00105-w> under the widely used Compound Symmetric Covariance structure. The package returns the estimation of the coefficients in random and fixed part of the mixed models by generalized inference.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 
    'partykit'. The fitting algorithm is described in more detail in Fokkema,
    Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).
    For detecting and modeling subgroups in growth curves with GLMM trees see
    Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).
Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) <https:>, Maiti and Pradhan (2009) <doi:10.1111/j.1541-0420.2008.01186.x>, Maity, Pradhan, and Das (2019) <doi:10.1080/00031305.2017.1407359> for further methodological details.
Efficient algorithms for fitting regularization paths for
        linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Conducts hierarchical partitioning to calculate individual contributions of each predictor (fixed effects) towards marginal R2 for generalized linear mixed-effect model (including lm, glm and glmm) based on output of r.squaredGLMM() in 'MuMIn', applying the algorithm of Lai J.,Zou Y., Zhang S.,Zhang X.,Mao L.(2022)glmm.hp: an R package for computing individual effect of predictors in generalized linear mixed models.Journal of Plant Ecology,15(6)1302-1307<doi:10.1093/jpe/rtac096>.
Fits generalized linear mixed models for a single grouping factor under
    maximum likelihood approximating the integrals over the random effects with an 
    adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) 
    <doi:10.1080/10618600.1995.10474663>.
Allows users to fit a cosinor model using the 'glmmTMB' framework. 
    This extends on existing cosinor modeling packages, including 'cosinor' 
    and 'circacompare', by including a wide range of available link functions 
    and the capability to fit mixed models. The cosinor model is described by 
    Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.
Fit generalized linear mixed models (GLMMs) with normal random
    effects using first-order Laplace, fully exponential Laplace (FEL) with
    mean-only corrections, and FEL with mean and covariance corrections in
    the E-step of an expectation-maximization (EM) algorithm. The current
    development version provides a matrix-based interface (y, X, Z) and
    supports binary logit and probit, and Poisson log-link models. An EM
    framework is used to update fixed effects, random effects, and a single
    variance component tau^2 for G = tau^2 I, with staged approximations
    (Laplace -> FEL mean-only -> FEL full) for efficiency and stability. A
    pseudo-likelihood engine glmmFEL_pl() implements the working-response /
    working-weights linearization approach of Wolfinger and O'Connell (1993)
    <doi:10.1080/00949659308811554>, and is adapted from the implementation
    used in the 'RealVAMS' package (Broatch, Green, and Karl (2018))
    <doi:10.32614/RJ-2018-033>. The FEL implementation follows Karl, Yang,
    and Lohr (2014) <doi:10.1016/j.csda.2013.11.019> and related work (e.g.,
    Tierney, Kass, and Kadane (1989) <doi:10.1080/01621459.1989.10478824>;
    Rizopoulos, Verbeke, and Lesaffre (2009)
    <doi:10.1111/j.1467-9868.2008.00704.x>; Steele (1996)
    <doi:10.2307/2532845>). Package code was drafted with assistance from
    generative AI tools.
Implements Bayesian spatial and spatiotemporal
    models that optionally allow for extreme spatial deviations through
    time. 'glmmfields' uses a predictive process approach with random
    fields implemented through a multivariate-t distribution instead of
    the usual multivariate normal.  Sampling is conducted with 'Stan'.
    References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.
Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) <https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, 
	see Groll and Tutz (2014) <doi:10.1007/s11222-012-9359-z>.
	See also Groll and Tutz (2017) <doi:10.1007/s10985-016-9359-y> for discrete survival models including heterogeneity.
Binomial and Poisson regression for clustered data, fixed
        and random effects with bootstrapping.
Fits high dimensional penalized generalized linear 
    mixed models using 
    the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. 
    The purpose of the package is to perform variable selection on both the fixed and 
    random effects simultaneously for generalized linear mixed models.
    The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
    supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
    is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>.
    The techniques used in the minimization portion of the procedure (the M-step) are
    derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) 
    <doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015)
    <doi:10.1007/s11222-013-9424-2>), with
    appropriate modifications to account for the estimation and penalization of
    the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, 
    and LASSO penalties.
Specification, analysis, simulation, and fitting of generalised linear mixed models. 
  Monte Carlo Maximum likelihood model fitting for a range of models, 
  non-linear fixed effect specifications, a wide range of flexible covariance functions including 
  Gaussian Process approximations. Methods described in Watson, Wang, and Giorgi (2026) <doi:10.48550/arXiv.2601.16022>.
Optimal design analysis algorithms for any study design that can be represented or
  modelled as a generalised linear mixed model including cluster randomised trials,
  cohort studies, spatial and temporal epidemiological studies, and split-plot designs.
  See <https://github.com/samuel-watson/glmmrBase/blob/master/README.md> for a
  detailed manual on model specification. A detailed discussion of the methods in this
  package can be found in Watson, Hemming, and Girling (2023) <doi:10.1177/09622802231202379>.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. 
    Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.
Provides tools for fitting sparse generalised linear mixed models with l0 
  regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects 
  must precede random effects. Uses coordinate descent and local search algorithms to rapidly 
  deliver near-optimal estimates. Gaussian and binomial response families are currently supported.
  For more details see Thompson, Wand, and Wang (2025) <doi:10.48550/arXiv.2506.20425>.
A Bayesian model selection approach for generalized linear mixed models.
    Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. 
    Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix.
    'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics.
    'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.
Using mixed effects models to analyse longitudinal gene expression can highlight differences between sample groups over time. The most widely used differential gene expression tools are unable to fit linear mixed effect models, and are less optimal for analysing longitudinal data. This package provides negative binomial and Gaussian mixed effects models to fit gene expression and other biological data across repeated samples. This is particularly useful for investigating changes in RNA-Sequencing gene expression between groups of individuals over time, as described in: Rivellese, F., Surace, A. E., Goldmann, K., Sciacca, E., Cubuk, C., Giorli, G., ... Lewis, M. J., & Pitzalis, C. (2022) Nature medicine <doi:10.1038/s41591-022-01789-0>.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model,  multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead.  The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.
Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal
 response dataset for fitting continuation ratio models for datasets  where the number of covariates exceeds the sample size or when
 there is collinearity among the covariates. The 'glmnet' fitting algorithm is used to fit the continuation ratio model after data restructuring.
Cross validation informed Relaxed LASSO (or more generally elastic net), gradient boosting machine ('xgboost'), Random Forest ('RandomForestSRC'), Oblique Random Forest ('aorsf'), Artificial Neural Network (ANN), Recursive Partitioning ('RPART') or step wise regression models are fit.  Cross validation leave out samples (leading to nested cross validation) or bootstrap out-of-bag samples are used to evaluate and compare performances between these models with results presented in tabular or graphical means.  Calibration plots can also be generated, again based upon (outer nested) cross validation or bootstrap leave out (out of bag) samples.
    Note, at the time of this writing, in order to fit gradient boosting machine models one must install the packages 'DiceKriging' and 'rgenoud' using the install.packages() function. 
    For some datasets, for example when the design matrix is not of full rank, 'glmnet' may have very long run times when fitting the relaxed lasso model, from our experience when fitting Cox models on data with many predictors and many patients, making it difficult to get solutions from either glmnet() or cv.glmnet().  This may be remedied by using the 'path=TRUE' option when calling glmnet() and cv.glmnet().  Within the 'glmnetr' package the approach of path=TRUE is taken by default. 
    other packages doing similar include 'nestedcv' <https://cran.r-project.org/package=nestedcv>, 'glmnetSE' <https://cran.r-project.org/package=glmnetSE> which may provide different functionality when performing a nested CV. 
    Use of the 'glmnetr' has many similarities to the 'glmnet' package and it could be helpful for the user of 'glmnetr' also become familiar with the 'glmnet' package <https://cran.r-project.org/package=glmnet>, with the "An Introduction to 'glmnet'" and "The Relaxed Lasso" being especially useful in this regard.
Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or
    'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value)
    for selected coefficients with no shrinkage applied for them. Model
    performance can be evaluated on test data and an automated alpha
    selection is implemented for Elastic Net. Parallelized computation is
    used to speed up the process. The methods are described in Friedman et
    al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011)
    <doi:10.18637/jss.v039.i05>.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
Provides generalized L-moments estimation methods for the
    generalized extreme value ('GEV') distribution. Implements both stationary
    'GEV' and non-stationary 'GEV11' models where location and scale parameters
    vary with time. Includes various penalty functions ('Martins'-'Stedinger',
    Park, Cannon, 'Coles'-Dixon) for shape parameter regularization.
    Also provides model averaging estimation ('ma.gev') that combines MLE and
    L-moment methods with multiple weighting schemes for robust high quantile
    estimation. The 'GLME' methodology is described in Shin et al. (2025a)
    <doi:10.48550/arXiv.2512.20385>. The non-stationary L-moment method
    is based on Shin et al. (2025b) <doi:10.1007/s42952-025-00325-3>.
    The model averaging method is described in Shin et al. (2026)
    <doi:10.1007/s00477-025-03167-x>.
    See also 'Hosking' (1990) <doi:10.1111/j.2517-6161.1990.tb01775.x> for
    L-moments theory and 'Martins' and 'Stedinger' (2000)
    <doi:10.1029/1999WR900330> for penalized likelihood methods.
Contains all the data and functions used in Generalized
    Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create
    all models, tables, and plots are included for each data set.
A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.
Provides a function for fitting a penalized constrained  continuation ratio model using the glmpath algorithm and methods for  extracting coefficient estimates, predicted class, class probabilities, and plots as described by Archer and Williams (2012) <doi:10.1002/sim.4484>.
Implements a generalized version of principal components analysis
    (GLM-PCA) for dimension reduction of non-normally distributed data such as
    counts or binary matrices.
    Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
    Townes FW (2019) <arXiv:1907.02647>.
In practical applications, the assumptions underlying generalized linear models frequently face violations, including incorrect specifications of the outcome variable's distribution or omitted predictors. These deviations can render the results of standard generalized linear models unreliable. As the sample size increases, what might initially appear as minor issues can escalate to critical concerns. To address these challenges, we adopt a permutation-based inference method tailored for generalized linear models. This approach offers robust estimations that effectively counteract the mentioned problems, and its effectiveness remains consistent regardless of the sample size.
Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
Creates ideal data for all distributions in the generalized
    linear model framework.
Fit spatio-temporal models within a (double) generalized linear modelling framework. The package includes functions for estimation, simulation and inference.
Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.
Set of tools for the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) Zero-inflated and zero-altered regression models to deal with zero-excess in count data; (6) generalized nonlinear models; (7) generalized estimating equations for cluster correlated data.
We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The details of methods can be found in "Tian, Y., & Feng, Y. (2023). Transfer learning under high-dimensional generalized linear models. Journal of the American Statistical Association, 118(544), 2684-2697.".
Automated model selection and model-averaging. Provides a
        wrapper for glm and other functions, automatically generating
        all possible models (under constraints set by the user) with
        the specified response and explanatory variables, and finding
        the best models in terms of some Information Criterion (AIC,
        AICc or BIC). Can handle very large numbers of candidate
        models. Features a Genetic Algorithm to find the best models
        when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
             including parametric links and heteroscedastic latent variables.
Provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by 'VGAM' package), beta regression model (estimated by 'betareg' package) and negative binomial regression model (estimated by 'MASS' package). Since most of functions implemented in 'glmxdiag' already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.
Second-order summary statistics K- and pair-correlation functions describe
    interactions in point pattern data. This package provides computations to estimate those
    statistics on inhomogeneous point processes, using the methods of
    in T Shaw, J Møller, R Waagepetersen, 2020 <doi:10.48550/arXiv.2004.00527>.
It provides more configurations on the option values such as validation
    and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown" or "free") objects in R expressions
    by code inspection using various strategies (ordered, liberal,
    conservative, or deep-first search). The objective of this package is to
    make it as simple as possible to identify global objects for the purpose
    of exporting them in parallel, distributed compute environments.
Google offers public access to global search volumes from its
    search engine through the Google Trends portal. The package downloads
    these search volumes provided by Google Trends and uses them to
    measure and analyze the distribution of search scores across countries
    or within countries. The package allows researchers and analysts to
    use these search scores to investigate global trends based on patterns
    within these scores. This offers insights such as degree of
    internationalization of firms and organizations or dissemination of
    political, social, or technological trends across the globe or within
    single countries.  An outline of the package's methodological
    foundations and potential applications is available as a working
    paper: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3969013>.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), <arXiv:2306.00686>.
Provides tools to access, search, and download global 
    3D building footprint datasets (3D-GloBFP) generated by 
    Che et al. (2024) <doi:10.5194/essd-16-5357-2024>. 
    The package includes functions to retrieve metadata, filter by bounding box, 
    and download building height tiles.
A fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) <doi:10.1109/MHS.1995.494215>, Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) <doi:10.1109/CEC.2004.1330875>, Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) <doi:10.1007/s11721-014-0095-1>, Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) <doi:10.1109/TCYB.2014.2322602> and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) <doi:10.1016/j.asoc.2024.111913>. For the DE algorithm, six types in Storn, R. & Price, K. (1997) <doi:10.1023/A:1008202821328> are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2.
Tools for the generalized logistic distribution (Type I,
             also known as skew-logistic distribution), encompassing
	     basic distribution functions (p, q, d, r, score), maximum
	     likelihood estimation, and structural change methods.
Functions for constructing Transformed and Relative Lorenz curves with survey sampling weights. Given a variable of interest measured in two groups with scaled survey weights so that their hypothetical populations are of equal size, tlorenz() computes the proportion of members of the group with smaller values (ordered from smallest to largest) needed for their sum to match the sum of the top qth percentile of the group with higher values. rlorenz() shows the fraction of the total value of the group with larger values held by the pth percentile of those in the group with smaller values. Fd() is a survey weighted cumulative distribution function and Eps() is a survey weighted inverse cdf used in rlorenz(). Ramos, Graubard, and Gastwirth (2025) <doi:10.1093/jrsssa/qnaf044>.
A user-friendly 'shiny' application for Bayesian machine
    learning analysis of marine species distributions. GLOSSA (Global
    Ocean Species Spatio-temporal Analysis) uses Bayesian Additive
    Regression Trees (BART; Chipman, George, and McCulloch (2010)
    <doi:10.1214/09-AOAS285>) to model species distributions with
    intuitive workflows for data upload, processing, model fitting, and
    result visualization. It supports presence-absence and presence-only
    data (with pseudo-absence generation), spatial thinning,
    cross-validation, and scenario-based projections. GLOSSA is designed
    to facilitate ecological research by providing easy-to-use tools for
    analyzing and visualizing marine species distributions across
    different spatial and temporal scales. Optionally, pseudo-absences can
    be generated within the environmental space using the external package
    'flexsdm' (not on CRAN), which can be downloaded from
    <https://github.com/sjevelazco/flexsdm>; this functionality is used
    conditionally when available and all core features work without it.
Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file.
Read examples with interlinear glosses from files
    or from text and print them in a way compatible with both
    Latex and HTML outputs.
Provides a framework for creating plots with glowing points.
Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
Provides a fully automated workflow for calibrating and analyzing
    light-level geolocation ('GLS') data from seabirds and other wildlife. The
    'glscalibrator' package auto-discovers birds from directory structures,
    automatically detects calibration periods from the first days of deployment,
    processes multiple individuals in batch mode, and generates standardized
    outputs including position estimates, diagnostic plots, and quality control
    metrics. Implements the established threshold workflow internally, following
    the methods described in 'SGAT' (Wotherspoon et al. (2016)
    <https://github.com/SWotherspoon/SGAT>), 'GeoLight' (Lisovski et al. (2012)
    <doi:10.1111/j.2041-210X.2012.00185.x>), and 'TwGeos' (Lisovski et al.
    (2019) <https://github.com/slisovski/TwGeos>).
When the response variable Y takes one of R > 1 values, the function 'glsm()' computes the maximum likelihood estimates (MLEs) of the parameters under four models: null, complete, saturated, and logistic. It also calculates the log-likelihood values for each model. This method assumes independent, non-identically distributed variables. For grouped data with a multinomial outcome, where observations are divided into J populations, the function 'glsm()' provides estimation for any number K of explanatory variables.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
Performs the Generalised Linear Step-up Procedure (GLSUP) with a flexible user-defined sizing function. Functions are also available for creating common sizing functions.
An implementation of interpreted string literals, inspired by
    Python's Literal String Interpolation
    <https://www.python.org/dev/peps/pep-0498/> and Docstrings
    <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted
    String Literals
    <https://docs.julialang.org/en/v1.3/manual/strings/#Triple-Quoted-String-Literals-1>.
Provides convenient wrapper functions around the 'glue' library for common string interpolation tasks. The package simplifies the process of combining 'glue' string templating with common R functions like message(), warning(), stop(), print(), cat(), and file writing operations. Instead of manually calling glue() and then passing the result to these functions, 'glueDo' provides direct wrapper functions that handle both steps in a single call. This is particularly useful for logging, error handling, and formatted output in R scripts and packages. The main reference for the underlying 'glue' package is Hester and Bryan (2022) <https://CRAN.R-project.org/package=glue>.
Ease the transition between R vectors and markdown text. With
    'gluedown' and 'rmarkdown', users can create traditional vectors in R,
    glue those strings together with the markdown syntax, and print those
    formatted vectors directly to the document. This package primarily
    uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous
    CommonMark specification by John MacFarlane (2019)
    <https://spec.commonmark.org/>.
Provides residual global fit indices for generalized latent variable models.
Provides functions to parse glycan structure text representations
    into 'glyrepr' glycan structures. Currently, it supports StrucGP-style,
    pGlyco-style, IUPAC-condensed, IUPAC-extended, IUPAC-short, WURCS,
    Linear Code, and GlycoCT format. It also provides an automatic parser
    to detect the format and parse the structure string.
Computational representations of glycan compositions and structures,
    including details such as linkages, anomers, and substituents. Supports varying
    levels of monosaccharide specificity (e.g., "Hex" or "Gal") and ambiguous linkages.
    Provides robust parsing and generation of IUPAC-condensed structure strings.
    Optimized for vectorized operations on glycan structures, with efficient handling
    of duplications. As the cornerstone of the glycoverse ecosystem, this package
    delivers the foundational data structures that power glycomics and glycoproteomics
    analysis workflows.
Provides a simple and intuitive high-level language for music
    representation. Generates and embeds music scores and audio files in
    'RStudio', 'R Markdown' documents, and R 'Jupyter Notebooks'.
    Internally, uses 'MusicXML' <https://github.com/w3c/musicxml> to represent
    music, and 'MuseScore' <https://musescore.org/> to convert 'MusicXML'.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/gr.216754.116>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the 'Gmail' 'RESTful' API.  Allows access to
    your 'Gmail' messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
Provides tools to compute the Generalized Measure of Correlation (GMC), 
  a dependence measure accounting for nonlinearity and asymmetry in the relationship 
  between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
A lightweight fork of 'gMCP' with functions for graphical
    described multiple test procedures introduced in
    Bretz et al. (2009) <doi:10.1002/sim.3495> and
    Bretz et al. (2011) <doi:10.1002/bimj.201000239>.
    Implements a flexible function using 'ggplot2' to create
    multiplicity graph visualizations.
    Contains instructions of multiplicity graph and graphical testing for
    group sequential design, described in
    Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>,
    with necessary unit testing using 'testthat'.
A template for a geometallurgical database and a fast and easy
    interface for accessing it.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to make short term forecasting for a univariate time series.
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).
Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko.
    Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms
    that performs gradually complicated polynomial models and selecting the best solution by an external criterion.
    In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and
    selecting an optimal structure of model or network.
    The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.
Fast algorithms for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
Supply implementation to model generalized multivariate functional
 data using Bayesian additive mixed models of R package 'bamlss' via a latent
 Gaussian process (see Umlauf, Klein, Zeileis (2018) 
 <doi:10.1080/10618600.2017.1407325>).
Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Support for geostatistical analysis of multivariate data, 
         in particular data with restrictions, e.g. positive amounts, 
         compositions, distributional data, microstructural data, etc. 
         It includes descriptive analysis and modelling for such data, both 
         from a two-point Gaussian perspective and multipoint perspective.
         The methods mainly follow Tolosana-Delgado, Mueller and van den
         Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.
Gaussian mixture graphical models include Bayesian networks and
    dynamic Bayesian networks (their temporal extension) whose local probability
    distributions are described by Gaussian mixture models. They are powerful
    tools for graphically and quantitatively representing nonlinear dependencies
    between continuous variables. This package provides a complete framework to
    create, manipulate, learn the structure and the parameters, and perform
    inference in these models. Most of the algorithms are described in the PhD
    thesis of Roos (2018) <https://theses.hal.science/tel-01943718>.
Utilities for common medical-statistics graphics
    and tables, including automatic creation of publication-ready "Table 1"
    summaries; transition (Sankey) plots; flow-chart helpers that extend the
    grid package; Bézier lines and arrows; and a singular-value-decomposition-
    based variable-selection method.
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
Likelihood-based boosting approaches for generalized mixed models are provided.
Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) <doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003) <doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of Dempster et al. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on model-based clustering includes Fraley and Raftery (2002) <doi:10.1198/016214502760047131> and McLachlan and Peel (2000, ISBN:9780471006268).
The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.
An implementation of maximum simulated likelihood method for the
    estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>.
    Specifically, it allows estimating models with continuous heterogeneity
    such as the mixed multinomial logit and the generalized multinomial logit.
    It also allows estimating models with discrete heterogeneity such as the
    latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Make 2D and 3D plots of linear programming (LP), 
    integer linear programming (ILP), or mixed integer linear programming (MILP) models 
    with up to three objectives. Plots of both the solution and criterion space are possible.
    For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models 
    (see vignettes for an overview). The package also contains an function for checking if a point
    is inside the convex hull.
Datasets analysed in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics".
Efficiently manage and process data from 'oTree' experiments.
    Import 'oTree' data and clean them by using functions that handle
    messy data, dropouts, and other problematic cases.  Create IDs,
    calculate the time, transfer variables between app data frames, and
    delete sensitive information.  Review your experimental data prior to
    running the experiment and automatically generate a detailed summary
    of the variables used in your 'oTree' code.  Information on 'oTree' is
    found in Chen, D. L., Schonger, M., & Wickens, C. (2016)
    <doi:10.1016/j.jbef.2015.12.001>.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Symbolic calculation (addition or multiplication) and evaluation of multivariate polynomials with rational coefficients.
Functions for graph matching via nodes' degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.
A system for fitting Gompertz Curve in a Time Series Data.
Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.
A collection of tools to create, use and maintain modularized model code written in the modeling 
    language 'GAMS' (<https://www.gams.com/>). Out-of-the-box 'GAMS' does not come with support for modularized
    model code. This package provides the tools necessary to convert a standard 'GAMS' model to a modularized one
    by introducing a modularized code structure together with a naming convention which emulates local
    environments. In addition, this package provides tools to monitor the compliance of the model code with
    modular coding guidelines.
Integrates game theory and ecological theory to construct 
    social-ecological models that simulate the management of populations and 
    stakeholder actions. These models build off of a previously developed 
    management strategy evaluation (MSE) framework to simulate all aspects of 
    management: population dynamics, manager observation of populations, manager
    decision making, and stakeholder responses to management decisions. The 
    newly developed generalised management strategy evaluation (GMSE) 
    framework uses genetic algorithms to mimic the decision-making process of 
    managers and stakeholders under conditions of change, uncertainty, and 
    conflict. Simulations can be run using gmse(), gmse_apply(), and
    gmse_gui() functions.
Two-Step Lasso (TS-Lasso) and compound minimum methods to recover 
             the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an 
             imputation method that handles various types of missing peaks simultaneously. This package 
             provides the procedure to generate missing peaks (or data) for simulation study, 
             as well as a tool to estimate and visualize the proportion of missing at random.
Interface between the GMT map-making software and R, enabling the
  user to manipulate geographic data within R and call GMT commands to draw and
  annotate maps in postscript format. The gmt package is about interactive data
  analysis, rapidly visualizing subsets and summaries of geographic data, while
  performing statistical analysis in the R console.
The multiple contrast tests for univariate were proposed by Munko, Ditzhaus, Pauly, Smaga, and Zhang (2023) <doi:10.48550/arXiv.2306.15259>. Recently, they were extended to the multivariate functional data in Munko, Ditzhaus, Pauly, and Smaga (2024) <doi:10.48550/arXiv.2406.01242>. These procedures enable us to evaluate the overall hypothesis regarding equality, as well as specific hypotheses defined by contrasts. In particular, we can perform post hoc tests to examine particular comparisons of interest. Different experimental designs are supported, e.g., one-way and multi-way analysis of variance for functional data.
Unconstrained and constrained maximum likelihood estimation of structural and reduced form 
    Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t
    mixture vector autoregressive models, quantile residual tests, graphical diagnostics,
    simulations, forecasting, and estimation of generalized impulse response function and generalized 
    forecast error variance decomposition.
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>,
    Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>,
    Savi Virolainen (in press) <doi:10.1016/j.ecosta.2025.09.003>.
Fit joint models of survival and multivariate longitudinal data. The longitudinal
    data is specified by generalised linear mixed models. The joint models are fit via maximum
    likelihood using an approximate expectation maximisation algorithm. 
    Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.
Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>.
             The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. 
             The 'gmwmx' package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.
Implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Voirol, L., Xu, H., Zhang, Y., Insolia, L., Molinari, R. and Guerrier, S. (2024) <doi:10.48550/arXiv.2409.05160>.
             The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals in presence of missing data.
             The 'gmwmx2' package provides functions to load and plot Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory and functions to estimate linear model model with correlated residuals in presence of missing data.
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots, for the underlying manuscript see Fischer, Oja (2015) <doi:10.18637/jss.v065.i09>.
The function plotLRT() draws pairwise graphical model checks for
      the Rasch Model (RM; Rasch, 1960), 
      the Partial Credit Model(PCM; Masters, 1982), and 
      the Rating Scale Model (RSM; Andrich, 1978)
  using the output object of eRm::LRtest().
  The function cLRT() provides a conditional Likelihood Ratio Test
  (Andersen, 1973), using the routines of 'psychotools'.
  Users may choose to plot
      the threshold parameters, 
      the cumulative thresholds, 
      the average thresholds per item, or
      the person parameters.
  Extended coloring options allow for automated item-wise or
  threshold-wise coloring. For multi-group splits, all pairwise
  group comparisons are drawn automatically.
  For more details see
      Andersen (1973) <doi:10.1007/BF02291180>,
      Andrich (1978) <doi:10.1007/BF02293814>,
      Masters (1982) <doi:10.1007/BF02296272> and
      Rasch (1960, ISBN:9780598554512).
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure, potentially with exogenous variables.  Such models are described in Knight et al. (2020) <doi:10.18637/jss.v096.i05> and Nason and Wei (2021) <doi:10.1111/rssa.12875>.  Diagnostic tools for GNAR(X) models can be found in Nason et al. (2023) <doi:10.48550/arXiv.2312.00530>.
Compute standard and generalized Nash Equilibria of non-cooperative games. 
 Optimization methods available are nonsmooth reformulation, fixed-point formulation, 
 minimization problem and constrained-equation reformulation. 
 See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.
Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
Functions to specify and fit generalized nonlinear models,
    including models with multiplicative interaction terms such as the
    UNIDIFF model from sociology and the AMMI model from crop science, and
    many others.  Over-parameterized representations of models are used
    throughout; functions are provided for inference on estimable
    parameter combinations, as well as standard methods for diagnostics
    etc.
Tools to set up, train, store, load, investigate and analyze
             generative neural networks. In particular, functionality for
	     generative moment matching networks is provided.
Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>.
    It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.
The goal of 'gnonadd' is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.
Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
Estimation of gross output production functions and productivity in the presence of numerous fixed (nonflexible) and a single flexible input using the nonparametric identification strategy specified in Gandhi, Navarro, and Rivers (2020) <doi:10.1086/707736>. Monte Carlo evidence from the paper demonstrates high performance in estimating production function elasticities.
Provides tools for interacting with the 'geographic name resolution service' ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The 'GNRS' is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The 'GNRS' resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the 'GNRS' attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The 'GNRS' works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the 'GNRS' does not attempt to match state or county.
Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances,  see Quarello (2020) <arXiv:2005.04683>.
Read data files readable by 'gnumeric' into 'R'. Can read
        whole sheet or a range, from several file formats, including
        the native format of 'gnumeric'. Reading is done by using
        'ssconvert' (a file converter utility included in the 'gnumeric'
        distribution <http://www.gnumeric.org>) to convert
        the requested part to CSV. From 'gnumeric' files (but not other
        formats) can list sheet names and sheet sizes or read all
        sheets.
This function converts mfpr, numeric, or character strings representing numbers to bigq format without loss of precision.
Solves goal programming problems of the weighted and  
    lexicographic type, as well as combinations of the two, as described 
    by Ignizio (1983) <doi:10.1016/0305-0548(83)90003-5>. Allows for 
    a simple human-readable input describing the problem as a series 
    of equations. Relies on the 'lpSolve' package to solve the underlying 
    linear optimisation problem.
Perform gene set enrichment analyses using the Gene set Ordinal
    Association Test (GOAT) algorithm and visualize your
    results. Koopmans, F. (2024) <doi:10.1038/s42003-024-06454-5>.
Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.
Define, simulate, and validate stock-flow consistent (SFC) macroeconomic models. The godley R package offers tools to dynamically define model structures by adding variables and specifying governing systems of equations. With it, users can analyze how different macroeconomic structures affect key variables, perform parameter sensitivity analyses, introduce policy shocks, and visualize resulting economic scenarios. The accounting structure of SFC models follows the approach outlined in the seminal study by Godley and Lavoie (2007, ISBN:978-1-137-08599-3), ensuring a comprehensive integration of all economic flows and stocks. The algorithms implemented to solve the models are based on methodologies from Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205>, Peressini and Sullivan (1988, ISBN:0-387-96614-5), and contributions by Joao Macalos.
A collection of functions useful in (vegetation) community analyses and ordinations. Includes automatic species selection for ordination diagrams, NMDS stress/scree plots, species response curves, merging of taxa as well as calculation and sorting of synoptic tables.
Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.
A post-estimation method for categorical response models (CRM). 
    Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), 
    vglm() and glm() are currently supported. Available tests include the 
    Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic 
    regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal 
    models. The proportional odds, adjacent-category, and constrained continuation-ratio 
    models are particularly supported at ordinal level. Tests for the proportional 
    odds assumptions in ordinal models are also possible with the Brant and the 
    Likelihood-Ratio tests. Moreover, several summary measures of predictive strength 
    (Pseudo R-squared), and some useful error metrics, including, the brier 
    score, misclassification rate and logloss are also available for the 
    binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) 
    <http://www.statmod.org/workshops_archive_proceedings_2018.html>.
Graphical tools and goodness-of-fit tests for right-censored data:
             1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests,
                which use the empirical distribution function for complete data 
                and are extended for right-censored data.
             2. Generalized chi-squared-type test, which is based on the squared 
                differences between observed and expected counts using random 
                cells with right-censored data.
             3. A series of graphical tools such as probability or cumulative hazard 
                plots to guide the decision about the most suitable parametric model 
                for the data.
                These functions share several features as they can handle both 
                complete and right-censored data, and they provide parameter 
                estimates for the distributions under study.
Performs goodness-of-fit tests for model-based clustering 
    based on the methodology developed in  <doi:10.48550/arXiv.2511.04206>. 
    It implements a test statistic 
    derived from empirical likelihood to verify the distribution of 
    clusters in multivariate Gaussian mixtures and other latent 
    class models.
Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.
Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.
Implementation of several goodness-of-fit tests for functional
    data. Currently, mostly related with the functional linear model with
    functional/scalar response and functional/scalar predictor. The package
    allows for the replication of the data applications considered in
    García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga
    (2021) <doi:10.1111/sjos.12486>.
We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.
We implement various tests for the composite hypothesis of
    testing the fit to the family of inverse Gaussian distributions. 
    Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) <doi:10.48550/arXiv.1910.14119>, 
    as well as two tests from Henze and Klar (2002) <doi:10.1023/A:1022442506681>.
    Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) <doi:10.1016/j.jmva.2015.05.013>. 
    For each test a parametric bootstrap procedure is implemented.
Integrates with your 'RMarkdown' documents to automatically publish
  figures to the <https://GoFigr.io> service. Supports both 'knitr' and interactive 
  execution within 'RStudio'.
Tests of goodness-of-fit based on a kernel smoothing of the data.
    References:
       Pavía (2015) <doi:10.18637/jss.v066.c01>.
Provides statistical methods to check if a parametric family of
  conditional density functions fits to some given dataset of covariates and 
  response variables. Different test statistics can be used to determine the 
  goodness-of-fit of the assumed model, see Andrews (1997) 
  <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>,
  Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta 
  (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the 
  corresponding p-values are approximated using a parametric bootstrap method.
An interactive document on  the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.
Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, 'Weibull', 'Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.
Provision of classes and methods for estimating generalized
   orthogonal GARCH models. This is an alternative approach to CC-GARCH models
   in the context of multivariate volatility modeling.
Tools for fitting statistical network models to dynamic network data.
   Can be used for fitting both dynamic network actor models ('DyNAMs') and
   relational event models ('REMs').
   Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>,
   Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>,
   Stadtfeld and Block (2017) <doi:10.15195/v4.a14>,
   Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.
Implements the Goldilocks adaptive trial design for a time to event
    outcome using a piecewise exponential model and conjugate Gamma prior
    distributions. The method closely follows the article by Broglio and
    colleagues <doi:10.1080/10543406.2014.888569>, which allows users to explore
    the operating characteristics of different trial designs.
An opinionated framework for building a production-ready
    'Shiny' application. This package contains a series of tools for
    building a robust 'Shiny' application from start to finish.
Fetch Professional Golfers' Association (PGA) Tour tournament data
    from ESPN <https://www.espn.com/golf/> including leaderboards and
    hole-by-hole scoring. Data is returned in tidy tibble format ready for
    analysis. Supports local storage via RDS or 'Apache Arrow' Parquet files
    for fast repeated access. Designed for golf analytics, data journalism,
    and fantasy sports research.
An efficient algorithm to generate group assignments for classroom settings while minimizing repeated pairings across multiple rounds.
In gene-expression microarray studies, for example, one generally obtains a list of
 dozens or hundreds of genes that differ in expression between samples and then asks
 'What does all of this mean biologically?' Alternatively, gene lists can be derived conceptually
 in addition to experimentally. For instance, one might want to analyze a group of genes known
 as housekeeping genes. The work of the Gene Ontology (GO) Consortium <geneontology.org>
 provides a way to address that question. GO organizes genes into hierarchical categories based
 on biological process, molecular function and subcellular localization. The role of 'GoMiner'
 is to automate the mapping between a list of genes and GO, and to provide a statistical
 summary of the results as well as a visualization.
Allows the user to animate text within 'rmarkdown' documents and 'shiny' applications. 
    The animations are activated using the 'Animate.css' library. See <https://animate.style/> for more information.
The gamma-Orthogonal Matching Pursuit (gamma-OMP) is a recently suggested modification of the OMP feature selection algorithm for a wide range of response variables. The package offers many alternative regression models, such linear, robust, survival, multivariate etc., including k-fold cross-validation. References: Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. <doi:10.1101/431734>. Tsagris M., Papadovasilakis Z., Lakiotaki K. and Tsamardinos I. (2022). "The gamma-OMP algorithm for feature selection with application to gene expression data". IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214--1224. <doi:10.1109/TCBB.2020.3029952>.
Estimates hazard ratios and mortality differentials for
    doubly-truncated data without population denominators. This method is
    described in Goldstein et al. (2023) <doi:10.1007/s11113-023-09785-z>.
Contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms
             Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.
Reconstruction of muscle fibers from image stacks using textural analysis. Includes functions for tracking, smoothing, cleaning, plotting and exporting muscle fibers. Also calculates basic fiber properties (e.g., length, angle and curvature).
Performing goodness-of-fit tests for stochastic block models used to fit network data. Among the three variants discussed in Karwa et al. (2023) <doi:10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the Erdos-Renyi (ER) and Beta versions.
Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).
Give advice about good practices when building R packages.
    Advice includes functions and syntax to avoid, package structure, code
    complexity, code formatting, etc.
A comprehensive toolkit for scraping and analyzing book data from <https://www.goodreads.com/>. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from 'Goodreads' data.
Collect  marketing data from 'Google Ads' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, User Activity API
  GA4's Data API and Admin API and Multi-Channel Funnel API.
Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.
Interact with Google Cloud Storage <https://cloud.google.com/storage/> 
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Interact with the 'Google Cloud Vision' <https://cloud.google.com/vision/>
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> project.
Send error reports to the Google Error Reporting service <https://cloud.google.com/error-reporting/> and view errors and assign error status in the Google Error Reporting user interface.
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
Access Google Cloud machine learning APIs for text and speech tasks. 
  Use the Cloud Translation API for text detection and translation, the Natural Language API
  to analyze sentiment, entities, and syntax, the Cloud Speech API to transcribe audio to text,
  and the Cloud Text-to-Speech API to synthesize text into audio files.
Interact with Google's Cloud Natural Language API
    <https://cloud.google.com/natural-language/> (v1) via R. The API has
    four main features, all of which are available through this
    R package: syntax analysis and part-of-speech tagging, entity
    analysis, sentiment analysis, and language identification.
Encodes simple feature ('sf') objects and coordinates, and decodes polylines 
    using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Provides a collection of functions to set up 'Google Public Data Explorer'
  <https://www.google.com/publicdata/> data visualization tool with your own data,
  building automatically the corresponding DataSet Publishing Language file, or
  DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to
  be published in 'Public Data Explorer'.
Provides an easy to use interface to the 'Google
	Pub/Sub' REST API <https://cloud.google.com/pubsub/docs/reference/rest>.
This is a wrapper for the command line tool 'googler', which can be
    found at the following URL: <https://github.com/jarun/googler>.
Interact with Google Sheets through the Sheets API v4
    <https://developers.google.com/sheets/api>. "API" is an acronym for
    "application programming interface"; the Sheets API allows users to
    interact with Google Sheets programmatically, instead of via a web
    browser. The "v4" refers to the fact that the Sheets API is currently
    at version 4. This package can read and write both the metadata and
    the cell data in a Sheet.
Interact with the 'Google Tag Manager' API <https://developers.google.com/tag-platform/tag-manager/api/v2>, enabling scripted deployments and updates across multiple tags, triggers, variables and containers.
Create geographically referenced traffic data from the Google Maps JavaScript API <https://developers.google.com/maps/documentation/javascript/examples/layer-traffic>.
R interface to Google's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required. The data 
    remains local and is not uploaded to Google.
Provides a mechanism to plot a 'Google Map' from 'R' and overlay
    it with shapes and markers. Also provides access to 'Google Maps' APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
Seamless integration between R and 'Goose' AI capabilities
    including memory management, visualization enhancements, and workflow
    automation. Save R objects to 'Goose' memory, apply Block branding to
    visualizations, and manage data science project workflows. 
    For more information about 'Goose' AI, see <https://github.com/block/goose>.
Implementation of multilayered visualizations for enhanced
    graphical representation of functional analysis data. It combines and integrates
    omics data derived from expression and functional annotation enrichment
    analyses. Its plotting functions have been developed with an hierarchical
    structure in mind: starting from a general overview to identify the most
    enriched categories (modified bar plot, bubble plot) to a more detailed one
    displaying different types of relevant information for the molecules in a given
    set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).
Informal implementation of some algorithms from Graph
  Theory and Combinatorial Optimization which arise in the subject
  "Graphs and Network Optimization" from first course of the EUPLA
  degree of Data Engineering in Industrial Processes.
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate linear models and generalised linear models.
Implements the generalized order-restricted information criterion
    approximation (GORICA), an AIC-like information criterion that can be
    utilized to evaluate informative hypotheses specifying directional
    relationships between model parameters in terms of (in)equality
    constraints (see Altinisik, Van Lissa, Hoijtink, Oldehinkel, & Kuiper,
    2021), <doi:10.31234/osf.io/t3c8g>. The GORICA is applicable not only to
    normal linear models, but also to generalized linear models (GLMs), 
    generalized linear mixed models (GLMMs), structural equation models
    (SEMs), and contingency tables. For contingency tables, restrictions on cell
    probabilities can be non-linear.
Methods to analyse experimental agriculture data, 
    from data synthesis to model selection and visualisation. 
    The package is named after W.S. Gosset aka ‘Student’, a pioneer 
    of modern statistics in small sample experimental design and analysis.
Add a scroll back to top 'Font Awesome' icon 
    <https://fontawesome.com/> in 'rmarkdown' documents and 'shiny'
    apps thanks to 'jQuery GoTop' <https://scottdorman.blog/jquery-gotop/>.
A suite of custom R Markdown formats and templates
    for authoring web pages styled with the GOV.UK Design System.
It can be necessary to limit the rate of execution of a loop or 
 repeated function call e.g. to show or gather data only at particular intervals.
 This package includes two methods for limiting this execution rate; speed governors
 and timers.  A speed governor will insert pauses during execution to meet
 a user-specified loop time.  Timers are alarm clocks which will indicate 
 whether a certain time has passed.  These mechanisms are implemented in 'C'
 to minimize processing overhead.
Access data provided by the United States Government Publishing Office (GPO) 'GovInfo' API (<https://github.com/usgpo/api>).
This package purposes to deal with public survey data of
        Japanese government via their Application Programming Interface
        (http://statdb.nstac.go.jp/)
Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.
Provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.
Implements a variant of the Self-Organizing Map (SOM) algorithm
    designed for mixed-attribute datasets. Similarity between observations is
    computed using the Gower distance, and categorical prototypes are updated
    via heuristic strategies (weighted mode and multinomial sampling).
    Provides functions for model fitting, mapping, visualization
    (U-Matrix and component planes), and evaluation, making SOM applicable
    to heterogeneous real-world data. For methodological details see
    Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.
A collection of tools which extract a model documentation from 'GAMS' code and comments. 
             In order to use the package you need to install 'pandoc' and 'pandoc-citeproc' 
             first (<https://pandoc.org/>).
Functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.
Aligning multiple visualisations by utilising generalised orthogonal Procrustes analysis (GPA) before combining coordinates into a single biplot display as described in Nienkemper-Swanepoel, le Roux and Lubbe (2023)<doi:10.1080/03610918.2021.1914089>. This is mainly suitable to combine visualisations constructed from multiple imputations, however, it can be generalised to combine variations of visualisations from the same datasets (i.e. resamples).
Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.
Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.
Derivative Free Gradient Projection Algorithms for Factor Rotation.
    For more details see ?GPArotateDF. Theory for these functions can be found in
    the following publications:
    Jennrich (2004) <doi:10.1007/BF02295647>.
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.
Gradient projection algorithms for orthogonal and oblique rotation
    of factor loadings matrices in factor analysis. Implements a comprehensive
    set of rotation criteria including quartimax, quartimin, oblimin, geomin,
    simplimax, the Crawford-Ferguson family, and target rotation, among others. 
    Supports multiple random starts. For details see Bernaards and Jennrich (2005)
    <doi:10.1177/0013164404272507>.
Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'.
An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.
Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.
Provides a framework to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM as proposed by Schauberger and Mair (2019) <doi:10.3758/s13428-019-01224-2>. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
This function performs genomic prediction of cross performance using genotype and phenotype data.
  It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data,
  fitting mixed models, and predicting cross performance based on weighted marker effects.
  For more information, see Labroo et al. (2023) <doi:10.1007/s00122-023-04377-z>.
Parameter estimation and prediction of Gaussian Process Classifier models as described in Bachoc et al. (2020) <doi:10.1007/S10898-020-00920-0>. Important functions : gpcm(), predict.gpcm(), update.gpcm().
Provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).
Functionalities for modelling functional data with 
    multidimensional inputs, multivariate functional data, and non-separable 
    and/or non-stationary covariance structure of function-valued processes. 
    In addition, there are functionalities for functional regression models where 
    the mean function depends on scalar and/or functional covariates and 
    the covariance structure depends on functional covariates. 
    The development version of the package can be found on 
    <https://github.com/gpfda/GPFDA-dev>.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Provides flexible tools for the visualization of genomic data. Supports interactive and static plots tailored for presentations and publications, with customizable features like colors, themes, and annotations to align with specific analytical and presentation goals.
Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <doi:10.48550/arXiv.1902.06565>.
Functions for fitting and doing predictions with
    Gaussian process models using Vecchia's (1988) approximation. 
    Package also includes functions for reordering input locations, 
    finding ordered nearest neighbors (with help from 'FNN' package), 
    grouping operations, and conditional simulations.
    Covariance functions for spatial and spatial-temporal data
    on Euclidean domains and spheres are provided. The original 
    approximation is due to Vecchia (1988) 
    <http://www.jstor.org/stable/2345768>, and the reordering and
    grouping methods are from Guinness (2018) 
    <doi:10.1080/00401706.2018.1437476>.
    Model fitting employs a Fisher scoring algorithm described
    in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.
Tools to build and work with bilateral generalized-mean
    price indexes (and by extension quantity indexes), and indexes composed of
    generalized-mean indexes (e.g., superlative quadratic-mean indexes, GEKS).
    Covers the core mathematical machinery for making bilateral price indexes,
    computing price relatives, detecting outliers, and decomposing indexes,
    with wrappers for all common (and many uncommon) index-number
    formulas. Implements and extends many of the methods in
    Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007,
    <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020,
    <doi:10.5089/9781484354841.069>).
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Build Open Geospatial Consortium 'GeoPackage' files (<https://www.geopackage.org/>). 'GDAL' utilities for reading and writing spatial data are provided by the 'terra' package. Additional 'GeoPackage' and 'SQLite' features for attributes and tabular data are implemented with the 'RSQLite' package.
Convert the chip ID of GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> 
    to GeneBank Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.
Implements the most common Gaussian process (GP) models using Laplace and
  expectation propagation (EP) approximations, maximum marginal likelihood
  (or posterior) inference for the hyperparameters, and sparse approximations
  for larger datasets.
Provides functions for estimating a generalized partial
	     linear model, a semiparametric variant of the generalized linear model
	     (GLM) which replaces the linear predictor by the sum of a linear
	     and a nonparametric function.
Various R programming tools for plotting data, including:
  - calculating and plotting locally smoothed summary function as
    ('bandplot', 'wapply'),
  - enhanced versions of standard plots ('barplot2', 'boxplot2',
    'heatmap.2', 'smartlegend'),
  - manipulating colors ('col2hex', 'colorpanel', 'redgreen',
    'greenred', 'bluered', 'redblue', 'rich.colors'),
  - calculating and plotting two-dimensional data summaries ('ci2d',
    'hist2d'),
  - enhanced regression diagnostic plots ('lmplot2', 'residplot'),
  - formula-enabled interface to 'stats::lowess' function ('lowess'),
  - displaying textual data in plots ('textplot', 'sinkplot'),
  - plotting dots whose size reflects the relative magnitude of the
    elements ('balloonplot', 'bubbleplot'),
  - plotting "Venn" diagrams ('venn'),
  - displaying Open-Office style plots ('ooplot'),
  - plotting multiple data on same region, with separate axes
    ('overplot'),
  - plotting means and confidence intervals ('plotCI', 'plotmeans'),
  - spacing points in an x-y plot so they don't overlap ('space').
We provides functions that employ splines to estimate generalized 
             partially linear single index models (GPLSIM), which extend the generalized linear 
             models to include nonlinear effect for some predictors. Please see Y. (2017) at
             <doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at <doi:10.1198/016214502388618861> for more details.
Combining a generalized linear model with an additional tree part 
          on the same scale. A four-step procedure is proposed to fit the model and test 
          the joint effect of the selected tree part while adjusting on confounding factors. 
          We also proposed an ensemble procedure based on the bagging to improve prediction 
          accuracy and computed several scores of importance for variable selection.
          See 'Cyprien Mbogning et al.'(2014)<doi:10.1186/2043-9113-4-6> and 
         'Cyprien Mbogning et al.'(2015)<doi:10.1159/000380850> 
          for an overview of all the methods implemented in this package.
Tools for studying genotype-phenotype maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 <doi:10.3389/fgene.2013.00216>.
Platform dedicated to the Global Modelling technique. Its aim
    is to obtain ordinary differential equations of polynomial form directly
    from time series. It can be applied to single or multiple time series under
    various conditions of noise, time series lengths, sampling, etc. This platform
    is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO),
    UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE.
    The developments were funded by the French program Les Enveloppes Fluides
    et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu). The
    French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged (projects
    Crops'IChaos and Musc & SlowFast). The method is described in the article :
    Mangiarotti S. and Huc M. (2019) <doi:10.1063/1.5081448>.
Estimates a counterfactual using Gaussian process projection. It takes a dataframe, creates missingness in the desired outcome variable and estimates counterfactual values based on all information in the dataframe. The package writes Stan code, checks it for convergence and adds artificial noise to prevent overfitting and returns a plot of actual values and estimated counterfactual values using r-base plot.
Implements maximum likelihood estimation for Gaussian processes, supporting both isotropic and separable models with predictive capabilities. Includes penalized likelihood estimation following Li and Sudjianto (2005, <doi:10.1198/004017004000000671>), with cross-validation guided by decorrelated prediction error (DPE) metric. DPE metric, motivated by Mahalanobis distance, serves as evaluation criteria that accounts for predictive uncertainty in tuning parameter selection (Mutoh, Booth, and Stallrich, 2025, <doi:10.48550/arXiv.2511.18111>). Designed specifically for small datasets.
A Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality.  The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by 'rstan' package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al. (2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.
This package has been deprecated and will not be updated. 
    New users should use the package 'gprofiler2' (<https://CRAN.R-project.org/package=gprofiler2>)
    for up-to-date data and improved functionality.
    Functional enrichment analysis, gene identifier conversion and
    mapping homologous genes across related organisms via the 'g:Profiler' toolkit
    (<https://biit.cs.ut.ee/gprofiler/>).
A toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via 'g:Profiler' (<https://biit.cs.ut.ee/gprofiler/>). 
    The main tools are: 
    (1) 'g:GOSt' - functional enrichment analysis and visualization of gene lists; 
    (2) 'g:Convert' - gene/protein/transcript identifier conversion across various namespaces;
    (3) 'g:Orth' - orthology search across species;
    (4) 'g:SNPense' - mapping SNP rs identifiers to chromosome positions, genes and variant effects.
    This package is an R interface corresponding to the 2019 update of 'g:Profiler' and provides access to 'g:Profiler' for versions 'e94_eg41_p11' and higher. See the package 'gProfileR' for accessing older versions from the 'g:Profiler' toolset.
General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) <arXiv:2201.06808>. Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) <doi:10.1214/ss/1038425655>. They are also related to the O-splines of O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) <doi:10.1007/s11222-022-10178-z>. It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework.
Focused on extracting important data from track points such as speed, distance, elevation difference and azimuth.(PLAZA, J. et al., 2022) <doi:10.1016/j.applanim.2022.105643>.
Implements the generalized propensity score cumulative distribution
    function proposed by Greene (2017)
    <https://digitalcommons.library.tmc.edu/dissertations/AAI10681743/>.
    A single scalar balancing score is calculated for any generalized propensity
    score vector with three or more treatments. This balancing score is used for
    propensity score matching and stratification in outcome analyses when analyzing
    either ordinal or multinomial treatments.
Applies sequential clustering algorithm to animal location data 
    based on user-defined parameters. Plots interactive cluster maps and 
    provides a summary dataframe with attributes for each cluster commonly
    used as covariates in subsequent modeling efforts. Additional functions
    provide individual keyhole markup language plots for quick assessment,
    and export of global positioning system exchange format files for
    navigation purposes. 
    Methods can be found at <doi:10.1111/2041-210X.13572>.
Provides Gaussian process (GP) regression tools for social
    science inference problems.  GPs combine flexible nonparametric
    regression with principled uncertainty quantification: rather than
    committing to a single model fit, the posterior reflects lesser
    knowledge at the edge of or beyond the observed data, where other
    approaches become highly model-dependent.  The package reduces
    user-chosen hyperparameters from three to zero and supplies
    convenience functions for regression discontinuity (gp_rdd()),
    interrupted time-series (gp_its()), and general GP fitting
    (gpss(), gp_train(), gp_predict()).  Methods are described in
    Cho, Kim, and Hazlett (2026) <doi:10.1017/pan.2026.10032>.
Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.
Gaussian processes are flexible distributions to model functional data. Whilst
    theoretically appealing, they are computationally cumbersome except for small datasets.
    This package implements two methods for scaling Gaussian process inference in 'Stan'. First, a
    sparse approximation of the likelihood that is generally applicable and, second, an exact method
    for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility
    functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface.
    References: Hoffmann and Onnela (2025) <doi:10.18637/jss.v112.i02>.
A convenient interface with the OpenAI 'ChatGPT' API <https://openai.com/api>. 'gptr' allows you to interact with 'ChatGPT', a powerful language model, for various natural language processing tasks. The 'gptr' R package makes talking to 'ChatGPT' in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With 'gptr', you can use 'ChatGPT' in R without any hassle, making it simpler for everyone to do cool things with language!
We implement and extend the Dividing Local Gaussian Process 
    algorithm by Lederer et al. (2020) <doi:10.48550/arXiv.2006.09446>. Its
    main use case is in online learning where it is used to train a network of
    local GPs (referred to as tree) by cleverly partitioning the input space. 
    In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of
    data. The package includes methods to create the tree and set its
    parameter, incorporating data points from a data stream as well as making
    joint predictions based on all relevant local GPs.
Large language models are readily accessible via API. This
    package lowers the barrier to use the API inside of your development
    environment.  For more on the API, see
    <https://platform.openai.com/docs/introduction>.
An R interface to the 'GPTZero' API (<https://gptzero.me/docs>). Allows 
    users to classify text into human and computer written with probabilities. Formats
    the data into data frames where each sentence is an observation. Paragraph-level
    and document-level predictions are organized to align with the sentences.
GPUs are great resources for data analysis, especially in statistics and linear algebra. Unfortunately, very few packages connect R to the GPU, and none of them are transparent enough to run the computations on the GPU without substantial changes to the code. The maintenance of these packages is cumbersome: several of the earlier attempts have been removed from their respective repositories. It would be desirable to have a properly maintained R package that takes advantage of the GPU with minimal changes to the existing code. We have developed the 'GPUmatrix' package (available on CRAN). 'GPUmatrix' mimics the behavior of the Matrix package and extends R to use the GPU for computations. It includes single(FP32) and double(FP64) precision data types, and provides support for sparse matrices. It is easy to learn, and requires very few code changes to perform the operations on the GPU. 'GPUmatrix' relies on either the 'Torch' or 'Tensorflow' R packages to perform the GPU operations. We have demonstrated its usefulness for several statistical applications and machine learning applications: non-negative matrix factorization, logistic regression and general linear models. We have also included a comparison of GPU and CPU performance on different matrix operations.
An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <doi:10.48550/arXiv.1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <doi:10.48550/arXiv.1706.02205> and MaxMin ordering proposed in Guinness (2018) <doi:10.48550/arXiv.1609.05372>.
Calculates the cost of crossing in terms of the number of individuals and generations, which is theoretically 
    formulated by Servin et al. (2004) <DOI:10.1534/genetics.103.023358>. This package has been designed for selecting 
    appropriate parental genotypes and find the most efficient crossing scheme for gene pyramiding, especially for plant breeding.
Server implementation of 'GraphQL'
    <http://spec.graphql.org/>, a query language originally created by
    Facebook for describing data requirements on complex application data
    models.  Visit <https://graphql.org> to learn more about 'GraphQL'.
Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.
Provides a comprehensive suite of genome-wide association study (GWAS) methods 
    specifically designed for biobank-scale data, including but not limited to, robust approaches 
    for time-to-event traits (Li et al., 2025 <doi:10.1038/s43588-025-00864-z>) and ordinal 
    categorical traits (Bi et al., 2021 <doi:10.1016/j.ajhg.2021.03.019>). The package also offers 
    general frameworks for GWAS of any trait type (Bi et al., 2020 <doi:10.1016/j.ajhg.2020.06.003>), 
    while accounting for sample relatedness (Xu et al., 2025 <doi:10.1038/s41467-025-56669-1>) or 
    population structure (Ma et al., 2025 <doi:10.1186/s13059-025-03827-9>). By accurately 
    approximating score statistic distributions using saddlepoint approximation (SPA), these 
    methods can effectively control type I error rates for rare variants and in the presence of 
    unbalanced phenotype distributions. Additionally, the package includes functions for simulating 
    genotype and phenotype data to support research and method development.
Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.
Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.
Provides functions for matching student-answers to teacher answers for a variety of data types.
After being given the location of your students' submissions and a test file, the function runs each file that is an R script, R Markdown file, or Quarto document, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test.
An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.
Implements LASSO regression using gradient descent with support for Gaussian, Binomial, Negative Binomial, and Zero-Inflated Negative Binomial (ZINB) families. Features cross-validation for determining lambda, stability selection, and bootstrapping for confidence intervals. Methods described in Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x> and Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.
Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.
Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.
Probability propagation in Bayesian networks, also known as graphical independence networks. Documentation
    of the package is provided in vignettes included in the package and in
    the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>).
    See 'citation("gRain")' for details.
Given a landscape resistance surface, creates minimum planar graph
    (Fall et al. (2007) <doi:10.1007/s10021-007-9038-7>) and grains of connectivity
    (Galpern et al. (2012) <doi:10.1111/j.1365-294X.2012.05677.x>) models that can be
    used to calculate effective distances for landscape connectivity at multiple scales.
    Documentation is provided by several vignettes, and a paper
    (Chubaty, Galpern & Doctolero (2020) <doi:10.1111/2041-210X.13350>).
A native R implementation of grammatical evolution (GE).
    GE facilitates the discovery of programs that can achieve a desired goal.
    This is done by performing an evolutionary optimisation over a population
    of R expressions generated via a user-defined context-free grammar (CFG)
    and cost function.
Numerical integration with Gram polynomials (based on <arXiv:2106.14875> [math.NA] 28 Jun 2021, by Irfan Muhammad [School of Computer Science, University of Birmingham, UK]).
Implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the 'randnet' package <https://CRAN.R-project.org/package=randnet>. For more details, see the "proposed method" section of Liu, Bi, and Li (2025) <doi:10.48550/arXiv.2507.00402>.
Nucleotide conversion sequencing experiments have been
  developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such 
  experiments require specialized tools for primary processing such as GRAND-SLAM,
  (see 'Jürges et al' <doi:10.1093/bioinformatics/bty256>) and specialized tools for 
  downstream analyses. 'grandR' provides a comprehensive toolbox for quality control,
  kinetic modeling, differential gene expression analysis and visualization of such data.
  Fast Wilcoxon tests are supported via the 'presto' package (available at <https://github.com/immunogenomics/presto>).
Contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne' and Montanari, 2018 <arXiv:1803.00374>.
Performs Granger causality tests on pairs of time series to determine
    causal relationships. Uses Vector Autoregressive (VAR) models to test whether
    one time series helps predict another beyond what the series' own past values
    provide. Returns structured results including p-values, test statistics, and
    causality conclusions for both directions.
This small collection of functions provides what we call elemental graphics for display of analysis of variance
    results, David C. Hoaglin, Frederick Mosteller and John W. Tukey (1991, ISBN:978-0-471-52735-0), Paul R. Rosenbaum (1989) 
    <doi:10.2307/2684513>, Robert M. Pruzek and James E. Helmreich <https://jse.amstat.org/v17n1/helmreich.html>. 
    The term elemental derives from the fact that each function is aimed at construction of
    graphical displays that afford direct visualizations of data with respect to the fundamental 
    questions that drive the particular analysis of variance methods. These functions can be 
    particularly helpful for students and non-statistician analysts. But these methods should be
    quite generally helpful for work-a-day applications of all kinds, as they can help to identify
    outliers, clusters or patterns, as well as highlight the role of non-linear transformations of data.
A minimal set of routines to calculate the Grantham distance
    <doi:10.1126/science.185.4154.862>. The Grantham distance attempts to
    provide a proxy for the evolutionary distance between two amino acids
    based on three key chemical properties: composition, polarity and
    molecular volume. In turn, evolutionary distance is used as a proxy for
    the impact of missense mutations. The higher the distance, the more
    deleterious the substitution is expected to be.
Turn arbitrary functions into binary operators.
Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format.
Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. Based on the 'vis.js' Graph3d module and the 'htmlwidgets' R package.
Build graphs for landscape genetics analysis. This set of 
	functions can be used to import and convert spatial and genetic data 
	initially in different formats, import landscape graphs created with 
	'GRAPHAB' software (Foltete et al., 2012) <doi:10.1016/j.envsoft.2012.07.002>, 
	make diagnosis plots of isolation by distance relationships in order to 
	choose how to build genetic graphs, create graphs with a large range of 
	pruning methods, weight their links with several genetic distances, plot 
	and analyse graphs,	compare them with other graphs. It uses functions from 
	other packages such as 'adegenet' 
	(Jombart, 2008) <doi:10.1093/bioinformatics/btn129> and 'igraph' (Csardi
	et Nepusz, 2006) <https://igraph.org/>. It also implements methods 
	commonly used in landscape genetics to create graphs, described by Dyer et 
	Nason (2004) <doi:10.1111/j.1365-294X.2004.02177.x> and Greenbaum et 
	Fefferman (2017) <doi:10.1111/mec.14059>, and to analyse distance data 
	(van Strien et al., 2015) <doi:10.1038/hdy.2014.62>.
Graph clustering using an agglomerative algorithm to maximize the
  integrated classification likelihood criterion and a mixture of stochastic
  block models. The method is described in the article "Model-based clustering 
  of multiple networks with a hierarchical algorithm" by 
  T. Rebafka (2022) <arXiv:2211.02314>.
A multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.
Computes marginal likelihood in Gaussian graphical models through a
  novel telescoping block decomposition of the precision matrix which allows
  estimation of model evidence. The top level function used to estimate marginal
  likelihood is called evidence(), which expects the prior name, data, and
  relevant prior specific parameters. This package also provides an MCMC prior
  sampler using the same underlying approach, implemented in prior_sampling(),
  which expects a prior name and prior specific parameters. Both functions also
  expect the number of burn-in iterations and the number of sampling iterations
  for the underlying MCMC sampler.
Statistical methodology for sparse multivariate extreme value models. Methods are
             provided for exact simulation and statistical inference for multivariate Pareto distributions
             on graphical structures as described in the paper 'Graphical Models for Extremes' by
             Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.
Multiple comparison procedures (MCPs) control the familywise error
    rate in clinical trials. Graphical MCPs include many commonly used
    procedures as special cases; see Bretz et al. (2011)
    <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et
    al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency
    implementation of graphical MCPs which allow mixed types of tests. It also
    includes power simulations and visualization of graphical MCPs.
Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
Several new layout algorithms to visualize networks are provided which are not part of 'igraph'. 
    Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>. 
    Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes.
Provides a not-so-comprehensive list of methods for estimating graphon,
    a symmetric measurable function, from a single or multiple of observed networks. 
    For a detailed introduction on graphon and popular estimation techniques, 
    see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>.
    It also contains several auxiliary functions for generating sample networks
    using various network models and graphons.
Generates dense or sparse graphs using graphon mixtures and graphettes. Graphon mixtures uses two graphons U and W to generate graphs. Sparse graphs are generated in this case using the inverse line graph (root) operation. Graphettes have 3 components, the graphon W, a real-valued sequence and a graph edit function. Both techniques can generate dense or sparse graphs. Kandanaarachchi and Ong (2026) <doi:10.48550/arXiv.2505.13864>, Wijesinghe et al (2026) <doi:10.48550/arXiv.2602.23566>.
Estimation and display of various types of population attributable 
    fraction and impact fractions. As well as the usual calculations of 
    attributable fractions and impact fractions, functions are provided for 
    attributable fraction nomograms and fan plots, continuous exposures, for 
    pathway specific population attributable fractions, and for joint, average 
    and sequential population attributable fractions.
Implement some models for 
  correlation/covariance matrices including two approaches 
  to model correlation matrices from a graphical structure.
  One use latent parent variables as proposed in
  Sterrantino et. al. (2024) <doi:10.1007/s10260-025-00788-y>.
  The other uses a graph to specify conditional 
  relations between the variables.
  The graphical structure makes correlation matrices 
  interpretable and avoids the quadratic increase of 
  parameters as a function of the dimension. 
  In the first approach a natural sequence of simpler 
  models along with a complexity penalization is used.
  The second penalizes deviations from a base model.
  These can be used as prior for model parameters,
  considering C code through the 'cgeneric' interface 
  for the 'INLA' package (<https://www.r-inla.org>). 
  This allows one to use these models as building 
  blocks combined and to other latent Gaussian models 
  in order to build complex data models.
Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL
    <https://graphql.org> syntax and exports the AST in JSON format.
Implements the Rank In Similarity Graph Edge-count two-sample test 
    (RISE) for high-dimensional and non-Euclidean data. 
    The method constructs similarity-based graphs, such as k-nearest neighbor graph (k-NNG),
    k-minimum spanning tree (k-MST), and k-minimum distance non-bipartite pairing (k-MDP),
    and evaluates rank-based within-sample edge counts 
    with asymptotic and permutation p-values.
    For methodological details, see Zhou and Chen (2023)
    <https://proceedings.mlr.press/v195/zhou23a.html>.
Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph'  structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate  correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.
Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. This package includes statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design.
    See Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report.
Provides a unified framework for sparse-group regularization and
    precision matrix estimation in Gaussian graphical models. It implements
    multiple sparse-group penalties, including sparse-group lasso, sparse-group
    adaptive lasso, sparse-group SCAD, and sparse-group MCP, and solves them
    efficiently using ADMM-based optimization. The package is designed for
    high-dimensional network inference where both sparsity and group structure
    are present.
Facilitates the citation of R packages used in analysis
    projects. Scans project for packages used, gets their citations, and
    produces a document with citations in the preferred bibliography
    format, ready to be pasted into reports or manuscripts. Alternatively,
    'grateful' can be used directly within an 'R Markdown' or 'Quarto' document.
Provides a coherent interface and implementation for creating
    grouped date classes.
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Create graticule lines and labels for maps. Control the creation
    of lines or tiles by setting their placement (at particular meridians and parallels)
    and extent (along parallels and meridians). Labels are created independently of
    lines.
Generates synthetic time series based on various univariate
    time series models including MAR and ARIMA processes. Kang, Y.,
    Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.
Utilities to cost and evaluate Australian tax policy, including fast
    projections of personal income tax collections, high-performance tax and 
    transfer calculators, and an interface to common indices from the Australian
    Bureau of Statistics.  Written to support Grattan Institute's Australian 
    Perspectives program, and related projects. Access to the Australian Taxation
    Office's sample files of personal income tax returns is assumed.
Using Australian Bureau of Statistics indices, provides functions
    that convert historical, nominal statistics to real, contemporary values 
    without worrying about date input quality, performance, or the ABS catalogue.
Wrappers for functions in the 'gRain' package to emulate some 'RHugin' 
  functionality, allowing the building of Bayesian networks consisting on discrete 
  chance nodes incrementally, through   adding nodes, edges and conditional probability 
  tables, the setting of evidence,   both 'hard' (boolean) or 'soft' (likelihoods), 
  querying marginal probabilities   and normalizing constants, and generating sets of
  high-probability configurations. Computations will typically not be so fast as they are
  with 'RHugin', but this package should assist users without access to 'Hugin' to use
  code written to use 'RHugin'.
Provides tools for systematically exploring large quantities of 
             temporal data across cyclic temporal granularities
             (deconstructions of time) by visualizing probability distributions.
             Cyclic time granularities can be circular, quasi-circular or 
             aperiodic. 'gravitas' computes cyclic
             single-order-up or multiple-order-up granularities, check the
             feasibility of creating plots for any two cyclic granularities
             and recommend probability distributions plots for exploring
             periodicity in the data.
A wrapper of different standard estimation methods for gravity models. 
  This package provides estimation methods for log-log models and multiplicative models.
Implements a one-sector Armington-CES gravity model with general equilibrium (GE) effects. This model is designed to analyze international and domestic trade by capturing the impacts of trade costs and policy changes within a general equilibrium framework. Additionally, it includes a local parameter to run simulations on productivity. The package provides functions for calibration, simulation, and analysis of the model.
Computes the gravitational and magnetic anomalies generated by
  3-D vertical rectangular prisms at specific observation points using the
  method of Plouff (1976) <doi:10.1190/1.1440645>.
The 'gRbase' package provides graphical modelling features
    used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements
    graph algorithms including (i) maximum cardinality search (for marked
    and unmarked graphs).
    (ii) moralization, (iii) triangulation, (iv) creation of junction tree.
    'gRbase' facilitates array operations,
    'gRbase' implements functions for testing for conditional independence.
    'gRbase' illustrates how hierarchical log-linear models may be
    implemented and describes concept of graphical meta
    data. 
    The facilities of the package are documented in the book by Højsgaard,
    Edwards and Lauritzen (2012,
    <doi:10.1007/978-1-4614-2299-0>) and in the paper by 
    Dethlefsen and Højsgaard, (2005, <doi:10.18637/jss.v014.i17>).
    Please see 'citation("gRbase")' for citation details.
Estimation, model selection and other aspects of
    statistical inference in Graphical Gaussian models with edge and
    vertex symmetries (Graphical Gaussian models with colours).
    Documentation about 'gRc' is provided in the paper by Hojsgaard and
    Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by
    Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).
When the number of treatments is large with limited experimental resources then Row-Column(RC) designs with multiple units per cell can be used. These designs are called Generalized Row-Column (GRC) designs and are defined as designs with v treatments in p rows and q columns such that the intersection of each row and column (cell) consists of k experimental units. For example (Bailey & Monod (2001)<doi:10.1111/1467-9469.00235>), to conduct an experiment for comparing 4 treatments using 4 plants with leaves at 2 different heights row-column design with two units per cell can be used. A GRC design is said to be structurally complete if corresponding to the intersection of each row and column, there appears at least two treatments. A GRC design is said to be structurally incomplete if corresponding to the intersection of any row and column, there is at least one cell which does not contain any treatment.
Provides tools for constructing row-column factorial
    experiment layouts for the estimation of main effects and two-factor
    interactions in factorial and fractional factorial experiments. The
    package implements generator-matrix based design construction methods
    motivated by 2fi-optimal row-column designs, where all main effects
    are estimable and as many two-factor interactions as possible are
    unconfounded; see Zhang, Pan and Shi (2025)
    <doi:10.1016/j.jspi.2024.106192>. It also includes theorem-based
    constructions, heuristic D-optimal search routines for unsupported or
    composite-level cases, utilities for building generator matrices, and
    diagnostic functions for evaluating aliasing and estimability
    properties of the generated designs.
Implement maximum likelihood estimation for Poisson generalized
  linear models with grouped and right-censored count data. Intended to be used
  for analyzing grouped and right-censored data, which is widely applied in
  many branches of social sciences. The algorithm implemented is described
  in Fu et al., (2021) <doi:10.1111/rssa.12678>.
A tool for registering (aligning) gene expression profiles
    between reference and query data.
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.
Computes experimental designs for two-arm experiments with
    covariates using multiple methods, including: (0) complete randomization
    and randomization with forced-balance; (1) greedy optimization of a balance
    objective function via pairwise switching; (2) numerical optimization via
    'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5)
    exhaustive enumeration for small sample sizes; (6) binary pair matching
    using 'nbpMatching'; (7) binary pair matching plus method (1) to further
    optimize balance; (8) binary pair matching plus method (3) to further
    optimize balance; (9) Hadamard designs; and (10) simultaneous multiple
    kernels. For the greedy, rerandomization, and related methods, three
    objective functions are supported: Mahalanobis distance, standardized sums
    of absolute differences, and kernel distances via the 'kernlab' library.
    This package is the result of a stream of research that can be found in
    Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random
    Designs with Greatly Improved Balance." Biometrika 106(3), 695-701
    <doi:10.1093/biomet/asz026>. Krieger, A. M., Azriel, D. A., and
    Kapelner, A. (2023). "Better experimental design by hybridizing binary
    matching with imbalance optimization." Canadian Journal of Statistics,
    51(1), 275-292 <doi:10.1002/cjs.11685>.
These are GreedyExperimentalDesign Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without GreedyExperimentalDesign.
An implementation of functions to display Greek letters on the 'RStudio' (include subscript and superscript indexes) and 'RGui' (without subscripts and only with superscript 1, 2 or 3; because 'RGui' doesn't support printing the corresponding Unicode characters as a string: all subscripts ranging from 0 to 9 and superscripts equal to 0, 4, 5, 6, 7, 8 or 9). The functions in this package do not work properly on the R console. Characters are used via Unicode and encoded as UTF-8 to ensure that they can be viewed on all operating systems. Other characters related to mathematics are included, such as the infinity symbol. All this accessible from very simple commands. This is a package that can be used for teaching purposes, the statistical notation for hypothesis testing can be written from this package and so it is possible to build a course from the 'swirlify' package. Another utility of this package is to create new summary functions that contain the functional form of the model adjusted with the Greek letters, thus making the transition from statistical theory to practice easier. In addition, it is a natural extension of the 'clisymbols' package.
Methods to calculate sensitivities of financial option prices for
 European, geometric and arithmetic Asian, and American options, with various
 payoff functions in the Black Scholes model, and in more general jump diffusion
 models. A shiny app to interactively plot the results is included. Furthermore,
 methods to compute implied volatilities are provided for a wide range of option
 types and  custom payoff functions. Classical formulas are implemented for
 European options in the Black Scholes Model, as is presented in Hull, J. C.
 (2017), Options, Futures, and Other Derivatives.
 In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see
 Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential
 Lévy processes. <doi:10.1007/s11009-023-10014-5>. For American
 options, the Binomial Tree  Method is implemented, as is presented in Hull,
 J. C. (2017).
Implements cost-benefit analysis primitives from HM
    Treasury Green Book guidance (HM Treasury, 2022, 2026):
    the kinked Social Time Preference Rate ('STPR'), discount factors,
    net present value ('NPV'), equivalent annual cost, and real-terms
    rebasing using the GDP deflator. Designed for UK central government
    appraisal and evaluation. Bundled parameter tables carry vintage
    metadata for reproducibility.
Implements a method of iteratively collapsing the rows of a
    contingency table, two at a time, by selecting the pair of categories whose
    combination yields a new table with the smallest loss of chi-squared, as
    described by Greenacre, M.J. (1988) <doi:10.1007/BF01901670>. The result is
    compatible with the class of object returned by the 'stats' package's
    hclust() function and can be used similarly (plotted as a dendrogram,
    cut, etc.). Additional functions are provided for automatic cutting and
    diagnostic plotting.
These Bayesian models written in the 'Stan' probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) <doi:10.1002/eap.2561>.
Tools and methods to apply the model Geospatial Regression Equation
    for European Nutrient losses (GREEN); 
    Grizzetti et al. (2005) <doi:10.1016/j.jhydrol.2004.07.036>; 
    Grizzetti et al. (2008);
    Grizzetti et al. (2012) <doi:10.1111/j.1365-2486.2011.02576.x>; 
    Grizzetti et al. (2021) <doi:10.1016/j.gloenvcha.2021.102281>.
Provides tools for downloading, processing, and reporting daily and finalized 'GreenFeed' data.
Quantification, analysis, and visualization of urban greenness within city networks using data from 'OpenStreetMap' <https://www.openstreetmap.org>.
Provides a set of accessible and automated functions to apply 
    statistical models such as Simple Linear Regression (RLS, from the Spanish 
    'Regresión Lineal Simple'), Multiple Linear Regression (RLM, from the Spanish 
    'Regresión Lineal Múltiple'), Generalized Linear Models (GLM), and time 
    series analysis through Autoregressive Integrated Moving Average (ARIMA) 
    models. Designed to support teaching at the Universidad Autónoma Chapingo, 
    it facilitates results interpretation and assumption validation through 
    automatic graphical diagnostics. Methods for regression and time series 
    are based on Montgomery et al. (2021, ISBN:978-1119570141) and 
    Box & Jenkins (1970, ISBN:978-0816211043).
Methods for manipulating regression models and for describing these in a style adapted for medical journals.
  Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model
  estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots.
  In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich'
  package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for
  time-splitting a dataset when modeling non-proportional hazards in Cox regressions.
Computes Gregory weights for a given number nodes and function order. Anthony Ralston and Philip Rabinowitz (2001) <ISBN:9780486414546>.
Functions which make using the Generalized Regression Estimator(GREG)
    J.N.K. Rao, Isabel Molina, (2015) <doi:10.3390/f11020244> 
    and the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY) easier. 
    The functions are designed to work well within a forestry context, and estimate multiple 
    estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when 
    describing the linear model.
We propose two distribution-free test statistics based on between-sample edge counts and measure the degree of relevance by standardized counts. Users can set edge costs in the graph to compare the parameters of the distributions. Methods for comparing distributions are as described in: Xiaoping Shi (2021) <arXiv:2107.00728>.
Fit linear mixed-effects models using restricted (or residual)
    maximum likelihood (REML) and with generalized inverse matrices to specify
    covariance structures for random effects. In particular, the package is
    suited to fit quantitative genetic mixed models, often referred to as
    'animal models'. Implements the average information algorithm as the main
    tool to maximize the restricted log-likelihood, but with other algorithms
    available.
We define generalized multipartite networks  as the joint observation of several networks implying some common pre-specified groups of individuals. The aim is to fit an adapted version of  the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020) <arXiv:1807.10138>.
Contains many functions useful for
    monitoring and reporting the results of clinical trials and other
    experiments in which treatments are compared.  LaTeX is
    used to typeset the resulting reports, recommended to be in the
    context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
    by 'greport' for high-level graphics.
Provides an interface to the system-level 'grep' utility for 
    efficiently reading, filtering, and aggregating data from multiple flat 
    files. By pre-filtering data at the command line before it enters the 
    R environment, the package reduces memory overhead and improves 
    ingestion speed. Includes functions for counting records across large 
    file systems and supports recursive directory searching.
The social network literature features numerous methods for assigning
    value to paths as a function of their ties. 'gretel' systemizes these approaches,
    casting them as instances of a generalized path value function indexed by 
    a penalty parameter. The package also calculates probabilistic path value and
    identifies optimal paths in either value framework. Finally, proximity 
    matrices can be generated in these frameworks that capture high-order connections 
    overlooked in primitive adjacency sociomatrices. Novel methods are described
    in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. 
    More traditional methods are also implemented, as described in Yang, Knoke (2001) 
    <doi:10.1016/S0378-8733(01)00043-0>.
It allows running 'gretl' (<http://gretl.sourceforge.net/index.html>) program from R, R Markdown and Quarto. 'gretl' ('Gnu' Regression, 'Econometrics', and Time-series Library) is a statistical software for Econometric analysis.  This package does not only integrate 'gretl' and 'R' but also serves  as a 'gretl' Knit-Engine for 'knitr' package. Write all your 'gretl' commands in 'R', R Markdown chunk.
Convert 'Ensembl' gene identifiers from Genotype-Tissue
    Expression (GTEx) data to identifiers in other annotation systems,
    including 'Entrez', 'HGNC', and 'UniProt'.
Implements functions and instruments for regression model building and its
             application to forecasting. The main scope of the package is in variables selection
             and models specification for cases of time series data. This includes promotional
             modelling, selection between different dynamic regressions with non-standard
             distributions of errors, selection based on cross validation, solutions to the fat
             regression model problem and more. Models developed in the package are tailored
             specifically for forecasting purposes. So as a results there are several methods
             that allow producing forecasts from these models and visualising them.
Testing, Implementation and Forecasting of Grey Model (GM(1, 1)). For method details see Hsu, L. and Wang, C. (2007). <doi:10.1016/j.techfore.2006.02.005>.
Forest-based statistical estimation and inference.
  GRF provides non-parametric methods for heterogeneous treatment effects estimation
  (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
  as well as least-squares regression, quantile regression, and survival regression,
  all with support for missing covariates.
Implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.
Functions for rendering Bezier
             curves (Pomax, 2018) <https://pomax.github.io/bezierinfo/>
             in 'grid'.  
             There is support for both quadratic and cubic Bezier curves.
             There are also functions for calculating points on curves,
             tangents to curves, and normals to curves.
Estimates grid type bivariate copula functions, calculates some association measures and provides several copula graphics.
Functions for drawing scene trees representing 
              scenes that have been drawn using grid graphics.
Tools for exploratory geospatial distribution dynamics with
    'sf' objects and tidy data. Provides pooled and time-specific
    classification of longitudinal spatial values, classic discrete Markov
    transition matrices, spatial Markov matrices conditioned on spatial-lag
    classes, endpoint and adjacent rank mobility summaries, and 'ggplot2'
    visualizations. Methods follow Rey (2001)
    <doi:10.1111/j.1538-4632.2001.tb00444.x> and Rey et al. (2016)
    <doi:10.1007/s10109-016-0234-x>; design is inspired by the Python
    'PySAL' 'giddy' package <https://pysal.org/giddy/>.
Provides a number of user-level functions to work with "grid"
    graphics, notably to arrange multiple grid-based plots on a page, and draw
    tables.
Functions for performing polygon geometry with 'grid' grobs.
             This allows complex shapes to be defined by combining simpler
             shapes.
Functions to convert a page of plots drawn with the 
  'graphics' package into identical output drawn with the 'grid' package.
  The result looks like the original 'graphics'-based plot, but consists
  of 'grid' grobs and viewports that can then be manipulated with 
  'grid' functions (e.g., edit grobs and revisit viewports).
Functions for drawing node-and-edge graphs that have been 
              laid out by 'graphviz'.  This provides an alternative 
              rendering to that provided by the 'Rgraphviz' package, with
              two main advantages:  the rendering provided by 'gridGraphviz'
              should be more similar to what 'graphviz' itself would draw;
              and rendering with 'grid' allows for post-hoc customisations
              using the named viewports and grobs that 'gridGraphviz'
              produces.
A simple and flexible tool designed to create enriched figures and tables by providing a way to add text 
    around them through predefined or custom layouts. 
    Any input which is convertible to 'grob' is supported, like 'ggplot', 'gt' or 'flextable'. 
    Based on R 'grid' graphics, for more details see Paul Murrell (2018) <doi:10.1201/9780429422768>.
Renders 'LaTeX' math equations as native R grid graphics objects
    (grobs) using the 'MicroTeX' 'C++' library as the layout engine. Produces
    resolution-independent vector output that works on any R graphics device,
    with no external 'LaTeX' installation required.
Discretize multivariate continuous data using a grid
 to capture the joint distribution that preserves clusters in
 original data. It can handle both labeled or unlabeled data.
 Both published methods (Wang et al 2020) <doi:10.1145/3388440.3412415>
 and new methods are included. Joint grid discretization
 can prepare data for model-free inference of association,
 function, or causality.
Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. It utilizes the Frank-Wolfe algorithm to approximate so-called pivot measures: One-dimensional transport plans that fully describe the full transport, see G. Auricchio (2023) <doi:10.4171/RLM/1026>. For these, it offers methods for visualization and to extract the corresponding transport plans and costs. Additionally, related functions for one-dimensional optimal transport are available.
Provides 'grid' grobs that fill in a user-defined area with various patterns.  Includes enhanced versions of the geometric and image-based patterns originally contained in the 'ggpattern' package as well as original 'pch', 'polygon_tiling', 'regular_polygon', 'rose', 'text', 'wave', and 'weave' patterns plus support for custom user-defined patterns.
Simulation tool to facilitate determination of
    required sample size to achieve category saturation
    for studies using multiple repertory grids in conjunction with
    content analysis.
An easy way to create responsive layouts with just a few lines of code. You can create boxes that are draggable and resizable and load predefined Layouts. The package serves as a wrapper to allow for easy integration of the 'gridstack.js' functionalities <https://github.com/gridstack/gridstack.js>.
Functions to export graphics drawn with package grid to SVG
  format.  Additional functions provide access to SVG features that
  are not available in standard R graphics, such as hyperlinks, 
  animation, filters, masks, clipping paths, and gradient and pattern fills.
Provides support for rendering of formatted text using 'grid' graphics. Text can be
    formatted via a minimal subset of 'Markdown', 'HTML', and inline 'CSS' directives, and it can be
    rendered both with and without word wrap.
Provides the following types of models: Models for contingency
    tables (i.e. log-linear models) Graphical Gaussian models for multivariate
    normal data (i.e. covariance selection models) Mixed interaction models.
    Documentation about 'gRim' is provided by vignettes included in this
    package and the book by Højsgaard, Edwards and Lauritzen (2012,
    <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.
Functions for converting, importing, and drawing PostScript 
              pictures in R plots.
Functions for importing external vector images and
  drawing them as part of 'R' plots.  This package is different from the
  'grImport' package because, where that package imports 'PostScript'
  format images, this package imports 'SVG' format images.  Furthermore,
  this package imports a specific subset of 'SVG', so external images
  must be preprocessed using a package like 'rsvg' to produce 'SVG'
  that this package can import.  'SVG' features that are not supported
  by 'R' graphics, e.g., gradient fills, can be imported and then
  exported via the 'gridSVG' package.
Improved version of 'GRIN' software that streamlines its use in practice to analyze genomic lesion data, accelerate its computing, and expand its analysis capabilities to answer additional scientific questions including a rigorous evaluation of the association of genomic lesions with RNA expression. Pounds, Stan, et al. (2013) <DOI:10.1093/bioinformatics/btt372>.
Uses simple Bayesian conjugate prior update rules to calculate 
    the win probability of each option, value remaining in the test, and 
    percent lift over the baseline for various marketing objectives.
    References: 
    Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>.
    Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf>.
Quantitative genetics tool supporting the modelling of multivariate 
 genetic variance structures in quantitative data. It allows fitting different 
 models through multivariate genetic-relationship-matrix (GRM) 
 structural equation modelling (SEM) in unrelated individuals, 
 using a maximum likelihood approach. Specifically, 
 it combines genome-wide genotyping information, as captured by GRMs, 
 with twin-research-based SEM techniques, 
 St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>, 
 Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.
Provides methods for recursive partitioning based on the 
  'Graded Response Model' ('GRM'), extending the 'MOB' algorithm from the 
  'partykit' package. The package allows for fitting
  'GRM' trees that partition the population into homogeneous 
  subgroups based on item response patterns and covariates. 
  Includes specialized plotting functions for visualizing 'GRM' trees
  with different terminal node displays (threshold regions, 
  parameter profiles, and factor score distributions). 
  For more details on the methods, see Samejima (1969) <doi:10.1002/J.2333-8504.1968.TB00153.X>, Komboz et al. (2018) <doi:10.1177/0013164416664394> and Arimoro et al. (2025) <doi:10.1007/s11136-025-04018-6>.
The program GRNN implements the algorithm proposed by
        Specht (1991).
This General Regression Neural Networks Package uses various distance functions.
     It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper 
     Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions, 
     although "euclidean" distance is used traditionally.
Workbench for testing genomic regression accuracy on
    (optionally noisy) phenotypes.
A tool which allows users the ability to intuitively create 
  flexible, reproducible portable document format reports comprised of 
  aesthetically pleasing tables, images, plots and/or text.
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.
A comprehensive suite of functions and 'RStudio' Add-ins leveraging the capabilities of open-source Large Language Models (LLMs) to support R developers. These functions offer a range of utilities, including text rewriting, translation, and general query capabilities. Additionally, the programming-focused functions provide assistance with debugging, translating, commenting, documenting, and unit testing code, as well as suggesting variable and function names, thereby streamlining the development process.
Make R scripts reproducible, by ensuring that
    every time a given script is run, the same version of the used packages are
    loaded (instead of whichever version the user running the script happens to
    have installed). This is achieved by using the command
    groundhog.library() instead of the base command library(), and including a
    date in the call. The date is used to call on the same version of the
    package every time (the most recent version available at that date).
    Load packages  from CRAN, GitHub, or Gitlab.
Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.
Comparing two independent or paired groups across a range of descriptive statistics, enabling the evaluation of potential differences in central tendency (mean, median), dispersion (variance, interquartile range), shape (skewness, kurtosis), and distributional characteristics (various quantiles). The analytical framework incorporates parametric t-tests, non-parametric Wilcoxon tests, permutation tests, and bootstrap resampling techniques to assess the statistical significance of observed differences.
Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).
Methods for dividing data into groups. 
    Create balanced partitions and cross-validation folds. 
    Perform time series windowing and general grouping and splitting of data. 
    Balance existing groups with up- and downsampling or collapse them to fewer groups.
To aggregate a hyper data frame, defined in the package 'spatstat.geom', according to a grouping structure.  To facilitate downstream analysis based on a "grouped" hyper data frame.  The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications.
An intuitive interface to simulate superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks.
These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).
Integer programming models to assign students to groups by maximising 
    diversity within groups, or by maximising preference scores for topics.
The 'groupr' package provides a more powerful version of grouped
  tibbles from 'dplyr'. It allows groups to be marked inapplicable,
  which is a simple but widely useful way to express structure in a dataset.
  It also provides powerful pivoting and other group manipulation functions.
Implement group response-adaptive randomization procedures, which also integrates standard non-group response-adaptive randomization methods as specialized instances. It is also uniquely capable of managing complex scenarios, including those with delayed and missing responses, thereby expanding its utility in real-world applications. This package offers 16 functions for simulating a variety of response adaptive randomization procedures. These functions are essential for guiding the selection of statistical methods in clinical trials, providing a flexible and effective approach to trial design. Some of the detailed methodologies and algorithms used in this package, please refer to the following references:
    LJ Wei (1979) <doi:10.1214/aos/1176344614>
    L. J. WEI and S. DURHAM (1978) <doi:10.1080/01621459.1978.10480109>
    Durham, S. D., FlournoY, N. AND LI, W. (1998) <doi:10.2307/3315771>
    Ivanova, A., Rosenberger, W. F., Durham, S. D. and Flournoy, N. (2000) <https://www.jstor.org/stable/25053121>
    Bai Z D, Hu F, Shen L. (2002) <doi:10.1006/jmva.2001.1987>
    Ivanova, A. (2003) <doi:10.1007/s001840200220>
    Hu, F., & Zhang, L. X. (2004) <doi:10.1214/aos/1079120137>
    Hu, F., & Rosenberger, W. F. (2006, ISBN:978-0-471-65396-7).
    Zhang, L. X., Chan, W. S., Cheung, S. H., & Hu, F. (2007) <https://www.jstor.org/stable/26432528>
    Zhang, L., & Rosenberger, W. F. (2006) <doi:10.1111/j.1541-0420.2005.00496.x>
    Hu, F., Zhang, L. X., Cheung, S. H., & Chan, W. S. (2008) <doi:10.1002/cjs.5550360404>.
Computes probabilities related to group sequential designs for
    normally distributed test statistics. Enables to derive critical
    boundaries, power, drift, and confidence intervals of such designs.
    Supports the alpha spending approach by Lan-DeMets (1994)
    <doi:10.1002/sim.4780131308>.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
A procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.
Fits weighted quantile sum (WQS) regressions for one or more chemical groups with continuous or binary outcomes. Wheeler D, Czarnota J.(2016) <doi:10.1289/isee.2016.4698>.
Functional denoising and functional ANOVA through wavelet-domain 
  Markov groves. Fore more details see: Ma L. and Soriano J. (2018) 
  Efficient functional ANOVA through wavelet-domain Markov groves. 
  <arXiv:1602.03990v2 [stat.ME]>.
Run grass growth simulations using a grass growth model based 
    on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting 
    Dynamics of Biomass, Structure and Digestibility of Herbage in Managed 
    Permanent Pastures. 1. Model Description." (2006) 
    <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in
    this package contains a few additions to the above cited version of ModVege,
    such as simulations of management decisions, and influences of snow cover.
    As such, the model is fit to simulate grass growth in mountainous 
    regions, such as the Swiss Alps. The package also contains routines for 
    calibrating the model and helpful tools for analysing model outputs and 
    performance.
Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.
Identifies implausible anthropometric (e.g., height,
    weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.
Simple and  user-friendly wrappers to the 'saemix' package for 
    performing linear and non-linear mixed-effects regression modeling for 
    growth data to account for clustering or longitudinal analysis via repeated
    measurements. The package allows users to fit a variety of growth
    models, including linear, exponential, logistic, and 'Gompertz'
    functions. For non-linear models, starting values are automatically
    calculated using initial least-squares estimates. The package includes 
    functions for summarizing models, visualizing data and results, 
    calculating doubling time and other key statistics, and generating model 
    diagnostic plots and residual summary statistics. It also provides 
    functions for generating publication-ready summary tables for reports. 
    Additionally, users can fit linear and non-linear least-squares 
    regression models if clustering is not applicable. The mixed-effects 
    modeling methods in this package are based on Comets, Lavenu, and 
    Lavielle (2017) <doi:10.18637/jss.v080.i03> as implemented in the 
    'saemix' package. Please contact us at models@dfci.harvard.edu 
    with any questions.
Fits the logistic equation to
    microbial growth curve data (e.g., repeated absorbance measurements
    taken from a plate reader over time). From this fit, a variety of
    metrics are provided, including the maximum growth rate,
    the doubling time, the carrying capacity, the area under the logistic
    curve, and the time to the inflection point. Method described in 
    Sprouffske and Wagner (2016) <doi:10.1186/s12859-016-1016-7>.
Decomposes observed growth in agricultural and livestock systems into interpretable component effects. Depending on the application, the total change in output can be attributed to components such as area effect, yield effect, herd or slaughter effect, productivity effect, and interaction effect. Details can be found in Rakshit and Bardhan (2026) <doi:10.1007/s11250-026-04988-w>.
A compilation of nonlinear growth models.
Assists in the plotting and functional smoothing of traits 
    measured over time and the extraction of features from these traits, 
    implementing the SET (Smoothing and Extraction of Traits) method 
    described in Brien et al. (2020) Plant Methods, 16. Smoothing of 
    growth trends for individual plants using natural cubic smoothing 
    splines or P-splines is available for removing transient effects and 
    segmented smoothing is available to deal with discontinuities in 
    growth trends. There are graphical tools for assessing the adequacy 
    of trait smoothing, both when using this and other packages, such as 
    those that fit nonlinear growth models. A range of per-unit (plant, 
    pot, plot) growth traits or features can be extracted from the 
    data, including single time points, interval growth rates and other 
    growth statistics, such as maximum growth or days to maximum growth. 
    The package also has tools adapted to inputting data from 
    high-throughput phenotyping facilities, such from a Lemna-Tec 
    Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> 
    for more information). The package 'growthPheno' can also be 
    installed from <http://chris.brien.name/rpackages/>.
A nonparametric empirical Bayes method for recovering
        gradients (or growth velocities) from observations of smooth
        functions (e.g., growth curves) at isolated time points.
A collection of methods to determine growth rates from
    experimental data, in particular from batch experiments and
    plate reader trials.
Offers tools for data formatting, anomaly detection, and classification
 of tree-ring data using spatial comparisons and cross-correlation. 
 Supports flexible detrending and climate–growth modeling via generalized
 additive mixed models (Wood 2017, ISBN:978-1498728331) and the 'mgcv'
 package (<https://CRAN.R-project.org/package=mgcv>), enabling robust
 analysis of non-linear trends and autocorrelated data. Provides
 standardized visual reporting, including summaries, diagnostics, and
 model performance. Compatible with '.rwl' files and tailored for the
 Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository
 (Girardin et al. (2021) <doi:10.1139/er-2020-0099>), offering
 a comprehensive and adaptable framework for dendrochronologists working
 with large and complex datasets.
Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.
Fits user-specified (GLM-) models with group lasso penalty.
Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2025) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), multivariate regression (multigaussian), smoothed support vector machines (svm1), squared support vector machines (svm2), logistic regression (binomial), proportional odds logistic regression (ordinal), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.
Efficient algorithms for fitting the regularization path of linear
  regression, GLM, and Cox regression models with grouped penalties.  This
  includes group selection methods such as group lasso, group MCP, and
  group SCAD as well as bi-level selection methods such as the group
  exponential lasso, the composite MCP, and the group bridge.  For more
  information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>,
  Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang
  (2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015)
  <doi:10.1111/biom.12300>, or visit the package homepage
  <https://pbreheny.github.io/grpreg/>.
Provides tools for sparse regression modelling with grouped predictors using the group 
    subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver 
    near optimal estimates. The group subset penalty can be combined with a group lasso or ridge 
    penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping 
    groups.
Design of group sequential trials, including non-binding futility analysis
  at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).
Group SLOPE (Group Sorted L1 Penalized Estimation) is
    a penalized linear regression method that is used for adaptive
    selection of groups of significant predictors in a high-dimensional
    linear model. The Group SLOPE method can control the (group) false
    discovery rate at a user-specified level (i.e., control the expected
    proportion of irrelevant among all selected groups of predictors).
    For additional information about the implemented methods please see
    Brzyski, Gossmann, Su, Bogdan (2018) <doi:10.1080/01621459.2017.1411269>.
Methods include converting series of event names to strings, finding common patterns
    in a group of strings, discovering "unique" patterns when comparing two groups of strings as well
    as the number and starting position of each pattern in each string, obtaining transition matrix, 
    computing transition entropy, statistically comparing the difference between two groups of strings,
    and clustering string groups. Event names can be any action names or labels such as events in log files or areas of interest (AOIs) in eye tracking research.
    An R Shiny application is available on GitHub.
Simulation and analysis of graded response data with different types of estimators. Also, an interactive shiny application is provided with graphics for characteristic and information curves. Samejima (2018) <doi:10.1007/978-1-4757-2691-6_5>.
Statistical testing procedures for detecting
  GxE (gene-environment) interactions. The main focus lies on
  GRSxE interaction tests that aim at detecting GxE interactions
  through GRS (genetic risk scores). Moreover, a novel testing
  procedure based on bagging and OOB (out-of-bag) predictions is
  implemented for incorporating all available observations at
  both GRS construction and GxE testing (Lau et al., 2023,
  <doi:10.1038/s41598-023-28172-4>).
Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.
Lightweight way to read 'NumPy' '.npy' and '.npz' files in R. All data types supported
    by 'NumPy', with all sizes (converted internally to R native size), both C and 'Fortran' order,
    and any shape, up to an arbitrary number of dimensions, are supported.
River hydrograph separation and daily runoff time series analysis. Provides
  various filters to separate baseflow and quickflow. Implements advanced separation 
  technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which involves 
  meteorological data to reveal genetic components of the runoff: ground, rain, thaw 
  and spring (seasonal thaw). High-performance C++17 computation, annually aggregated 
  variables, statistical testing and numerous plotting functions for high-quality 
  visualization.
A tool to sensitivity analysis using SOBOL (Sobol, 1993) and AMA (Dell'Oca et al. 2017 <doi:10.5194/hess-21-6219-2017>) indices.
            It allows to identify the most sensitive parameter or parameters of a model.
Provides utility functions used by the Genome Analysis Toolkit ('GATK') to load tables and plot data. The 'GATK' is a toolkit for variant discovery in high-throughput sequencing data.
Computing Global Sensitivity Indices from given data using Optimal Transport, as defined in Borgonovo et al (2024) <doi:10.1287/mnsc.2023.01796>. You provide an input sample, an output sample, decide the algorithm, and compute the indices.
Write SARIMA models in (finite) AR representation and simulate 
	generalized multiplicative seasonal autoregressive moving average (time) series 
	with Normal / Gaussian, Poisson or negative binomial distribution. 
	The methodology of this method is described in Briet OJT, Amerasinghe PH, and 
	Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.
Group Sequential Operating Characteristics for Clinical,
        Bayesian two-arm Trials with known Sigma and Normal Endpoints,
	as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.
Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.
Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>.
    It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.
Provides utilities for manipulating time series of location-based counts of events to detect geo-spatial clusters. Significance of these clusters is determined using a set of models that classify based on a learned relationship between observed and the log(observed/expected) ratio of counts. The approach implemented here is similar to prospective space-time estimation of clusters using the scan statistic.
Design and analysis of group sequential designs for negative
    binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.
A genomic simulation approach for creating biologically
    informed individual genotypes from empirical data that 1) samples alleles
    from populations without replacement, 2) segregates alleles based on species-specific
    recombination rates. 'gscramble' is a flexible simulation approach that allows users
    to create pedigrees of varying complexity in order to simulate admixed genotypes.
    Furthermore, it allows users to track haplotype blocks from the source populations
    through the pedigrees.
Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013) <doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014) <https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".
The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.
Derives group sequential clinical trial designs and describes
    their properties. Particular focus on time-to-event, binary, and
    continuous outcomes. Largely based on methods described in
    Jennison, Christopher and Turnbull, Bruce W., 2000,
    "Group Sequential Methods with Applications to Clinical Trials"
    ISBN: 0-8493-0316-8.
The goal of 'gsDesign2' is to enable fixed or group sequential
    design under non-proportional hazards. To enable highly flexible enrollment,
    time-to-event and time-to-dropout assumptions, 'gsDesign2' offers piecewise
    constant enrollment, failure rates, and dropout rates for a stratified
    population. This package includes three methods for designs:
    average hazard ratio, weighted logrank tests in Yung and Liu (2019)
    <doi:10.1111/biom.13196>, and MaxCombo tests.
    Substantial flexibility on top of what is in the 'gsDesign' package
    is intended for selecting boundaries.
Provides tools for planning and simulating recurrent event trials
    with overdispersed count endpoints analyzed using negative binomial (or
    Poisson) rate models. Implements sample size and power calculations for
    fixed designs with variable accrual, dropout, maximum follow-up, and event
    gaps, including methods of Zhu and Lakkis (2014) <doi:10.1002/sim.5947> and
    Friede and Schmidli (2010) <doi:10.3414/ME09-02-0060>. Supports group
    sequential designs by adding calendar-time analysis schedules compatible
    with the 'gsDesign' package and by estimating blinded information at
    interim looks. Includes simulation utilities for recurrent events
    (including seasonal rates), interim data truncation, and Wald-based
    inference for treatment rate ratios.
Provides systematic, dependency-aware exploration of
    group sequential designs created with 'gsDesign'.
    Supports reproducible grid and random search over user-defined
    candidate sets, parallel evaluation via the 'future' framework,
    standardized metric extraction, and auditable reporting for
    design-space evaluation and trade-off analysis.
    Methods for group sequential design are described in
    Anderson (2025) <doi:10.32614/CRAN.package.gsDesign>.
    The 'future' framework for parallel processing is described in
    Bengtsson (2021) <doi:10.32614/RJ-2021-048>.
Robust Estimation of Multivariate Location and Scatter in the
        Presence of Cellwise and Casewise Contamination and Missing Data.
Methods to construct and power group sequential clinical trial 
  designs for outcomes at multiple times. Outcomes at earlier times provide
  information on the final (primary) outcome. A range of recruitment and 
  correlation models are available as are methods to simulate data in order to 
  explore design operating characteristics. For more details see Parsons (2024)
  <doi:10.1186/s12874-024-02174-w>.
R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).
Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.
Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.
Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.
'R' version of 'G-Series', Statistics Canada's generalized system devoted
    to the benchmarking and reconciliation of time series data. The methods
    used in 'G-Series' essentially come from Dagum, E. B., and P. Cholette 
    (2006) <doi:10.1007/0-387-35439-5>.
Simple package to download Google Sheets using just the sharing
    link. Spreadsheets can be downloaded as a data frame, or as plain text to parse
    manually. Google Sheets is the new name for Google Docs Spreadsheets <https://www.google.com/sheets/about>.
R implementation of the 'Octave' package 'signal', containing
    a variety of signal processing tools, such as signal generation and
    measurement, correlation and convolution, filtering, filter design,
    filter analysis and conversion, power spectrum analysis, system
    identification, decimation and sample rate change, and windowing.
A set of high efficient functions to decode identifiers of National 
  Football League players.
An R wrapper for some of the functionality of the
 Gnu Scientific Library.
An R interface to weighted nonlinear least-squares optimization with the GNU Scientific Library (GSL), see M. Galassi et al. (2009, ISBN:0954612078). The available trust region methods include the Levenberg-Marquardt algorithm with and without geodesic acceleration, the Steihaug-Toint conjugate gradient algorithm for large systems and several variants of Powell's dogleg algorithm. Multi-start optimization based on quasi-random samples is implemented using a modified version of the algorithm in Hickernell and Yuan (1997, OR Transactions). Robust nonlinear regression can be performed using various robust loss functions, in which case the optimization problem is solved by iterative reweighted least squares (IRLS). Bindings are provided to tune a number of parameters affecting the low-level aspects of the trust region algorithms. The interface mimics R's nls() function and returns model objects inheriting from the same class.
Contains published data sets for global benthic d18O data for 
    0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based 
    on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.
Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
It provides functions to generate operating characteristics and to calculate Sequential Conditional Probability Ratio Tests(SCPRT) efficacy and futility boundary values along with sample/event size of Multi-Arm Multi-Stage(MAMS) trials for different outcomes. The package is based on Jianrong Wu, Yimei Li, Liang Zhu (2023) <doi:10.1002/sim.9682>, Jianrong Wu, Yimei Li (2023) "Group Sequential Multi-Arm Multi-Stage Survival Trial Design with Treatment Selection"(Manuscript accepted for publication) and Jianrong Wu, Yimei Li, Shengping Yang (2023) "Group Sequential Multi-Arm Multi-Stage Trial Design with Ordinal Endpoints"(In preparation).
Simulating composite endpoints with recurrent and terminal events under staggered entry, and for constructing one- and two-sample group sequential test statistics and monitoring boundaries based on the mean frequency function. Details will be available in an upcoming publication.
Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.
Provides automated downloading, parsing, cleaning, unit
    conversion and formatting of Global Surface Summary of the Day
    ('GSOD') weather data from the from the USA National Centers for
    Environmental Information ('NCEI').  The data were retired on
    2025-08-29 and are no longer updated. Units are converted from from
    United States Customary System ('USCS') units to International System
    of Units ('SI').  Stations may be individually checked for number of
    missing days defined by the user, where stations with too many missing
    observations are omitted.  Only stations with valid reported latitude
    and longitude values are permitted in the final data.  Additional
    useful elements, saturation vapour pressure ('es'), actual vapour
    pressure ('ea') and relative humidity ('RH') are calculated from the
    original data using the improved August-Roche-Magnus approximation
    (Alduchov & Eskridge 1996) and included in the final data set.  The
    resulting metadata include station identification information,
    country, state, latitude, longitude, elevation, weather observations
    and associated flags.  For information on the 'GSOD' data from 'NCEI',
    please see the 'GSOD' 'readme.txt' file available from,
    <https://www.ncei.noaa.gov/pub/data/gsod/readme.txt>.
Proposes a new file format ('gson') for storing gene set and related information, and provides read, write and other utilities to process this file format.
Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".
Generalization of supervised principal component regression (SPCR; 
    Bair et al., 2006, <doi:10.1198/016214505000000628>) to support continuous, 
    binary, and discrete variables as outcomes and predictors 
    (inspired by the 'superpc' R package <https://cran.r-project.org/package=superpc>).
A group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.
A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. 
    See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.
A comprehensive package for structural multivariate
        function estimation using smoothing splines.
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
Mapper-based survival analysis with transcriptomics data is designed to carry out. 
			Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) 
			where survival data is taken into account in the filtering function. More 
			details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, 
			A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap 
			identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics
			and methylation studies" <doi:10.1101/2022.09.08.507080>.
Multivariate time series analysis based on Generalized Space-Time Autoregressive Model by Ruchjana et al.(2012) <doi:10.1063/1.4724118>.
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions; supports sf and stars.
Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.  See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.
Implementations of the algorithms present article 
    Generalized Spatial-Time Sequence Miner, original title
    (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio
    ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de
    Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD -
    Simpósio Brasileiro de Banco de Dados, 2021 <doi:10.5753/sbbd.2021.17891>).
The gsubfn function is like gsub but can take a replacement 
   function or certain other objects instead of the replacement string.
   Matches and back references are input to the replacement function and 
   replaced by the function output.   gsubfn can be used to split strings 
   based on content rather than delimiters and for quasi-perl-style string 
   interpolation. The package also has facilities for translating formulas 
   to functions and allowing such formulas in function calls instead of 
   functions.  This can be used with R functions such as apply, sapply,
   lapply, optim, integrate, xyplot, Filter and any other function that 
   expects another function as an input argument or functions like cat
   or sql calls that may involve strings where substitution is desirable.
   There is also a facility for returning multiple objects from functions
   and a version of transform that allows the RHS to refer to LHS used in
   the same transform.
Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C library, version 3.06-16-0 (commit '657216dd4f5ea079b5f0e021a4163e2d26893371', dated 2022-10-11, available at <https://github.com/TEOS-10/GSW-C>, which stems from 'Matlab' and other code written by members of Working Group 127 of 'SCOR'/'IAPSO' (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans).
Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.
Conducts causal inference with interactive fixed-effect models. It
    imputes counterfactuals for each treated unit using control group
    information based on a linear interactive fixed effects model that
    incorporates unit-specific intercepts interacted with time-varying
    coefficients. This method generalizes the synthetic control method to the
    case of multiple treated units and variable treatment periods, and improves
    efficiency and interpretability. See Xu (2017)
    <doi:10.1017/pan.2016.2> for details.
Build display tables from tabular data with an easy-to-use
    set of functions. With its progressive approach, we can construct
    display tables with a cohesive set of table parts. Table values can be
    formatted using any of the included formatting functions. Footnotes
    and cell styles can be precisely added through a location targeting
    system. The way in which 'gt' handles things for you means that you
    don't often have to worry about the fine details.
Provides tools to measure the reliability of an Information Retrieval test collection.
  It allows users to estimate reliability using Generalizability Theory and map those estimates onto
  well-known indicators such as Kendall tau correlation or sensitivity.
Tools to make it easier to work with "tables" of 'grobs'. The
    'gtable' package defines a 'gtable' grob class that specifies a grid
    along with a list of grobs and their placement in the grid. Further
    the package makes it easy to manipulate and combine 'gtable' objects
    so that complex compositions can be built up sequentially.
Provides functions to analyze data exported from 'Google Takeout'. The package supports unzipping archives and extracting user review data from Google Business Profile exports into tidy data frames for further analysis.
Tools to streamline the extraction, processing, and visualization of
    Computable General Equilibrium (CGE) results from 'GTAP' models. Designed for
    compatibility with both .har and .sl4 files, the package enables users to
    automate data preparation, apply mapping metadata, and generate high-quality
    plots and summary tables with minimal coding. 'GTAPViz' supports flexible export
    options (e.g., Text, CSV, 'Stata', or 'Excel' formats).
    This facilitates efficient post-simulation analysis for
    economic research and policy reporting. Includes helper functions to filter,
    format, and customize outputs with reproducible styling.
Understanding how features influence a specific response variable becomes crucial in classification problems, with applications ranging from medical diagnosis to customer behavior analysis. 
			 This packages provides tools to compute such an influence measure grounded on game theory concepts.
			 In particular, the influence measures presented in Davila-Pena, Saavedra-Nieves, and Casas-Méndez (2024) <doi:10.48550/arXiv.2408.02481> can be obtained.
Finite candidate-set approximate optimal designs for group   
  testing and related experiments, using convex optimization and equivalence
  checks. Implements the information matrix and cost structure for the
  prevalence / sensitivity / specificity model used in Huang and colleagues
    (2020), as in Chi-Kuang Yeh, Weng Kee Wong, and Julie Zhou (<doi:10.48550/arXiv.2508.08445>).
Computes the  probability density, survival function,
   the hazard rate functions and generates random samples from the 
   GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. 
   The likelihood estimates, the randomized quantile (Louzada, F., et al. 
   (2020) <doi:10.1109/ACCESS.2020.3040525>)
   residuals and the normally transformed randomized survival 
   probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>)
   residuals are obtained for the GTDL model.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
    (2011).
Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.
Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.
New multi-sample tests for testing whether multiple samples are from the same distribution. They work well particularly for high-dimensional data.
    Song, H. and Chen, H. (2022) 
    <arXiv:2205.13787>.
Implements three nonparametric two-sample tests for multivariate paired data
    and pair matching. Methods are described in the associated preprint:
    <doi:10.48550/arXiv.2007.01497>.
A convenient R interface to the Genotype-Tissue Expression (GTEx) 
    Portal API. The GTEx project is a comprehensive public resource for studying 
    tissue-specific gene expression and regulation in human tissues. Through 
    systematic analysis of RNA sequencing data from 54 non-diseased tissue sites 
    across nearly 1000 individuals, GTEx provides crucial insights into the 
    relationship between genetic variation and gene expression. This data is 
    accessible through the GTEx Portal API enabling programmatic access to human 
    gene expression data. For more information on the API, see 
    <https://gtexportal.org/api/v2/redoc>.
Provides additional functions for creating beautiful tables
    with 'gt'. The functions are generally wrappers around boilerplate or
    adding opinionated niche capabilities and helpers functions.
Generalizes application of gray-level co-occurrence matrix
    (GLCM) metrics to objects outside of images. The current focus is to
    apply GLCM metrics to the study of biological networks and fitness
    landscapes that are used in studying evolutionary medicine and
    biology, particularly the evolution of cancer resistance. The package was
    developed as part of the author's publication in Physics in Medicine and Biology
    Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. 
    A general reference to learn more about mathematical oncology can be found at
    Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.
A bottom up model to estimate the emission levels of public transport systems based on General Transit Feed Specification (GTFS) data. The package requires two main inputs: i) Public transport data in the GTFS standard format; and ii) Some basic information on fleet characteristics such as fleet age, technology, fuel and Euro stage. As it stands, the package estimates several pollutants at high spatial and temporal resolutions. Pollution levels can be calculated for specific transport routes, trips, time of the day or for the transport system as a whole. The output with emission estimates can be extracted in different formats, supporting analysis on how emission levels vary across space, time and by fleet characteristics. A full description of the methods used in the 'gtfs2emis' model is presented in Vieira, J. P. B.; Pereira, R. H. M.; Andrade, P. R. (2022) <doi:10.31219/osf.io/8m2cy>.
Convert general transit feed specification (GTFS) data to global positioning system (GPS) records in 'data.table' format. It also has some functions to subset GTFS data in time and space and to convert both representations to simple feature format.
Tools for the development of packages related to General
    Transit Feed Specification (GTFS) files. Establishes a standard for
    representing GTFS feeds using R data types. Provides fast and flexible
    functions to read and write GTFS feeds while sticking to this
    standard. Defines a basic 'gtfs' class which is meant to be extended
    by packages that depend on it. And offers utility functions that
    support checking the structure of GTFS objects.
GTFS-realtime is a format transit agencies use to provide current vehicle positions, predicted
    arrival times, and service alerts. This package provides efficient functions to read this format into
    data frames. It can be used to retrieve current data or to process archived data.
Use 'GTFS' (General Transit Feed Specification) data for routing
    from nominated start and end stations, for extracting 'isochrones', and
    travel times from any nominated start station to all other stations.
Utility functions to read, manipulate, analyse and write
    transit feeds in the General Transit Feed Specification (GTFS) data
    format.
Provides a small, beginner-friendly interface for estimating
    variance components in simple generalizability theory designs. The package
    currently supports a fully crossed persons-by-items design and a simple
    items-within-person nested design, along with design-study summaries for
    relative and absolute decisions.
Insert tables created by the 'gt' R package into 'Microsoft Word' 
    documents. This gives users the ability to add to their existing word documents
    the tables made in 'gt' using the familiar 'officer' package and syntax from
    the 'officeverse'.
Functions to assist in R programming, including:
  - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
    'getDependencies', 'keywords', 'scat'),
  - calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
  - test if a value is missing, empty or contains only NA and NULL values ('invalid'),
  - manipulate R's .Last function ('addLast'),
  - define macros ('defmacro'),
  - detect odd and even integers ('odd', 'even'),
  - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
  - perform a binary search ('binsearch'),
  - sort strings containing both numeric and character components ('mixedsort'),
  - create a factor variable from the quantiles of a continuous variable ('quantcut'),
  - enumerate permutations and combinations ('combinations', 'permutation'),
  - calculate and convert between fold-change and log-ratio ('foldchange',
    'logratio2foldchange', 'foldchange2logratio'),
  - calculate probabilities and generate random numbers from Dirichlet distributions
    ('rdirichlet', 'ddirichlet'),
  - apply a function over adjacent subsets of a vector ('running'),
  - modify the TCP_NODELAY ('de-Nagle') flag for socket objects,
  - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
  - generate significance stars from p-values ('stars.pval'),
  - convert characters to/from ASCII codes ('asc', 'chr'),
  - convert character vector to ASCII representation ('ASCIIfy'),
  - apply title capitalization rules to a character vector ('capwords').
The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight.
Creates tables suitable for regulatory agency submission by
    leveraging the 'gtsummary' package as the back end. Tables can be exported
    to HTML, Word, PDF and more. Highly customized outputs are
    available by utilizing existing styling functions from 'gtsummary' as
    well as custom options designed for regulatory tables.
Simplifies regression modeling in R by integrating multiple modeling and summarization
  tools into a cohesive, user-friendly interface. Designed to be accessible for researchers,
  particularly those in Low- and Middle-Income Countries (LMIC). Built upon widely accepted 
  statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), 
  log-binomial regression (Spiegelman and Hertzmark 2005 <doi:10.1093/aje/kwi188>), 
  Poisson and robust Poisson regression (Zou 2004 <doi:10.1093/aje/kwh090>), 
  negative binomial regression (Hilbe 2011, ISBN:9780521179515), and linear regression 
  (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure 
  high-quality output and generate reproducible, publication-ready tables in alignment with
  best practices in epidemiology and applied statistics.
Connects to the 'Google Trends for Health' API hosted at
    <https://trends.google.com/trends/>, allowing projects authorized to use the
    health research data to query 'Google Trends'.
An interface for retrieving and displaying the information
        returned online by Google Trends is provided. Trends (number of
        hits) over the time as well as geographic representation of the
        results can be displayed.
A collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.
Creates presentation-ready tables summarizing data sets,
    regression models, and more. The code to create the tables is concise
    and highly customizable. Data frames can be summarized with any
    function, e.g. mean(), median(), even user-written functions.
    Regression models are summarized and include the reference rows for
    categorical variables. Common regression models, such as logistic
    regression and Cox proportional hazards regression, are automatically
    identified and the tables are pre-filled with appropriate column
    headers.
Quantitative trait loci mapping and genome wide association analysis are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) <doi:10.1007/s00122-011-1631-7> and Gusev A. (2016) <doi:10.1038/ng.3506> to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect. F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic. The best linear model can be obtained by stepwise regression analysis.
Datos de nombres inscritos en Chile
    entre 1920 y 2021, de acuerdo al Servicio de Registro Civil.
    English: Chilean baby names registered from 1920 to 2021
    by the Civil Registry Service.
Access to 'The Guardian' newspaper's open API
  <https://open-platform.theguardian.com/>, containing all articles published 
  in 'The Guardian' from 1999 to the present, including article text, metadata,
  tags and contributor information. An API key and registration is required.
Provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.
Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and 
	graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
	development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.
Real-time quantitative polymerase chain reaction (qPCR) data
    by Guescini et al. (2008) <doi:10.1186/1471-2105-9-326> in tidy format.
    This package provides two data sets where the amplification efficiency has
    been modulated: either by changing the amplification mix concentration, or
    by increasing the concentration of IgG, a PCR inhibitor.
    Original raw data files:
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/MediaObjects/12859_2008_2311_MOESM1_ESM.xls> and
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/MediaObjects/12859_2008_2311_MOESM5_ESM.xls>.
Provides tools to adjust estimates of learning for guessing-related 
    bias in educational and survey research. Implements standard guessing 
    correction methods and a sophisticated latent class model that leverages 
    informative pre-post test transitions to account for guessing behavior. 
    The package helps researchers obtain more accurate estimates of actual 
    learning when respondents may guess on closed-ended knowledge items. 
    For theoretical background and empirical validation, see Cor and Sood (2018) 
    <https://gsood.com/research/papers/guess.pdf>.
We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.
Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.
Companion package for the manual
  'guide-R : Guide pour l’analyse de données d’enquêtes avec R' available at
  <https://larmarange.github.io/guide-R/>. 'guideR' implements miscellaneous
  functions introduced in 'guide-R' to facilitate statistical analysis and
  manipulation of survey data.
'Guild AI' is an open-source tool for managing machine learning
    experiments. It's for scientists, engineers, and researchers who want to
    run scripts, compare results, measure progress, and automate machine
    learning workflow. 'Guild AI' is a light weight, external tool that runs
    locally. It works with any framework, doesn't require any changes to
    your code, or access to any web services. Users can easily record
    experiment metadata, track model changes, manage experiment artifacts,
    tune hyperparameters, and share results. 'Guild AI' combines features
    from 'Git', 'SQLite', and 'Make' to provide a lab notebook for machine
    learning.
A collection of sampling formulas for the unified neutral
    model of biogeography and biodiversity. Alongside the sampling
    formulas, it includes methods to perform maximum likelihood
    optimization of the sampling formulas, methods to generate data given
    the neutral model, and methods to estimate the expected species
    abundance distribution. Sampling formulas included in the GUILDS
    package are the Etienne Sampling Formula (Etienne 2005), the guild
    sampling formula, where guilds are assumed to differ in dispersal
    ability (Janzen et al. 2015), and the guilds sampling formula
    conditioned on guild size (Janzen et al. 2015).
Create a user-friendly plotting GUI for 'R'.
    In addition, one purpose of creating the 'R' package is to facilitate third-party software to call 'R' for drawing, for example, 'Phoenix WinNonlin' software calls 'R' to draw the drug concentration versus time curve.
This 'R' package provides a single procedure guix.install(),
  which allows users to install 'R' packages via 'Guix' right from within
  their running 'R' session.  If the requested 'R' package does not exist
  in 'Guix' at this time, the package and all its missing dependencies
  will be imported recursively and the generated package definitions
  will be written to ~/.Rguix/packages.scm.  This record of imported
  packages can be used later to reproduce the environment, and to add
  the packages in question to a proper 'Guix' channel (or 'Guix' itself).
  guix.install() not only supports installing packages from CRAN, but
  also from Bioconductor or even arbitrary 'git' or 'mercurial'
  repositories, replacing the need for installation via 'devtools'.
Implements general unilateral loading  estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions.  A  unified  interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) <doi:10.1016/j.apm.2025.116280>.
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Uses jackknife and bootstrap methods to quantify the sampling uncertainty in goodness-of-fit statistics. Full details are in Clark et al. (2021), "The abuse of popular performance metrics in hydrologic modeling", Water Resources Research, <doi:10.1029/2020WR029001>.
A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances,  Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and  (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.
For plant physiologists, converts conductance (e.g. stomatal conductance) to different units: m/s, mol/m^2/s, and umol/m^2/s/Pa.
Statistical analysis of monthly background checks of gun purchases for the New York Times 
 story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at 
 <https://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html> 
 is provided.
Provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.
Download and process public domain works in the Project
    Gutenberg collection <https://www.gutenberg.org/>. Includes metadata
    for all Project Gutenberg works, so that they can be searched and
    retrieved.
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).
Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.
Several tools for Global Value Chain ('GVC') analysis are
    implemented.
Provides tools for decomposing Global Value Chain (GVC) participation 
    and value-added trade. It implements the frameworks proposed by Borin and 
    Mancini (2023) 10.1080/09535314.2022.2153221> for source-based and 
    sink-based decompositions, and by Borin, Mancini, and Taglioni (2025) 
    10.1093/wber/lhaf017> for tripartite and output-based GVC measures.
Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
The 'Geocoordinate Validation Service' (GVS) runs checks of coordinates in latitude/longitude format. It returns annotated coordinates with additional flags and metadata that can be used in data cleaning.  Additionally, the package has functions related to attribution and metadata information. More information can be found at <https://github.com/ojalaquellueva/gvs/tree/master/api>.
Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive 'Tableau'-like interface, leveraging the 'graphic-walker' JavaScript library and the 'htmlwidgets' package.
Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using 'ggplot2'.
Provides a reproducible pipeline to conduct genome-wide association studies
    (GWAS) and extract single-nucleotide polymorphisms (SNPs) for a human trait or
    disease. Given aggregated GWAS dataset(s) and a user-defined significance threshold,
    the package retrieves significant SNPs from the GWAS Catalog using supported trait
    identifiers, annotates their gene context, and can write a
    harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data
    (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered
    regularly interspaced short palindromic repeats (CRISPR) guide design. The existing
    efo_id argument is retained for backward compatibility. The package prepares
    computational artifacts for downstream workflows; it does not perform biological
    causality testing, clinical interpretation, therapeutic design, or wet-lab validation.
    For details on the resources and methods see:
    Buniello et al. (2019) <doi:10.1093/nar/gky1120>;
    Sollis et al. (2023) <doi:10.1093/nar/gkac1010>;
    Jinek et al. (2012) <doi:10.1126/science.1225829>.
Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.
Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
  as typically obtained in a Genome Wide Association Study (GWAS).
Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).
Performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).
When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report.
'GWAS' R 'API' Data Download.
    This package provides easy access to the 'NHGRI'-'EBI' 'GWAS' Catalog data by
    accessing the 'REST' 'API' <https://www.ebi.ac.uk/gwas/rest/docs/api/>.
Fit a regression model for when the response variable is presented as a ratio or proportion. This adjustment can occur globally, with the same estimate for the entire study space, or locally, where a beta regression model is fitted for each region, considering only influential locations for that area. Da Silva, A. R. and Lima, A. O. (2017) <doi:10.1016/j.spasta.2017.07.011>.
Application of multi-site models for daily precipitation and temperature data. 
  This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland.
  It applies fitting procedures and provides weather generators described in the following references:
  - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.5194/hess-22-655-2018>.
  - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.1007/s00704-018-2404-x>.
Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.
Methods and tools for the analysis of Genome Wide 
    Identity-by-Descent ('gwid') mapping data, focusing on testing whether there 
    is a higher occurrence of Identity-By-Descent (IBD) segments around potential causal variants 
    in cases compared to controls, which is crucial for identifying rare 
    variants. To enhance its analytical power, 'gwid' incorporates a Sliding 
    Window Approach, allowing for the detection and analysis of signals from 
    multiple Single Nucleotide Polymorphisms (SNPs).
Re-implementation of the 'gWidgets' API. The API is defined in this
    package. A second, toolkit-specific package is required to use it. At this point only 'gWidgets2tcltk' is viable.
Port of the 'gWidgets2' API for the 'tcltk' package.
Performs geographically weighted Lasso regressions. Find optimal bandwidth, fit a geographically weighted lasso or ridge regression, and make predictions.
    These methods are specially well suited for ecological inferences. Bandwidth selection algorithm is from A. Comber and P. Harris (2018) <doi:10.1007/s10109-018-0280-7>.
Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.
Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Implements a geographically weighted non-negative principal
    components analysis, which consists of the fusion of geographically
    weighted and sparse non-negative principal components analyses
    <doi:10.17608/k6.auckland.9850826.v1>.
Computes G-Wishart normalising constants through a Fourier approach. Either exact analytical results, numerical integration or Monte Carlo estimation are employed. Details at C. Wong, G. Moffa and J. Kuipers (2024), <doi:10.48550/arXiv.2404.06803>. Also includes approximations of the ratio of normalising constants, see details at C. Wong, G. Moffa and J. Kuipers (2025), <doi:10.48550/arXiv.2503.13046>.
Implements a geographically weighted partial correlation which is 
    an extension from gwss() function in the 'GWmodel' package 
    (Percival and Tsutsumida (2017) <doi:10.1553/giscience2017_01_s36>).
An interactive mapping tool for geographically weighted correlation and partial correlation. Geographically weighted partial correlation coefficients are calculated following (Percival and Tsutsumida, 2017)<doi:10.1553/giscience2017_01_s36> and are described in greater detail in (Tsutsumida et al., 2019)<doi:10.5194/ica-abs-1-372-2019> and (Percival et al., 2021)<arXiv:2101.03491>.
A modern, first implementation of Geographically Weighted Panel Regression (GWPR) for spatial panel data. The package provides a unified public API supporting Gaussian and binomial family models, within/pooling/random panel effects, three bandwidth search strategies (grid, Stochastic Gradient Descent, random), five kernel functions, and optional parallel execution via the 'future' framework. Diagnostic tools include spatial Moran's I, local F-test, Hausman test, and Lagrange Multiplier test.
Fits Weighted Quantile Sum (WQS) regression  (Carrico et al. (2014) <doi:10.1007/s13253-014-0180-3>), a random subset implementation of WQS (Curtin et al. (2019) <doi:10.1080/03610918.2019.1577971>), a repeated holdout validation WQS (Tanner et al. (2019) <doi:10.1016/j.mex.2019.11.008>) and a WQS with 2 indices (Renzetti et al. (2023) <doi:10.3389/fpubh.2023.1289579>) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.
It implements a hybrid spatial model for improved spatial prediction by combining the variable selection capability of
             LASSO (Least Absolute Shrinkage and Selection Operator) with the Geographically Weighted Regression (GWR) model that 
             captures the spatially varying relationship efficiently. For method details see, Wheeler, D.C.(2009).<DOI:10.1068/a40256>.
             The developed hybrid model efficiently selects the relevant variables by using LASSO as the first step; these selected variables
             are then incorporated into the GWR framework, allowing the estimation of spatially varying regression coefficients at unknown locations
             and finally predicting the values of the response variable at unknown test locations while taking into account the spatial heterogeneity of the data. 
             Integrating the LASSO and GWR models enhances prediction accuracy by considering spatial heterogeneity and capturing the local relationships between 
             the predictors and the response variable. The developed hybrid spatial model can be useful for spatial modeling, especially in scenarios involving complex 
             spatial patterns and large datasets with multiple predictor variables.
Statistical functions to fit, validate and describe a Generalized
    Waring Regression Model (GWRM).
Computes the sample probability value (p-value)
    for the estimated coefficient from a standard
    genome-wide univariate regression.  It computes the exact
    finite-sample p-value under the assumption that the measured
    phenotype (the dependent variable in the regression) has
    a known Bernoulli-normal mixture distribution.
    Finite-sample genome-wide regression p-values (Gwrpv) with
    a non-normally distributed phenotype (Gregory Connor and
    Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).
Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.
Shiny application for the analysis of groundwater
    monitoring data, designed to work with simple time-series data for
    solute concentration and ground water elevation, but can also plot
    non-aqueous phase liquid (NAPL) thickness if required. Also provides
    the import of a site basemap in GIS shapefile format.
The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.
Fits a geographically weighted regression model
	using zero inflated probability distributions. Has the zero 
	inflated negative binomial distribution (zinb) as default, 
	but also accepts the zero inflated Poisson (zip), negative
	binomial (negbin) and Poisson distributions. Can also fit 
	the global versions of each regression model.
	Da Silva, A. R. & De Sousa, M. D. R. (2023). "Geographically weighted zero-inflated negative binomial regression: A general case for count data", Spatial Statistics <doi:10.1016/j.spasta.2023.100790>.
	Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). "Geographically weighted regression: a method for exploring spatial nonstationarity", Geographical Analysis, <doi:10.1111/j.1538-4632.1996.tb00936.x>.
	Yau, K. K. W., Wang, K., & Lee, A. H. (2003). "Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros", Biometrical Journal, <doi:10.1002/bimj.200390024>.
A novel PRS model is introduced to enhance the prediction accuracy by utilising GxE effects. This package performs Genome Wide Association Studies (GWAS) and Genome Wide Environment Interaction Studies (GWEIS) using a discovery dataset. The package has the ability to obtain polygenic risk scores (PRSs) for a target sample. Finally it predicts the risk values of each individual in the target sample. Users have the choice of using existing models (Li et al., 2015) <doi:10.1093/annonc/mdu565>, (Pandis et al., 2013) <doi:10.1093/ejo/cjt054>, (Peyrot et al., 2018) <doi:10.1016/j.biopsych.2017.09.009> and (Song et al., 2022) <doi:10.1038/s41467-022-32407-9>, as well as newly proposed models for genomic risk prediction (refer to the URL for more details).
Tools to run genome-wide association study (GWAS) and
  genome-wide by environment interaction study (GWEIS) scans using the genetic
  data stored in a binary dosage file. The user provides a data frame with
  the subject's covariate data and the information about the binary dosage
  file returned by the BinaryDosage::getbdinfo() routine.
OpenAI Gym is a open-source Python toolkit for developing and comparing
    reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API,
    and enables access to an ever-growing variety of environments.
    For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>.
    For more details on the OpenAI Gym API specification, please see here:
    <https://github.com/openai/gym-http-api>.
We provide a toolbox to conduct a Bayesian meta-analysis for estimating the current expansion rate of the Universe, called the Hubble constant H0, via time delay cosmography. The input data are Fermat potential difference and time delay estimates. For a robust inference, we assume a Student's t error for these inputs. Given these inputs, the meta-analysis produces posterior samples of the model parameters including the Hubble constant via Metropolis-Hastings within Gibbs. The package provides an option to implement repelling-attracting Metropolis-Hastings within Gibbs in a case where the parameter space has multiple modes.
R interface for 'H2O', the scalable open source machine learning
    platform that offers parallelized implementations of many supervised and
    unsupervised machine learning algorithms such as Generalized Linear
    Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests,
    Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes,
    Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection,
    Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Interface to 'H2O4GPU' <https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.
Enhances the H2O platform by providing tools for detailed evaluation of machine learning models. It includes functions for bootstrapped performance evaluation, extended F-score calculations, and various other metrics, aimed at improving model assessment.
Implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.
Provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile 'h3-js' <https://github.com/uber/h3-js> and 'V8' <https://github.com/jeroen/v8>.
'H3' is a hexagonal hierarchical spatial index developed by 'Uber' <https://h3geo.org/>.
  This package exposes the source code of 'H3' (written in 'C') to routines that are callable through 'R'.
A dependency free interface to the H3 geospatial indexing system utilizing the Rust library 'h3o' <https://github.com/HydroniumLabs/h3o> via the 'extendr' library <https://github.com/extendr/extendr>.
Provides access to Uber's 'H3' geospatial indexing system via 'h3lib' 
  <https://CRAN.R-project.org/package=h3lib>. 'h3r' is designed to mimic the 'H3' 
  Application Programming Interface (API) <https://h3geo.org/docs/api/indexing/>, 
  so that any function in the API is also available in 'h3r'.
Provides tools for species distribution modeling using H3
  hexagonal grids (Uber Technologies Inc., 2022, <https://h3geo.org>).
  Facilitates retrieval of species occurrence records, generation of H3 grids,
  computation of landscape metrics, and preparation of spatial data for modern
  species distribution models workflows. Designed for biodiversity and landscape ecology research.
A user-friendly interface for the Hierarchical Data Format 5 
    ('HDF5') library designed to "just work." It bundles the necessary 
    system libraries to ensure easy installation on all platforms. Features 
    smart defaults that automatically map R objects (vectors, matrices, 
    data frames) to efficient 'HDF5' types, removing the need to manage 
    low-level details like dataspaces or property lists. Uses the 'HDF5' 
    library developed by The HDF Group <https://www.hdfgroup.org/>.
A Haar-Fisz algorithm for Poisson intensity estimation.
	Will denoise Poisson distributed sequences where
        underlying intensity is not constant. Uses the multiscale
        variance-stabilization method called the Haar-Fisz transform.
        Contains functions to carry out the forward and inverse
        Haar-Fisz transform and denoising on near-Gaussian sequences.
        Can also carry out cycle-spinning.
	Main reference: Fryzlewicz, P. and Nason, G.P. (2004)
	"A Haar-Fisz algorithm for Poisson intensity estimation."
        Journal of Computational and Graphical Statistics,
        13, 621-638. <doi:10.1198/106186004X2697>.
Simple tools for converting columns to new data types. Intuitive functions for columns with missing values.
A collection of functions for sampling and simulating 3D surfaces and objects 
    and estimating metrics like rugosity, fractal dimension, convexity, sphericity, circularity, 
    second moments of area and volume, and more.
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Use the Official Hacker News API through R. Retrieve
    posts, articles and other items in form of convenient R objects.
Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.
Generates Hadamard matrices using different construction methods. For those who want to generate Hadamard matrix, a generic function, Hadamard_matrix() is provided. For those who want to generate Hadamard matrix using a particular method, separate functions are available. See Horadam (2007, ISBN:9780691119212) Hadamard Matrices and their applications, Princeton University Press for more information on Hadamard Matrices.
Functions for processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by 
             Mass Spectrometry experiments (HDX-MS) (<doi:10.1093/bioinformatics/btaa587>). 'HaDeX' introduces a new 
             standardized and reproducible workflow for the analysis of the HDX-MS data, including novel 
             uncertainty intervals. Additionally, it covers data exploration, quality control and generation of 
             publication-quality figures. All functionalities are also available in the in-built 'Shiny' app.
Processing, analysis and visualization of Hydrogen Deuterium eXchange monitored by 
             Mass Spectrometry experiments (HDX-MS). 'HaDeX2' introduces a new 
             standardized and reproducible workflow for the analysis of the HDX-MS data, including uncertainty propagation, data aggregation and visualization on 3D structure.
             Additionally, it covers data exploration, quality control and generation of publication-quality 
             figures. All functionalities are also available in the accompanying 'shiny' app.
Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices.
Analysis of plant pathogen pathotype survey data.  Functions
    provided calculate distribution of susceptibilities, distribution of
    complexities with statistics, pathotype frequency distribution, as
    well as diversity indices for pathotypes.  This package is meant to be
    a direct replacement for Herrmann, Löwer and Schachtel's (1999)
    <doi:10.1046/j.1365-3059.1999.00325.x> Habgood-Gilmour Spreadsheet,
    'HaGiS', previously used for pathotype analysis.
Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at <https://github.com/kharchenkolab/hahmmr/>.
Initializes a class that obtains API credentials and provides
    a method to use those credentials to make GET requests to the 'Hakai'
    API server. Usage instructions are documented at
    <https://hakaiinstitute.github.io/hakai-api/>.
A scalable implementation of the highly adaptive lasso algorithm,
  including routines for constructing sparse matrices of basis functions of the
  observed data, as well as a custom implementation of Lasso regression tailored
  to enhance efficiency when the matrix of predictors is composed exclusively of
  indicator functions. For ease of use and increased flexibility, the Lasso
  fitting routines invoke code from the 'glmnet' package by default. The highly
  adaptive lasso was first formulated and described by MJ van der Laan (2017)
  <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance
  given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This
  implementation of the highly adaptive lasso algorithm was described by Hejazi,
  Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.
An algorithm for flexible conditional density estimation based on
    application of pooled hazard regression to an artificial repeated measures
    dataset constructed by discretizing the support of the outcome variable. To
    facilitate flexible estimation of the conditional density, the highly
    adaptive lasso, a non-parametric regression function shown to estimate
    cadlag (RCLL) functions at a suitably fast convergence rate, is used. The
    use of pooled hazards regression for conditional density estimation as
    implemented here was first described for by Díaz and van der Laan (2011)
    <doi:10.2202/1557-4679.1356>. Building on the conditional density estimation
    utilities, non-parametric inverse probability weighted (IPW) estimators of
    the causal effects of additive modified treatment policies are implemented,
    using conditional density estimation to estimate the generalized propensity
    score. Non-parametric IPW estimators based on this can be coupled with
    undersmoothing of the generalized propensity score estimator to attain the
    semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan
    <doi:10.48550/arXiv.2205.05777>).
Build better balance in causal inference models. 'halfmoon'
    helps you assess propensity score models for balance between groups
    using metrics like standardized mean differences and visualization
    techniques like mirrored histograms. 'halfmoon' supports both
    weighting and matching techniques.
Conducts analyses for healthcare program evaluations or intervention 
    studies. Calculates regression analyses for standard ordinary least squares 
    (OLS or linear) or logistic models. Performs regression models used for 
    causal modeling such as differences-in-differences (DID) and interrupted 
    time series (ITS) models. Provides limited interpretations of model 
    results and a ranking of variable importance in models. Performs 
    propensity score models, top-coding of model outcome variables, and 
    can return new data with the newly formed variables. Conducts Bayesian 
    analysis summaries and graphs, decision curve analysis, and produces some 
    Shewhart control charts. Also performs Cronbach's  alpha for various scale 
    items (e.g., survey questions). See Github URL for examples in the README 
    file. For more details on the statistical methods, see 
    Allen & Yen (1979, ISBN:0-8185-0283-5), 
    Angrist & Pischke (2009, ISBN:9780691120355), 
    Cohen (1988, ISBN:0-8058-0283-5),
    Gebski (2012) <doi:10.1017/S0950268812000179>,
    Gelman & Goodrich (2019) <doi:10.1080/00031305.2018.1549100>,
    Harrell (2016, ISBN:978-3-319-19424-0), 
    Kline (1999, ISBN:9780415211581),  
    Kruschke (2014, ISBN:9780124058880), 
    Linden (2015) <doi:10.1177/1536867X1501500208>,
    Merlo (2006) <doi:10.1136/jech.2004.029454>,
    Muthen & Satorra (1995) <doi:10.2307/271070>, 
    Rabe-Hesketh & Skrondal (2008, ISBN:978-1-59718-040-5),
    Ryan (2011, ISBN:978-0-470-59074-4), and
    Vickers & Elkin (2006) <doi:10.1177/0272989X06295361>.
Various functions and algorithms are provided here for solving optimal matching 
  tasks in the context of preclinical cancer studies. Further, various helper and plotting 
  functions are provided for unsupervised and supervised machine learning as well as longitudinal 
  mixed-effects modeling of tumor growth response patterns.
The goal of 'HAMMER' is to provide factor analytic representation 
  learningand associated determinacy metrics for very-high-dimensional data. 
  It projects high-dimensional data onto low-dimensional generative latent 
  sources and assesses the uncertainty in the projection. The projection is 
  distribution-free, scale-equivariant, and efficient.
Shiny-App that allows to annotate vectors of texts to predefined categories by hand.
Converts among many citation formats, including 'BibTeX',
    'Citeproc', 'Codemeta', 'RDF XML', 'RIS', 'Schema.org', and
    'Citation File Format'. A low level 'R6' class is provided, as well
    as stand-alone functions for each citation format for both read
    and write.
An S4 implementation of Eq. (3) and Eq. (7) by
    David J. Hand and Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.
Perform statistical writership analysis of scanned handwritten documents.
             Webpage provided at: <https://github.com/CSAFE-ISU/handwriter>.
Perform statistical writership analysis of scanned handwritten documents with a 'shiny' app for 'handwriter'.
Perform forensic handwriting analysis of two scanned handwritten documents. This package implements the statistical method described by Madeline Johnson and Danica Ommen (2021) <doi:10.1002/sam.11566>. Similarity measures and a random forest produce a score-based likelihood ratio that quantifies the strength of the evidence in favor of the documents being written by the same writer or different writers.
Several handy plots for quickly looking at the relationship between two numeric vectors of equal length. Quickly visualize scatter plots, residual plots, qq-plots, box plots, confidence intervals, and prediction intervals.
Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, <doi:10.1073/pnas.79.8.2554>). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, <doi:10.48550/ARXIV.1606.01164>). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with 'OpenMP' is used if available during compilation.
The haversine is a function used to calculate the distance between a pair of 
    latitude and longitude points while accounting for the assumption that the points 
    are on a spherical globe. This package provides a fast, dataframe compatible, 
    haversine function. For the first publication on the haversine calculation see 
    Joseph de Mendoza y Ríos (1795) <https://books.google.cat/books?id=030t0OqlX2AC> (In Spanish).
A simple and time saving multiple linear regression function (OLS) with interpretation, optional bootstrapping, effect size calculation and all tested requirements.
Convert Chinese characters into Hanyu Pinyin (the official 
    romanization system for Standard Chinese) with support for tones, 
    toneless output, initials, URL slugs, and valid R variable names. 
    The package was inspired by the now-orphaned CRAN package 'pinyin' 
    (archived in April 2026 after the maintainer became unreachable). 
    'hanyupinyin' is a ground-up rewrite using the authoritative Unicode 
    Unihan database, a vectorized engine, and modern R practices. 
    Dictionary data are derived from the Unicode Unihan Database 
    (Unicode Consortium, 2025) <https://www.unicode.org/reports/tr38/>.
The following R functions are used for inference of trait
        associations with haplotypes and other covariates in
        generalized linear models.  The functions are developed
        primarily for data collected in cohort or cross-sectional
        studies. They can accommodate uncertain haplotype phase and
        handle missing genotypes at some SNPs.
Inference of chromosome-length haplotypes using a few haploid 
	gametes of an individual. The gamete genotype data may be generated from various platforms 
	including genotyping arrays and sequencing even with low-coverage. Hapi simply takes 
	genotype data of known hetSNPs in single gamete cells as input and report the high-resolution 
	haplotypes as well as confidence of each phased hetSNPs. The package also includes a module 
	allowing downstream analyses and visualization of identified crossovers in the gametes.
Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in "Gjessing HK and Lie RT. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396".
Routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.
Used for predicting a genotype's allelic state at a specific locus/QTL/gene. This is accomplished by using both a genotype matrix and a separate file which has categorizations about loci/QTL/genes of interest for the individuals in the genotypic matrix. A training population can be created from a panel of individuals who have been previously screened for specific loci/QTL/genes, and this previous screening could be summarized into a category. Using the categorization of individuals which have been genotyped using a genome wide marker platform, a model can be trained to predict what category (haplotype) an individual belongs in based on their genetic sequence in the region associated with the locus/QTL/gene. These trained models can then be used to predict the haplotype of a locus/QTL/gene for individuals which have been genotyped with a genome wide platform yet not genotyped for the specific locus/QTL/gene. This package is based off work done by Winn et al 2021. For more specific information on this method, refer to <doi:10.1007/s00122-022-04178-w>.
Provides functions to estimate population genetics summary statistics from haplo-diploid systems, where one sex is haploid and the other diploid (e.g. Hymenoptera insects). It implements a theoretical model assuming equal sex ratio, random mating, no selection, no mutation, and no gene flow, deriving expected genotype frequencies for both sexes under these equilibrium conditions. The package includes windowed calculations (operating over genomic sliding windows from VCF input) for allele and genotype frequencies, the inbreeding coefficient (Fis), pairwise Fst, Nei's H (gene diversity), Watterson's Theta, and sex-specific reference allele frequencies. Most statistics are agnostic to ploidy, allowing the package to be applied to both strictly haplo-diploid and fully diploid systems.
Simulate haplotypes through meioses. Allows specification
        of population parameters.
Function to  identify  haplotypes
  within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL. This function groups
  together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, haplotyper groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, haplotyper calculates such
  probability from relative frequencies.
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel-coding method (only simple indel-coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapsing identical DNA sequences into haplotypes or inferring haplotypes using user-provided absolute pairwise character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks.
A local haplotyping tool for use in trait association and trait prediction analyses pipelines.
    'HaploVar' enables users take single nucleotide polymorphisms (SNPs) (in VCF 
    format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the 
    output to be compatible with a wide range of trait association and trait prediction tools. 
    The local haplotypes are calculated from the LD matrix using a clustering algorithm called 
    density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) 
    <ISBN: 1577350049>.
Automatic open data acquisition from resources of IGN
    ('Institut National de Information Geographique et forestiere')
    (<https://www.ign.fr/>). Available datasets include various types of
    raster and vector data, such as digital elevation models, state
    borders, spatial databases, cadastral parcels, and more. 'happign' also 
    provide access to API Carto (<https://apicarto.ign.fr/api/doc/>).
By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.
Building modeling packages is hard. A large amount of effort
    generally goes into providing an implementation for a new method that
    is efficient, fast, and correct, but often less emphasis is put on the
    user interface. A good interface requires specialized knowledge about
    S3 methods and formulas, which the average package developer might not
    have. The goal of 'hardhat' is to reduce the burden around building
    new modeling packages by providing functionality for preprocessing,
    predicting, and validating input.
Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908;  Weinberg, 1908) for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included (Graffelman & Weir, 2016) <doi:10.1038/hdy.2016.20>, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots. The functionality of the package is explained in detail in a related JSS paper <doi:10.18637/jss.v064.i03>.
The harmonic mean p-value (HMP) test combines p-values and corrects for multiple testing while controlling the strong-sense family-wise error rate. It is more powerful than common alternatives including Bonferroni and Simes procedures when combining large proportions of all the p-values, at the cost of slightly lower power when combining small proportions of all the p-values. It is more stringent than controlling the false discovery rate, and possesses theoretical robustness to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is certain to be significant and conversely when the superset is non-significant, the constituent tests are certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem. For detailed examples type vignette("harmonicmeanp") after installation. Version 3.0 addresses errors in versions 1.0 and 2.0 that led function p.hmp to control the familywise error rate only in the weak sense, rather than the strong sense as intended.
Several functions are provided to harmonize CN8 (Combined Nomenclature 
	8 digits) and PC8 (Production Communautaire 8 digits) product codes over 
	time and the classification systems HS6 and BEC. Harmonization of CN8 
	codes are possible by default from 1995 to 2022 and of PC8 from 2001 to 
	2021, respectively.
Implementation of the Harmony algorithm for single cell integration, described in Patikas, Yao, et al. <doi:10.64898/2026.03.16.711825>. Package includes a standalone Harmony function and interfaces to external frameworks.
'Harmony' is a tool using AI which allows you to compare items from questionnaires and identify similar content. You can try 'Harmony' at <https://harmonydata.ac.uk/app/> and you can read our blog at <https://harmonydata.ac.uk/blog/> or at <https://fastdatascience.com/how-does-harmony-work/>. Documentation at <https://harmonydata.ac.uk/harmony-r-released/>.
A bootstrapper that launches a command-line coding agent of the
    user's choice in a terminal tab pre-configured for a professional R role.
    Each role is described by a curated harness: a subset of community skills,
    a system prompt, a folder layout, and quality gates. The package does not
    run an agent loop and does not call a language model; it discovers the
    chosen coder binary, generates its configuration, links the curated skills,
    and opens the terminal. Code written by the agent is run manually by the
    user, by design, so that every generated script passes through a human
    audit gate before execution.
Provides tools for processing and analyzing .har and .sl4 files, making it easier 
    for 'GEMPACK' users and 'GTAP' researchers to handle large economic datasets. 
    It simplifies the management of multiple experiment results, enabling faster 
    and more efficient comparisons without complexity. Users can extract, restructure, 
    and merge data seamlessly, ensuring compatibility across different tools. 
    The processed data can be exported and used in 'R', 'Stata', 'Python', 'Julia', or 
    any software that supports Text, CSV, or 'Excel' formats.
HAR files are generated and consumed by 'GEMPACK' applications. This package reads/writes 'HAR' files (and 'SL4' files) directly using basic R functions.
Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr'
    implements some function to manipulate distance matrices and phylogenetic trees
    to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse'
    tools.
Implementation of characteristic palettes inspired in the Wizarding World and the Harry Potter movie franchise.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
Implements a data structure similar to hashes in Perl and dictionaries in Python but with a purposefully R flavor. For objects of appreciable size, access using hashes outperforms native named lists and vectors.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
A fast, vectorized hashmap that is built on top of 'C++' std::unordered_map <https://en.cppreference.com/w/cpp/container/unordered_map.html>.
    The map can hold any 'R' object as key / value as long as it is serializable and supports
    vectorized insertion, lookup, and deletion.
Apply an adaptation of the SuperFastHash algorithm to any R
    object. Hash whole R objects or, for vectors or lists, hash R objects to obtain
    a set of hash values that is stored in a structure equivalent to the input. See
    <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash
    algorithm.
The Ljung-Box test is one of the most important tests for time series diagnostics and model selection. 
    The  Hassani SACF (Sum of the Sample Autocorrelation Function) Theorem , however, indicates that the sum of sample autocorrelation function is always fix for 
    any stationary time series with arbitrary length. This package confirms for sensitivity of the Ljung-Box test to 
    the number of lags involved in the test and therefore it should be used with extra caution.
    The Hassani SACF Theorem has been described in : Hassani, Yeganegi and M. R. (2019) <doi:10.1016/j.physa.2018.12.028>.
A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) 
  test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve
  two distinct purposes. Initially, the test seeks to determine whether there exists a 
  statistically significant difference between the distribution of forecast errors, and 
  secondly it exploits the principles of stochastic dominance to determine whether the 
  forecasts with the lower error also reports a stochastically smaller error than forecasts 
  from a competing model, and thereby enables distinguishing between the predictive accuracy 
  of forecasts. KSPA test has been described in : Hassani and Silva (2015) <doi:10.3390/econometrics3030590>.
Returns a Hasse diagram of the layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918173> or the restricted layout structure (Bate and Chatfield (2016)) <doi:10.1080/00224065.2016.11918174> of an experimental design.
Predict hatch and emergence timing for a wide range of wild
    fishes using the effective value framework (Sparks et al., (2019)
    <DOI:10.1139/cjfas-2017-0468>). 'hatchR' offers users access to
    established phenological models and the flexibility to incorporate
    custom parameterizations using external datasets.
Implements the Hatemi-J (2008) cointegration test which allows 
    for two unknown structural breaks (regime shifts) in the cointegrating 
    relationship. The test provides three test statistics: ADF* (Augmented 
    Dickey-Fuller), Zt* (Phillips-Perron Z_t), and Za* (Phillips-Perron Z_alpha), 
    along with endogenously determined break dates. Critical values are based 
    on simulations from Hatemi-J (2008) <doi:10.1007/s00181-007-0175-9>.
Estimates survey indicators using complex survey designs.
    Supports mean, proportion, and ratio estimation with multi-stage stratified sampling,
    weights, and finite population correction. The output is designed to be comparable
    to results from 'SPSS' (Statistical Package for the Social Sciences) Complex Samples procedures.
Computes the test statistic and p-values of the one-sample and two-sample Hausdorff (H) goodness-of-fit tests. The H statistic measures the Hausdorff distance under the Chebyshev (l-infinity) metric, between the two cumulative distribution functions (cdfs) underlying the corresponding one-sample and two-sample null hypothesis. It coincides to the side length of the largest axis-aligned square (hypercube) that can be inscribed between the two cdfs. The following cases are covered: (i) one-sample, univariate; (ii) two-sample univariate; and (iii) two-sample bivariate.  Exact one-sample p-values are computed in O(n^2 log n) time via the 'Exact-KS-FFT' method of Dimitrova, Kaishev, and Tan (2020) <doi:10.18637/jss.v095.i10>; two-sample p-values are obtained by permutation. A key advantage of the H test is that its sensitivity can be directed towards the left tail, body, or right tail of the distribution by tuning a scale parameter sigma, and therefore maximizing its power which as shown numerically is significantly higher than the power of the classical tests such as the Kolmogorov-Smirnov,  Cramer-von Mises, and Anderson-Darling test, especially when the right tail of the distribution is targeted. The sensitivity of the test (left tail, body, or right tail) is governed by two parameters psi1 and psi2, whose values needs to be input. Then the optimal value of the scale parameter sigma is automatically computed.
Plot an 'R' package's recursive dependency graph and tabulate the 
    number of unique downstream dependencies added by top-level dependencies. 
    This helps 'R' package developers identify which of their declared 
    dependencies add the most downstream dependencies in order to prioritize
    them for removal if needed. Uses graph stress minimization adapted from 
    Schoch (2023) <doi:10.21105/joss.05238> and originally reported in Gansner 
    et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.
Import foreign statistical formats into R via the embedded
    'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.
The conditional treatment effect for competing risks data in observational studies is estimated. While it is described as a constant difference between the hazard functions given the covariates, we do not assume specific functional forms for the covariates. Rava, D. and Xu, R. (2021) <arXiv:2112.09535>.
Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.
Implements hierarchically regularized entropy balancing proposed by Xu and Yang (2022) <doi:10.1017/pan.2022.12>. The method adjusts the covariate distributions of the control group to match those of the treatment group. 'hbal' automatically expands the covariate space to include higher order terms and uses cross-validation to select variable penalties for the balancing conditions.
Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte
    Carlo via 'Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological 
    positions of survey respondents and political actors on a common scale using positional survey 
    data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform 
    other versions both in terms of yielding meaningful posterior distributions for respondent 
    positions and in terms of recovering true respondent positions in simulations. The package 
    contains functions for preparing data, fitting models, extracting estimates, plotting key 
    results, and comparing models using cross-validation. The original version of the default 
    model is described in Bølstad (2024) <doi:10.1017/pan.2023.18>.
Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.
Contains functions for fitting hierarchical versions of
        EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
        our gamma signal detection model to recognition memory
        confidence-ratings data.
Functions to compute small area estimates based on a basic area or
    unit-level model. The model is fit using restricted maximum likelihood, or
    in a hierarchical Bayesian way. In the latter case numerical integration is
    used to average over the posterior density for the between-area variance.
    The output includes the model fit, small area estimates and corresponding
    mean squared errors, as well as some model selection measures. Additional functions
    provide means to compute aggregate estimates and mean squared errors, to minimally
    adjust the small area estimates to benchmarks at a higher aggregation
    level, and to graphically compare different sets of small area estimates.
Fits area-level Hierarchical Bayesian Small Area Estimation
    models. The methodological foundation follows the standard area-level
    Small Area Estimation literature, primarily Rao and Molina (2015,
    ISBN: 9781118735787) <doi:10.1002/9781118735855>, while computational
    implementation is adapted to the parameterisation and
    prior-specification conventions of the 'brms' package
    <doi:10.18637/jss.v080.i01>, which targets the
    Stan back-end. Supports a principled Bayesian workflow
    <doi:10.48550/arXiv.2011.01808>, with prior
    predictive checks, convergence diagnostics, model comparison,
    spatial random effects, custom distributions, missing-data
    handling, and a bilingual 'shiny' application for non-programmer
    analysts.
Fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.
Texts for H.C. Andersens fairy tales, ready for
    text analysis. Fairy tales in German, Danish, English, Spanish and
    French.
Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at <https://raw.githubusercontent.com/prdm0/hcci/master/references/dissertacao_mestrado.pdf>. The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) <doi:10.1080/00949650801935327>, while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) <doi:10.2307/2336685>. The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) <doi:10.1214/aos/1176350142>. This bootstrap scheme is known as weighted or wild bootstrap.
Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) <arXiv:1810.01509>. The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities.
Simulate and analyze hierarchical composite endpoints. Includes implementation for the kidney hierarchical composite endpoint as defined in Heerspink HL et al (2023) “Development and validation of a new hierarchical composite end point for clinical trials of kidney disease progression” (Journal of the American Society of Nephrology 34 (2): 2025–2038, <doi:10.1681/ASN.0000000000000243>). Win odds, also called Wilcoxon-Mann-Whitney or success odds, is the main analysis method. Other win statistics (win probability, win ratio, net benefit) are also implemented in the univariate case, provided there is no censoring. The win probability analysis is based on the Brunner-Munzel test and uses the DeLong-DeLong-Clarke-Pearson variance estimator, as described by Brunner and Konietschke (2025) in “An unbiased rank-based estimator of the Mann–Whitney variance including the case of ties” (Statistical Papers 66 (1): 20, <doi:10.1007/s00362-024-01635-0>). Includes implementation of a new Wilson-type, compatible confidence interval for the win odds, as proposed by Schüürhuis, Konietschke, Brunner (2025) in “A new approach to the nonparametric Behrens–Fisher problem with compatible confidence intervals.” (Biometrical Journal 67 (6), <doi:10.1002/bimj.70096>). Stratification and covariate adjustment are performed based on the methodology presented by Koch GG et al. in “Issues for covariance analysis of dichotomous and ordered categorical data from randomized clinical trials and non-parametric strategies for addressing them” (Statistics in Medicine 17 (15-16): 1863–92). For a review, see Gasparyan SB et al (2021) “Adjusted win ratio with stratification: Calculation methods and interpretation” (Statistical Methods in Medical Research 30 (2): 580–611, <doi:10.1177/0962280220942558>).
By binding R functions and the 'Highmaps' <https://www.highcharts.com.cn/products/highmaps> chart library, 'hchinamap' package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan.
A collection of datasets of human-computer interaction (HCI) experiments. 
    Each dataset is from an HCI paper, with all fields described and the original publication 
    linked. All paper authors of included data have consented to the inclusion of their data 
    in this package. The datasets include data from a range of HCI studies, such as pointing 
    tasks, user experience ratings, and steering tasks. 
    Dataset sources: 
    Bergström et al. (2022) <doi:10.1145/3490493>;
    Dalsgaard et al. (2021) <doi:10.1145/3489849.3489853>;
    Larsen et al. (2019) <doi:10.1145/3338286.3340115>;
    Lilija et al. (2019) <doi:10.1145/3290605.3300676>;
    Pohl and Murray-Smith (2013) <doi:10.1145/2470654.2481307>;
    Pohl and Mottelson (2022) <doi:10.3389/frvir.2022.719506>.
Computes heteroskedasticity-consistent covariance matrix
    estimators for ordinary least squares regression models. The published HC0
    through HC5m estimators implemented in the package follow White (1980)
    <doi:10.2307/1912934>, Hinkley (1977)
    <doi:10.1080/00401706.1977.10489550>, Horn et al. (1975)
    <doi:10.1080/01621459.1975.10479877>, MacKinnon and White (1985)
    <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004)
    <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto and da Silva
    (2011) <doi:10.1007/s10182-010-0141-2>, Cribari-Neto et al. (2007)
    <doi:10.1080/03610920601126589>, and Li et al. (2016)
    <doi:10.1080/00949655.2016.1198906>. The package also includes HCbeta, a
    new estimator proposed by the package authors. It provides normal Wald tests,
    confidence intervals, diagnostics, and S3 output for applied inference.
Univariate agglomerative hierarchical clustering with a comprehensive list of choices of a linkage function in O(n*log n) time. The better algorithmic time complexity is paired with an efficient 'C++' implementation.
Facilitates hierarchical clustering analysis with functions
    to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization
    techniques to the dataset, perform hierarchical clustering and
    construct scatter plot from principal component analysis to evaluate
    the groups obtained.
Implements hierarchical clustering methods (single linkage, complete linkage, average linkage, and centroid linkage) 
    with stepwise printing and dendrograms for didactic purposes.
Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.
Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level
    splitting and within-cluster subsampling to accommodate dependence, and
    inverse-probability weighting to correct distribution shift induced by missingness.
    Conditional densities are estimated by inverting fitted conditional quantiles
    (linear quantile regression or quantile regression forests), and p-values are
    aggregated across resampling and splitting steps using the Cauchy combination test.
This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. 
    We implement an effective solution to recover the above hidden compact representation under the likelihood framework. 
    Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.
Tools to estimate, compare, and visualize healthcare resource utilization using
    data derived from electronic health records or real-world evidence sources.
    The package supports pre index and post index analysis, patient cohort comparison, 
    and customizable summaries and visualizations for clinical and health economics research. 
    Methods implemented are based on Scott et al. (2022) <doi:10.1080/13696998.2022.2037917>
    and Xia et al. (2024) <doi:10.14309/ajg.0000000000002901>.
It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) <doi:10.1002/pst.1756> and Jianrong Wu, Yimei Li (2020) <doi:10.1080/10543406.2019.1684305>.
Provides functions for designing phase II clinical trials adjusting for the heterogeneity of the population using known subgroups or historical controls.
A comprehensive R package for accessing and working with publicly 
    available and free resources from the Agency for Healthcare Research and Quality 
    (AHRQ) Healthcare Cost and Utilization Project (HCUP). The package provides 
    streamlined access to HCUP's Clinical Classifications Software Refined (CCSR) 
    mapping files and Summary Trend Tables, enabling researchers and analysts to 
    efficiently map ICD-10-CM diagnosis codes and ICD-10-PCS procedure codes to 
    CCSR categories and access HCUP statistical reports. Key features include: 
    direct download from HCUP website, multiple output formats (long/wide/default), 
    cross-classification support, version management, citation generation, and 
    intelligent caching. The package does not redistribute HCUP data files but 
    facilitates direct download from the official HCUP website, ensuring users 
    always have access to the latest versions and maintain compliance with HCUP 
    data use policies. This package only accesses free public tools and reports; 
    it does NOT access HCUP databases (NIS, KID, SID, NEDS, etc.) that require 
    purchase. For more information, see <https://hcup-us.ahrq.gov/>.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) 
   "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". 
   This implements and imports a  collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting 
   functions, multiple real data sets and four vignettes covering a range different applications.
Provides seamless access to the WEkEO Harmonised Data Access (HDA) API, enabling 
  users to query, download, and process data efficiently from the HDA platform. 
  With 'hdar', researchers and data scientists can integrate the extensive HDA 
  datasets into their R workflows, enhancing their data analysis capabilities. 
  Comprehensive information on the API functionality and usage is available 
  at <https://gateway.prod.wekeo2.eu/hda-broker/docs>.
User-friendly functions for leveraging (multiple) historical data set(s) in Bayesian analysis of generalized 
    linear models (GLMs) and survival models, along with support for Bayesian model averaging (BMA). The package provides 
    functions for sampling from posterior distributions under various informative priors, including the prior induced by the 
    Bayesian hierarchical model, power prior by Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, normalized power prior by 
    Duan et al. (2006) <doi:10.1002/env.752>, normalized asymptotic power prior by Ibrahim et al. (2015) <doi:10.1002/sim.6728>, 
    commensurate prior by Hobbs et al. (2011) <doi:10.1111/j.1541-0420.2011.01564.x>, robust meta-analytic-predictive 
    prior by Schmidli et al. (2014) <doi:10.1111/biom.12242>, latent exchangeability prior by Alt et al. (2024) 
    <doi:10.1093/biomtc/ujae083>, and a normal (or half-normal) prior. The package also includes functions for computing
    model averaging weights, such as BMA, pseudo-BMA, pseudo-BMA with the Bayesian bootstrap, and stacking (Yao et al.,
    2018 <doi:10.1214/17-BA1091>), as well as for generating posterior samples from the ensemble distributions to reflect 
    model uncertainty. In addition to GLMs, the package supports survival models including: (1) accelerated failure time 
    (AFT) models, (2) piecewise exponential (PWE) models, i.e., proportional hazards models with piecewise constant
    baseline hazards, and (3) mixture cure rate models that assume a common probability of cure across subjects, paired 
    with a PWE model for the non-cured population. Functions for computing marginal log-likelihoods under each implemented 
    prior are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package.
Functions implementing change point detection methods using the maximum pairwise Bayes factor approach.
    Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques.
Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.
Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.
Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation.
A two-step double-robust method to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the covariate(s) of interest. The CATE estimator implemented in this package not only allows for high-dimensional data, but also has the “double robustness” property: either the model for the propensity score or the models for the conditional means of the potential outcomes are allowed to be misspecified (but not both). This package is based on the paper by Fan et al., "Estimation of Conditional Average Treatment Effects With High-Dimensional Data" (2022), Journal of Business & Economic Statistics <doi:10.1080/07350015.2020.1811102>.
Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) <doi:10.48550/arXiv.2306.04702> , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) <doi:10.1111/rssb.12243>, and the method of Pilliat et al (2023) <doi:10.1214/23-EJS2126>.
Discriminant analysis and data clustering methods for high
    dimensional data, based on the assumption that high-dimensional data live in
    different subspaces with low dimensionality proposing a new parametrization of
    the Gaussian mixture model which combines the ideas of dimension reduction and
    constraints on the model.
Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <https://jmlr.org/papers/v18/16-342.html>.
Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.
Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.
A procedure that fits derivative curves based on a sequence of quotient differences.  In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves.  In a non-hierarchical setting the package produces a single derivative curve.
Hard drive data: Class of data allowing the easy importation/manipulation of out of memory data sets. The data sets are located on disk but look like in-memory, the syntax for manipulation is similar to 'data.table'. Operations are performed "chunk-wise" behind the scene.
Provides a self-contained, static build of the 'HDF5'
    (Hierarchical Data Format 5) 'C' library (release 2.1.1) for R
    package developers. Designed for use in the 'LinkingTo' field,
    it enables zero-dependency integration by building the library
    entirely from source during installation. Additionally, it compiles
    and internally links a comprehensive suite of advanced compression
    filters and their 'HDF5' plugins (Zstd, LZ4, Blosc/Blosc2, Snappy,
    ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are
    integrated out-of-the-box, allowing downstream packages to utilize
    high-performance compression directly through the standard 'HDF5'
    API while keeping the underlying third-party headers fully
    encapsulated. 'HDF5' is developed by The HDF Group
    <https://www.hdfgroup.org/>.
'HDF5' is a data model, library and file format for storing
    and managing large amounts of data. This package provides a nearly
    feature complete, object oriented  wrapper for the 'HDF5' API
    <https://support.hdfgroup.org/documentation/hdf5/latest/_r_m.html> using R6 classes.
    Additionally, functionality is added so that 'HDF5' objects behave very
    similar to their corresponding R counterparts.
Some methods to manipulate 'HDF5' files, extending the 'hdf5r' package. Reading and writing R objects to 'HDF5' formats follow the specification of 'AnnData' <https://anndata.readthedocs.io/en/latest/fileformat-prose.html>.
Provides a forecasting method that efficiently maps vast
    numbers of (scalar-valued) signals into an aggregate density forecast
    in a time-varying and computationally fast manner. The method proceeds
    in two steps: First, it transforms a predictive signal into a density
    forecast and, second, it combines the resulting candidate density
    forecasts into an ultimate aggregate density forecast. For a detailed
    explanation of the method, please refer to Adaemmer et al. (2025)
    <doi:10.1080/07350015.2025.2526424>.
Provides an interface to 'HDFql' <https://www.hdfql.com/> 
    and helper functions for reading data from and writing data to 'HDF5' files. 'HDFql' 
    provides a high-level language for managing 'HDF5' data that is platform independent.
    For more information, see the reference manual 
    <https://www.hdfql.com/resources/HDFqlReferenceManual.pdf>.
Offers methods for visualising, modelling, and forecasting high-dimensional functional time series, also known as functional panel data. Documentation about 'hdftsa' is initially provided via the paper by Cristian F. Jimenez-Varon, Ying Sun and Han Lin Shang (2024, Journal of Computational and Graphical Statistics).
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in high-dimensional models.
A correlation-based batch process for fast, accurate imputation for 
    high dimensional missing data problems via chained random forests.
    See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on 'hdImpute',
    Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on 'missForest', 
    and Mayer (2022) <https://github.com/mayer79/missRanger> for more on 'missRanger'.
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) <doi:10.1007/s11634-021-00457-4>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.
The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.
Testing homogeneity of k multivariate distributions is a classical and challenging problem in
             statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size.
             We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. 
             These tests are easy to implement and distribution-free in finite sample situations. Under appropriate 
             regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the 
             dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale 
             approach, where the results for different number of partitions are aggregated judiciously. Details are in 
             Biplab Paul, Shyamal K De and Anil K Ghosh (2021) <doi:10.1016/j.jmva.2021.104897>; Soham Sarkar and Anil K Ghosh (2019) 
             <doi:10.1109/TPAMI.2019.2912599>; William M Rand (1971) <doi:10.1080/01621459.1971.10482356>;  
             Cyrus R Mehta and Nitin R Patel (1983) <doi:10.2307/2288652>; Joseph C Dunn (1973) 
             <doi:10.1080/01969727308546046>; Sture Holm (1979) <doi:10.2307/4615733>; 
             Yoav Benjamini and Yosef Hochberg (1995) <doi: 10.2307/2346101>.
Implementation of selected high-dimensional statistical and
    econometric methods for estimation and inference. Efficient estimators and
    uniformly valid confidence intervals for various low-dimensional causal/
    structural parameters are provided which appear in high-dimensional
    approximately sparse models. Including functions for fitting heteroscedastic
    robust Lasso regressions with non-Gaussian errors and for instrumental variable
    (IV) and treatment effect estimation in a high-dimensional setting. Moreover,
    the methods enable valid post-selection inference and rely on a theoretically
    grounded, data-driven choice of the penalty.
    Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.
Penalized regression for generalized linear models for
  measurement error problems (aka. errors-in-variables). The package
  contains a version of the lasso (L1-penalization) which corrects
  for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). 
  It also contains an implementation of the Generalized Matrix Uncertainty 
  Selector, which is a version the (Generalized) Dantzig Selector for the 
  case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
High-dimensional matrix factor models have drawn much attention in view of the fact that observations are usually well structured to be an array such as in macroeconomics and finance. In addition, data often exhibit heavy-tails and thus it is also important to develop robust procedures. We aim to address this issue by replacing the least square loss with Huber loss function. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's Frobenius norm, which leads to a weighted iterative projection approach to compute and learn the parameters and thereby named as Robust-Matrix-Factor-Analysis (RMFA), see the details in He et al. (2023)<doi:10.1080/07350015.2023.2191676>. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm (IHR), see the details in He et al. (2023) <arXiv:2306.03317>. In this package, we also provide the algorithm for alpha-PCA by Chen & Fan (2021) <doi:10.1080/01621459.2021.1970569>, the Projected estimation (PE) method by Yu et al. (2022)<doi:10.1016/j.jeconom.2021.04.001>. In addition, the methods for determining the pair of factor numbers are also given.
A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) <doi:10.1080/01621459.2020.1765785>.
Estimates parameters in Mixture Transition Distribution (MTD) models, a class of high-order Markov chains. The set of relevant pasts (lags) is selected using either the Bayesian Information Criterion or the Forward Stepwise and Cut algorithms. Other model parameters (e.g. transition probabilities and oscillations) can be estimated via maximum likelihood estimation or the Expectation-Maximization algorithm. Additionally, 'hdMTD' includes a perfect sampling algorithm that generates samples of an MTD model from its invariant distribution. For theory, see Ost & Takahashi (2023) <http://jmlr.org/papers/v24/22-0266.html>.
Creates nomogram visualizations for penalized Cox regression
    models, with the support of reproducible survival model building,
    validation, calibration, and comparison for high-dimensional data.
Contains functions to construct high-dimensional orthogonal maximin distance designs in two, four, eight, and sixteen levels from rotating the Kronecker product of sub-Hadamard matrices.
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers.
In high-dimensional settings:
	Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.
	Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.
	Adjust the shrinkage bias in the predicted PC scores.
	Dey, R. and Lee, S. (2019) <doi:10.1016/j.jmva.2019.02.007>.
Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis <DOI:10.1017/pan.2019.13> and <doi:10.18637/jss.v107.i10>.
Implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.
Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.
Factor models have been widely applied in areas such as economics and finance, and the well-known heavy-tailedness of macroeconomic/financial data should be taken into account when conducting factor analysis. We propose two algorithms to do robust factor analysis by considering the Huber loss. One is based on minimizing the Huber loss of the idiosyncratic error's L2 norm, which turns out to do Principal Component Analysis (PCA) on the weighted sample covariance matrix and thereby named as Huber PCA. The other one is based on minimizing the element-wise Huber loss, which can be solved by an iterative Huber regression algorithm. In this package we also provide the code for traditional PCA, the Robust Two Step (RTS) method by He et al. (2022) and the Quantile Factor Analysis (QFA) method by Chen et al. (2021) and He et al. (2023).
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
Constructs shrinkage estimators of high-dimensional mean-variance portfolios and performs 
    high-dimensional tests on optimality of a given portfolio. The techniques developed in 
    Bodnar et al. (2018 <doi:10.1016/j.ejor.2017.09.028>, 2019 <doi:10.1109/TSP.2019.2929964>, 
    2020 <doi:10.1109/TSP.2020.3037369>, 2021 <doi:10.1080/07350015.2021.2004897>) 
    are central to the package. They provide simple and feasible estimators and tests for optimal 
    portfolio weights, which are applicable for 'large p and large n' situations where p is the 
    portfolio dimension (number of stocks) and n is the sample size. The package also includes tools
    for constructing portfolios based on shrinkage estimators of the mean vector and covariance matrix
    as well as a new Bayesian estimator for the Markowitz efficient frontier recently developed by 
    Bauder et al. (2021) <doi:10.1080/14697688.2020.1748214>.
Contains ten datasets used in the chapters and exercises of Paul, Alice (2023) "Health Data Science in R" <https://alicepaul.github.io/health-data-science-using-r/>.
A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.
Implements an efficient algorithm for fitting the entire regularization path of support vector machine models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.
Efficient sampling from high-dimensional truncated Gaussian
 distributions, or multivariate truncated normal (MTN). Techniques include 
 zigzag Hamiltonian Monte Carlo as in Akihiko Nishimura, Zhenyu Zhang and 
 Marc A. Suchard (2024) <doi:10.1080/01621459.2024.2395587>, and  harmonic Monte Carlo in Ari Pakman 
 and Liam Paninski (2014) <doi:10.1080/10618600.2013.788448>.
Provides uniform testing procedures for existence and heterogeneity of threshold 
    effects in high-dimensional nonparametric panel regression models. The package accompanies
    the paper Chen, Keilbar, Su and Wang (2023) "Inference on many jumps in nonparametric panel
    regression models". arXiv preprint <doi:10.48550/arXiv.2312.01162>.
An implementation for high-dimensional time series analysis methods, including factor model for vector time series 
      proposed by Lam and Yao (2012) <doi:10.1214/12-AOS970> and Chang, Guo and Yao (2015)
      <doi:10.1016/j.jeconom.2015.03.024>, martingale difference test proposed by 
      Chang, Jiang and Shao (2023) <doi:10.1016/j.jeconom.2022.09.001>, principal 
      component analysis for vector time series proposed by Chang, Guo and Yao (2018) <doi:10.1214/17-AOS1613>,
      cointegration analysis proposed by Zhang, Robinson and Yao (2019)
      <doi:10.1080/01621459.2018.1458620>, unit root test proposed by Chang, Cheng and Yao (2022)
      <doi:10.1093/biomet/asab034>, white noise tests proposed by Chang, Yao and Zhou (2017)
      <doi:10.1093/biomet/asw066> and Chang et al. (2026+), CP-decomposition for matrix time 
      series proposed by Chang et al. (2023) <doi:10.1093/jrsssb/qkac011> and
      Chang et al. (2026+) <doi:10.48550/arXiv.2410.05634>, and statistical inference for
      spectral density matrix proposed by Chang et al. (2025) 
      <doi:10.1080/01621459.2025.2468013>.
The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods.
A protocol that facilitates the processing and analysis of Hydrogen-Deuterium Exchange Mass Spectrometry data using p-value statistics and Critical Interval analysis. 
    It provides a pipeline for analyzing data from 'HDXExaminer' (Sierra Analytics, Trajan Scientific), 
    automating matching and comparison of protein states through Welch's T-test and the Critical Interval statistical framework. 
    Additionally, it simplifies data export, generates 'PyMol' scripts, and ensures calculations meet publication standards. 
    'HDXBoxeR' assists in various aspects of hydrogen-deuterium exchange data analysis, including reprocessing data, calculating parameters, identifying significant peptides, 
    generating plots, and facilitating comparison between protein states. 
    For details check papers by Hageman and Weis (2019) <doi:10.1021/acs.analchem.9b01325> 
    and Masson et al. (2019) <doi:10.1038/s41592-019-0459-y>.
    'HDXBoxeR' citation: Janowska et al. (2024) <doi:10.1093/bioinformatics/btae479>.
Create dynamic, data-driven text. Given two values, a list of 
    talking points is generated and can be combined using string
    interpolation. Based on the 'glue' package.
Allows for painless use of the 'Metopio' health atlas APIs
    <https://metopio.com/health-atlas> to explore and import data.
    'Metopio' health atlases store open public health data. See what topics
    (or indicators) are available among specific populations, periods, and
    geographic layers. Download relevant data along with geographic 
    boundaries or point datasets. Spatial datasets are returned as 'sf' 
    objects.
Provides easy access to Brazilian public health data from multiple
    sources including VIGITEL (Surveillance of Risk Factors for Chronic Diseases
    by Telephone Survey), PNS (National Health Survey), 'PNAD' Continua (Continuous
    National Household Sample Survey), 'POF' (Household Budget Survey with food
    security and consumption data), 'Censo Demografico' (population denominators
    via 'SIDRA' API), SIM (Mortality Information System), SINASC (Live Birth
    Information System), 'SIH' (Hospital Information System),
    'SIA' (Outpatient Information System), 'SINAN' (Notifiable Diseases Surveillance),
    'CNES' (National Health Facility Registry),
    'SI-PNI' (National Immunization Program - aggregated 1994-2019 via FTP,
    individual-level 'microdata' 2020+ via 'OpenDataSUS' API),
    'SISAB' (Primary Care Health Information System - coverage indicators via
    REST API), ANS ('Agencia Nacional de Saude Suplementar' - supplementary
    health beneficiaries, consumer complaints, and financial statements),
    'ANVISA' ('Agencia Nacional de Vigilancia Sanitaria' - product registrations,
    'pharmacovigilance', 'hemovigilance', 'technovigilance', and controlled
    substance sales via 'SNGPC'),
    and other health information systems. Data is downloaded
    from the Brazilian Ministry of Health and 'IBGE' repositories. Data is returned
    in tidy format following tidyverse conventions.
Health Calculator helps to find different parameters like basal metabolic rate, body mass index etc. related to fitness and health of a person.
Antitrust analysis of
    healthcare markets. Contains functions to implement the 
    semiparametric estimation technique described in Raval, Rosenbaum,
    and Tenn (2017) "A Semiparametric Discrete Choice Model: An Application
    to Hospital Mergers" <doi:10.1111/ecin.12454>.
A system for identifying diseases or events from healthcare databases and
    preparing data for epidemiological studies. It includes capabilities not
    supported by 'SQL', such as matching strings by 'stringr' style regular
    expressions, and can compute comorbidity scores (Quan et al. (2005)
    <doi:10.1097/01.mlr.0000182534.19832.83>) directly on a database server. The
    implementation is based on 'dbplyr' with full 'tidyverse' compatibility.
Compute 21 summary measures of health inequality and its
    corresponding confidence intervals for ordered and non-ordered
    dimensions using disaggregated data. Measures for ordered dimensions
    (e.g., Slope Index of Inequality, Absolute Concentration Index) also
    accept individual and survey data.
Provides a shiny interface for a free, open-source managerial
    accounting-like system for health care practices. This package allows
    health care administrators to project revenue with monthly adjustments
    and procedure-specific boosts up to a 3-year period. Granular data
    (patient-level) to aggregated data (department- or hospital-level) can
    all be used as valid inputs provided historical volume and revenue
    data is available. For more details on managerial accounting
    techniques, see Brewer et al. (2015, ISBN:9780078025792).
This R package has been developed with a focus on air pollution and noise but can be applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package.
 Selection of relevant references (in chronological order): 
 WHO (2003) <https://www.who.int/publications/i/item/9241546204>, 
 Murray et al. (2003) <doi:10.1186/1478-7954-1-1>, 
 Miller & Hurley (2003) <doi:10.1136/jech.57.3.200>, 
 Steenland & Armstrong (2006) <doi:10.1097/01.ede.0000229155.05644.43>, 
 WHO (2011) <https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27>,
 GBD 2019 Risk Factors Collaborators (2020) <doi:10.1016/S0140-6736(20)30752-2>.
Provides a broad collection of datasets focused on health, biomechanics, and human motion.
    It includes clinical, physiological, and kinematic information from diverse sources,
    covering aspects such as surgery outcomes, vital signs, rheumatoid arthritis, osteoarthritis,
    accelerometry, gait analysis, motion sensing, and biomechanics experiments.
    Designed for researchers, analysts, and students, the package facilitates exploration
    and analysis of data related to health monitoring, physical activity, and rehabilitation.
Efficient tools for parsing and standardizing Australian 
    addresses from textual data. It utilizes optimized algorithms to accurately identify and 
    extract components of addresses, such as street names, types, and postcodes, especially  
    for large batched data in contexts where sending addresses to internet services may be 
    slow or inappropriate. The core functionality is built on fast string processing techniques 
    to handle variations in address formats and abbreviations commonly found in Australian 
    address data. Designed for data scientists, urban planners, and logistics analysts, the 
    package facilitates the cleaning and normalization of address information, supporting 
    better data integration and analysis in urban studies, geography, and related fields.
Hospital data analysis workflow tools, modeling, and automations. This library
    provides many useful tools to review common administrative hospital data. Some 
    of these include average length of stay, readmission rates, average net pay
    amounts by service lines just to name a few. The aim is to provide a simple
    and consistent verb framework that takes the guesswork out of everything.
Hospital machine learning and ai data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative 
    hospital data. Some of these include predicting length of stay, and readmits.
    The aim is to provide a simple and consistent verb framework that takes the 
    guesswork out of everything.
Provides data for functions typically used in the 'healthyR' package.
Hospital time series data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative time 
    series hospital data. Some of these include average length of stay, and 
    readmission rates. The aim is to provide a simple and consistent verb 
    framework that takes the guesswork out of everything.
Provides methods for correcting heaping (digit preference) in
    survey data at the individual record level. Age heaping, where respondents
    disproportionately report ages ending in 0 or 5, is a common phenomenon that
    can distort demographic analyses. Unlike traditional smoothing methods that
    only correct aggregated statistics, this package corrects individual values
    by replacing a calculated proportion of heaped observations with draws from
    fitted truncated distributions (log-normal, normal, or uniform). Supports
    5-year and 10-year heaping patterns, single heap correction, and optional
    model-based adjustment to preserve covariate relationships.
Makes it easy to download a large number of files such as PDF files 
    and CSV files, while automatically slowing down requests, letting you know 
    where it is up to, and adjusting for files that have already been downloaded.
Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system (<https://electricblue.eu/pulse>) from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting.
    Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) <doi:10.3390/a5040588>. Check original code at <https://github.com/ig248/pyampd>.
    ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (<https://electricblue.eu>).
The heatex package calculates heat storage in the body and
        the components of heat exchange (conductive, convective,
        radiative, and evaporative) between the body and the
        environment during physical activity based on the principles of
        partitional calorimetry. The program enables heat exchange
        calculations for a range of environmental conditions when
        wearing various clothing ensembles.
Implements the simpler and faster heat index, which matches the values of the original 1979 heat index and its 2022 extension for air temperatures above 300 K (27 C, 80 F) and with only minor differences at lower temperatures. Also implements an algorithm for calculating the thermodynamic (and psychrometric) wet-bulb (and ice-bulb) temperature.
An improved heatmap package. Completely
    compatible with the original R function 'heatmap',
    and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
    binary dependent variables, and calculates the related heatmap fit
    statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
A set of tools supporting more flexible heatmaps. The graphics is grid-like using
  the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions
  constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming
  and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set,
  e.g., clustered and ordered by a supervised clustering method.
Create interactive cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
    show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
    method for visualizing high-dimensional data, in which a table of numbers
    are encoded as a grid of colored cells. The rows and columns of the matrix
    are ordered to highlight patterns and are often accompanied by 'dendrograms'.
    'Heatmaps' are used in many fields for visualizing observations, correlations,
    missing values patterns, and more. Interactive 'heatmaps' allow the inspection
    of specific value by hovering the mouse over a cell, as well as zooming into
    a region of the 'heatmap' by dragging a rectangle around the relevant area.
    This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
    similar 'heatmaps' to 'heatmap.2' with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.
Provides a lightweight framework for creating high quality, complex 
    heatmaps using base graphics. Supports hierarchical clustering with 
    dendrograms, column and row scaling, cluster sub-divisions, customizable 
    cell colours, shapes and sizes, legends, and flexible layouts for arranging 
    multiple heatmaps. Designed to return plot objects that can be easily 
    arranged with other plots without sacrificing resolution. Methods for 
    hierarchical clustering and distance computations are described in 
    Murtagh and Contreras (2012) <doi:10.1002/wics.53>. Dendrogram 
    visualisation methods are based on the 'ggdendro' package by 
    de Vries and Ripley (2020) <https://CRAN.R-project.org/package=ggdendro>.
Creates self-contained widgets for interactive phylogenetic tree
    visualization. This package wraps the 'JavaScript' 'heat-tree' package using
    the 'htmlwidgets' R package.
The different methods for defining, detecting, and categorising the extreme events 
    known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) <doi: 10.1016/j.pocean.2015.12.014> 
    and Hobday et al. (2018) <https://www.jstor.org/stable/26542662>. The functions in this package work on both air 
    and water temperature data of hourly and daily temporal resolution. These detection algorithms may be used on 
    non-temperature data as well.
Implements the estimators and algorithms described in Chapters 8 and 9 of the book "The Fundamentals of Heavy Tails: Properties, Emergence, and Estimation" by Nair et al. (2022, ISBN:9781009053730). These include the Hill estimator, Moments estimator, Pickands estimator, Peaks-over-Threshold (POT) method, Power-law fit, and the Double Bootstrap algorithm.
Provides a case conversion between common cases like CamelCase and 
    snake_case. Using the 'rust crate heck' <https://github.com/withoutboats/heck>
    as the backend for a highly performant case conversion for 'R'.
It performs maximum likelihood estimation for the Heckman selection model (Normal, Student-t or Contaminated normal) using an EM-algorithm <doi:10.1016/j.jmva.2021.104737>. It also performs influence diagnostic through global and local influence for four possible perturbation schema.
Tools for the estimation of Heckman selection models with robust 
  variance-covariance matrices. It includes functions for computing the bread 
  and meat matrices, as well as clustered standard errors for generalized Heckman 
  models, see Fernando de Souza Bastos and Wagner Barreto-Souza and Marc G. Genton 
  (2022, ISSN: <https://www.jstor.org/stable/27164235>). The package also offers 
  cluster-robust inference with sandwich estimators, and tools for handling issues 
  related to eigenvalues in covariance matrices.
Implements Heckman selection models using a Bayesian approach via 'Stan' and compares the performance of normal, Student’s t, and contaminated normal distributions in addressing complexities and selection bias (Heeju Lim, Victor E. Lachos, and Victor H. Lachos, Bayesian analysis of flexible Heckman selection models using Hamiltonian Monte Carlo, 2025, under submission).
This tool identifies hydropeaking events from raw time-series flow record, a rapid flow variation induced by the hourly-adjusted electricity market. The novelty of 'HEDA' is to use vector angle instead of the first-order derivative to detect change points which not only largely improves the computing efficiency but also accounts for the rate of change of the flow variation. More details <doi:10.1016/j.jhydrol.2021.126392>.
Helper functions designed to make
    dynamically generating R Markdown documents easier by providing a
    simple and tidy way to create report pieces, shape them to your data,
    and combine them for exporting into a single R Markdown document.
Hedgehog will eat all your bugs.
  'Hedgehog' is a property-based testing package in the spirit
  of 'QuickCheck'. With 'Hedgehog', one can test properties
  of their programs against randomly generated input, providing
  far superior test coverage compared to unit testing. One of the
  key benefits of 'Hedgehog' is integrated shrinking of
  counterexamples, which allows one to quickly find the cause of
  bugs, given salient examples when incorrect behaviour occurs.
An implementation of the modelling and reporting features described 
    in reference textbook and guidelines (Briggs, Andrew, et al. Decision 
    Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011;
    Siebert, U. et al. State-Transition Modeling. Medical Decision Making 
    32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, 
    heterogeneity analysis, time dependency on state-time and model-time 
    (semi-Markov and non-homogeneous Markov models), etc.
In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. 
  It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts.
  Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.
Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.
Calculate and visualize Healthy Eating Index (HEI) scores
    from National Health and Nutrition Examination Survey 24-hour dietary
    recall data utilizing three methods recommended by the National Cancer
    Institute (2024)
    <https://epi.grants.cancer.gov/hei/hei-methods-and-calculations.html#:~:text=To%20use%20the%20simple%20HEI,the%20total%20scores%20across%20individuals.>.
    Effortlessly analyze HEI scores across different demographic groups
    and years.
Contains the National Health and Nutrition Examination Survey
    24-hour dietary recall data and Healthy Eating Index scoring standards
    used by the 'heiscore' package.
Create publication-quality, 2-dimensional visualizations of alpha-helical peptide
  sequences. Specifically, allows the user to programmatically generate
  helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of
  alpha-helical oligopeptides. See Wadhwa RR, et al. (2018)
  <doi:10.21105/joss.01008> for more information.
Empirical value of the Hellinger correlation, a measure of dependence between
	     two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) <arXiv:1810.10276v4>.
Base R's default setting for 'stringsAsFactors' within
    'data.frame()' and 'as.data.frame()' is supposedly the most often complained
    about piece of code in the R infrastructure. The 'hellno' package provides
    an explicit solution without changing R itself or having to mess around with
    options. It tries to solve this problem by providing alternative
    'data.frame()' and 'as.data.frame()' functions that are in fact simple
    wrappers around base R's 'data.frame()' and 'as.data.frame()' with
    'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
    by default.
Provides datasets used for analysis 
  and visualizations in the open-access Hello Data Science book.
Prints a user-supplied message alongside a small ASCII-art
    frog in one of several moods (default, sleepy, angry, party, ninja).
    Designed as a quirky alternative to 'print' for adding visual flair
    to console output in scripts, demos, teaching materials, and
    command-line tools.
A dummy package to demonstrate how to interface to a jar
        file that resides inside an R package.
Template R package with minimal setup to use Rust code in R without
    hacks or frameworks. Includes basic examples of importing cargo dependencies,
    spawning threads and passing numbers or strings from Rust to R. Cargo crates
    are automatically 'vendored' in the R source package to support offline
    installation. The GitHub repository for this package has more details and also 
    explains how to set up CI. This project was first presented at 'Erum2018' to 
    showcase R-Rust integration <https://jeroen.github.io/erum2018/>; for a real
    world use-case, see the 'gifski' package on 'CRAN'.
Contains miscellaneous functions useful for managing 'NetCDF' files (see <https://en.wikipedia.org/wiki/NetCDF>), get moon phase and time for sun rise and fall, tide level, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data APIs in the Helsinki
    region in Finland. Current data sources include the Service Map API,
    Linked Events API, and Helsinki Region Infoshare statistics API.
Tools to calculate Mean Corpuscular Volume, Mean Corpuscular
             Hemoglobin, and Mean Corpuscular Hemoglobin Concentration,
             which are essential for assessing red blood cell health and 
             diagnosing blood disorders.
Import and classify canopy fish-eye images, estimate angular gap fraction and derive canopy attributes like leaf area index and openness.  Additional information is provided in the study by Chianucci F., Macek M. (2023) <doi:10.1016/j.agrformet.2023.109470>.
A visualization suite primarily designed for single-cell
    RNA-sequencing data analysis applications but well-suited for
    other purposes as well. It introduces novel plots to represent two-variable
    and frequency data and optimizes some commonly used plotting options
    (e.g., correlation, network, density, alluvial and volcano plots) 
    for ease of usage and flexibility.
Provides HE plot and other functions for visualizing hypothesis
    tests in multivariate linear models. HE plots represent sums-of-squares-and-products 
    matrices for linear hypotheses and for error using ellipses (in two
    dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models
    such as robust methods and homogeneity of variance covariance matrices.
    The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when
    there are more than a few response variables.
Set of R functions to be coupled with the 'xeus-r' 'jupyter' kernel
    in order to drive execution of code in notebook input cells, how R objects
    are to be displayed in output cells, and handle two way communication
    with the front end through comms.
Constructs paths to your project's files.
    Declare the relative path of a file within your project with 'i_am()'.
    Use the 'here()' function as a drop-in replacement for 'file.path()',
    it will always locate the files relative to your project root.
Interface to the 'HERE' REST APIs <https://developer.here.com/develop/rest-apis>:
  (1) geocode and autosuggest addresses or reverse geocode POIs using the 'Geocoder' API;
  (2) route directions, travel distance or time matrices and isolines using the 'Routing', 'Matrix Routing' and 'Isoline Routing' APIs;
  (3) request real-time traffic flow and incident information from the 'Traffic' API;
  (4) find request public transport connections and nearby stations from the 'Public Transit' API;
  (5) request intermodal routes using the 'Intermodal Routing' API;
  (6) get weather forecasts, reports on current weather conditions, astronomical
  information and alerts at a specific location from the 'Destination Weather' API.
  Locations, routes and isolines are returned as 'sf' objects.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
Reporting heritability estimates is an important to quantitative genetics 
  studies and breeding experiments. Here we provide functions to calculate various broad-sense
  heritabilities from 'asreml' and 'lme4' model objects. All methods we have implemented 
  in this package have extensively discussed in the article by Schmidt et al. (2019) <doi:10.1534/genetics.119.302134>.
Probability functions and other utilities for the generalized Hermite distribution.
Facilitates estimation of full univariate and bivariate 
  probability density functions and cumulative distribution functions along with
  full quantile functions (univariate) and nonparametric correlation 
  (bivariate) using Hermite series based estimators. These estimators are 
  particularly useful in the sequential setting (both stationary and 
  non-stationary) and one-pass batch estimation setting for large data sets. 
  Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 <doi:10.1214/17-EJS1245>, 
  Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) <doi:10.1007/s00184-020-00785-z> and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) <doi:10.1016/j.jmva.2021.104783>.
An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>.    A hero is a specific type of sandwich.  Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) <doi:10.1016/j.spasta.2020.100413>.
A modular and computationally efficient R package for  
  parameterizing, simulating, and analyzing health economic simulation 
  models. The package supports cohort discrete time state transition models 
  (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
  N-state partitioned survival models (Glasziou et al. 1990)
  <doi:10.1002/sim.4780091106>, and individual-level continuous 
  time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
  encompassing both Markov (time-homogeneous and time-inhomogeneous) and 
  semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is 
  quantified with standard graphical and tabular summaries of a probabilistic 
  sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, 
  <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
  make individual-patient simulation, probabilistic sensitivity analysis, 
  and incorporation of patient heterogeneity fast.
Implementation of the HespDiv framework for hierarchical spatial subdivision of geographical occurrence data. The main function hespdiv() performs iterative spatially constrained subdivision of a study area to identify topologically contiguous clusters in geographic space using user-defined or preset subdivision methods. Additional functions provide tools for analysing subdivision results, visualizing hierarchical spatial structures, and evaluating robustness through sensitivity analyses and statistical testing. Some examples use the optional HDData data package, which is available from GitHub at Liudas-Dau/hespdiv_data. The methodology is described in Daumantas and Spiridonov (2024) <doi:10.1111/pala.12702>.
Provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation.
Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features,  and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review <doi:10.1111/insr.70016>. This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of ‘The SMILE Project: Statistical Modelling and Inference to Live the Environment’, funded by the European Union – Next Generation EU (Punzo).
Distribution free heteroscedastic tests for functional data. 
    The following tests are included in this package: test of no main treatment or contrast
    effect and no simple treatment effect given in 
    Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>,
    no main time effect, and no interaction effect based on 
    original observations given in Wang and Akritas (2010a) 
    <doi:10.1080/10485250903171621>
    and tests based on ranks given in Wang and Akritas (2010b)
    <doi:10.1016/j.jmva.2010.03.012>.
A method for estimating the correlation matrix of the Gaussian copula from the observed data. This package also contains a penalized estimation of the corresponding precision matrix, and enables to generate random vectors that are distributed according to a Gaussian copula.
A comprehensive suite of spatial 
  functions created to analyze and assess data heterogeneity and climate variability 
  in spatial datasets. This package is specifically designed to address the challenges associated
  with characterizing and understanding complex spatial patterns in environmental and climate-related data.
Published meta-analyses routinely present one of the
   measures of heterogeneity introduced in Higgins and Thompson
   (2002) <doi:10.1002/sim.1186>. For critiquing articles
   it is often better to convert to another of
   those measures. Some
   conversions are provided here and confidence intervals are also
   available.
A multi-core R package that allows for the statistical modeling of multi-group multivariate mixed data using Gaussian graphical models. Combining the Gaussian copula framework with the fused graphical lasso penalty, the 'heteromixgm' package can handle a wide variety of datasets found in various sciences. The package also includes an option to perform model selection using the AIC, BIC and EBIC information criteria, a function that plots partial correlation graphs based on the selected precision matrices, as well as simulate mixed heterogeneous data for exploratory or simulation purposes and one multi-group multivariate mixed agricultural dataset pertaining to maize yields. The package implements the methodological developments found in Hermes et al. (2024) <doi:10.1080/10618600.2023.2289545>.
Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Homomorphic encryption (Brakerski and Vaikuntanathan (2014) <doi:10.1137/120868669>) using Ring Learning with Errors (Lyubashevsky et al. (2012) <https://eprint.iacr.org/2012/230>) is a form of Learning with Errors (Regev (2005) <doi:10.1145/1060590.1060603>) using polynomial rings over finite fields. Functions to generate the required polynomials (using 'polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided.
Cellular responses to perturbations are highly heterogeneous
    and depend largely on the initial state of cells. Connecting
    post-perturbation cells via cellular trajectories to untreated cells
    (e.g. by leveraging metabolic labeling information) enables
    exploitation of intercellular heterogeneity as a combined knock-down
    and overexpression screen to identify pathway modulators, termed
    Heterogeneity-seq (see 'Berg et al' <doi:10.1101/2024.10.28.620481>).
    This package contains functions to generate cellular trajectories based
    on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for
    metabolic labelling sequencing) time courses, functions to identify
    pathway modulators and to visualize the results.
Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.
Provides functions to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data), and to test for heterogeneity across multiple time points.  More details are available in Parast et al (2024) <doi:10.1002/sim.10122>.
Functions for the fitting and summarizing of heteroscedastic t-regression.
Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.
Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <doi:10.48550/arXiv.1412.5000>,
    and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <doi:10.48550/arXiv.1605.06566>
    for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
    the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
    implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.
Structural handling of Finnish identity codes (natural persons and 
    organizations); extract information, check ID validity and diagnostics.
Implements the Clarke-Wright algorithm to find a quasi-optimal solution to the Capacitated Vehicle Routing Problem. See Clarke, G. and Wright, J.R. (1964) <doi:10.1287/opre.12.4.568> for details. The implementation is accompanied by helper functions to inspect its solution.
Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.  Take The Best was first described in: Gigerenzer, G.
    & Goldstein, D. G. (1996) <doi:10.1037/0033-295X.103.4.650>.  All
    of these heuristics were run on many data sets and analyzed in:
    Gigerenzer, G., Todd, P. M., & the ABC Group (1999).
    <ISBN:978-0195143812>.
Binning and plotting functions for hexagonal bins.
Kernel density estimation with hexagonal grid for bivariate data.
      Hexagonal grid has many beneficial properties like equidistant neighbours
      and less edge bias, making it better for spatial analyses than the more
      commonly used rectangular grid.
      Carr, D. B. et al. (1987) <doi:10.2307/2289444>.
      Diggle, P. J. (2010) <doi:10.1201/9781420072884>.
      Hill, B. (2017) <https://blog.bruce-hill.com/meandering-triangles>.
      Jones, M. C. (1993) <doi:10.1007/BF00147776>.
Contains most of the hex font files from the 'GNU Unifont Project' <https://unifoundry.com/unifont/> compressed by 'xz'.  'GNU Unifont' is a duospaced bitmap font that attempts to cover all the official Unicode glyphs plus several of the artificial scripts in the '(Under-)ConScript Unicode Registry' <https://www.kreativekorp.com/ucsur/>.  Provides a convenience function for loading in several of them at the same time as a 'bittermelon' bitmap font object for easy rendering of the glyphs in an 'R' terminal or graphics device.
Provides functions to build and use hexagonal discrete global grids
    using the 'Snyder' 'ISEA' projection ('Snyder' 1992
    <doi:10.3138/27H7-8K88-4882-1752>) and the 'H3' hierarchical hexagonal system
    ('Uber' Technologies). Implements the 'ISEA' discrete global grid system
    ('Sahr', 'White' and 'Kimerling' 2003 <doi:10.1559/152304003100011090>).
    Includes a fast 'C++' core for 'ISEA' projection and aperture quantization,
    an included 'H3' v4.4.1 C library for native 'H3' grid operations, and
    'sf'/'terra'-compatible R wrappers for grid generation and coordinate assignment.
    Output is compatible with 'dggridR' for interoperability.
Creates a responsive HTML file with tiled hexagonal logos for packages
    in an R session. Tiles can be also be generated for a custom set of packages
    specified with a character vector. Output can be saved as a static screenshot in PNG format using a headless browser.
Helper functions for creating reproducible hexagon sticker purely in R.
Simple and integrated tool that automatically extracts and folds all hairpin sequences from raw genome-wide data. It predicts the secondary structure of several overlapped segments, with longer length than the mean length of sequences of interest for the species under processing, ensuring that no one is lost nor inappropriately cut.
Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
Provides functionality to download and cache files from 'Hugging Face Hub' <https://huggingface.co/models>. 
    Uses the same caching structure so files can be shared between different client libraries.
A data only package containing commercial domestic flights that
    departed Houston (IAH and HOU) in 2011.
Provides functions for the estimation, plotting, predicting and cross-validation of hierarchical feature regression models as described in Pfitzinger (2024). Cluster Regularization via a Hierarchical Feature Regression. Econometrics and Statistics (in press). <doi:10.1016/j.ecosta.2024.01.003>.
Implementation of the Hysteretic and Gatekeeping Depressions Model (HGDM) which calculates variable connected/contributing areas and resulting discharge volumes in prairie basins dominated by depressions ("slough" or "potholes"). The small depressions are combined into a single "meta" depression which explicitly models the hysteresis between the storage of water and the connected/contributing areas of the depressions. The largest (greater than 5% of the total depressional area) depression (if it exists) is represented separately to model its gatekeeping, i.e. the blocking of upstream flows until it is filled. The methodolgy is described in detail in Shook and Pomeroy (2025, <doi:10.1016/j.jhydrol.2025.132821>).
Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.
This data-only package was created for distributing data used in the examples of the 'hglm' package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
  constants of unnormalized probability distributions by utilizing holonomic 
  systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
  to find maximal likelihood estimates by utilizing the HGM.
Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.
A set of routines to quickly download and import the
    HUGO Gene Nomenclature Committee (HGNC) data set on mapping of
    gene symbols to gene entries in other genomic databases or resources.
Contains functions for
 identifying and correcting HGNC human gene symbols and MGI mouse gene symbols 
 which have been converted to date format by Excel, withdrawn, or aliased.
 Also contains functions for reversibly converting between HGNC
 symbols and valid R names.
It is used to travel graphs, by using DFS and BFS to get the path from node to each leaf node.
  Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure.
  Traversal means visiting all the nodes of a graph.
  Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria.
  It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level.
  Also, it provides the matrix which is reachable between each node.
  Implement reference about Baruch Awerbuch (1985) <doi:10.1016/0020-0190(85)90083-3>.
Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.
A handy collection of utility functions designed to aid in
    package development, plotting and scientific research.  Package
    development functionalities includes among others tools such as
    cross-referencing package imports with the description file, analysis
    of redundant package imports, editing of the description file and the
    creation of package badges for GitHub.  Some of the other
    functionalities include automatic package installation and loading,
    plotting points without overlap, creating nice breaks for plots,
    overview tables and many more handy utility functions.
This model divides coefficients into three types,
        i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022)<doi:10.1177/23998083211063885>.
        If data have spatial hierarchical structures (especially are overlapping on some locations),
        it is worth trying this model to reach better fitness.
Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Provides a streamlined pipeline to simulate household infection 
    dynamics, estimate transmission parameters, and visualize epidemic timelines. 
    Uses a Bayesian approach with 'Stan' that models transmission probability as a
    function of viral load, seasonality and infectivity, multiple infection 
    episodes (reinfections), and waning immunity modeling.
    Li et al. (2026) <doi:10.64898/2026.04.01.26349903>).
A Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases.
Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.
Allows users to create high-quality heatmaps from labelled, hierarchical data. Specifically, for data with a two-level hierarchical structure, it will produce a heatmap where each row and column represents a category at the lower level. These rows and columns are then grouped by the higher-level group each category belongs to, with the names for each category and groups shown in the margins. While other packages (e.g. 'dendextend') allow heatmap rows and columns to be arranged by groups only, 'hhmR' also allows the labelling of the data at both the category and group level.
In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. 
             Reference: 
             Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.
Develops algorithms for fitting, prediction, simulation 
  and initialization of the following models
  (1)- hidden hybrid Markov/semi-Markov model, 
  introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, 
  (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015
  <doi:10.1111/biom.12282>), 
  (3)- regime switching regression model 
  (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive 
  hidden hybrid Markov/semi-Markov model, 
  (4)- spline-based nonparametric 
  estimation of additive state-switching models  
  (Langrock et al., 2018 <doi:10.1111/stan.12133>)
  (5)- robust emission model proposed by 
  Qin et al, 2024 <doi:10.1007/s10479-024-05989-4>
  (6)- several emission distributions, including mixture of multivariate normal  
  (which can also handle missing data using EM algorithm) and multi-nomial emission 
  (for modeling polymer or DNA sequences)
  (7)- tools for prediction of future state sequence, computing the score of a new sequence,
  splitting the samples and sequences to train and test sets, computing the information measures of 
  the models, computing the residual useful lifetime (reliability) and many other useful tools ...
  (read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its 
  arxiv version: <doi:10.48550/arXiv.2109.12489>).
Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.
A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including:
    (1) fixed effects and coefficients of covariates,
    (2) environmental random effects, and its corresponding variance, 
    (3) genetic variance, 
    (4) residual variance, 
    (5) heritability, 
    (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals,
    (7) SNP effect size, 
    (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, 
    (9) posterior probability of association of the genomic window (WPPA), 
    (10) posterior inclusive probability (PIP). 
    The functions are not limited, we will keep on going in enriching it with more features. 
    References: Lilin Yin et al. (2025) <doi:10.18637/jss.v114.i06>; Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
             It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
             clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
             geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance
             thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
             with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
             multivariate clustering (MVC), cluster validation, visualization of regionalization results, and
             exporting region map and mean timeseries into NetCDF-4 file.
             The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.
Provides an example HiC dataset and two examples of 'HiCociety' 
  outputs from a function named hic2community(). 
  The data are intended for demonstration purposes only and kept small 
  enough to be distributed via CRAN.
The Harmonised Index of Consumer Prices (HICP) is the key economic figure to measure inflation in the euro area.
              The methodology underlying the HICP is documented in the HICP Methodological Manual (<https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/w/ks-gq-24-003>).
              Based on the manual, this package provides functions to access and work with HICP data from Eurostat's public database (<https://ec.europa.eu/eurostat/data/database>).
Perform Hi-C data differential analysis based on pixel-level
    differential analysis and a post hoc inference strategy to quantify
    signal in clusters of pixels. Clusters of pixels are obtained through
    a connectivity-constrained two-dimensional hierarchical clustering.
Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Generates HIDECAN plots that summarise and combine 
  the results of genome-wide association studies (GWAS) and transcriptomics 
  differential expression analyses (DE), along with manually curated candidate genes of interest. The HIDECAN plot 
  is presented in Angelin-Bonnet et al. (2023) (currently in review).
Performs linear discriminant analysis in high dimensional
        problems based on reliable covariance estimators for problems
        with (many) more variables than observations. Includes routines
        for classifier training, prediction, cross-validation and
        variable selection.
Calculate clinical scores for hidradenitis suppurativa (HS), a
  dermatologic disease.  The scores are typically used for evaluation of
  efficacy in clinical trials.  The scores are not commonly used in clinical
  practice.  The specific scores implemented are Hidradenitis Suppurativa 
  Clinical Response (HiSCR) (Kimball, et al. (2015) <doi:10.1111/jdv.13216>),
  Hidradenitis Suppurativa Area and Severity Index Revised (HASI-R) (Goldfarb,
  et al. (2020) <doi:10.1111/bjd.19565>), hidradenitis suppurativa Physician
  Global Assessment (HS PGA) (Marzano, et al. (2020) <doi:10.1111/jdv.16328>),
  and the International Hidradenitis Suppurativa Severity Score System (IHS4)
  (Zouboulis, et al. (2017) <doi:10.1111/bjd.15748>).
Pure set data visualization approaches are often limited in
    scalability due to the combinatorial explosion of distinct set families as
    the number of sets under investigation increases. hierarchicalSets applies
    a set centric hierarchical clustering of the sets under investigation and
    uses this hierarchy as a basis for a range of scalable visual
    representations. hierarchicalSets is especially well suited for collections
    of sets that describe comparable comparable entities as it relies on the
    sets to have a meaningful relational structure.
Bipartite graph-based hierarchical clustering, developed for pharmacogenomic 
    datasets and datasets sharing the same data structure. The goal is to 
    construct a hierarchical clustering of groups of samples based on 
    association patterns between two sets of variables. In the context of
    pharmacogenomic datasets, the samples are cell lines, and the two sets of 
    variables are typically expression levels and drug sensitivity values. 
    For this method, sparse canonical correlation analysis from 
    Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638> 
    is first applied to extract association patterns for each group of samples. 
    Then, a nuclear norm-based dissimilarity measure is used to construct a 
    dissimilarity matrix between groups based on the extracted associations. 
    Finally, hierarchical clustering is applied.
Estimates hierarchical F-statistics from haploid or
    diploid genetic data with any numbers of levels in the hierarchy, following the
    algorithm of Yang (Evolution(1998), 52:950).
    Tests via randomisations the significance
    of each F and variance components, using the likelihood-ratio statistics G
    (Goudet et al. (1996) <https://academic.oup.com/genetics/article/144/4/1933/6017091>).
    Estimates genetic diversity statistics
    for haploid and diploid genetic datasets in various formats, including inbreeding and
    coancestry coefficients, and population specific F-statistics following
    Weir and Goudet (2017) <https://academic.oup.com/genetics/article/206/4/2085/6072590>.
Efficient implementation of penalized regression with hierarchical nested parametrization for grouped data. The package provides penalized regression methods that decompose subgroup specific effects into shared global effects, Major subgroup specific effects, and Minor subgroup specific effects, enabling structured borrowing of information across related clinical subgroups. Both lasso and hierarchical overlapping group lasso penalties are supported to encourage sparsity while respecting the nested subgroup structure. Efficient computation is achieved through a modified design matrix representation and a custom algorithm for overlapping group penalties.
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
Machine learning hierarchical risk clustering portfolio allocation strategies. 
 The implemented methods are:
  Hierarchical risk parity (De Prado, 2016) <DOI: 10.3905/jpm.2016.42.4.059>.
  Hierarchical clustering-based asset allocation (Raffinot, 2017)  
  <DOI: 10.3905/jpm.2018.44.2.089>.
  Hierarchical equal risk contribution portfolio (Raffinot, 2018)
  <DOI: 10.2139/ssrn.3237540>.
  A Constrained Hierarchical Risk Parity Algorithm with Cluster-based Capital Allocation (Pfitzingera and Katzke, 2019)
  <https://www.ekon.sun.ac.za/wpapers/2019/wp142019/wp142019.pdf>.
Provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.
An implementation of the nonnegative garrote method that incorporates hierarchical relationships among variables. The core function, HiGarrote(), offers an automated approach for analyzing experiments while respecting hierarchical structures among effects. For methodological details, refer to Yu and Joseph (2025) <doi:10.1080/00224065.2025.2513508>. This work is supported by U.S. National Science Foundation grant DMS-2310637.
A wrapper for the 'Highcharts' library including
    shortcut functions to plot R objects. 'Highcharts'
    <https://www.highcharts.com/> is a charting library offering
    numerous chart types with a simple configuration syntax.
Tests for two high-dimensional population mean vectors. The user has the option to compute the asymptotic, the permutation or the bootstrap based p-value of the test. Some references are: Chen S.X. and Qin Y.L. (2010). <doi:10.1214/09-AOS716>, Cai T.T., Liu W., and Xia Y. (2014) <doi:10.1111/rssb.12034> and Yu X., Li D., Xue L. and Li, R. (2023) <doi:10.1080/01621459.2022.2061354>.
Provides a backend-agnostic 'API' for creating data visualizations
    using 'highcharter' (interactive) or 'ggplot2' (static). Figures are
    defined once via a specification object and can be rendered to either
    backend without modifying the calling code. Supports both declarative
    and layered workflows, flexible theming and colour palettes, optional
    'JavaScript' enhancements, and tools for exporting figures and interactive
    exploration via a 'shiny' app.
Implements the high-dimensional two-sample test 
    proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>. 
    It also implements the test proposed by Srivastava, Katayama, 
    and Kano (2013) <doi:10.1016/j.jmva.2012.08.014>. These tests 
    are particularly suitable to high dimensional data from two populations 
    for which the classical multivariate Hotelling's T-square test fails due 
    to sample sizes smaller than dimensionality. In this case, the ZWL and ZWLm 
    tests proposed by Zhang (2019) <http://hdl.handle.net/2097/40235>, 
    referred to as zwl_test() in this package, provide a reliable and powerful test.
Computes the scores and ranks candidates according to voting rules electing the highest median grade.
    Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>.
    Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.
A modern idiomatic header-only 'C++'' interface for 'libhdf5'. Original software can be found at <https://github.com/highfive-devs/highfive/>.
Provide functionality to manage, clean and match highfrequency
    trades and quotes data, calculate various liquidity measures, estimate and
    forecast volatility, detect price jumps and investigate microstructure noise and intraday
    periodicity. A detailed vignette can be found in the open-access paper 
    "Analyzing Intraday Financial Data in R: The highfrequency Package" 
    by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>).
Syntax highlighter for R code based on the results of the R
    parser. Rendering in HTML and latex markup. Custom Sweave driver
    performing syntax highlighting of R code chunks.
Code Syntax Highlighting made easy for code snippets or complete
    files. Whether you're documenting your data analysis or creating interactive
    'shiny' apps.
A tool to format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.
Input multiple versions of a source document, 
    and receive HTML code for a highlighted version of the source document
    indicating the frequency of occurrence of phrases in the different versions.
    This method is described in Chapter 3 of 
    Rogers (2024) <https://digitalcommons.unl.edu/dissertations/AAI31240449/>.
A unified, flexible framework for high dimensional feature
    selection in the presence of a survival outcome. Provides multiple
    machine learning approaches (Cox elastic net, random survival forest,
    accelerated oblique random survival forest, gradient-boosted Cox,
    stability selection, classical univariate Cox screening, pseudo-
    observation bridging to arbitrary regression learners, and Fine-Gray
    competing risks selection) under a single interface. Adds causal
    survival forest estimation of heterogeneous treatment effects on
    survival (experimental), conformal survival prediction with finite-
    sample coverage guarantees, and time-dependent 'SHAP' explanations via
    'SurvSHAP(t)'. Methodology is based on regularised Cox regression
    (2011) <doi:10.18637/jss.v039.i05>, random survival forests (2008)
    <doi:10.1214/08-AOAS169>, oblique random survival forests (2024)
    <doi:10.1080/10618600.2023.2231048>, stability selection (2010)
    <doi:10.1111/j.1467-9868.2010.00740.x>, causal survival forests (2023)
    <doi:10.1111/rssb.12538>, time-dependent survival explanations (2023)
    <doi:10.1016/j.knosys.2022.110234>, conformal survival prediction (2023)
    <doi:10.1093/biomet/asad043>, the Fine-Gray model for competing
    risks (1999) <doi:10.1080/01621459.1999.10474144>,
    and pseudo-observation regression (2010)
    <doi:10.1177/0962280209105020>.
The classical Markowitz's mean-variance portfolio formulation ignores 
    heavy tails and skewness. High-order portfolios use higher order moments to
    better characterize the return distribution. Different formulations and fast 
    algorithms are proposed for high-order portfolios based on the mean, variance, 
    skewness, and kurtosis.
    The package is based on the papers:
    R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via 
    Successive Convex Approximation Algorithms." <arXiv:2008.00863>.
    X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable 
    High-Order Portfolios Design via Parametric Skew-t Distribution." <arXiv:2206.02412>.
Provides syntax highlighting for R source code. Currently it
    supports LaTeX and HTML output. Source code of other languages is supported
    via Andre Simon's highlight package (<https://gitlab.com/saalen/highlight>).
Functions for determining and evaluating high-risk zones and
    simulating and thinning point process data, as described in 'Determining
    high risk zones using point process methodology - Realization by building
    an R package' Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> 
    and 'Determining high-risk zones for unexploded World War II bombs by using point 
    process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.
R interface to 'HiGHS', an optimization solver for solving mixed integer
        optimization problems with quadratic or linear objective and linear constraints.
HIGHT(HIGh security and light weigHT) algorithm is a block cipher encryption algorithm developed to provide confidentiality in computing environments that demand low power consumption and lightweight, such as RFID(Radio-Frequency Identification) and USN(Ubiquitous Sensor Network), or in mobile environments that require low power consumption and lightweight, such as smartphones and smart cards. Additionally, it is designed with a simple structure that enables it to be used with basic arithmetic operations, XOR, and circular shifts in 8-bit units. This algorithm was designed to consider both safety and efficiency in a very simple structure suitable for limited environments, compared to the former 128-bit encryption algorithm SEED. In December 2010, it became an ISO(International Organization for Standardization) standard. The detailed procedure is described in Hong et al. (2006) <doi:10.1007/11894063_4>.
Implements the method developed by Cao and Kosorok (2011) for the 
    significance analysis of thousands of features in high-dimensional 
    biological studies. It is an asymptotically valid data-driven procedure 
    to find critical values for rejection regions controlling the 
    k-familywise error rate, false discovery rate, and the tail probability 
    of false discovery proportion.
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
    a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
    In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
    See Su and Zhu (2018) <arXiv:1802.04876> for details.
Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires
    some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid,
    the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve
    each sample can be visualized as a simple density plot, and the distance between samples can be calculated from
    the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences
    between samples can identified using a simple bootstrap procedure.
Makes it easy to extract and combine variables from the HILDA (Household, Income and Labour Dynamics in Australia) survey maintained by the Melbourne Institute <https://melbourneinstitute.unimelb.edu.au/hilda>.
Calculate taxonomic, functional and phylogenetic diversity measures 
    through Hill Numbers proposed by Chao, Chiu and Jost (2014) 
    <doi:10.1146/annurev-ecolsys-120213-091540>.
A set of tools to create georeferenced hillshade relief 
    raster maps using ray-tracing and other advanced hill-shading 
    techniques. It includes a wrapper function to create a georeferenced,
    ray-traced hillshade map from a digital elevation model, and other
    functions that can be used in a rayshader pipeline.
Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques. Methods used in the package refer to Zhang H, Zheng Y, Hou L, Liu L, HIMA: An R Package for High-Dimensional Mediation Analysis. Journal of Data Science. (2025). <doi:10.6339/25-JDS1192>.
For supersonic aircraft, flying subsonic over land,
    find the best route between airports. Allow for coastal buffer and
    potentially closed regions. Use a minimal model of aircraft
    performance: the focus is on time saved versus subsonic flight, rather
    than on vertical flight profile. For modelling and forecasting, not for planning your
    flight!
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
Hypergeometric Intersection distributions are a
    broad group of distributions that describe the probability of picking
    intersections when drawing independently from two (or more) urns
    containing variable numbers of balls belonging to the same n
    categories. <arXiv:1305.0717>.
Provides helper functions for analysing patient data in
    hyperthermic intraperitoneal chemotherapy (HIPEC) workflows.
    Includes functions to estimate peritoneal surface area (PSA),
    summarise registry data, and produce reporting graphics.
    Body surface area calculations are based on Du Bois and Du Bois
    (1916) <doi:10.1001/archinte.1916.00080130010002>.
Can be used for paternity and maternity assignment and outperforms 
  conventional methods where closely related individuals occur in the pool of 
  possible parents. The method compares the genotypes of offspring with any 
  combination of potentials parents and scores the number of mismatches of these 
  individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). 
  It elaborates on a prior exclusion method based on the Homozygous Opposite Test 
  (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the 
  additional exclusion criterion HIPHOP (Homozygous Identical Parents, 
  Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential 
  parents are excluded if they have more mismatches than can 
  be expected due to genotyping error and mutation, and thereby one can identify the 
  true genetic parents and detect situations where one (or both) of the true 
  parents is not sampled. Package 'hiphop' can deal with (a) the case where there 
  is contextual information about parentage of the mother (i.e. a female has been 
  seen to be involved in reproductive tasks such as nest building), but paternity 
  is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, 
  because there is no contextual information on which female laid eggs and which 
  male fertilized them (e.g. polygynandrous mating system where multiple females 
  and males deposit young in a common nest, or organisms with external fertilisation 
  that breed in aggregations).
    For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L.,
       Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. 
       (in revision). HIPHOP: improved paternity assignment among close relatives 
       using a simple exclusion method for bi-allelic markers. 
       Molecular Ecology Resources, DOI to be added upon acceptance.
An 'RStudio' Addin for Hippie Expand (AKA Hippie Code
  Completion or Cyclic Expand Word). This type of completion searches for 
  matching tokens within the user's current source editor file, regardless of
  file type. By searching only within the current source file, 'hippie' offers a
  fast way to identify and insert completions that appear around the user's
  cursor.
Read hierarchical fixed width files like those commonly used by 
    many census data providers. Also allows for reading of data in chunks,
    and reading 'gzipped' files without storing the full file in memory.
Identifying labeled compounds in a 13C-tracer experiment in
    non-targeted fashion is a cumbersome process. This package facilitates
    such type of analyses by providing high level quality control plots,
    deconvoluting and evaluating spectra and performing a multitude of
    tests in an automatic fashion.  The main idea is to use changing
    intensity ratios of ion pairs from peak list generated with 'xcms' as
    candidates and evaluate those against base peak chromatograms and
    spectra information within the raw measurement data automatically.
    The functionality is described in Hoffmann et al. (2018)
    <doi:10.1021/acs.analchem.8b00356>.
Read 'PLINK' 1.9 binary datasets (BED/BIM/FAM) and generate the CSV
    files required by the Erasmus MC 'HIrisPlex' / 'HIrisPlex-S' webtool <https://hirisplex.erasmusmc.nl/>. It maps
    'PLINK' alleles to the webtool's required 'rsID_Allele' columns (0/1/2/NA).
    No external tools (e.g., 'PLINK CLI') are required.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
In some cases you will have data in a histogram format, where
  you have a vector of all possible observations, and a vector of how many
  times each observation appeared. You could expand this into a single 1D
  vector, but this may not be advisable if the counts are extremely large.
  'HistDat' allows for the calculation of summary statistics without the need
  for expanding your data.
The 'HistData' package provides a collection of small data sets
    that are interesting and important in the history of statistics and data
    visualization. The goal of the package is to make these available, both for
    instructional use and for historical research. Some of these present interesting
    challenges for graphics or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new
    approach to the statistical analysis of multi-valued data, we consider
    histogram-valued data, i.e., data described by univariate histograms. The
    methods and the basic statistics for histogram-valued data are mainly based
    on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
    between quantile functions. The package contains unsupervised classification
    techniques, least square regression and tools for histogram-valued data and for
    histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.
Using the MDL principle, it is possible to estimate
	parameters for a histogram-like model. The package contains
	the implementation of such an estimation method.
Automatic construction of regular and irregular histograms as described in Rozenholc/Mildenberger/Gather (2010).
Provides a number of utility functions useful for
    manipulating large histograms.  This includes methods to trim, subset,
    merge buckets, merge histograms, convert to CDF, and calculate
    information loss due to binning.  It also provides a protocol buffer
    representation of R's native histogram class to allow histograms over
    large data sets to be computed and combined in distributed analytical
    pipelines.  Implements bin-by-bin histogram distance measures
    described in Rubner, Tomasi and Guibas (2000)
    <doi:10.1023/A:1026543900054>, Swain and Ballard (1991)
    <doi:10.1007/BF00130487>, and Puzicha, Hofmann and Buhmann (1997)
    <doi:10.1109/CVPR.1997.609331>, and average shifted histograms as
    in Scott (2015, ISBN:9781118575536).
Historical borrowing in clinical trials can improve
  precision and operating characteristics. This package supports
  a hierarchical model and a mixture model to borrow historical
  control data from other studies to better characterize the
  control response of the current study. It also quantifies
  the amount of borrowing through benchmark models (independent
  and pooled). Some of the methods are discussed by
  Viele et al. (2013) <doi:10.1002/pst.1589>.
Historical borrowing in clinical trials can improve
  precision and operating characteristics. This package supports
  a longitudinal hierarchical model to borrow historical
  control data from other studies to better characterize the
  control response of the current study. It also quantifies
  the amount of borrowing through longitudinal benchmark models (independent
  and pooled). The hierarchical model approach to historical borrowing
  is discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.
These sample data sets are intended for historians
    learning R. They include population, institutional, religious,
    military, and prosopographical data suitable for mapping,
    quantitative analysis, and network analysis.
A histogram slider input binding for use in 'Shiny'. Currently supports creating histograms from numeric, date, and 'date-time' vectors.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.
We provide the monthly number of HIV and antiretroviral therapy (ART) cases of male, female, children and transgender as well as for the whole of Pakistan reported at various treatment centers in Pakistan from January 2016 to December 2021. Related works include: 
  a) Imran, M., Nasir, J. A., & Riaz, S. (2018). Regional pattern of HIV cases in Pakistan. Journal of Postgraduate Medical Institute, 32(1), 9-13. 
  <https://jpmi.org.pk/index.php/jpmi/article/view/2108>.
Hadoop InteractiVE facilitates distributed 
             computing via the MapReduce paradigm through R and Hadoop. An easy to use 
             interface to Hadoop, the Hadoop Distributed File System (HDFS), 
	     and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
An interactive 'Shiny' dashboard for visualizing and exploring key metrics related to HIV/AIDS, including prevalence, incidence, mortality, and treatment coverage. The dashboard is designed to work with a dataset containing specific columns with standardized names. These columns must be present in the input data for the app to function properly: year: Numeric year of the data (e.g. 2010, 2021); sex: Gender classification (e.g. Male, Female); age_group: Age bracket (e.g. 15–24, 25–34); hiv_prevalence: Estimated HIV prevalence percentage; hiv_incidence: Number of new HIV cases per year; aids_deaths: Total AIDS-related deaths; plhiv: Estimated number of people living with HIV; art_coverage: Percentage receiving antiretroviral therapy (ART); testing_coverage: HIV testing services coverage; causes: Description of likely HIV transmission cause (e.g. unprotected sex, drug use). The dataset structure must strictly follow this column naming convention for the dashboard to render correctly.
This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).
Datasets related to Hong Kong, including information on the 2019 elected District Councillors (<https://www.districtcouncils.gov.hk> and <https://dce2019.hk01.com/>) and traffic collision data from the Hong Kong Department of Transport (<https://www.td.gov.hk/>). All
    of the data in this package is available in the public domain.
Several procedures for the hierarchical kernel extreme value process of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>, including simulation, estimation and spatial extrapolation. The spatial latent variable model <DOI:10.1214/11-STS376> is also included.
Functions, Shiny apps and data for the book "Introduction to Statistics" by
   Wolfgang Karl Härdle, Sigbert Klinke, and Bernd Rönz (2015) <doi:10.1007/978-3-319-17704-5>.
A streamlined tool for eplet analysis of donor and recipient HLA (human leukocyte antigen) mismatch. Messy, low-resolution HLA typing data is cleaned, and imputed to high-resolution using the NMDP (National Marrow Donor Program) haplotype reference database <https://haplostats.org/haplostats>. High resolution data is analyzed for overall or single antigen eplet mismatch using a reference table (currently supporting 'HLAMatchMaker' <http://www.epitopes.net> versions 2 and 3). Data can enter or exit the workflow at different points depending on the user's aims and initial data quality.
A toolkit for the analysis and management of data for genes in the so-called "Human Leukocyte Antigen" (HLA) region. Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA 'GitHub' repository (ANHIG/IMGTHLA) <https://github.com/ANHIG/IMGTHLA>, validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods.
Provides access to datasets published by 'Hlídač státu' <https://www.hlidacstatu.cz/>, 
    a Czech watchdog, via their API.
A suite of diagnostic tools for hierarchical
    (multilevel) linear models. The tools include
    not only leverage and traditional deletion diagnostics (Cook's
    distance, covratio, covtrace, and MDFFITS) but also 
    convenience functions and graphics for residual analysis. Models
    can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package.
Provides functions for visualization and decomposition in
    hierarchical linear models (HLM) for applications in education, psychology,
    and the social sciences. Includes variance decomposition for two-level and
    three-level data structures following Snijders and Bosker (2012,
    ISBN:9781849202015), intraclass correlation (ICC) estimation and design
    effect computation as described in Shrout and Fleiss (1979)
    <doi:10.1037/0033-2909.86.2.420>, and contextual effect decomposition via
    the Mundlak (1978) <doi:10.2307/1913646> specification distinguishing
    within- and between-cluster components. Supports visualization of random
    slopes and cross-level interactions following Hofmann and Gavin (1998)
    <doi:10.1177/014920639802400504> and Hamaker and Muthen (2020)
    <doi:10.1037/met0000239>. Multilevel models are estimated using 'lme4'
    (Bates et al., 2015 <doi:10.18637/jss.v067.i01>). An optional 'Shiny'
    application enables interactive exploration of model components and
    parameter variation. The implementation follows the multilevel modeling
    framework of Raudenbush and Bryk (2002, ISBN:9780761919049).
Provides constructors for hl-rhotrices of dimension 2, 4 and 6,
    together with computation of the determinant, adjoint, inverse and
    eigenvalues under the Robust Multiplication Method (RMM).
    A 'ggplot2'-based function visualises the rhomboidal layout and the
    decomposition into principal minors.
Fits latent space models for single networks and hierarchical latent space models for ensembles of networks as described in Sweet, Thomas & Junker (2013).
Higher-order latent trait theory (item response theory). We
    implement the generalized partial credit model with a second-order latent
    trait structure. Latent regression can be done on the second-order latent
    trait. For a pre-print of the methods,
    see, "Latent Regression in Higher-Order Item Response Theory with the R
    Package hlt" <https://mkleinsa.github.io/doc/hlt_proof_draft_brmic.pdf>.
Provides interpretable high-dimensional mean comparison methods (HMC). 
    For example, users can apply these methods to assess the difference in 
    gene expression between two treatment groups. It is not a gene-by-gene 
    comparison. Instead, the methods focus on the interplay between features 
    and identify those that are predictive of the group label. The tests are 
    valid frequentist procedures and yield sparse estimates indicating which 
    features contribute to the group differences.
Fitting hidden Markov models of learning under the cognitive diagnosis framework.
  The estimation of the hidden Markov diagnostic classification model,
  the first order hidden Markov model, the reduced-reparameterized unified learning model,
  and the joint learning model for responses and response times.
Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models.  All of the code is written in R.  Theoretical references are listed below:.
    Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418, 
    Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" <arXiv:1701.02434>, 
    Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:2006.16194>,
    Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955, 
    Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034, 
    Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.
Holistic Multimodel Domain Analysis (HMDA) is a robust and transparent framework designed for exploratory machine learning research, aiming to enhance the process of feature assessment and selection. HMDA addresses key limitations of traditional machine learning methods by evaluating the consistency across multiple high-performing models within a fine-tuned modeling grid, thereby improving the interpretability and reliability of feature importance assessments. Specifically, it computes Weighted Mean SHapley Additive exPlanations (WMSHAP), which aggregate feature contributions from multiple models based on weighted performance metrics. HMDA also provides confidence intervals to demonstrate the stability of these feature importance estimates. This framework is particularly beneficial for analyzing complex, multidimensional datasets common in health research, supporting reliable exploration of mental health outcomes such as suicidal ideation, suicide attempts, and other psychological conditions. Additionally, HMDA includes automated procedures for feature selection based on WMSHAP ratios and performs dimension reduction analyses to identify underlying structures among features. For more details see Haghish (2025) <doi:10.13140/RG.2.2.32473.63846>.
Wrapper for 'Stan' that offers a number of in-built models to implement a hierarchical Bayesian longitudinal model for repeat observation data. Model choice selects the differential equation that is fit to the observations. Single and multi-individual models are available. O'Brien et al. (2024) <doi:10.1111/2041-210X.14463>.
Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<https://www.fertilitydata.org>), The Japanese Mortality Database (<https://www.ipss.go.jp/p-toukei/JMD/index-en.html>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
A set of objects and functions for Bayes Linear emulation and history matching.
  Core functionality includes automated training of emulators to data, diagnostic functions
  to ensure suitability, and a variety of proposal methods for generating 'waves' of points.
  For details on the mathematical background, there are many papers available on the topic
  (see references attached to function help files or the below references); for details of 
  the functions in this package, consult the manual or help files.
  Iskauskas, A, et al. (2024) <doi:10.18637/jss.v109.i10>.
  Bower, R.G., Goldstein, M., and Vernon, I. (2010) <doi:10.1214/10-BA524>.
  Craig, P.S., Goldstein, M., Seheult, A.H., and Smith, J.A. (1997) <doi:10.1007/978-1-4612-2290-3_2>.
Implementation of Bayesian regressions over the meta-d' model of 
    psychological data from two alternative forced choice tasks with 
    ordinal confidence ratings. For more information, see 
    Maniscalco & Lau (2012) <doi:10.1016/j.concog.2011.09.021>.
    The package is a front-end to the 'brms' package, which facilitates a wide
    range of regression designs, as well as tools for efficiently extracting 
    posterior estimates, plotting, and significance testing.
Contains many functions useful for data
	analysis, high-level graphics, utility operations, functions for
	computing sample size and power, simulation, importing and annotating datasets,
	imputing missing values, advanced table making, variable clustering,
	character string manipulation, conversion of R objects to LaTeX and html code,
	recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.
An algorithm for time series analysis that leverages hidden Markov models, cluster analysis, and mixture distributions to segment data, detect patterns and predict future sequences.
Easy to use library to setup, apply and make inference with discrete time and discrete space Hidden Markov Models.
Fits hidden Markov models with discrete non-parametric 
	observation distributions to data sets.  The observations may
	be univariate or bivariate. Simulates data from such models.
	Finds most probable underlying hidden states, the most
	probable sequences of such states, and the log likelihood
	of a collection of observations given the parameters of
	the model.  Auxiliary predictors are accommodated in the
        univariate setting.
Estimation procedures and goodness-of-fit test for several Markov regime switching models and mixtures of  bivariate copula models. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses Rosenblatt's transform and parametric bootstrap to estimate the p-value. The proposed methodologies are described in Nasri, Remillard and Thioub (2020) <doi:10.1002/cjs.11534>.
Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions.
Provides flexible maximum likelihood estimation and inference for Hidden
    Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs), as well as the
    underlying systems in which they operate. The package supports a wide range
    of observation and dwell-time distributions, offering a flexible modelling
    framework suitable for diverse practical data. Efficient implementations of 
    the forward-backward and Viterbi algorithms are provided via 'Rcpp' for enhanced 
    computational performance. Additional functionality includes model simulation, 
    residual analysis, non-initialised estimation, local and global decoding, 
    calculation of diverse information criteria, computation of confidence intervals 
    using parametric bootstrap methods, numerical covariance matrix estimation, and 
    comprehensive visualisation functions for interpreting the data-generating 
    processes inferred from the models. Methods follow standard approaches described 
    by Guédon (2003) <doi:10.1198/1061860032030>, 
    Zucchini and MacDonald (2009, ISBN:9781584885733), and 
    O'Connell and Højsgaard (2011) <doi:10.18637/jss.v039.i04>.
Functions for specifying and fitting marginal models for contingency tables proposed by Bergsma and Rudas (2002) <doi:10.1214/aos/1015362188> here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci, Colombi and Forcina (2007) <https://www.jstor.org/stable/24307737>; multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency tables proposed by Lang (2004) <doi:10.1214/aos/1079120140> and Lang (2005) <doi:10.1198/016214504000001042>. Inequality constraints on the parameters are allowed and can be tested.
Analysing time-series accelerometer data to quantify length and 
             intensity of physical activity using hidden Markov models. 
             It also contains the traditional cut-off point method.
             Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014).
             <doi:10.1371/journal.pone.0114089>.
Datasets and code examples that accompany our book Visser & Speekenbrink (2021), "Mixture and Hidden Markov Models with R", <https://depmix.github.io/hmmr/>.
Reliability Analysis and Maintenance Optimization using Hidden Markov Models (HMM). The use of HMMs to model the state of a system which is not directly observable and instead certain indicators (signals) of the true situation are provided via a control system. A hidden model can provide key information about the system dependability, such as the reliability of the system and related measures. An estimation procedure is implemented based on the Baum-Welch algorithm. Classical structures such as K-out-of-N systems and Shock models are illustrated. Finally, the maintenance of the system is considered in the HMM context and two functions for new preventive maintenance strategies are considered. Maintenance efficiency is measured in terms of expected cost. Methods are described in Gamiz, Limnios, and Segovia-Garcia (2023) <doi:10.1016/j.ejor.2022.05.006>.
Solves Hidden Markov Models (HMMs) via matrix and tensor
    decomposition. Converts observation sequences to co-occurrence
    matrices/tensors and applies Symmetric Non-negative Matrix
    Factorization (symNMF), Singular Value Decomposition (SVD),
    CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT)
    decomposition to recover HMM parameters.
    Also provides standard HMM algorithms (Forward, Backward, Viterbi,
    Baum-Welch) for comparison.
    The spectral learning approach for HMMs is based on
    Hsu, Kakade, and Zhang (2012) <doi:10.1016/j.jcss.2011.12.025>.
    The symNMF method is described in
    Kuang, Yun, and Park (2015) <doi:10.1007/s10898-014-0247-2>.
    The Tensor-Train decomposition is described in
    Oseledets (2011) <doi:10.1137/090752286>.
Fitting hidden Markov models using automatic differentiation 
  and Laplace approximation, allowing for fast inference and flexible covariate
  effects (including random effects and smoothing splines) on model parameters.
  The package is described by Michelot (2025) <doi:10.18637/jss.v114.i05>.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Provides functions to download, parse, and tidy statistical
    data published by 'HM Revenue and Customs' ('HMRC') on 'GOV.UK'.
    Returns annotated 'hmrc_tbl' data frames with provenance metadata
    (source URL, fetch time, vintage, cell methods) for reproducible
    fiscal research. Covers monthly tax receipts (41 tax heads from 2008),
    'VAT' (from 1973), fuel duties (from 1990), tobacco duties (from 1991),
    annual 'Corporation Tax' receipts, stamp duty, research and development
    tax credit statistics (from 2000), tax gap estimates, 'Income Tax'
    liabilities by income range, and monthly property transaction counts.
    File URLs are resolved at runtime via the 'GOV.UK' Content API
    <https://www.gov.uk/api/content>, so data is always current without
    hardcoded URLs. Files are cached locally between sessions.
Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.
Hierarchical Modelling of Species Communities (HMSC) is
   a model-based approach for analyzing community ecological data. 
   This package implements it in the Bayesian framework with Gibbs
   Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020)
   <doi:10.1111/2041-210X.13345>).
A collection of datasets and supporting functions accompanying Health Metrics and the Spread of Infectious Diseases by Federica Gazzelloni (2024). This package provides data for health metrics calculations, including Disability-Adjusted Life Years (DALYs), Years of Life Lost (YLLs), and Years Lived with Disability (YLDs), as well as additional tools for analyzing and visualizing health data. Federica Gazzelloni (2024) <doi:10.5281/zenodo.10818338>.
The HMS (Hierarchic Memetic Strategy) is a composite global optimization
  strategy consisting of a multi-population evolutionary strategy and some
  auxiliary methods. The HMS makes use of a dynamically-evolving data structure
  that provides an organization among the component populations. It is a tree
  with a fixed maximal height and variable internal node degree. Each component
  population is governed by a particular evolutionary engine. This package
  provides a simple R implementation with examples of using different genetic
  algorithms as the population engines. References: J. Sawicki, M. Łoś,
  M. Smołka, J. Alvarez-Aramberri (2022) <doi:10.1007/s11047-020-09836-w>.
Tracks elapsed clock time using a `hms::hms()` scalar. 
 It was was originally developed to time Bayesian model runs. 
 It should not be used to estimate how long extremely fast code takes to execute 
 as the package code adds a small time cost.
The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.
Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
The Hierarchical Neyman-Pearson (H-NP) classification framework
    extends the Neyman-Pearson classification paradigm to multi-class settings
    where classes have a natural priority ordering. This is particularly useful
    for classification in unbalanced dataset, for example, disease severity
    classification, where under-classification errors (misclassifying patients
    into less severe categories) are more consequential than other
    misclassifications. The package implements H-NP umbrella algorithms that
    controls under-classification errors under user specified control levels
    with high probability. It supports the creation of H-NP classifiers using
    scoring functions based on built-in classification methods (including
    logistic regression, support vector machines, and random forests), as well
    as user-trained scoring functions. For theoretical details, please refer to 
    Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong (2024) <doi:10.1080/01621459.2023.2270657>.
Suite of tools for managing cached files, targeting
    use in other R packages. Uses 'rappdirs' for cross-platform paths.
    Provides utilities to manage cache directories, including targeting
    files by path or by key; cached directories can be compressed and
    uncompressed easily to save disk space.
Allows to evaluate Higher Order Assortativity of complex networks defined through objects of class 'igraph' from the package of the same name. The package returns a result also for directed and weighted graphs. References, Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2017) <doi:10.1016/j.ejor.2017.04.028> Arcagni, A., Grassi, R., Stefani, S., & Torriero, A. (2021) <doi:10.1016/j.jbusres.2019.10.008> Arcagni, A., Cerqueti, R., & Grassi, R. (2023) <doi:10.48550/arXiv.2304.01737>.
Processing of CSV files generated by HOBO weather stations and 
  data loggers. The package automatically imports multiple HOBO data records, 
  removes duplicate records, identifies impossible values, subsets user-defined 
  time ranges, and summarizes environmental data.
Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project <https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/home>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: <doi:10.3334/CDIAC/ATG.009>.
Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code.
Estimates treatment effects  using covariate adjustment methods in Randomized Clinical Trials (RCT) motivated by higher-order influence functions (HOIF). Provides point estimates, oracle bias, variance, and approximate variance for HOIF-adjusted estimators. For methodology details, see Zhao et al. (2024) <doi:10.48550/arXiv.2411.08491> and Gu et al. (2025) <doi:10.48550/arXiv.2512.20046>.
Higher order likelihood inference is a promising approach for
    analyzing small sample size data. The 'holi' package provides web applications
    for higher order likelihood inference. It currently supports linear, logistic,
    and Poisson generalized linear models through the rstar_glm() function, based
    on Pierce and Bellio (2017) <doi:10.1111/insr.12232> and 'likelihoodAsy'.
    The package offers two main features: LA_rstar(), which launches an interactive
    'shiny' application allowing users to fit models with rstar_glm() through their
    web browser, and sim_rstar_glm_pgsql(), which streamlines the process of
    launching a web-based 'shiny' simulation application that saves results to a
    user-created 'PostgreSQL' database.
Convenience date tools for identifying weekends, business
    days, and Canadian holidays, including R wrappers for the Canada
    Holidays API <https://canada-holidays.ca/>.
Holistic generalized linear models (HGLMs) extend generalized linear models (GLMs) by enabling the possibility to add further constraints to the model. The 'holiglm' package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the 'README' and the accompanying paper <doi:10.18637/jss.v108.i07>.
Offers a convenient way to compute parameters in the framework of the theory of vocational choice introduced by J.L. Holland, (1997). A comprehensive summary to this theory  of vocational choice is given in Holland, J.L. (1997). Making vocational choices. A theory of vocational personalities and work environments.  Lutz, FL: Psychological Assessment.
Enables chat completion and text annotation with local and 'OpenAI' <https://openai.com/> language models, supporting batch processing, multiple annotators, and consistent output formats.
We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faith’s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. All functions rely on construction of a core community phylogeny, which is a phylogeny where branches are defined based on their presence in multiple samples from a single type of habitat. Our package provides two options for constructing the core community phylogeny, a tip-based approach, where the core community phylogeny is identified based on incidence of leaf nodes and a branch-based approach, where the core community phylogeny is identified based on incidence of individual branches. We suggest use of the microViz package.
Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets
    with groups of variables with different degrees of variance, covariance, and effect size.
A 'shiny' application, which allows you to perform single- and
    multi-omics analyses using your own omics datasets. After the upload 
    of the omics datasets and a metadata file, single-omics is performed
    for feature selection and dataset reduction. These datasets are used 
    for pairwise- and multi-omics analyses, where automatic tuning is 
    done to identify correlations between the datasets - the end goal 
    of the recommended 'Holomics' workflow.
    Methods used in the package were implemented in the package 'mixomics'
    by Florian Rohart,Benoît Gautier,Amrit Singh,Kim-Anh Lê Cao (2017) <doi:10.1371/journal.pcbi.1005752>
    and are described there in further detail.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
Implements indirect demographic methods for estimating adult
    mortality from orphanhood data. The package includes the standard
    Brass and Hill (1973) method
    <https://scholar.google.com/scholar_lookup?&title=Estimating%20Adult%20Mortality%20from%20Orphanhood&pages=111-23&publication_year=1973&author=Brass%2CW.&author=Hill.%2CK.>,
    the regression-based approach developed by Timaeus (1992)
    <https://pubmed.ncbi.nlm.nih.gov/12317481/>, and the adjustments
    proposed by Luy (2012) <doi:10.1007/s13524-012-0101-4> for
    low-mortality populations. A relational model is used to harmonize
    estimates into comparable adult mortality indicators. The package
    also provides diagnostic tools to assess the sensitivity of results
    to assumptions about the mean age of childbearing and the choice of
    model life table family.
A simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (Biometrika 106, 841-856, 2019).
Most common exact, asymptotic and resample based tests are provided for testing the 
            homogeneity of variances of k normal distributions under normality. 
            These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. 
            Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters.  
            Bartlett, M. S. (1937) <doi:10.1098/rspa.1937.0109> 
            Bhandary, M., & Dai, H. (2008) <doi:10.1080/03610910802431011>
            Brown, M. B., & Forsythe, A. B. (1974).<doi:10.1080/01621459.1974.10482955>
            Chang, C. H., Pal, N., & Lin, J. J. (2017) <doi:10.1080/03610918.2016.1202277>
            Gokpinar E. & Gokpinar F. (2017) <doi:10.1080/03610918.2014.955110>
            Liu, X., & Xu, X. (2010) <doi:10.1016/j.spl.2010.05.017>
            Levene, H. (1960) <https://cir.nii.ac.jp/crid/1573950400526848896>
            Gökpınar, E. (2020) <doi:10.1080/03610918.2020.1800037>.
A wrapper for the homologene database by the National Center for
    Biotechnology Information ('NCBI'). It allows searching for gene homologs across 
    species. Data in this package can be found at <ftp://ftp.ncbi.nih.gov/pub/HomoloGene/build68/>.
    The package also includes an updated version of the homologene database where 
    gene identifiers and symbols are replaced with their latest (at the time of
    submission) version and functions to fetch latest annotation data to keep updated.
Homomorphic computations in R for privacy-preserving applications. Currently only
             the Paillier Scheme is implemented.
Implements the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2016) <https://eprint.iacr.org/2016/421.pdf> schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures.
Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2023) <doi:10.1093/restud/rdad018>, "A More Credible Approach to Parallel Trends" [Previously titled "An Honest Approach..."]. Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets.
A utility to quickly obtain clean and tidy men's
    basketball play by play data. Provides functions to access
    live play by play and box score data from ESPN<https://www.espn.com> with shot locations
    when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper.
    It is also a scraping and aggregating interface for Ken Pomeroy's
    men's college basketball statistics website<https://kenpom.com>. It provides users with an
    active subscription the capability to scrape the website tables and
    analyze the data for themselves.
Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.
Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.
Supplement for the book "Handbook of Regression Methods" by D. S. Young.  Some datasets used in the book are included and documented.  Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis <doi:10.1007/BF02289447>). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
We provide extensions to the classical dataset "Example 4:
             Death by the kick of a horse in the Prussian Army" first
	           used by Ladislaus von Bortkeiwicz in his treatise on
	           the Poisson distribution "Das Gesetz der kleinen Zahlen",
	           <DOI:10.1017/S0370164600019453>.  As well as an
	           extended time series for the horse-kick death data, we also
             provide, in parallel, deaths by falling from a horse and by
	     drowning.
Package that accesses the 'Lichess' API (<https://lichess.org/api>).
  Supports both authenticated and unauthenticated requests. Basic functionality
  for game and player analysis.
Automatically displays the order and spatial weighting matrix of the distance between locations. This concept was derived from the research of Mubarak, Aslanargun, and Siklar (2021) <doi:10.52403/ijrr.20211150> and Mubarak, Aslanargun, and Siklar (2022) <doi:10.17654/0972361722052>. Distance data between locations can be imported from 'Ms. Excel', 'maps' package or created in 'R' programming directly. This package also provides 5 simulations of distances between locations derived from fictitious data, the 'maps' package, and from research by Mubarak, Aslanargun, and Siklar (2022) <doi:10.29244/ijsa.v6i1p90-100>.
Set of tools to help interested researchers to build hospital networks 
  from data on hospitalized patients transferred between hospitals. Methods provided 
  have been used in Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715>, 
  and Nekkab N, Crépey P, Astagneau P, Opatowski L, Temime L. (2020) <doi:10.1038/s41598-020-71212-6>.
A data set of the Portuguese 'NHS' hospitals.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
A set of R functions which implements Hotelling's T^2 test and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations.
Functions to calculate the Hotelling’s T-squared statistic and corresponding confidence ellipses. Provides the semi-axes of the Hotelling’s T-squared ellipses at 95% and 99% confidence levels. Enables users to obtain the coordinates in two or three dimensions at user-defined confidence levels, allowing for the construction of 2D or 3D ellipses with customized confidence levels. Bro and Smilde (2014) <DOI:10.1039/c3ay41907j>. Brereton (2016) <DOI:10.1002/cem.2763>.
Provides utilities for runtime hotpatching of locked R package namespaces. The package enables dynamic injection of function patches into sealed package environments without rebuilding or redeploying the package. This is particularly useful for legacy containerized workflows where package versions are frozen in place. The core functionality includes inject_patch() to inject patches into package namespaces, undo_patch() to restore original functions, apply_hotfix_file() to apply patches from external R scripts, and test_patched_dir() to run test suites against patched packages. The package implements namespace surgery techniques that allow internal callers to automatically see patched functions.
Contains data for software hotspot analysis, along with a function performing the analysis itself.
The hotspots package is designed to look within a set of measured values of a variable and identify values that are disproportionately high based on both the deviance of any given value from a statistical distribution and its similarity to other values. Because this relative magnitude of each value is taken into account, a value that is a statistical outlier may not always be a hot spot if other values are similarly large.
Manipulate data through memory-mapped files, as vectors, matrices or arrays.
    Basic arithmetic functions are implemented, but currently no matrix arithmetic.
    Can write and read descriptor files for compatibility with the 'bigmemory' package.
Lecțiile prof/cls trebuie completate cu un câmp "ora", astfel ca
    oricare două lecții prof/cls/ora să nu se suprapună într-o aceeași oră.
    The prof/cls lessons must be completed with a "hour" field ('ora), so that
    any two prof/cls/ora lessons do not overlap in the same hour.
    <https://vlad.bazon.net/>.
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016.  Additional data sets containing geographical information and links to Wikipedia are also included.
A wrapper around a CSS library called 'Hover.css', intended for use 
  in 'shiny' applications.
Audio interactivity within 'shiny' applications using 'howler.js'. Enables the
    status of the audio player to be sent from the UI to the server, and events such as
    playing and pausing the audio can be triggered from the server.
When performing multiple imputations, while 5-10 imputations are
             sufficient for obtaining point estimates, a larger number of
             imputations are needed for proper standard error estimates.
             This package allows you to calculate how many imputations are
             needed, following the work of von Hippel (2020)
             <doi:10.1177/0049124117747303>.
Helping to calculate cricket specific problems in a tidy & simple manner.
Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on the Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.
By binding R functions and the 'Highcharts' <http://www.highcharts.com/> charting library, 'hpackedbubble' package provides a simple way to draw split packed bubble charts.
Probabilistic models describing the behavior 
	of workload and queue on a High Performance Cluster and computing GRID 
	under FIFO service discipline basing on modified Kiefer-Wolfowitz 
	recursion. Also sample data for inter-arrival times, service times, 
	number of cores per task and waiting times of HPC of Karelian 
	Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
	Functions provided to import/export workload traces in Standard Workload Format (swf).
	Stability condition of the model may be verified either exactly, or approximately.
	Stability analysis: see Rumyantsev and Morozov (2017) <doi:10.1007/s10479-015-1917-2>,
	workload recursion: see Rumyantsev (2014) <doi:10.1109/PDCAT.2014.36>.
Provides two functions that implement the one-sided and
    two-sided versions of the Hodrick-Prescott filter. The one-sided
    version is a Kalman filter-based implementation, whereas the two-
    sided version uses sparse matrices for improved efficiency.
    References:
    Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682>
    Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x>
    Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html>
    For more references, see the vignette.
Compute house price indexes and series using a variety of different methods and
    models common through the real estate literature.  Evaluate index 'goodness' based
    on accuracy, volatility and revision statistics. Background on basic model construction
    for repeat sales models can be found at: Case and Quigley (1991) 
    <https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html> and for hedonic pricing models at: 
    Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the 
    random forest approach to house price indexes can be found at: <http://www.github.com/andykrause/hpi_research>.
Package that simplifies the use of the 'HPZone' API. Most of the annoying and labor-intensive parts of the interface are handled by wrapper functions. Note that the API and its details are not publicly available. Information can be found at <https://www.ggdghorkennisnet.nl/groep/726-platform-infectieziekte-epidemiologen/documenten/map/9609> for those with access.
Provides univariate and indexed (multivariate) nonparametric smoothed kernel estimators for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025)  <doi:10.1093/biomet/asaf008>.
Miscellaneous convenience functions and wrapper functions 
	to convert frequencies between Hz, semitones, mel and Bark, 
	to create a matrix of dummy columns from a factor, 
	to determine whether x lies in range [a,b], 
	and to add a bracketed line to an existing plot.
	This package also contains an example data set of a stratified sample
	of 80 talkers of Dutch.
Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.
Methods for data engineering in the human resources (HR) corporate domain. Designed for HR analytics practitioners and workforce-oriented data sets.
A compilation of extra 'ggplot2' themes, scales and utilities, including a
    spell check function for plot label fields and an overall emphasis on typography.
    A copy of the 'Google' font 'Roboto Condensed' is also included.
Nonparametric cumulative-incidence based estimation of the ratios of sub-hazard ratios to cause-specific hazard ratios using the approach from Ng et al. (2020).
Computes the hemodynamic response function (HRF) for task 
    functional magnetic resonance imaging (fMRI) data. Also includes functions
    for constructing a design matrix from task fMRI event timings, and for
    comparing multiple design matrices in a general linear model (GLM). A
    wrapper function is provided for GLM analysis of CIFTI-format data. Lastly,
    there are supporting functions which provide visual summaries of the
    HRFs and design matrices.
A program that conducts group variable selection for quantile and robust mean 
              regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for
              group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss.
              Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check 
              function can be approximated by the Huber loss for the median and the tilted version of 
              Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and
              has also been shown to be statistical consistent.
Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" <doi:10.48550/arXiv.2104.12597>, which will appear as <doi:10.1017/S0266466623000269>.
It is used to construct run sequences with minimum changes for half replicate of two level factorial run order. Experimenter can save time and resources by minimizing the number of changes in levels of individual factor and therefore the total number of changes. It consists of the function minimal_hrtlf(). This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. In Design of Experiments (DOE) theory, two level of a factor can be represented as integers e.g. - 1 for low and 1 for high. User is expected to enter total number of factors to be considered in the experiment. minimal_hrtlf() provides the required run sequences for the input number of factors.  The output also gives the number of changes of each factor along with total number of changes in the run sequence. Due to restricted randomization the minimally changed run sequences of half replicate of two level factorial run order will be affected by trend effect. The output also provides the Trend Factor value of the run order. Trend factor value will lies between 0 to 1. Higher the values, lesser the influence of trend effects on the run order.
The presence of outliers in a dataset can substantially bias the
    results of statistical analyses. To correct for outliers, micro edits are 
    manually performed on all records. A set of constraints and decision rules
    is typically used to aid the editing process. However, straightforward
    decision rules might overlook anomalies arising from disruption of linear
    relationships. Computationally efficient methods are provided to 
    identify historical, tail, and relational anomalies at the data-entry 
    level (Sartore et al., 2024; <doi:10.6339/24-JDS1136>). A score statistic
    is developed for each anomaly type, using a distribution-free approach
    motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used
    to detect cellwise outliers resulting from different types of anomalies.
    Each data entry is individually scored and individual scores are combined
    into a final score to determine anomalous entries. In contrast to fuzzy
    logic, Bayesian bootstrap and a Bayesian test based on empirical 
    likelihoods are also provided as studied by Sartore et 
    al. (2024; <doi:10.3390/stats7040073>). These algorithms allow for a more 
    nuanced approach to outlier detection, as it can identify outliers at 
    data-entry level which are not obviously distinct from the rest of the 
    data.
    ---
    This research was supported in part by the U.S. Department of Agriculture,
    National Agriculture Statistics Service. The findings and conclusions in
    this publication are those of the authors and should not be construed to
    represent any official USDA, or US Government determination or policy.
The book "Semiparametric Regression with R" by J. Harezlak, D. Ruppert & M.P. Wand (2018, Springer; ISBN: 978-1-4939-8851-8) makes use of datasets and scripts to explain semiparametric regression concepts. Each of the book's scripts are contained in this package as well as datasets that are not within other R packages. Functions that aid semiparametric regression analysis are also included.
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
Functions, data sets, analyses and examples from the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2006). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the 
  second edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Brian S. Everitt and Torsten
  Hothorn, Chapman & Hall/CRC, 2008). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  the package contains Sweave code for producing slides for selected
  chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the 
  third edition of the book 
  ''A Handbook of Statistical Analyses Using R'' (Torsten Hothorn and Brian S.
  Everitt, Chapman & Hall/CRC, 2014). The first chapter
  of the book, which is entitled ''An Introduction to R'', 
  is completely included in this package, for all other chapters,
  a vignette containing all data analyses is available. In addition,
  Sweave source code for slides of selected chapters is included in 
  this package (see HSAUR3/inst/slides). The publishers web page is 
  '<https://www.routledge.com/A-Handbook-of-Statistical-Analyses-using-R/Hothorn-Everitt/p/book/9781482204582>'.
The theoretical covariance between pairs of markers is calculated
    from either paternal haplotypes and maternal linkage disequilibrium (LD) or 
    vise versa. A genetic map is required. Grouping of markers is based on the 
    correlation matrix and a representative marker is suggested for each group.
    Employing the correlation matrix, optimal sample size can be derived for 
    association studies based on a SNP-BLUP approach.
    The implementation relies on paternal half-sib families and biallelic 
    markers. If maternal half-sib families are used, the roles of sire/dam are 
    swapped. Multiple families can be considered.
    Wittenburg, Bonk, Doschoris, Reyer (2020) "Design of Experiments for 
    Fine-Mapping Quantitative Trait Loci in Livestock Populations" 
    <doi:10.1186/s12863-020-00871-1>.
    Carlson, Eberle, Rieder, Yi, Kruglyak, Nickerson (2004) "Selecting a 
    maximally informative set of single-nucleotide polymorphisms for association
    analyses using linkage disequilibrium" <doi:10.1086/381000>.
User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 <doi:10.1890/04-0609>). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
We present this package for fitting structural equation models using the hierarchical likelihood method. This package allows extended structural equation model, including dynamic structural equation model. We illustrate the use of our packages with well-known data sets. Therefore, this package are able to handle two serious problems inadmissible solution and factor indeterminacy <doi:10.3390/sym13040657>.
Implementation of S4 class of sets and multisets of numbers.
    The implementation is based on the hash table from the package 'hash'.
    Quick operations are allowed when the set is a dynamic object.
    The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.
To test the homogeneity of stratum effects in stratified paired binary data.
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data.
Spatial heterogeneity can be specified in various ways. 'hspm' is an ambitious project that aims at implementing various methodologies to control for heterogeneity in spatial models. The current version of 'hspm' deals with spatial and (non-spatial) regimes models. In particular, the package allows to estimate a general spatial regimes model with additional endogenous variables, specified in terms of a spatial lag of the dependent variable, the spatially lagged regressors, and, potentially, a spatially autocorrelated error term. Spatial regime models are estimated by instrumental variables and generalized methods of moments (see Arraiz et al., (2010) <doi:10.1111/j.1467-9787.2009.00618.x>, Bivand and Piras, (2015) <doi:10.18637/jss.v063.i18>, Drukker et al., (2013) <doi:10.1080/07474938.2013.741020>, Kelejian and Prucha, (2010) <doi:10.1016/j.jeconom.2009.10.025>).
Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.
Paternal recombination rate and maternal linkage disequilibrium 
    (LD) are estimated for pairs of biallelic markers such as single nucleotide 
    polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The 
    implementation relies on paternal half-sib families. If maternal half-sib 
    families are used, the roles of sire/dam are swapped. Multiple families can 
    be considered. For parameter estimation, at least one sire has to be double 
    heterozygous at the investigated pairs of SNPs.
    Based on recombination rates, genetic distances between markers can be 
    estimated. Markers with unusually large recombination rate to markers in 
    close proximity (i.e. putatively misplaced markers) shall be discarded in 
    this derivation.
    *A pipeline is available at GitHub*
    <https://github.com/wittenburg/hsrecombi>
    Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of 
    recombination rate and maternal linkage disequilibrium in half-sibs" 
    <doi:10.3389/fgene.2018.00186>.
    Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium 
    in half-sib families" <doi:10.1534/genetics.111.137521>.
Linear and logistic regression models penalized with hierarchical
  shrinkage priors for selection of biomarkers (or more general variable
  selection), which can be fitted using Stan (Carpenter et al. (2017)
  <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
  horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
  as well as the projection predictive selection approach to recover a sparse
  set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
  <doi:10.1214/20-EJS1711>).
Fast, model-agnostic implementation of different H-statistics
    introduced by Jerome H. Friedman and Bogdan E. Popescu (2008)
    <doi:10.1214/07-AOAS148>.  These statistics quantify interaction
    strength per feature, feature pair, and feature triple.  The package
    supports multi-output predictions and can account for case weights.
    In addition, several variants of the original statistics are provided.
    The shape of the interactions can be explored through partial
    dependence plots or individual conditional expectation plots. 'DALEX'
    explainers, meta learners ('mlr3', 'tidymodels', 'caret') and most
    other models work out-of-the-box.
Implements a structured, reproducible framework for budget impact
    modelling (BIM) in health technology assessment (HTA), following the ISPOR
    Task Force guidelines (Sullivan et al. (2014) <doi:10.1016/j.jval.2013.08.2291>
    and Mauskopf et al. (2007) <doi:10.1111/j.1524-4733.2007.00187.x>).
    Provides functions for epidemiology-driven population estimation, market
    share modelling with flexible uptake dynamics, per-patient cost calculation
    across multiple cost categories, multi-year budget projections, payer
    perspective analysis, deterministic sensitivity analysis (DSA), and
    probabilistic sensitivity analysis (PSA) via Monte Carlo simulation.
    Produces submission-quality outputs including ISPOR-aligned summary tables,
    scenario comparison tables, per-patient cost breakdowns, tornado diagrams,
    PSA histograms, and text and HTML reports compatible with NICE, CADTH, and
    EU-HTA dossier formats. Ships with an interactive 'shiny' dashboard built on
    'bslib' for point-and-click model building and exploration.
Implements hierarchical Bayesian inference, robust frequentist
    inference, and distribution-free inference for dependent and unbalanced
    data under strong-mixing conditions. Supports triangular-array,
    weighted-sum and mixingale convergence regimes with Whittle and composite
    likelihoods, heteroskedasticity-and-autocorrelation-consistent variance
    estimation, block bootstrap with automatic block length, fixed-bandwidth
    HAR inference, adaptive conformal prediction, Bayesian decision under
    Region of Practical Equivalence, bridge-sampling Bayes factors, and
    predictive comparison via the Widely Applicable Information Criterion and
    leave-future-out cross-validation. Methods follow Andrews (1991)
    <doi:10.2307/2938229>, Kiefer and Vogelsang (2005)
    <doi:10.1017/S0266466605050565>, Patton, Politis and White (2009)
    <doi:10.1080/07474930802459016>, Vehtari, Gelman and Gabry (2017)
    <doi:10.1007/s11222-016-9696-4>, Kruschke (2018)
    <doi:10.1177/2515245918771304>, and Gibbs and Candes (2021)
    <doi:10.48550/arXiv.2106.00170>.
A collection of reweighted marginal hypothesis tests for clustered data, based
    on reweighting methods of Williamson, J., Datta, S., and Satten, G. (2003) <doi:10.1111/1541-0420.00005>.
    The tests in this collection are clustered analogs to well-known hypothesis tests
    in the classical setting, and are appropriate for data with cluster- and/or group-size 
    informativeness. The syntax and output of functions are modeled after common, 
    recognizable functions native to R. Methods used in the package refer to 
    Gregg, M., Datta, S., and Lorenz, D. (2020) <doi:10.1177/0962280220928572>, 
    Nevalainen, J., Oja, H., and Datta, S. (2017) <doi:10.1002/sim.7288>
    Dutta, S. and Datta, S. (2015) <doi:10.1111/biom.12447>, 
    Lorenz, D., Datta, S., and Harkema, S. (2011) <doi:10.1002/sim.4368>, 
    Datta, S. and Satten, G. (2008) <doi:10.1111/j.1541-0420.2007.00923.x>, 
    Datta, S. and Satten, G. (2005) <doi:10.1198/016214504000001583>.
Estimating heterogeneous treatment effects with tree-based machine
    learning algorithms and visualizing estimated results in flexible and 
    presentation-ready ways. For more information, see Brand, Xu, Koch, 
    and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package 
    first started as a fork of the 'causalTree' package on 'GitHub' and we 
    greatly appreciate the authors for their extremely useful and free package.
Two papers published in the early 2000's (Zeeberg, B.R., Feng, W., Wang, G. et al. 
  (2003) <doi:10.1186/gb-2003-4-4-r28>) and (Zeeberg, B.R., Qin, H., Narashimhan, S., et al. (2005)
  <doi:10.1186/1471-2105-6-168>) implement 'GoMiner' and 'High Throughput GoMiner'
  ('HTGM') to map lists of genes to the Gene Ontology (GO) <https://geneontology.org>. Until recently,
  these were hosted on a server at The National Cancer Institute (NCI). In order to continue
  providing these services to the bio-medical community, I have developed stand-alone versions.
  The current package 'HTGM' builds upon my recent package 'GoMiner'.
  The output of 'GoMiner' is a heatmap showing the relationship of a single list
  of genes and the significant categories into which they map. 'High Throughput GoMiner'
  ('HTGM') integrates the results of the individual 'GoMiner' analyses. The output of 'HTGM'
  is a heatmap showing the relationship of the significant categories derived from each gene list.
  The heatmap has only 2 axes, so the identity of the genes are unfortunately
  "integrated out of the equation." Because the graphic for the heatmap is implemented
  in Scalable Vector Graphics (SVG) technology, it is relatively easy to hyperlink each
  picture element to the relevant list of genes. By clicking on the desired picture
  element, the user can recover the "lost" genes.
The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. Microarray studies are usually analyzed with BP, whereas
  proteomics researchers often prefer CC. To capture the benefit of both of those ontologies,
  I developed a two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'). I generate a
  2D heat map whose axes are any two of BP, MF, or CC, and the value within
  a picture element of the heat map reflects the Jaccard metric p-value for the number of genes
  in common for the corresponding pair.
The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. To capture the benefit of all three ontologies,
  I developed 'HTGM3D', a three-dimensional version of 'GoMiner'.
The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. Microarray studies are usually analyzed with BP, whereas
  proteomics researchers often prefer CC. To capture the benefit of both of those ontologies,
  I now present an enhancement of the existing two-dimensional version of 'High-Throughput GoMiner' ('HTGM2D'),
  which is called 'HTGM4D'. The original 'HTGM2D' is augmented by adding two instances of the original
  'GoMiner' genes versus categories heatmaps, aligned with the categories axes of the 'HTGM2D' heatmap.
Efficient Bayesian multinomial logistic regression based on heavy-tailed
  (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters
  is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and
  Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed
  description of the method: Li and Yao (2018), 
  Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <doi:10.48550/arXiv.1405.3319>.
Convert a html document to plain texts by stripping off all html tags.
Provides functions for converting local 'HTML' and 'XHTML' files to 'PDF' using an external backend. The package includes helper functions for backend setup, file conversion, and an optional 'Tk' graphical interface.
Provides a 'Shiny' app allowing to convert 'HTML' code to 'R' code (e.g. '<span>Hello</span>' to 'tags$span("Hello")'), for usage in a 'Shiny' UI.
Generates valid HTML tag strings for HTML5 elements documented by Mozilla. 
    Attributes are passed as named lists, with names being the attribute name and values being the attribute value. 
    Attribute values are automatically double-quoted. To declare a DOCTYPE, wrap html() with function doctype().
    Mozilla's documentation for HTML5 is available here: <https://developer.mozilla.org/en-US/docs/Web/HTML/Element>.
    Elements marked as obsolete are not included.
Create compressed, interactive 'HTML' (Hypertext Markup Language) reports with embedded 'Python' code, custom 'JS' ('JavaScript') and 'CSS' (Cascading Style Sheets), and wrappers for
	'CanvasXpress' plots, networks and more. Based on <https://pypi.org/project/py-report-html/>, its sister project.
Tables with state-of-the-art layout elements such as row spanners,
    column spanners, table spanners, zebra striping, and more. While allowing
    advanced layout, the underlying css-structure is simple in order to maximize
    compatibility with common word processors. The package also contains a few
    text formatting functions that help outputting text compatible with HTML/LaTeX.
Facilitates automated HTML report creation, in particular
        framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
    contexts including the R console, 'R Markdown' documents, and 'Shiny'
    web applications.
A lightweight framework for building server-driven web
    applications in 'R'. 'htmxr' combines the simplicity of 'htmx' for
    partial page updates with the power of 'plumber2' for non-blocking
    HTTP endpoints. Build interactive dashboards and data applications
    without writing 'JavaScript', using familiar 'R' patterns inspired by
    'Shiny'. For more information on 'htmx', see <https://htmx.org>.
Analysis of Surface Plasmon Resonance (SPR) and Biolayer Interferometry data, with automations for high-throughput SPR. This version of the package fits the 1: 1 binding model, with and without bulkshift. It offers optional local or global Rmax fitting. The user must provide a sample sheet and a Carterra output file in Carterra's current format. There is a utility function to convert from Carterra's old output format. The user may run a custom pipeline or use the provided 'Runscript', which will produce a pdf file containing fitted Rmax, ka, kd and standard errors, a plot of the sensorgram and fits, and a plot of residuals. The script will also produce a .csv file with all of the relevant parameters for each spot on the SPR chip.
Detection of haplotype patterns that include single nucleotide polymorphisms (SNPs) and non-contiguous haplotypes that are associated with a phenotype. Methods for implementing HTRX are described in Yang Y, Lawson DJ (2023) <doi:10.1093/bioadv/vbad038> and Barrie W, Yang Y, Irving-Pease E.K, et al (2024) <doi:10.1038/s41586-023-06618-z>.
Provides methods for analysing and forecasting hierarchical and 
    grouped time series. The available forecast methods include bottom-up,
    top-down, optimal combination reconciliation (Hyndman et al. 2011) 
    <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation
    (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.
A Poisson mixture model is implemented to cluster genes from high-
    throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is
    performed using either the EM or CEM algorithm, and the slope heuristics are
    used for model selection (i.e., to choose the number of clusters).
Takes the MinT implementation of the 'hts'<https://cran.r-project.org/package=hts> package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024)<doi:10.15488/17729>. The MinT algorithm is based on Wickramasuriya et al. (2019)<doi:10.1080/01621459.2018.1448825>.
The seed germination process starts with water uptake by the seed and ends with the protrusion of radicle and plumule under varying temperatures and soil water potential. Hydrotime is a way to describe the relationship between water potential and seed germination rates at germination percentages. One important quantity before applying hydrotime modeling of germination percentages is to consider the proportion of viable seeds that could germinate under saturated conditions. This package can be used to apply correction factors at various water potentials before estimating parameters like stress tolerance, and uniformity of the hydrotime model. Three different distributions namely, Gaussian, Logistic, and Extreme value distributions have been considered to fit the model to the seed germination time course. Details can be found in Bradford (2002) <https://www.jstor.org/stable/4046371>, and Bradford and Still(2004) <https://www.jstor.org/stable/23433495>.
Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) <doi:10.1093/jxb/50.334.655> and Bradford (2002) <https://www.jstor.org/stable/4046371>.
Functions for the management and treatment of hydrology and 
  meteorology time-series stored in a 'Sqlite' data base.
A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity.
High throughput toxicokinetics ("HTTK") is the 
             combination of 1) chemical-specific in vitro measurements or in
             silico predictions and 2) generic mathematical models, to
             predict absorption, distribution, metabolism, and excretion by the
             body. HTTK methods have been described by Pearce et al. (2017) 
             (<doi:10.18637/jss.v079.i04>) and Breen et al. (2021) (<doi:10.1080/17425255.2021.1935867>).
             Here we provide examples (vignettes) applying HTTK to solve various
             problems in bioinformatics, toxicology, and exposure science.
             In accordance with Davidson-Fritz et al. (2025) (<doi:10.1371/journal.pone.0321321>),
             whenever a new HTTK model is developed, the code to generate the 
             figures evaluating that model is added as a new vignettte.
In order to improve performance for HTTP API clients, 'httpcache'
    provides simple tools for caching and invalidating cache. It includes the
    HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in
    replacements for those in the 'httr' package. These functions are cache-aware
    and provide default settings for cache invalidation suitable for RESTful
    APIs; the package also enables custom cache-management strategies.
    Finally, 'httpcache' includes a basic logging framework to facilitate the
    measurement of HTTP request time and cache performance.
Find and explain the meaning of 'HTTP' status codes.
    Functions included for searching for codes by full or partial number,
    by message, and get appropriate dog and cat images for many
    status codes.
A graphics device for R that is accessible via network protocols.
    This package was created to make it easier to embed live R graphics in 
    integrated development environments and other applications.
    The included 'HTML/JavaScript' client (plot viewer) aims to provide a better overall user experience when dealing with R graphics.
    The device asynchronously serves graphics via 'HTTP' and 'WebSockets'.
A suite of functions to ping 'URLs' and to time
    'HTTP' 'requests'. Designed to work with 'httr'.
Tools for emitting the 'Problem Details' structure defined in
  'RFC' 7807 <https://tools.ietf.org/html/rfc7807> for reporting errors from
  'HTTP' servers in a standard way.
Testing and documenting code that communicates with remote servers
    can be painful. Dealing with authentication, server state,
    and other complications can make testing seem too costly to
    bother with. But it doesn't need to be that hard. This package enables one
    to test all of the logic on the R sides of the API in your package without
    requiring access to the remote service. Importantly, it provides three
    contexts that mock the network connection in different ways, as well as
    testing functions to assert that HTTP requests were---or were
    not---made. It also allows one to safely record real API responses to use as
    test fixtures. The ability to save responses and load them offline also
    enables one to write vignettes and other dynamic documents that can be
    distributed without access to a live server.
Testing and documenting code that communicates with remote servers
    can be painful. This package helps with writing tests for packages that
    use 'httr2'. It enables testing all of the logic
    on the R sides of the API without requiring access to the
    remote service, and it also allows recording real API responses to use as
    test fixtures. The ability to save responses and load them offline also
    enables writing vignettes and other dynamic documents that can be
    distributed without access to a live server.
Provides low-level socket and protocol support for handling
    HTTP and WebSocket requests directly from within R. It is primarily
    intended as a building block for other packages, rather than making it
    particularly easy to create complete web applications using httpuv
    alone.  httpuv is built on top of the libuv and http-parser C
    libraries, both of which were developed by Joyent, Inc. (See LICENSE
    file for libuv and http-parser license information.)
Useful tools for working with HTTP organised by HTTP verbs
    (GET(), POST(), etc). Configuration functions make it easy to control
    additional request components (authenticate(), add_headers() and so
    on).
Tools for creating and modifying HTTP requests, then
    performing them and processing the results. 'httr2' is a modern
    re-imagining of 'httr' that uses a pipe-based interface and solves
    more of the problems that API wrapping packages face.
Collection of functions to help retrieving data from
    'Hub'Eau' the free and public French National APIs on water
    <https://hubeau.eaufrance.fr/>.
Functions for combining model outputs (e.g. predictions or
    estimates) from multiple models into an aggregated ensemble model
    output.
Core set of low-level utilities common across the 
  'hubverse'. Used to interact with 'hubverse' schema, Hub configuration files 
  and model outputs and designed to be primarily used internally by other 
  'hubverse' packages. See Reich et al. (2022) <doi:10.2105/AJPH.2022.306831> 
  for an overview of Collaborative Hubs.
Provides functions to access data from the US Department of Housing and Urban Development <https://www.huduser.gov/portal/dataset/fmr-api.html>.
Creating effective colour palettes for figures is 
    challenging. This package generates and plot palettes of optimally 
    distinct colours in perceptually uniform colour space, based on 
    'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>. 
    This is done through k-means clustering of CIE Lab colour space, 
    according to user-selected constraints on hue, chroma, and 
    lightness.
Provides a general framework for
        high-dimensional undirected graph estimation. It integrates
        data preprocessing, neighborhood screening, graph estimation,
        and model selection techniques into a pipeline. In
        preprocessing stage, the nonparanormal(npn) transformation is
        applied to help relax the normality assumption. In the graph
        estimation stage, the graph structure is estimated by
        Meinshausen-Buhlmann graph estimation, the graphical lasso,
        or the TIGER (tuning-insensitive graph estimation and
        regression) method, and the first two can be further
        accelerated by the lossy screening rule preselecting the
        neighborhood of each variable by correlation thresholding. We
        target on high-dimensional data analysis usually d >> n, and
        the computation is memory-optimized using the sparse matrix
        output. We also provide a computationally efficient approach,
        correlation thresholding graph estimation. Three
        regularization/thresholding parameter selection methods are
        included in this package: (1)stability approach for
        regularization selection (2) rotation information criterion (3)
        extended Bayesian information criterion which is only available
        for the graphical lasso.
An open-source R package to deploys reproducible and flexible labels using layers. 
  The 'huito' package is part of the 'inkaverse' project for developing different procedures and
  tools used in plant science and experimental designs. 
  Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
             to discriminate the class labels, visualizing the ROC curve for two or three class labels
             (Natalia Novoselova, Cristina Della Beffa, Junxi Wang, Jialiang Li, Frank Pessler, Frank Klawonn
             (2014) <doi:10.1093/bioinformatics/btu086>).
Format quantities of time or bytes into human-friendly strings.
Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.
Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling,
  in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>).
  The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).
Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.
Low level spell checker and morphological analyzer based on the 
    famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
    or check individual words as well as parse text, latex, html or xml documents.
    For a more user-friendly interface use the 'spelling' package which builds on
    this package to automate checking of files, documentation and vignettes in all
    common formats.
Contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.
The HURRECON model estimates wind speed, wind direction, enhanced 
    Fujita scale wind damage, and duration of EF0 to EF5 winds as a function 
    of hurricane location and maximum sustained wind speed. Results may be 
    generated for a single site or an entire region. Hurricane track and 
    intensity data may be imported directly from the US National Hurricane 
    Center's HURDAT2 database. For details on the original version of the 
    model written in Borland Pascal, see: Boose, Chamberlin, and Foster (2001) 
    <doi:10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2> and Boose, Serrano, 
    and Foster (2004) <doi:10.1890/02-4057>.
Allows users to create time series of tropical storm
    exposure histories for chosen counties for a number of hazard metrics
    (wind, rain, distance from the storm, etc.). This package interacts
    with data available through the 'hurricaneexposuredata' package, which
    is available in a 'drat' repository. To access this data package, see the 
    instructions at <https://github.com/geanders/hurricaneexposure>. 
    The size of the 'hurricaneexposuredata' package is
    approximately 20 MB. This work was supported in part by grants from the National
    Institute of Environmental Health Sciences (R00ES022631), the National Science
    Foundation (1331399), and a NASA Applied Sciences Program/Public Health Program
    Grant (NNX09AV81G).
Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.
Provides utility functions that are simply, frequently used, 
    but may require higher performance that what can be obtained from base R.
    Incidentally provides support for 'reverse geocoding', such as matching a point
    with its nearest neighbour in another array. Used as a complement to package
    'hutils' by sacrificing compilation or installation time for higher running 
    speeds. The name is a portmanteau of the author and 'Rcpp'.
Creates styled tables for data presentation. Export to HTML, LaTeX,
  RTF, 'Word', 'Excel', 'PowerPoint', 'typst', SVG and PNG. Simple, modern 
  interface to manipulate borders, size, position, captions, colours, 
  text styles and number formatting. Table cells can span multiple rows and/or columns.
  Includes  a 'huxreg' function to create regression tables, and 'quick_*' 
  one-liners to print tables to a new document.
Facilitates building topology preserving maps for data analysis.
Fits models for genotypic disequilibria, as described in
  Huttley and Wilson (2000) <doi:10.1093/genetics/156.4.2127>, Weir (1996) and Weir and Wilson (1986).
  Contrast terms are available that account for first order interactions
  between loci.  Also implements, for a single locus in a single
  population, a conditional exact test for Hardy-Weinberg equilibrium.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
Inference concerning equilibrium and random mating in 
    autopolyploids. Methods are available to test for equilibrium 
    and random mating at any even ploidy level (>2) in the presence
    of double reduction at biallelic loci. For autopolyploid populations
    in equilibrium, methods are available to estimate the degree of 
    double reduction. We also provide functions to calculate genotype 
    frequencies at equilibrium, or after one or several rounds of 
    random mating, given rates of double reduction. The main function is
    hwefit(). This material is based upon work supported by the
    National Science Foundation under Grant No. 2132247. The opinions,
    findings, and conclusions or recommendations expressed are those of
    the author and do not necessarily reflect the views of the National
    Science Foundation. For details of these methods, see 
    Gerard (2023a) <doi:10.1111/biom.13722> and
    Gerard (2023b) <doi:10.1111/1755-0998.13856>.
The half-weight index gregariousness (HWIG) is an association 
             index used in social network analyses. It extends the half-weight 
             association index (HWI), correcting for level of gregariousness 
             in individuals. It is calculated using group by individual 
             data according to methods described in Godde et al. (2013) 
             <doi:10.1016/j.anbehav.2012.12.010>.
Provides a way to display word clouds in R. The word cloud is a html widget, so you can use it in interactive documents and 'shiny' applications.
Easy-to-use and versatile functions to output R objects in
        HTML format.
Programmatic interface to the Harmonized World Soil Database 
    'HWSD' web services (<https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247>).
    Allows for easy downloads of 'HWSD' soil data directly to your R workspace 
    or your computer. Routines for both single pixel data downloads and
    gridded data are provided.
Provides methods to test whether time series is consistent
	with white noise. Two new tests based on Haar wavelets and general
	wavelets described by Nason and Savchev (2014)
	<doi:10.1002/sta4.69> are provided and, for comparison purposes
	this package also implements the
	B test of Bartlett (1967) <doi:10.2307/2333850>. Functionality
	is provided to compute an approximation to the theoretical
	power of the general wavelet test in the case of general
	ARMA alternatives.
The Hybrid design is a combination of model-assisted design (e.g., the modified 
             Toxicity Probability Interval design) with dose-toxicity model-based design for 
             phase I dose-finding studies. The hybrid design controls the overdosing 
             toxicity well and leads to a recommended dose closer to the true maximum tolerated 
             dose (MTD) due to its ability to calibrate for an intermediate dose. More details 
             can be found in Liao et al. 2022 <doi:10.1002/ijc.34203>.
Generates synthetic electronic health record data, including
    patients, encounters, vitals, laboratory results, medications,
    procedures, and allergies. The package supports optional SARS-focused
    and computed tomography (CT) research views and export to CSV, SQLite,
    and Excel formats for research and development workflows.
A set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz").
Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks. It is a metapopulation model in which each
    node in the network is a sub-population and disease spreads within nodes
    and among them, combining two approaches: stochastic simulation algorithm
    (<doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based
    approach, respectively. Equations that models spread within nodes are
    customizable and there are two link types among nodes: migration and
    influence (commuting). More information in Fernando S. Marques,
    Jose H. H. Grisi-Filho, Marcos Amaku et al. (2020) <doi:10.18637/jss.v094.i06>.
Using hybrid data, this package created a vividly colored hybrid heat map.
    The input is two files which are auto-selected.
    The first file has three columns, the first two for pairs of species,
    with the third column for the hybrid experiment code (an integer).
    The second file is a list of code and their descriptions in two 
    columns. The output is a figure showing the hybrid heat map with a color legend.
Method and tool for generating hybrid time series forecasts using
        an error remodeling approach. These forecasting approaches utilize a recursive 
        technique for modeling the linearity of the series using a linear method 
        (e.g., ARIMA, Theta, etc.) and then models (forecasts) the residuals of the linear forecaster
        using non-linear neural networks (e.g., ANN, ARNN, etc.). The hybrid architectures comprise three steps: 
        firstly, the linear patterns of the series are forecasted which are followed by an error re-modeling step, 
        and finally, the forecasts from both the steps are combined to produce the final output. This method additionally 
        provides the confidence intervals as needed. Ten different models can be implemented using this package.
        This package generates different types of hybrid error correction models for time series forecasting 
        based on the algorithms by Zhang. (2003), Chakraborty et al. (2019), Chakraborty et al. (2020), 
        Bhattacharyya et al. (2021), Chakraborty et al. (2022), and Bhattacharyya et al. (2022)
        <doi:10.1016/S0925-2312(01)00702-0> <doi:10.1016/j.physa.2019.121266> 
        <doi:10.1016/j.chaos.2020.109850> <doi:10.1109/IJCNN52387.2021.9533747> 
        <doi:10.1007/978-3-030-72834-2_29> <doi:10.1007/s11071-021-07099-3>.
An S4 class and several functions which utilize internally stored
    datasets and gauging data enable 1d water level interpolation. The S4 class
    (WaterLevelDataFrame) structures the computation and visualisation
    of 1d water level information along the German federal waterways Elbe and
    Rhine. 'hyd1d' delivers 1d water level data - extracted from the 'FLYS'
    database - and validated gauging data - extracted from the hydrological
    database 'WISKI7' - package-internally. For computations near real time
    gauging data are queried externally from the 'PEGELONLINE REST API' 
    <https://pegelonline.wsv.de/webservice/dokuRestapi>.
Raster based flood modelling internally using 'hyd1d', an R package
    to interpolate 1d water level and gauging data. The package computes flood
    extent and duration through strategies originally developed for 'INFORM',
    an 'ArcGIS'-based hydro-ecological modelling framework. It does not provide
    a full, physical hydraulic modelling algorithm, but a simplified, near real
    time 'GIS' approach for flood extent and duration modelling. Computationally
    demanding annual flood durations have been computed already and data
    products were published by Weber (2022) <doi:10.1594/PANGAEA.948042>.
Calculate an optimal embedding of a set of data points into low-dimensional hyperbolic space. This uses the strain-minimizing hyperbolic embedding of Keller-Ressel and Nargang (2019), see <arXiv:1903.08977>.
Functions for basic hydraulic calculations related to 
    water flow in circular pipes both flowing full (under pressure), and 
    partially full (gravity flow), and trapezoidal open channels. For 
    pressure flow this includes friction loss calculations by solving 
    the Darcy-Weisbach equation for head loss, flow or diameter, 
    plotting a Moody diagram, matching a pump characteristic curve to a system 
    curve, and solving for flows in a pipe network using the Hardy-Cross method. 
    The Darcy-Weisbach friction factor is calculated using the Colebrook 
    (or Colebrook-White equation), the basis of the Moody diagram, the original 
    citation being Colebrook (1939) <doi:10.1680/ijoti.1939.13150>. For gravity 
    flow, the Manning equation is used, again solving for missing parameters. 
    The derivation of and solutions using the Darcy-Weisbach equation and the
    Manning equation are outlined in many fluid mechanics texts such as 
    Finnemore and Maurer (2024, ISBN:978-1-264-78729-6). Some gradually- and 
    rapidly-varied flow functions are included. For the Manning equation
    solutions, this package uses modifications of original code from the 'iemisc' 
    package by Irucka Embry.
The 'hydReng' package provides a set of functions for hydraulic engineering tasks and natural hazard assessments. It includes basic hydraulics (wetted area, wetted perimeter, flow, flow velocity, flow depth, and maximum flow) for open channels with arbitrary geometry under uniform flow conditions. For structures such as circular pipes, weirs, and gates, the package includes calculations for pressure flow, backwater depth, and overflow over a weir crest. Additionally, it provides formulas for calculating bedload transport. The formulas used can be found in standard literature on hydraulics, such as Bollrich (2019, ISBN:978-3-410-29169-5) or Hager (2011, ISBN:978-3-642-77430-0).
Estimates frictional constants for hydraulic analysis 
    of rivers. This HYDRaulic ROughness CALculator (HYDROCAL) was previously 
    developed as a spreadsheet tool and accompanying documentation by McKay and
    Fischenich (2011, <https://erdc-library.erdc.dren.mil/jspui/bitstream/11681/2034/1/CHETN-VII-11.pdf>).
Pfafstetter Hydrological Codes 
    as cited in Verdin and Verdin (1999) <doi: 10.1016/S0022-1694(99)00011-6> 
    are decoded for upstream or downstream queries.
Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) <DOI:10.1029/2007WR006716>.
Provides a unified, extensible interface to discover
    hydrologic stations and download daily time series (e.g., water
    discharge, water level, water temperature, and several other water
    quality parameter) from national and regional public APIs. Includes a
    provider registry, S3 generics 'stations' and 'timeseries', licensing
    metadata, date-range and 'complete history' modes, rate limiting and
    retries, optional authentication via environment variables, tidy
    outputs, UTF-8 to ASCII transliteration, and WGS84 coordinates.
    Designed for reproducible workflows and straightforward addition of
    new providers.
	Background and use cases are described in Farber et al. (2025)
    <doi:10.5194/essd-17-4613-2025> and Farber et al. (2023)
    <doi:10.57757/IUGG23-2838>.
Computes scalar performance metrics and goodness-of-fit summaries
    for comparing simulated and observed hydrological or regression values.
    Provides error metrics, percentage bias, Nash-Sutcliffe efficiency,
    Kling-Gupta efficiency variants, correlation measures, agreement indices,
    and comparison tables via gof() and gof_compare(). Metric definitions are
    registry-governed with shared validation, provenance-aware wrappers, and
    explicit handling of undefined or degenerate cases. Methods include Nash
    and Sutcliffe (1970) <doi:10.1016/0022-1694(70)90255-6>, Gupta et al.
    (2009) <doi:10.1016/j.jhydrol.2009.08.003>, Kling et al. (2012)
    <doi:10.1016/j.jhydrol.2012.01.011>, and Willmott et al. (2012)
    <doi:10.1002/joc.2419>.
Events from individual hydrologic time series are extracted, and events
 are matched across multiple time series. The package has been applied in studies
 such as Wasko and Guo (2022) <doi:10.1002/hyp.14563> and Mohammadpour Khoie,
 Guo and Wasko (2025) <doi:10.1016/j.envsoft.2025.106521>.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
A collection of utilities that support creation of network attributes for hydrologic networks. Methods and algorithms implemented are documented in Moore et al. (2019) <doi:10.3133/ofr20191096>), Cormen and Leiserson (2022) <ISBN:9780262046305> and Verdin and Verdin (1999) <doi:10.1016/S0022-1694(99)00011-6>.
Estimates the parameters of infiltration and water retention models using the curve-fitting methods as shown in Omuto and Gumbe (2009) <doi:10.1016/j.cageo.2008.08.011>. The models considered are those that are commonly used in soil science. Version 2 of the package has new models for water retention characteristic curves.
State-of-the-art Multi-Objective Particle Swarm Optimiser (MOPSO), based on the algorithm developed by Lin et al. (2018) <doi:10.1109/TEVC.2016.2631279> with improvements described by Marinao-Rivas & Zambrano-Bigiarini (2020) <doi:10.1109/LA-CCI48322.2021.9769844>. This package is inspired by and closely follows the philosophy of the single objective 'hydroPSO' R package ((Zambrano-Bigiarini & Rojas, 2013) <doi:10.1016/j.envsoft.2013.01.004>), and can be used for global optimisation of non-smooth and non-linear R functions and R-base models (e.g., 'TUWmodel', 'GR4J', 'GR6J'). However, the main focus of 'hydroMOPSO' is optimising environmental and other real-world models that need to be run from the system console (e.g., 'SWAT+'). 'hydroMOPSO' communicates with the model to be optimised through its input and output files, without requiring modifying its source code. Thanks to its flexible design and the availability of several fine-tuning options, 'hydroMOPSO' can tackle a wide range of multi-objective optimisation problems (e.g., multi-objective functions, multiple model variables, multiple periods). Finally, 'hydroMOPSO' is designed to run on multi-core machines or network clusters, to alleviate the computational burden of complex models with long execution time.
An important environmental impact on running water ecosystems 
    is caused by hydropeaking - the discontinuous release of turbine water 
    because of peaks of energy demand. An event-based algorithm is implemented 
    to detect flow fluctuations referring to increase events (IC) and decrease 
    events (DC). For each event, a set of parameters related to the fluctuation 
    intensity is calculated. The framework is introduced in Greimel et al. (2016) 
    "A method to detect and characterize sub-daily flow fluctuations" 
    <doi:10.1002/hyp.10773> and can be used to identify different fluctuation 
    types according to the potential source: e.g., sub-daily flow fluctuations 
    caused by hydropeaking, rainfall, or snow and glacier melt.
    This is a companion to the package 'hydroroute', which is used to detect and 
    follow hydropower plant-specific hydropeaking waves at the sub-catchment 
    scale and to describe how hydropeaking flow parameters change along the
    longitudinal flow path as proposed and validated in Greimel et al. (2022).
Statistical functions used in the French 'HydroPortail' <https://hydro.eaufrance.fr/>.
    This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities.
    Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.
Implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) <doi:10.1002/rra.3978>. The identification of associated events is based on the event detection implemented in 'hydropeak'.
Identifies regime changes in streamflow runoff not explained by variations in precipitation. The package builds a flexible set of Hidden Markov Models of annual, seasonal or monthly streamflow runoff with precipitation as a predictor. Suites of models can be built for a single site, ranging from one to three states and each with differing combinations of error models and auto-correlation terms. The most parsimonious model is easily identified by AIC, and useful for understanding catchment drought non-recovery: Peterson TJ, Saft M, Peel MC & John A (2021) <doi:10.1126/science.abd5085>.
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
Read, plot, manipulate and process hydro-meteorological data records (with special features for Argentina and Chile data-sets).
Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling,
    data resampling, gap filler of precipitation, bias correction of forecasting
    data, flexible time series plot, and spatial map generation. It is a good pre-
    processing and post-processing tool for hydrological and hydraulic modellers.
Facilitates the analysis and evaluation of hydrologic model output and 
    time-series data with functions focused on comparison of modeled (simulated) and observed data, 
    period-of-record statistics, and trends.
High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
An interactive HTML widget of the perspective plot for generalized additive models.  An alternative solution of the function mgcv::vis.gam().  The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications.
A suite of routines for the hyperdirichlet distribution
   and reified Bradley-Terry; supersedes the 'hyperdirichlet' package;
   uses 'disordR' discipline <doi:10.48550/ARXIV.2210.03856>.  To cite
   in publications please use Hankin 2017 <doi:10.32614/rj-2017-061>,
   and for Generalized Plackett-Luce likelihoods use Hankin 2024
   <doi:10.18637/jss.v109.i08>.
Implements Data Envelopment Analysis (DEA) with a hyperbolic orientation using a non-linear programming solver. It enables flexible estimations with weight restrictions, non-discretionary variables, and a generalized distance function. Additionally, it allows for the calculation of slacks and super-efficiency scores. The methods are detailed in Öttl et al. (2023), <doi:10.1016/j.dajour.2023.100343>. Furthermore, the package provides a non-linear profitability estimation built upon the DEA framework.
Maintenance has been discontinued for this package. It has been
	     superseded by 'GeneralizedHyperbolic'. 'GeneralizedHyperbolic' 
	     includes all the functionality of 'HyperbolicDist' and more 
	     and is based on a more rational design. 'HyperbolicDist' 
	     provides functions for the hyperbolic and related 
	     distributions. Density, distribution and quantile functions 
	     and random number generation are provided for the hyperbolic 
	     distribution, the generalized hyperbolic distribution, 
	     the generalized inverse Gaussian distribution and the 
	     skew-Laplace distribution. Additional functionality is 
	     provided for the hyperbolic distribution, including 
	     fitting of the hyperbolic to data.
Provides functions and methods for organizing data in hypercubes
  (i.e., a multi-dimensional cube). Cubes are generated from molten data frames.
  Each cube can be manipulated with five operations: rotation (change.dimensionOrder()),
  dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()),
  and rolling up (remove.aggregation()).
Implements various tools for storing and analyzing hypergraphs.
  Handles basic undirected, unweighted hypergraphs, and various ways of 
  creating hypergraphs from a number of representations, and
  converting between graphs and hypergraphs.
Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.
The Gaussian hypergeometric function for complex numbers.
Computation of generalized hypergeometric function with tunable high precision in a vectorized manner, with the floating-point datatypes from 'mpfr' or 'gmp' library. The computation is limited to real numbers.
Uses support vector machines to identify a perfectly separating hyperplane (linear or curvilinear) between two entities in high-dimensional space. If this plane exists, the entities do not overlap. Applications include overlap detection in morphological, resource or environmental dimensions. More details can be found in: Brown et al. (2020) <doi:10.1111/2041-210X.13363> .
Comfortable ways to work with hyperspectral data sets.
    I.e. spatially or time-resolved spectra, or spectra with any other kind
    of information associated with each of the spectra. The spectra can be data
    as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
    etc. More generally, any data that is recorded over a discretized variable,
    e.g. absorbance = f(wavelength), stored as a vector of absorbance values
    for discrete wavelengths is suitable.
Provides a deterministic, framework-agnostic Domain-Specific Language for building
    'HTML' nodes and rendering them to a string.
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
Work with model files (setup, input, output) from
    the hydrological catchment model HYPE: Streamlined file import and export, standard 
    evaluation plot routines, diverse post-processing and aggregation routines 
    for hydrological model analysis. The HYPEtools package is also archived at <doi:10.5281/zenodo.7627955>
    and can be cited in publications with Brendel et al. (2024) <doi:10.1016/j.envsoft.2024.106094>.
An implementation of Random Forest-based two-sample tests as introduced in Hediger & Michel & Naef (2022).
Add, share and manage annotations for 'Shiny' applications and R Markdown documents via 'hypothes.is'.
Interact with the application programming interface for the web
    annotation service 'Hypothes.is' (See <http://hypothes.is> for more
    information.) Allows users to download data about public annotations, and
    create, retrieve, update, and delete their own annotations.
Provides a consistent API for hypothesis testing built on
    principles from 'Structure and Interpretation of Computer Programs':
    data abstraction, closure (combining tests yields tests), and
    higher-order functions (transforming tests). Implements z-tests,
    Wald tests, likelihood ratio tests, Fisher's method for combining
    p-values, and multiple testing corrections. Designed for use by
    other packages that want to wrap their hypothesis tests in a
    consistent interface.
Translation between experimental null hypotheses, hypothesis matrices, and contrast matrices as used in linear regression models. The package is based on the method described in Schad et al. (2019) <doi:10.1016/j.jml.2019.104038> and Rabe et al. (2020) <doi:10.21105/joss.02134>.
The hybrid model likelihood as described by Ramos-Goñi et al. (2017) <doi:10.1097/MLR.0000000000000283> is implemented and
      and embedded in a latent class framework. The package is based on 'flexmix' and among others contains an M-step-driver as described by Leisch (2004) <doi:10.18637/jss.v011.i08>.
      Users can, for example, estimate latent classes for EQ-5D value sets and address preference heterogeneity. Both uncensored and censored data are supported. 
      Furthermore, heteroscedasticity can be taken into account. It is possible to control for different covariates on the continuous and dichotomous  data and start values can differ
      between the expected latent classes.
Construction and analysis of multivalued zero-sum matrix games over the abstract space of probability distributions, which describe the losses in each scenario of defense vs. attack action. The distributions can be compiled directly from expert opinions or other empirical data (insofar available). The package implements the methods put forth in the EU project HyRiM (Hybrid Risk Management for Utility Networks), FP7 EU Project Number 608090. The method has been published in Rass, S., König, S., Schauer, S., 2016. Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions. PLoS ONE 11, e0168583. <doi:10.1371/journal.pone.0168583>, and applied for advanced persistent thread modeling in Rass, S., König, S., Schauer, S., 2017. Defending Against Advanced Persistent Threats Using Game-Theory. PLoS ONE 12, e0168675. <doi:10.1371/journal.pone.0168675>. A volume covering the wider range of aspects of risk management, partially based on the theory implemented in the package is the book edited by S. Rass and S. Schauer, 2018. Game Theory for Security and Risk Management: From Theory to Practice. Springer, <doi:10.1007/978-3-319-75268-6>, ISBN 978-3-319-75267-9.
We provide a stage-wise selection method using genetic algorithms, designed to efficiently identify main and two-way interactions within high-dimensional linear regression models. Additionally, it implements simulated annealing algorithm during the mutation process. The relevant paper can be found at: Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.
Enhance package 'testthat' by allowing tests to be attached to the function/object they test. 
    This allows to keep functional and unit test code together.
Estimate parameters of the hysteretic threshold autoregressive
    (HysTAR) model, using conditional least squares.
    In addition, you can generate time series data from the HysTAR model.
    For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.
Fit, summarize and plot sinusoidal hysteretic processes using:
    two-step simple harmonic least squares, ellipse-specific non-linear least
    squares, the direct method, geometric least squares or linear least squares. See 
    Yang, F and A. Parkhurst, "Efficient Estimation of Elliptical Hysteresis with 
    Application to the Characterization of Heat Stress" <DOI:10.1007/s13253-015-0213-6>.
An R API wrapper for the 'Hystreet' project <https://hystreet.com>. 'Hystreet' provides pedestrian counts in different cities in Germany.
Error type I and Optimal critical values to test statistical hypothesis based on Neyman-Pearson Lemma and Likelihood ratio test 
    based on random samples from several distributions. The families of distributions are Bernoulli, Exponential, Geometric, Inverse Normal, 
    Normal, Gamma, Gumbel, Lognormal, Poisson, and Weibull.
    This package is an ideal resource to help with the teaching of Statistics.
    The main references for this package are Casella G. and Berger R. (2003,ISBN:0-534-24312-6 , "Statistical Inference. Second Edition", Duxbury Press) and 
    Hogg, R., McKean, J., and Craig, A. (2019,ISBN:013468699, "Introduction to Mathematical Statistic. Eighth edition", Pearson).
Inference approach for jointly modeling correlated count and binary outcomes. This formulation allows simultaneous modeling of zero inflation via the Bernoulli component while providing a more accurate assessment of the Hierarchical Zero-Inflated Poisson's parsimony (Lizandra C. Fabio, Jalmar M. F. Carrasco, Victor H. Lachos and Ming-Hui Chen, Likelihood-based inference for joint modeling of correlated count and binary outcomes with extra variability and zeros, 2026, under submission).
Up-to-date data from the 'Unicode CLDR Project' (where 'CLDR'
    stands for 'Common Locale Data Repository') are available here as a series
    of easy-to-parse datasets. Several functions are provided for extracting
    key elements from the tabular datasets.
Suite of functions that help simulate elections under different electoral systems, which are then used to compute incentives generated by these systems in terms of the inter- and intra-party dimensions of electoral competition.
Contains data frames and functions used in the book "An Introduction to Acceptance Sampling and SPC with R". This book is available electronically at <https://bookdown.org/>. A physical copy will be published by CRC Press.
Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>.
Calculate false ring proportions from data frames of intra annual 
  density fluctuations.
Provides functions to conduct a model-agnostic asymptotic hypothesis test for the identification of interaction effects in black-box machine learning models. The null hypothesis assumes that a given set of covariates does not contribute to interaction effects in the prediction model. The test statistic is based on the difference of variances of partial dependence functions (Friedman (2008) <doi:10.1214/07-AOAS148> and Welchowski (2022) <doi:10.1007/s13253-021-00479-7>) with respect to the original black-box predictions and the predictions under the null hypothesis. The hypothesis test can be applied to any black-box prediction model, and the null hypothesis of the test can be flexibly specified according to the research question of interest. Furthermore, the test is computationally fast to apply as the null distribution does not require resampling or refitting black-box prediction models.
An interface to the algorithms of 'Interpretable AI'
    <https://www.interpretable.ai> from the R programming language.
    'Interpretable AI' provides various modules, including 'Optimal Trees' for
    classification, regression, prescription and survival analysis, 'Optimal
    Imputation' for missing data imputation and outlier detection, and 'Optimal
    Feature Selection' for exact sparse regression. The 'iai' package is an
    open-source project. The 'Interpretable AI' software modules are proprietary
    products, but free academic and evaluation licenses are available.
Provides a monthly summary of Iowa liquor (class E) sales from January 2015
    to October 2020. See the package website for more information, 
    documentation and examples. Data source: 
    Iowa Data portal <https://data.iowa.gov/resource/m3tr-qhgy.csv>.
The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) <arXiv:2301.00360>.
Implementation of some of the formulations for the thermodynamic
	and transport properties released by the International Association for
	the Properties of Water and Steam (IAPWS). More specifically, the
	releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97,
	R9-97, R10-06(2009), R11-24, R12-08, R15-11, R16-17(2018),
	R17-20 and R18-21 at <https://iapws.org>.
An implementation of the International Association for the
    Properties of Water (IAPWS) Formulation 1995 for the Thermodynamic Properties of Ordinary
    Water Substance for General and Scientific Use and on the releases for viscosity,
    conductivity, surface tension and melting pressure.
Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).
Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>.    The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample.    The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent).    Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds).    A sample dataset (titled 'sampledata') is included in this package to practice with.
This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).
An implementation of the iterative bootstrap procedure of 
    Kuk (1995) <doi:10.1111/j.2517-6161.1995.tb02035.x> to correct the estimation bias of a fitted model object. This
    procedure has better bias correction properties than the 
    bootstrap bias correction technique.
A collection of useful functions and datasets for the Data Science
  Course at IBAW.
Call wrappers for Istanbul Metropolitan
    Municipality's Open Data Portal (Turkish: İstanbul Büyükşehir
    Belediyesi Açık Veri Portalı) at <https://data.ibb.gov.tr/en/>.
Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.
Implements multiple variants of the Information Bottleneck ('IB') method 
    for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables.
    The package provides deterministic, agglomerative, generalized, 
    and standard 'IB' clustering algorithms that preserve relevant information while 
    forming interpretable clusters. The Deterministic Information Bottleneck is described in 
    Costa et al. (2026) <doi:10.1016/j.patcog.2026.113580>. The standard 'IB' method 
    originates from Tishby et al. (2000) <doi:10.48550/arXiv.physics/0004057>, 
    the agglomerative variant from Slonim and Tishby (1999) <https://papers.nips.cc/paper/1651-agglomerative-information-bottleneck>, 
    and the generalized 'IB' from Strouse and Schwab (2017) <doi:10.1162/NECO_a_00961>.
A collection of several utility functions related to binary incomplete block designs. Contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. Contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.
Implements continuous-time hidden Markov models (HMMs) to
    infer identity-by-descent (IBD) segments shared by two individuals
    from their single-nucleotide polymorphism (SNP) genotypes. Provides
    posterior probabilities at each marker (forward-backward algorithm),
    prediction of IBD segments (Viterbi algorithm), and functions for
    visualising results. Supports both autosomal data and X-chromosomal
    data.
This R package implements methods for estimation and inference under Incomplete Block Designs and Balanced Incomplete Block Designs within a design-based finite-population framework. Based on 'Koo and Pashley' (2026) <doi:10.1093/biomet/asag013>, it includes block-level estimators and extends to unit-level effects using 'Horvitz-Thompson' and 'Hájek' estimators. The package also provides asymptotic confidence intervals to support valid statistical inference.
Identity by Descent (IBD) distributions in pedigrees. A Hidden Markov
    Model is used to compute identity coefficients, simulate IBD segments and
    to derive the distribution of total IBD sharing and segment count across
    chromosomes. The methods are applied in Kruijver (2025) 
    <doi:10.3390/genes16050492>. The probability that the total IBD sharing
    is zero can be computed using the method of Donnelly (1983)
    <doi:10.1016/0040-5809(83)90004-7>.
Simulation of segments shared identical-by-descent (IBD) by
    pedigree members. Using sex specific recombination rates along the
    human genome (Halldorsson et al. (2019)
    <doi:10.1126/science.aau1043>), phased chromosomes are simulated for
    all pedigree members. Applications include calculation of realised
    relatedness coefficients and IBD segment distributions. 'ibdsim2' is
    part of the 'pedsuite' collection of packages for pedigree analysis.
    A detailed presentation of the 'pedsuite', including a separate
    chapter on 'ibdsim2', is available in the book 'Pedigree analysis in
    R' (Vigeland, 2021, ISBN:9780128244302). A 'Shiny' app for visualising
    and comparing IBD distributions is available at
    <https://magnusdv.shinyapps.io/ibdsim2-shiny/>.
Some basic functions to implement belief functions including:
    transformation between belief functions using the method introduced by
    Philippe Smets <arXiv:1304.1122>, evidence combination, evidence
    discounting, decision-making, and constructing masses. Currently, thirteen
    combination rules and six decision rules are supported. It can also be
    used to generate different types of random masses when working on belief
    combination and conflict management.
The initial basic feasible solution (IBFS) is a significant step to achieve the minimal total cost (optimal solution) of the transportation problem. However, the existing methods of IBFS do not always provide a good feasible solution which can reduce the number of iterations to find the optimal solution. This initial basic feasible solution can be obtained by using any of the following methods. 
             a) North West Corner Method. 
             b) Least Cost Method. 
             c) Row Minimum Method. 
             d) Column Minimum Method. 
             e) Vogel's Approximation Method.
             etc.
             For more technical details about the algorithms please refer below URLs.
             <https://theintactone.com/2018/05/24/ds-u2-topic-8-transportation-problems-initial-basic-feasible-solution/>.
             <https://www.brainkart.com/article/Methods-of-finding-initial-Basic-Feasible-Solutions_39037/>.
             <https://myhomeworkhelp.com/row-minima-method/>.
             <https://myhomeworkhelp.com/column-minima-method/>.
'Tidyverse'-friendly interface to the Brazilian Institute of Geography and Statistics ('IBGE') aggregate data 'API' 
    <https://servicodados.ibge.gov.br/api/docs/agregados?versao=3>. Query aggregates, variables, localities, periods, and metadata
    from surveys and censuses conducted by 'IBGE'.
Provides a lightweight R interface to the Interactive Brokers (IBKR)
    Client Portal REST API. Functions cover session management, account and portfolio
    queries, market data retrieval, and order placement and cancellation. Requires a
    locally running IBKR Client Portal Gateway.
Implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.
Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) 
  and methods to create new ones, particularly for population dynamics models (reproduction, 
  mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed.
Authentication can be the most difficult part about
  working with a new API. 'ibmAcousticR' facilitates making a
  connection to the 'IBM Acoustic' email campaign management API
  and executing various queries. The 'IBM Acoustic' API 
  documentation is available at
  <https://developer.ibm.com/customer-engagement/docs/>. This
  package is not supported by 'IBM'.
It provides a generic set of tools for initializing a synthetic
         population with each individual in specific disease states, and
         making transitions between those disease states according to the rates
         calculated on each timestep. The new version 1.0.0 has C++ code 
         integration to make the functions run faster. It has also a higher level
         function to actually run the transitions for the number of timesteps
         that users specify. Additional functions will follow for changing
         attributes on demographic, health belief and movement.
Functionality required to efficiently use R with IBM(R) Db2(R)
        Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in
        conjunction with IBM Db2 Analytics Accelerator for z/OS.
        Many basic and complex R operations are pushed down into the database, 
        which removes the main memory boundary of R and allows to make full 
        use of parallel processing in the underlying database.
        For executing R-functions in a multi-node environment in parallel the idaTApply() function
        requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>).
        The optional 'ggplot2' package is needed for the plot.idaLm() function only.
Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) <doi:10.48550/arXiv.2303.06183>. 
    The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). 
    Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating  mortality forecasts. 
    This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms  based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface.
Generates Personality Insights sunburst diagrams based on
    'IBM Watson' Personality Insights service output.
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Model agnostic tool for decomposition of predictions from black boxes.
    Supports additive attributions and attributions with interactions.
    The Break Down Table shows contributions of every variable to a final prediction. 
    The Break Down Plot presents variable contributions in a concise graphical way. 
    This package works for classification and regression models. 
    It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>,
    with new and faster strategies for orderings. 
    It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). 
    The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420>
    This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Provides native R access to Interactive Brokers Trader Workstation API.
Several functions to calculate two important indexes (IBR (Integrated Biomarker Response) and IBRv2 (Integrated Biological Response version 2)), it also calculates the standardized values for enzyme activity for each index, and it has a graphing function to perform radarplots that make great data visualization for this type of data. Beliaeff, B., & Burgeot, T. (2002). <https://pubmed.ncbi.nlm.nih.gov/12069320/>. Sanchez, W., Burgeot, T., & Porcher, J.-M. (2013).<doi:10.1007/s11356-012-1359-1>. Devin, S., Burgeot, T., Giambérini, L., Minguez, L., & Pain-Devin, S. (2014). <doi:10.1007/s11356-013-2169-9>. Minato N. (2022). <https://minato.sip21c.org/msb/>.
Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function.
Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible)
             using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is 
             evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
             are computed for variable selection. The thresholds values for variable selection are 
             computed using a nonparametric permutation test. 
             See 'Cyprien Mbogning' and 'Philippe Broet' (2016)<doi:10.1186/s12859-016-1090-x> for 
             an overview about the methods implemented in this package.
Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
Implementation of the classifier described in the paper Ali HR et al (2014) <doi:10.1186/s13059-014-0431-1>. It uses copy number and/or expression form breast cancer data, trains a Tibshirani's 'pamr' classifier with the features available and predicts the iC10 group.
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
A simple wrapper around the 'ical.js' library executing 
    'Javascript' code via 'V8' (the 'Javascript' engine driving the 'Chrome' 
    browser and 'Node.js' and accessible via the 'V8' R package). 
    This package enables users to parse 'iCalendar' files ('.ics', '.ifb', 
    '.iCal', '.iFBf') into lists and 'data.frames' to ultimately do statistics
    on events, meetings, schedules, birthdays, and the like.
To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) <doi:10.1038/s41467-020-18560-z>. It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) <doi:10.1038/ismej.2015.142> based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) <doi:10.1038/ismej.2013.93>. It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa).
Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) <doi:10.32614/RJ-2022-043>, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.
Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection.
Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.
Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.
Assist in the estimation of the Intraclass Correlation Coefficient
    (ICC) from variance components of a one-way analysis of variance and also
    estimate the number of individuals or groups necessary to obtain an ICC
    estimate with a desired confidence interval width.
Provides functions to calculate the requisite sample size for studies where ICC is
    the primary outcome. Can also be used for calculation of power. In both cases it
    allows the user to test the impact of changing input variables by calculating the outcome
    for several different values of input variables. Based off the work of Zou.
    Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with
    precision and assurance. Statistics in medicine, 31(29), 3972-3981.
Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.
A function and vignettes for computing an intraclass correlation
    described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>.
    This package quantifies the share of variance in a dependent variable that
    is attributed to group heterogeneity in slopes.
Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.
Provides methods for testing the equality of dependent intraclass correlation coefficients (ICCs) estimated using linear mixed-effects models. Several of the implemented approaches are based on the work of Donner and Zou (2002) <doi:10.1111/1467-9884.00324>.
Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) <doi:10.1111/j.1541-0420.2009.01335.x>.
The functions compute the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). Profiles comprising correlations are automatically transformed according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104> for details.
A comprehensive toolkit for intraclass correlation coefficient (ICC) analysis,
    integrating three core functionalities: (1) Closed-form sample size calculation for ICC
    estimation with assurance probability, based on Zou (2012) <doi:10.1002/sim.5466>;
    (2) Full implementation of all 10 ICC types (6 common + 4 supplementary) for point
    estimation, exact confidence interval calculation, and formal hypothesis testing,
    following the methods of McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and
    the standard decision framework; (3) An interactive 'shiny' application that guides users
    through ICC type selection, performs calculations, and provides reliability evaluation
    based on the Koo & Li (2016) <doi:10.1016/j.jcm.2016.02.012> criteria.
    Compared to existing packages, it provides a unified decision workflow and supports
    all less common ICC variants.
Implements the conditional inference forest approach 
  to modeling interval-censored survival data. It also provides 
  functions to tune the parameters and evaluate the model fit. See 
  Yao et al. (2019) <arXiv:1901.04599>.
Assists in generating categorical clustered outcome data, estimating the Intracluster Correlation Coefficient (ICC) for nominal or ordinal data with 2+ categories under the resampling and method of moments (MoM) methods, with confidence intervals.
Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) <doi:10.1080/13658810601073315> and the discrete Fréchet distance (Magdy et al. 2015) <doi:10.1109/IntelCIS.2015.7397286>.
Data from the United States Center for Medicare and Medicaid
    Services (CMS) is included in this package. There are ICD-9 and ICD-10
    diagnostic and procedure codes, and lists of the chapter and sub-chapter 
    headings and the ranges of ICD codes they encompass. There are also two 
    sample datasets. These data are used by the 'icd' package for finding
    comorbidities.
Provides convenient access to the German modification of the International Classification of Diagnoses, 10th revision (ICD-10-GM). It provides functionality to aid in the identification, specification and historisation of ICD-10 codes. Its intended use is the analysis of routinely collected data in the context of epidemiology, medical research and health services research. The underlying metadata are released by the German Institute for Medical Documentation and Information <https://www.dimdi.de>, and are redistributed in accordance with their license.
Provides tools for mapping International Classification of Diseases codes to comorbidity, 
    enabling the identification and analysis of various medical conditions within healthcare data.
Provides an estimator for generalized linear models with incomplete
    data for discrete covariates. The estimation is based on the EM algorithm by the
    method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.
Provides utilities to retrieve and manage 'ICD-10' code descriptions from 'Excel' files.
    Supports vectors, data frame columns, and association rule outputs (e.g., 'arules' objects)
    that require mapping to 'ICD-10' descriptions. Designed to simplify processing of healthcare datasets.
Categorization and scoring of injury severity typically involves
    trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function
    that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS')
    from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury 
    severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update 
    that accepts both 'ICD-9' and 'ICD-10' codes.
Functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject.
    They return original data with injury categorizations and severity scores added.
Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Programmatic access to NSIDC's sea ice concentration CDR <https://nsidc.org/data/g02202> via ERDAPP server and Sea Ice index <https://nsidc.org/data/g02135>. Supports caching results and optional fixes for some inconsistencies of the raw files.
Provides user-friendly and configurable print debugging via a
    single function, ic(). Wrap an expression in ic() to print the
    expression, its value and (where available) its source location.
    Debugging output can be toggled globally without modifying code.
Estimate test-retest reliability for complex sampling strategies 
    and extract variances using IntraClass Effect Decomposition. Developed by
    Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" <doi:10.7554/eLife.35718> 
    Also includes functions to simulate data based on sampling strategy. 
    Unofficial version release name: "Good work squirrels".
Generates the equiplot, an iconic dot-plot graph for visualizing inequalities, as well as three complex inequality measures: the slope index of inequality, the concentration index and the mean absolute difference to the mean. For more details see World Health Organization (2013) <https://www.who.int/docs/default-source/gho-documents/health-equity/handbook-on-health-inequality-monitoring/handbook-on-health-inequality-monitoring.pdf>.
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550>  and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Regression models for interval censored data. Currently supports
    Cox-PH, proportional odds, and accelerated failure time models. Allows for
    semi and fully parametric models (parametric only for accelerated failure
    time models) and Bayesian parametric models. Includes functions for easy visual
    diagnostics of model fits and imputation of censored data.
Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" <https://www.routledge.com/Survival-Analysis-with-Interval-Censored-Data-A-Practical-Approach-with/Bogaerts-Komarek-Lesaffre/p/book/9781420077476>. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.
We consider studies in which information from error-prone
    diagnostic tests or self-reports are gathered sequentially to determine the
    occurrence of a silent event. Using a likelihood-based approach
    incorporating the proportional hazards assumption, we provide functions to
    estimate the survival distribution and covariate effects. We also provide 
    functions for power and sample size calculations for this setting.
    Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) 
    <doi: 10.1214/15-AOAS810>, Xiangdong Gu and Raji Balasubramanian (2016) 
    <doi: 10.1002/sim.6962>, Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes,
    Yunsheng Ma, and Raji Balasubramanian (2020) <doi: 10.1186/s12911-020-01223-w>.
The app will calculate the ICER (incremental cost-effectiveness 
             ratio) Rawlins (2012) <doi:10.1016/B978-0-7020-4084-9.00044-6> from the mean 
             costs and quality-adjusted life years (QALY) Torrance and Feeny (2009) <doi:10.1017/S0266462300008461>
             for a set of treatment options, and draw the efficiency frontier 
             in the costs-effectiveness plane. The app automatically identifies 
             and excludes dominated and extended-dominated options from the ICER 
             calculation.
A collection of functions that facilitate computational steps
  related to advice for fisheries management, according to ICES guidelines.
  These include methods for calculating reference points and model diagnostics.
Programmatic connection to the 'OpenAltimetry' API <https://openaltimetry.earthdatacloud.nasa.gov/data/openapi/swagger-ui/index.html/> to download and process 'ATL03' (Global 'Geolocated' Photon Data), 'ATL06' (Land Ice Height), 'ATL07' (Sea Ice Height), 'ATL08' (Land and Vegetation Height), 'ATL10' (Sea Ice 'Freeboard'), 'ATL12' (Ocean Surface Height) and 'ATL13' (Inland Water Surface Height) 'ICESat-2' Altimeter Data. The user has the option to download the data by selecting a bounding box from a 1- or 5-degree grid globally utilizing a shiny application. The 'ICESat-2' mission collects 'altimetry' data of the Earth's surface. The sole instrument on 'ICESat-2' is the Advanced Topographic Laser Altimeter System (ATLAS) instrument that measures ice sheet elevation change and sea ice thickness, while also generating an estimate of global vegetation biomass. 'ICESat-2' continues the important observations of ice-sheet elevation change, sea-ice 'freeboard', and vegetation canopy height begun by 'ICESat' in 2003.
Provides user tokens for ICES web services that require
  authentication and authorization.  Web services covered by this
  package are ICES VMS database, the ICES DATSU web services, and the
  ICES SharePoint site <https://www.ices.dk/data/tools/Pages/WebServices.aspx>.
R interface to access the web services of the ICES (International
             Council for the Exploration of the Sea) DATRAS trawl survey
             database <https://datras.ices.dk/WebServices/Webservices.aspx>.
Functions to Interact with the ICES Data Submission Utility (DATSU)
  <https://datsu.ices.dk/web/index.aspx>.
Run quality checks on data sets using the same checks that are conducted
  on the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk>.
R interface to access the web services of the ICES Stock Assessment
             Graphs database <https://sg.ices.dk>.
R interface to access the web services of the ICES Stock Database <https://sd.ices.dk>.
Functions to support the ICES Transparent Assessment Framework
  <https://taf.ices.dk> to organize data, methods, and results used in ICES
  assessments. ICES is an organization facilitating international collaboration
  in marine science.
R interface to access the Vocabularies REST API of the ICES
  (International Council for the Exploration of the Sea) Vocabularies database
  <https://vocab.ices.dk/services/>.
It is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
Calculate various information criteria in literature for "lm" and "glm" objects.
Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data.
An implementation of 'Ichimoku Kinko Hyo', also commonly
    known as cloud charts. Static and interactive visualizations with
    tools for creating, backtesting and development of quantitative
    ichimoku strategies. As described in Sasaki (1996, ISBN:4925152009),
    the technique is a refinement on candlestick charting, originating
    from Japan and now in widespread use in technical analysis worldwide.
    Translating as one-glance equilibrium chart, it allows the price
    action and market structure of financial securities to be determined
    at-a-glance. Incorporates an interface with the 'OANDA fxTrade' API
    <https://developer.oanda.com/> for retrieving historical and live
    streaming price data for major currencies, metals, commodities,
    government bonds and stock indices.
A GUI designed to support the analysis of financial-economic time
    series data.
We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).
A variational Bayesian approach for fast integrative
    clustering and feature selection, facilitating the analysis of
    multi-view, mixed type, high-dimensional datasets with applications in
    fields like cancer research, genomics, and more.
Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.
Allows for the non-parametric estimation of transition intensities in interval-censored
    multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or 
    Gu et al. (2023) <doi:10.1093/biomet/asad073>.
Sieve semiparametric likelihood methods for analyzing 
 interval-censored failure time data from an outcome-dependent sampling (ODS) 
 design and from a case-cohort design. 
 Zhou, Q., Cai, J., and Zhou, H. (2018) <doi:10.1111/biom.12744>; 
 Zhou, Q., Zhou, H., and Cai, J. (2017)  <doi:10.1093/biomet/asw067>.
Implements the Information Combination (IComb) approach proposed by Nguyen, 
      Vahid and Wickramasuriya (2025)<https://www.monash.edu/business/ebs/research/publications/ebs/2025/wp11-2025.pdf> for hierarchical forecast reconciliation. The method
      combines information from base forecasts constructed using different information
      sets while ensuring coherence. It is implemented using a penalized regression-based
      framework.
Single Layer Feed-forward Neural networks (SLFNs) have many applications in various fields of statistical modelling, especially for time-series forecasting. However, there are some major disadvantages of training such networks via the widely accepted 'gradient-based backpropagation' algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) <doi:10.1109/72.761722>, namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) <doi:10.1080/14786440109462720>, i.e., the PCA based ELM model given by Castano et al. (2013) <doi:10.1007/s11063-012-9253-x>, from which the implemented ICA based algorithm is greatly inspired.
Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs.
Survival analysis of interval-censored data with proportional hazards, and an explicit smooth estimate of the baseline log-hazard with P-splines.
Reproducible, programmatic retrieval of datasets from the
    Inter-university Consortium for Political and Social Research archive.
Provides functions to compute and plot Krippendorff's inter-coder 
    reliability coefficient alpha and bootstrapped uncertainty estimates 
    (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to
    make use of parallel threads where supported.
Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009,
            <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's
            (AJS, 2006, <https://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) method of two different
            scatter matrices to obtain an invariant coordinate system or independent
            components, depending on the underlying assumptions.
Implementation of tandem clustering with invariant coordinate 
              selection with different scatter matrices and several choices for the 
              selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2024) <doi:10.1016/j.ecosta.2024.03.002>.
Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. The paper describing these methods is " Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes" by Sun R, Zhu L, Li Y, Yasui Y, & Robison L (Biometrics 2023, <doi:10.1111/biom.13636>).
Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.
The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) <https://www.jstor.org/stable/2290916> detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. For series of moderate size (i.e. 200 observations and beyond), the ICSS algorithm offers results comparable to those obtained by a Bayesian approach or by likelihood ration tests, without the heavy computational burden required by these approaches.
Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2018) <doi:10.1016/j.csda.2018.06.011>) using a shiny app.
We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Provides a unified and consistent interface for computing 
    classical and bootstrap confidence intervals for means, variances, 
    proportions, variance ratios, and regression coefficients. The package 
    offers a standardized output structure, S3 classes, and user-friendly 
    methods to facilitate statistical analysis and reproducibility.
Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.
Functions for converting time series of spatial abundance or density 
    data in raster format to vector fields of population movement using the digital 
    image correlation technique. More specifically, the functions in the package 
    compute cross-covariance using discrete fast Fourier transforms for computational 
    efficiency. Vectors in vector fields point in the direction of highest two 
    dimensional cross-covariance. The package has a novel implementation of the 
    digital image correlation algorithm that is designed to detect persistent 
    directional movement when image time series extend beyond a sequence of 
    two raster images.
Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.
Provides datasets and functions for the class "Modelling and Data Analysis for Pharmaceutical Sciences". 
             The datasets can be used to present various methods of data analysis and statistical modeling.
             Functions for data visualization are also implemented.
Use R to make requests to the US Census Bureau's International Data Base API.
             Results are returned as R data frames.  For more information about the IDB API, visit
             <https://www.census.gov/data/developers/data-sets/international-database.html>.
The digits of the old version (before 2000 year) of 'Chinese ID Card Number' is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given 'ID' is right or not.
Provides access to the 
    Idea Data Center (IDC) application for conducting 
    nonresponse bias analysis (NRBA). The IDC NRBA app is an
    interactive, browser-based Shiny application that can be used to 
    analyze survey data with respect to response rates,
    representativeness, and nonresponse bias. This app provides a user-friendly
    interface to statistical methods implemented by the 'nrba' package.
    Krenzke, Van de Kerckhove, and Mohadjer (2005) 
    <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf>
    and Lohr and Riddles (2016) 
    <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR>
    provide an overview of the statistical methods implemented in the application.
The Integro-Difference Equation model is a linear, dynamical model used to model
   phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011,
   ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the
   heart of the model is the kernel, which dictates how the process evolves from one time
   point to the next. Both process and parameter reduction are used to facilitate computation,
   and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed
   to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted
   by maximum likelihood, and estimation is carried out using an evolution algorithm.
When added to an existing shiny app, users may subset any
    developer-chosen R data.frame on the fly. That is, users are empowered to
    slice & dice data by applying multiple (order specific) filters using the
    AND (&) operator between each, and getting real-time updates on the number
    of rows effected/available along the way. Thus, any downstream processes
    that leverage this data source (like tables, plots, or statistical procedures)
    will re-render after new filters are applied. The shiny module’s user interface has
    a 'minimalist' aesthetic so that the focus can be on the data &
    other visuals. In addition to returning a reactive (filtered) data.frame,
    'IDEAFilter' as also returns 'dplyr' filter statements used to actually slice
    the data.
Offers item-response theory (IRT) ideal-point measurement modeling for diverse distributions, missing data, and over-time variation. Full and approximate Bayesian sampling with 'Stan' (<https://mc-stan.org/>).
Time series plain text conversion and data visualization. It allows
    to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales)
    daily series from plain text to CSV files or data frames in R. Additionally,
    it is possible to obtain exploratory graphs from times series. IDEAM’s data
    is freely delivered under formal request through the official web page
    <http://www.ideam.gov.co/solicitud-de-informacion>.
A suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. ‘ideanet’ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, ‘ideanet’ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.
Collection of tools to automate the processing of data collected though the IDEA4 method (see Zahm et al. (2018) <doi:10.1051/cagri/2019004> ). Starting from the original data collecting files this packages provides functions to compute IDEA indicators, draw modern and aesthetic plots, and produce a wide range of reporting materials.
Generates efficient designs for discrete choice experiments based on
              the multinomial logit model, and individually adapted designs for the mixed 
              multinomial logit model. The generated designs can be presented on screen
              and choice data can be gathered using a shiny application. Traets F, Sanchez G, 
              and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.
Interactive dendrogram that enables the user to select and
    color clusters, to zoom and pan the dendrogram, and to visualize
    the clustered data not only in a built-in heat map, but also in
    'GGobi' interactive plots and user-supplied plots. 
    This is a backport of Qt-based 'idendro' 
    (<https://github.com/tsieger/idendro>) to base R graphics and 
    Tcl/Tk GUI.
An implementation of various methods for estimating intrinsic
    dimension of vector-valued dataset or distance matrix. Most methods implemented
    are based on different notion of fractal dimension such as the capacity
    dimension, the box-counting dimension, and the information dimension.
The IDetect provides efficient implementation of the ID methodology 
    for the consistent estimation of the number and location of multiple 
    change-points in one-dimensional data sequences from the `deterministic 
    + noise' model. Currently implemented scenarios are: piecewise-constant signal, 
    piecewise-constant signal with a heavy-tailed noise, continuous piecewise-linear 
    signal, continuous piecewise-linear signal with a heavy-tailed noise.
Intensity-duration-frequency (IDF) curves are a widely used analysis-tool
              in hydrology to assess extreme values of precipitation
              [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>].
              The package 'IDF' provides functions to estimate IDF parameters for given
              precipitation time series on the basis of a duration-dependent
              generalized extreme value distribution
              [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].
A joint mixture model has been developed by Majumdar et al. (2025) <doi:10.48550/arXiv.2412.17511> that integrates information from gene expression data and methylation data at the modelling stage to capture their inherent dependency structure, enabling simultaneous identification of differentially methylated cytosine-guanine dinucleotide (CpG) sites and differentially expressed genes. The model leverages a joint likelihood function that accounts for the nested structure in the data, with parameter estimation performed using an expectation-maximisation algorithm.
A toolkit for detecting Differential Item Functioning (DIF) 
    using Logistic Regression (LR) as described in Swaminathan and Rogers 
    (1990) <doi:10.1111/j.1745-3984.1990.tb00754.x>, the IRT Likelihood Ratio Test 
    (LRT) following Thissen, Steinberg & Wainer
    (1993, ISBN:0-8058-0972-4), and model-based recursive partitioning 
    (MOB) as implemented in 'strucchange' following Strobl, Kopf and Zeileis 
    (2015) <doi:10.1007/s11336-013-9388-3>. Designed for both standard two-group 
    and intersectional multi-group designs, 'iDIFr' prioritises effect size 
    reporting alongside statistical significance, clear guidance on group 
    construction, and interpretable output suitable for applied testing 
    contexts. Built-in Intersectional Contrast Analysis (ICA) classifies 
    items as amplified, pure-intersection, obscured, or none by comparing 
    single-variable and intersectional analyses.
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).
Carry out comparative authorship analysis of disputed and undisputed texts within the Likelihood Ratio Framework for expressing evidence in forensic science. This package contains implementations of well-known algorithms for comparative authorship analysis, such as Smith and Aldridge's (2011) Cosine Delta <doi:10.1080/09296174.2011.533591> or Koppel and Winter's (2014) Impostors Method <doi:10.1002/asi.22954>, as well as functions to measure their performance and to calibrate their outputs into Log-Likelihood Ratios.
A toolkit for idionomic science, a research philosophy that places
    the unit of the ensemble (individual/couple/group) at the center of analysis.
    Rather than assuming a common distribution, a similar enough process for each
    unit, and fitting a single model to the whole ensemble, idionomic methods model
    each unit separately, then aggregate upward if sensible. The group-level picture
    emerges from individual results, not the other way around, while explicitly
    evaluating whether aggregation is reasonable given the measured level of
    heterogeneity of effects. The package is built around intensive longitudinal
    data where each participant contributes a time series. It provides a pipeline
    from preprocessing through modeling to group-level summaries. Current functions:
    data quality screening (i_screener()), within-person standardization
    (pmstandardize()), linear detrending (i_detrender()), per-subject
    ARIMAX (AutoRegressive Integrated Moving Average with eXogenous inputs)
    modeling and meta-analysis (iarimax()), individual p-values (i_pval()),
    Sign Divergence and Equisyncratic Null tests (sden_test()), and directed
    loop detection (looping_machine()). Methods are described in
    Hernandez et al. (2024) <doi:10.1007/978-3-030-77644-2_136-1>,
    Ciarrochi et al. (2024) <doi:10.1007/s10608-024-10486-w>, and
    Sahdra et al. (2024) <doi:10.1016/j.jcbs.2024.100728>.
A personalized dynamic latent factor model (Zhang et al. (2024) <doi:10.1093/biomet/asae015>) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series.
Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.
Interpreting the differences between mean scale scores across various
    forms of an assessment can be challenging. This difficulty arises from different
    mappings between raw scores and scale scores, complex mathematical relationships,
    adjustments based on judgmental procedures, and diverse equating functions applied
    to different assessment forms. An alternative method involves running simulations
    to explore the effect of incrementing raw scores on mean scale scores. The
    'idmact' package provides an implementation of this approach based on the
    algorithm detailed in Schiel (1998)
    <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which
    was developed to help interpret differences between mean scale scores on the
    American College Testing (ACT) assessment. The function idmact_subj() within
    the package offers a framework for running simulations on subject-level scores.
    In contrast, the idmact_comp() function provides a framework for conducting
    simulations on composite scores.
Utilities to work with data from the Internal Displacement
    Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>),
    with convenient functions for loading events data from the IDMC API
    and transforming events data to daily displacement estimates.
Provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".
Contains techniques for mining large and high-dimensional data
    sets by using the concept of Intrinsic Dimension (ID). Here the ID is
    not necessarily an integer. It is extended to fractal dimensions. And
    the Morisita estimator is used for the ID estimation, but other
    tools are included as well.
A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs.
Most existing approaches for network reconstruction can only infer an overall network 
	and, also, fail to capture a complete set of network properties. To address these issues, 
	a new model has been developed, which converts static data into their 'dynamic' form. 
	'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, 
	omnidirectional and personalized networks. For more information on functional 
	clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, 
	Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, 
	see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) 
	<doi:10.1080/19490976.2022.2106103>.
A method to integrate molecular profiles of cancer patients (gene copy number and mRNA abundance) to 
	identify candidate gain of function alterations. These candidate alterations can be subsequently further tested  
	to discover cancer driver alterations. Briefly, this method tests of genomic correlates of mRNA dysregulation and prioritise 
	those where DNA gains/amplifications are associated with elevated mRNA expression of the same gene. For details see,
	Haider S et al. (2016) "Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia", Genome Biology, <https://pubmed.ncbi.nlm.nih.gov/27358048/>.
Implements a nonparametric maximum likelihood method for assessing 
  potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection 
  under staggered enrollment and time-varying community transmission, allowing 
  crossover of placebo volunteers to the vaccine arm. 
  Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) 
  <doi:10.1093/cid/ciab630>.
Different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra, converting humidity measures.
Functions to perform robust
		 nonparametric survival analysis with right censored 
		 data using a prior near-ignorant Dirichlet Process.
		 Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.
This is a package for estimating the copula mixture model and plotting correspondence curves. Details are in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel.
Generate random or human readable and pronounceable identifiers.
A fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the 'IDSL.IPA' workflow <doi:10.1021/acs.jproteome.2c00120>. The 'IDSL.CSA' package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The 'IDSL.CSA' package was introduced in <doi:10.1021/acs.analchem.3c00376>.
The 'IDSL.FSA' package was designed to annotate standard .msp (mass spectra format) and .mgf (Mascot generic format) files using mass spectral entropy similarity, dot product (cosine) similarity, and normalized Euclidean mass error (NEME) followed by intelligent pre-filtering steps for rapid spectra searches. 'IDSL.FSA' also provides a number of modules to convert and manipulate .msp and .mgf files. The 'IDSL.FSA' workflow was integrated in the 'IDSL.CSA' and 'IDSL.NPA' packages introduced in <doi:10.1021/acs.analchem.3c00376>.
A multi-layered untargeted pipeline for high-throughput LC/HRMS data processing to extract signals of organic small molecules. The package performs ion pairing, peak detection, peak table alignment, retention time correction, aligned peak table gap filling, peak annotation and visualization of extracted ion chromatograms (EICs) and total ion chromatograms (TICs). The 'IDSL.IPA' package was introduced in <doi:10.1021/acs.jproteome.2c00120> .
A tiny parser to extract mass spectra data and metadata table of mass spectrometry acquisition properties from mzML, mzXML and netCDF files introduced in <doi:10.1021/acs.jproteome.2c00120>.
A pipeline to process nominal mass spectrometry data to create .msp files for untargeted analyses.
A simplified version of the 'IDSL.UFA' package to calculate isotopic profiles and adduct formulas from molecular formulas with no dependency on other R packages for online tools and educational mass spectrometry courses. The 'IDSL.SUFA' package also provides an ancillary module to process user-defined adduct formulas.
A pipeline to annotate chromatography peaks from the 'IDSL.IPA' workflow <doi:10.1021/acs.jproteome.2c00120> with molecular formulas of a prioritized chemical space using an isotopic profile matching approach. The 'IDSL.UFA' workflow only requires mass spectrometry level 1 (MS1) data for formula annotation. The 'IDSL.UFA' methods was described in <doi:10.1021/acs.analchem.2c00563> .
A pipeline to annotate a number of peaks from the 'IDSL.IPA' peaklists using an exhaustive chemical enumeration-based approach. This package can perform elemental composition calculations using the following 15 elements : C, B, Br, Cl, K, S, Si, N, H, As, F, I, Na, O, and P.
Implements various novel and standard clustering statistics and
  other analyses useful for understanding the spread of infectious disease.
Convert files to and from IDX format to vectors, matrices and arrays.
    IDX is a very simple file format designed for storing vectors and multidimensional matrices in
    binary format. The format is described on the website from Yann LeCun
    <http://yann.lecun.com/exdb/mnist/>.
A collection of Irucka Embry's miscellaneous USGS functions
    (processing .exp and .psf files, statistical error functions,
    "+" dyadic operator for use with NA, creating ADAPS and QW
    spreadsheet files, calculating saturated enthalpy). Irucka created these
    functions while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
A collection of Irucka Embry's miscellaneous USGS data sets (USGS
    Parameter codes with fixed values, USGS global time zone codes, and US Air
    Force Global Engineering Weather Data). Irucka created these data sets
    while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
A pair of functions for getting and setting the IEEE rounding 
  mode for floating point computations.
Integrated toolbox supporting common file formats used for intracranial 
    Electroencephalography (iEEG) and deep-brain stimulation (DBS) study.
An eclectic collection of short stories and poetry with topics on
    climate strange, connecting the geopolitical dots, the myth of us versus
    them, and the idiocy of war. Please refer to the COPYRIGHTS file and the
    text_citation.cff file for the reference copyright information and for the
    complete citations of the reference sources, respectively.
Computes characteristics of independent rainfall events (duration, total rainfall depth, and intensity) 
  extracted from a sub-daily rainfall time series based on the inter-event time definition (IETD) method. To have a 
  reference value of IETD, it also analyzes/computes IETD values through three methods: autocorrelation analysis, the 
  average annual number of events analysis, and coefficient of variation analysis. Ideal for analyzing the sensitivity 
  of IETD to characteristics of independent rainfall events.
  Adams B, Papa F (2000) <ISBN: 978-0-471-33217-6>.
  Joo J et al. (2014) <doi:10.3390/w6010045>.
  Restrepo-Posada P, Eagleson P (1982) <doi:10.1016/0022-1694(82)90136-6>.
Used in testing if the indirect effect from linear regression mediation analysis is equal to 0. Includes established methods such as the Sobel Test, Joint Significant test (maxP), and tests based off the distribution of the Product or Normal Random Variables. Additionally, this package adds more powerful tests based on Intersection-Union theory. These tests are the S-Test, the ps-test, and the ascending squares test. These new methods are uniformly more powerful than maxP, which is more powerful than Sobel and less anti-conservative than the Product of Normal Random Variables. These methods are explored by Kidd and Lin, (2024) <doi:10.1007/s12561-023-09386-6> and Kidd et al., (2025) <doi:10.1007/s10260-024-00777-7>.
Interface to the 'OpenGWAS' database API <https://api.opengwas.io/api/>. Includes a wrapper
    to make generic calls to the API, plus convenience functions for
    specific queries.
Tools, tutorials, and demos of Item Factor Analysis using 'OpenMx'.
    This software is described in Pritikin & Falk (2020) <doi:10.1177/0146621620929431>.
Contains several tools to treat imaging flow cytometry data from 'ImageStream®' and 'FlowSight®' cytometers ('Amnis®' 'Cytek®'). Provides an easy and simple way to read and write .fcs, .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved for each single cell. In addition, raw data such as images stored can also be accessed. Users, may hopefully increase their productivity thanks to dedicated functions to extract, visualize, manipulate and export 'IFC' data. Toy data example can be installed through the 'IFCdata' package of approximately 32 MB, which is available in a 'drat' repository <https://gitdemont.github.io/IFCdata/>. See file 'COPYRIGHTS' and file 'AUTHORS' for a list of copyright holders and authors.
Automatically detects Copy Number Variations (CNV) from Next Generation Sequencing data using a machine learning algorithm, Isolation forest. More details about the method can be found in the paper by Cabello-Aguilar (2022) <doi:10.1101/2022.01.03.474771>.
Provides utility functions to deal with Italian fiscal
    code ('codice fiscale').
Implements an S7 class for estimates based on influence functions, 
  with forward mode automatic differentiation defined for standard arithmetic 
  operations.
Implements an algorithm for fitting a generative model with an intractable likelihood using only box constraints on the parameters.  The implemented algorithm consists of two phases. The first phase (global search) aims to identify the region containing the best solution, while the second phase (local search) refines this solution using a trust-region version of the Fisher scoring method to solve a quasi-likelihood equation. See Guido Masarotto (2025) <doi:10.48550/arXiv.2511.08180> for the details of the algorithm and supporting results.
Implementation of two multi-criteria decision making methods (MCDM): Intuitionistic Fuzzy Synthetic Measure (IFSM) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IFTOPSIS) for intuitionistic fuzzy data sets for multi-criteria decision making problems. References describing the methods: Jefmański (2020) <doi:10.1007/978-3-030-52348-0_4>; Jefmański, Roszkowska, Kusterka-Jefmańska (2021) <doi:10.3390/e23121636>.
Download ifo business survey data and more time series from
    ifo institute <https://www.ifo.de/en/ifo-time-series>.
Compute onestep and multistep time series forecasts for machine learning models.
Imputation of missing values using the last observation carried forward technique on Indonesia food prices data that is time series data. Also, this technique applies imputation to data whose dates do not appear directly. So that the series assumptions in the time series data are met.
Iterated Function Systems Estimator as in Iacus and La Torre (2005) <doi:10.1155/JAMDS.2005.33>.
This function predicts item response probabilities and item 
  responses using the item-focused tree model. The item-focused tree model
  combines logistic regression with recursive partitioning to detect 
  Differential Item Functioning in dichotomous items. The model applies 
  partitioning rules to the data, splitting it into homogeneous subgroups, and 
  uses logistic regression within each subgroup to explain the data. 
  Differential Item Functioning detection is achieved by examining potential 
  group differences in item response patterns. This method is useful for 
  understanding how different predictors, such as demographic or psychological 
  factors, influence item responses across subgroups.
Implements the "Smith-Pittman" community detection algorithm  
    for network analysis using 'igraph' objects. This algorithm combines node 
    degree and betweenness centrality measures to identify communities within 
    networks, with a gradient evident in social partitioning. The package 
    provides functions for community detection, visualization, and analysis of 
    the resulting community structure. Methods are based on results from Smith, 
    Pittman and Xu (2024) <doi:10.48550/arXiv.2411.01394>.
A dataset of the top colours of photos from Instagram 
 taken in 2014 in the city of Vancouver, British Columbia, Canada.
 It consists of: top colour and counts data. This data was
 obtained using the Instagram API. Instagram is a web photo 
 sharing service. It can be found at: <https://instagram.com>.
 The Instagram API is documented at: <https://instagram.com/developer/>.
Functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments.
  This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>.
  Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.
A collection of statistical tests for genetic association studies and summary data based Mendelian randomization.
Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <doi:10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.
Compute distributional quantities for an
    Integrated Gamma (IG) or Integrated Gamma Limit (IGL) copula, such
    as a cdf and density. Compute corresponding conditional quantities
    such as the cdf and quantiles. Generate
    data from an IG or IGL copula. See the vignette for formulas,
    or for a derivation, see Coia, V (2017) "Forecasting of Nonlinear 
    Extreme Quantiles Using Copula Models." PhD Dissertation, 
    The University of British Columbia.
Provides functions to fetch market data, search historical prices, execute trades, and get account details from the 'IG Trading REST API' <https://labs.ig.com>. Returns tidy tibbles for easy analysis. Trading contracts for difference (CFDs), options and spread bets carries a high risk of losing money. This package is not financial or trading advice.
An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) <doi:10.1080/01621459.2025.2565851>. 'igml' extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.
Implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series plots, lasagna plots and ambulatory glucose profile report.
Tools for searching, extracting and recoding information from
    the Intergovernmental Organizations ('IGO') Database (v3), distributed
    by the Correlates of War Project <https://correlatesofwar.org/>. See
    also Pevehouse, J. C. et al. (2020) <doi:10.1177/0022343319881175>.
Provides function to read data from the 'Igor Pro' data analysis
    program by 'Wavemetrics'. The data formats supported are 'Igor' packed 
    experiment format ('pxp') and 'Igor' binary wave ('ibw'). See: 
    <https://www.wavemetrics.com/> for details. Also includes functions to load 
    special 'pxp' files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp' 
    packages for recording and analysing neuronal data. See 
    <https://github.com/SilverLabUCL/NeuroMatic> for details.
Convert between Irish grid references and Irish Grid coordinates. Irish grid 
    references can also be converted to or from an 'sf' object in any coordinate reference 
    system. Precisions from 1 m to 100 km including 2 km (tetrads) are supported, as are 
    datasets with mixed precision. Conversion to 'sf' polygons is precision-aware.
Routines for simple graphs and network analysis. It can
    handle large graphs very well and provides functions for generating
    random and regular graphs, graph visualization, centrality methods and
    much more.
A small collection of various network data sets,
    to use with the 'igraph' package: the Enron email network, various food webs,
    interactions in the immunoglobulin protein, the karate club network,
    Koenigsberg's bridges, visuotactile brain areas of the macaque monkey,
    UK faculty friendship network, domestic US flights network, etc.
Using 'shiny' to demo 'igraph' package makes learning graph theory easy and fun.
Versatile tools and data for graph matching analysis with various forms of prior information
    that supports working with 'igraph' objects, matrix objects, or lists of either.
This program facilitates exporting igraph graphs to the
        SoNIA file format
Interesting 'igraph' datasets from Melanie Walsh's sample social network datasets repository <https://github.com/melaniewalsh/sample-social-network-datasets>.
The 14th generation International Geomagnetic Reference Field (IGRF).
 A standard spherical harmonic representation of the Earth's main field.
To integrate multiple GSEA studies, we propose a hybrid strategy,
    iGSEA-AT, for choosing random effects (RE) versus fixed effect (FE) models,
    with an attempt to achieve the potential maximum statistical efficiency as 
    well as stability in performance in various practical situations. In addition
    to iGSEA-AT, this package also provides options to perform integrative GSEA
    with testing based on a FE model (iGSEA-FE) and testing based on a RE model
    (iGSEA-RE). The approaches account for different set sizes when testing a
    database of gene sets. The function is easy to use, and the three approaches
    can be applied to both binary and continuous phenotypes.
Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.
Provides a set of tools to
  i) identify geographic areas with significant change over time in drug utilization, and 
  ii) characterize common change over time patterns among the time series for multiple geographic areas.
  For reference, see below:
    1. Song, J., Carey, M., Zhu, H., Miao, H., Ram´ırez, J. C., & Wu, H. (2018) <doi:10.1504/IJCBDD.2018.10011910>
    2. Wu, S., Wu, H. (2013) <doi:10.1186/1471-2105-14-6>
    3. Carey, M., Wu, S., Gan, G. & Wu, H. (2016) <doi:10.1016/j.idm.2016.07.001>.
Make complex, interactive heatmaps. 'iheatmapr' includes a modular 
    system for iteratively building up complex heatmaps, as well as the 
    iheatmap() function for making relatively standard heatmaps.
A set of tools for writing documents
    according to Geneva Graduate Institute conventions and regulations.
    The most common use is for writing and compiling theses or thesis
    chapters, as drafts or for examination with correct preamble formatting. 
    However, the package also offers users to create HTML presentation
    slides with 'xaringan', complete problem sets, format posters, and, 
    for course instructors, prepare a syllabus.
    The package includes additional functions for institutional color palettes,
    an institutional 'ggplot' theme, a function for counting manuscript words,
    and a bibliographical analysis toolkit.
Web scraping the <https://www.dallasfed.org> for
    up-to-date data on international house prices and exuberance
    indicators. Download data in tidy format.
Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.
An offline suite of tools to clean, aggregate, and harmonise data 
    from the 'Malawi Integrated Household Survey' ('IHS'). Provides crop-specific 
    unit conversions, stratified winsorization, and automatic cross-round harmonisation 
    for complex survey designs.
A novel machine learning method for plant viruses diagnostic using 
    genome sequencing data. This package includes three different machine 
    learning models, random forest, XGBoost, and elastic net, to train and 
    predict mapped genome samples. Mappability profile and unreliable regions 
    are introduced to the algorithm, and users can build a mappability profile 
    from scratch with functions included in the package. Plotting mapped sample 
    coverage information is provided.
Computes individual contributions to the overall Gini and 
    Theil's T and Theil's L measures and their decompositions by groups 
    such as race, gender, national origin, with the three functions of 
    iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019)
    <doi:10.1177/0049124119875961>.
The Importance Index (I.I.) can determine the loss and solution sources for a system in certain knowledge areas (e.g., agronomy), when production (e.g., fruits) is known (Demolin-Leite, 2021). Events (e.g., agricultural pest) can have different magnitudes (numerical measurements), frequencies, and distributions (aggregate, random, or regular) of event occurrence, and I.I. bases in this triplet (Demolin-Leite, 2021) <https://cjascience.com/index.php/CJAS/article/view/1009/1319>. Usually, the higher the magnitude and frequency of aggregated distribution, the greater the problem or the solution (e.g., natural enemies versus pests) for the system (Demolin-Leite, 2021). However, the final production of the system is not always known or is difficult to determine (e.g., degraded area recovery). A derivation of the I.I. is the percentage of Importance Index-Production Unknown (% I.I.-PU) that can detect the loss or solution sources, when production is unknown for the system (Demolin-Leite, 2024) <DOI:10.1590/1519-6984.253218>.
These datasets and functions accompany Wolfe and Schneider (2017) - Intuitive Introductory Statistics (ISBN:  978-3-319-56070-0) <doi:10.1007/978-3-319-56072-4>. They are used in the examples throughout the text and in the end-of-chapter exercises. The datasets are meant to cover a broad range of topics in order to appeal to the diverse set of interests and backgrounds typically present in an introductory Statistics class.
Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.
Provides a function to calculate infinite-jackknife-based
    standard errors for fixed effects parameters in 'brms' models, handling
    both clustered and independent data. 
    References: Ji et al. (2024) <doi:10.48550/arXiv.2407.09772>; Giordano et al. (2024) <doi:10.48550/arXiv.2305.06466>.
General purpose TIFF file I/O for R users.  Currently the
    only such package with read and write support for TIFF files with
    floating point (real-numbered) pixels, and the only package that can
    correctly import TIFF files that were saved from 'ImageJ' and write
    TIFF files than can be correctly read by 'ImageJ'
    <https://imagej.net/ij/>. Also supports text image I/O.
Analyst oriented utility functions to handle the different quirks of
  the Israeli CBS municipal data, harmonize id's and bring together data points
  from different years.
Simple handling of survey data. Smart handling of meta-information like e.g. variable-labels value-labels and scale-levels. Easy access and validation of meta-information. Useage of value labels and values respectively for subsetting and recoding data.
The Indian Alien Flora Information (ILORA) database contains 14 invasion-relevant variables for 1388 alien plant species in India. The package enables exploration of the database using user-defined criteria. Using this package, users can retrieve variable-specific and species-level data from the database. The package also supports exploratory data analysis and visualization to give users an idea of the variables of interest. Further details about the database are available at <https://iloradb.wixsite.com/alienflora>.
Convert irregularly spaced longitudinal data into regular intervals for further analysis, 
             and perform clustering using advanced machine learning techniques. 
             The package is designed for handling complex longitudinal datasets, 
             optimizing them for research in healthcare, demography, and other fields 
             requiring temporal data modeling.
It performs interlaboratory studies (ILS) to detect those
    laboratories that provide non-consistent results when comparing to others. It
    permits to work simultaneously with various testing materials, from standard
    univariate, and functional data analysis (FDA) perspectives. The univariate
    approach based on ASTM E691-08 consist of estimating the Mandel's h and
    k statistics to identify those laboratories that provide more significant
    different results, testing also the presence of outliers by Cochran and Grubbs
    tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey
    tests) to test differences in means corresponding to different laboratories per
    each material. Taking into account the functional nature of data retrieved in
    analytical chemistry, applied physics and engineering (spectra, thermograms,
    etc.). ILS package provides a FDA approach for finding the Mandel's k and h
    statistics distribution by smoothing bootstrap resampling.
Merges and downloads 'SPSS' data from different International Large-Scale Assessments (ILSA), including: Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), and other studies from <https://www.iea.nl/>.
Calculates point estimates and standard errors using replicate weights and plausible values for International Large-Scale Assessments (ILSA), including: means, proportions, quantiles, correlations, singlelevel regressions, and multilevel regressions.
In view of the analysis of the structural characteristics of the tripartite 
    network has been complete, however, there is still a lack of a unified 
    operation that can quickly obtain the corresponding characteristics of the 
    tripartite network. 
    To solve this insufficiency, 'ILSM' was designed for supporting calculating 
    such metrics of tripartite networks by functions of this R package.
Provides functionality to perform a likelihood-free method for estimating the parameters of complex models
    that results in a simulated sample from the posterior distribution of model parameters given targets. The method begins
    with a accept/reject approximate bayes computation (ABC) step applied to a sample of points from the prior distribution
    of model parameters. Accepted points result in model predictions that are within the initially specified tolerance
    intervals around the target points. The sample is iteratively updated by drawing additional points from a mixture of
    multivariate normal distributions, accepting points within tolerance intervals. As the algorithm proceeds, the
    acceptance intervals are narrowed. The algorithm returns a set of points and sampling weights that account for the
    adaptive sampling scheme. For more details see Rutter, Ozik, DeYoreo, and Collier (2018) <arXiv:1804.02090>.
Improve optical character recognition by binarizing images. The package focuses primarily on local adaptive thresholding algorithms. 
    In English, this means that it has the ability to turn a color or gray scale image into a black and white image. This is particularly useful
    as a preprocessing step for optical character recognition or handwritten text recognition.
An implementation of the Canny Edge Detector for detecting edges in images. The package provides an interface to the algorithm available at <https://github.com/Neseb/canny>.
An implementation of the Unsupervised Smooth Contour Detection algorithm for digital images as described in the paper: "Unsupervised Smooth Contour Detection" by Rafael Grompone von Gioi, and Gregory Randall (2016). 
    The algorithm is explained at <doi:10.5201/ipol.2016.175>.
An implementation of the "FAST-9" corner detection algorithm explained in the paper 'FASTER and better: A machine learning approach to corner detection' by Rosten E., Porter R. and Drummond T. (2008), available at <doi:10.48550/arXiv.0810.2434>.
    The package allows to detect corners in digital images.
An implementation of the Harris Corner Detection as described in the paper "An Analysis and Implementation of the Harris Corner Detector" by Sánchez J. et al (2018) available at <doi:10.5201/ipol.2018.229>. 
    The package allows to detect relevant points in images which are characteristic to the digital image.
An open source library for face detection in images. 
    Provides a pretrained convolutional neural network based on <https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.
An implementation of the Line Segment Detector on digital images described in the paper: "LSD: A Fast Line Segment Detector with a False Detection Control" by Rafael Grompone von Gioi et al (2012). 
    The algorithm is explained at <doi:10.5201/ipol.2012.gjmr-lsd>.
An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". The algorithm is explained at <doi:10.5201/ipol.2016.158>.
The goal of 'image2data' is to extract images and return
    them into a data set, 
    especially for teaching data manipulation and data visualization. 
    Basically, the eponymous function takes an 
    image file ('png', 'tiff', 'jpeg', 'bmp') and turn it into a data set,
    pixels being rows (subjects) and columns (variables) being their coordinate positions (x- and y-axis) and their respective color (in hex codes). 
    The function can return a complete image or a range of color (i.e., contour, silhouette).
    The data can then be manipulated as would any data set by either creating other related variables (to hide the image) or as a genuine toy data set.
Note that 'imageData' has been superseded by 'growthPheno'. 
    The package 'growthPheno' incorporates all the functionality of 
    'imageData' and has functionality not available in 'imageData', 
    but some 'imageData' functions have been renamed.  
    The 'imageData' package is no longer maintained, but is retained 
    for legacy purposes.
Get image statistics based on processing fluency theory. The
    functions provide scores for several basic aesthetic principles that
    facilitate fluent cognitive processing of images: contrast,
    complexity / simplicity, self-similarity, symmetry, and typicality.
    See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr
    (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.
Synthesize images into characteristic features for time-series analysis or machine learning applications.  The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity.  However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.
Fast image processing for images in up to 4 dimensions (two spatial
    dimensions, one time/depth dimension, one colour dimension). Provides most
    traditional image processing tools (filtering, morphology, transformations,
    etc.) as well as various functions for easily analysing image data using R. The
    package wraps 'CImg', <https://cimg.eu>, a simple, modern C++ library for image
    processing.
Provides advanced functions for image processing based on the package 'imager'.
Tools for manipulating, visualizing, and exporting raster images in R.
 Designed as an educational resource for students learning the basics of remote sensing,
 the package provides user-friendly functions to apply color ramps, export RGB composites,
 and create multi-frame visualizations. Built on top of the 'terra' and 'ggplot2' packages.
 See <https://github.com/ducciorocchini/imageRy> for more details and examples.
A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arXiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.
Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.
Provides functions to measure and test imaginary cognitive social structure (CSS) motifs, which are patterns of perceived relationships among individuals in a social network. Includes tools for calculating motif frequencies, comparing observed motifs to expected distributions, and visualizing motif structures. Implements methods described in Tanaka and Vega Yon (2023) <doi:10.1016/j.socnet.2023.11.005>.
Simulate general insurance policies, losses and loss emergence. The functions contemplate 
  deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative
  to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency
  distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation 
  uses standard loss distributions for claim amounts.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
This is an Automatic Item Generator for Psychological Assessment. Items created with the 'IMak' package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.
A model that provides researchers with a powerful tool for the classification
    and study of native corn by aiding in the identification of racial complexes
    which are fundamental to Mexico's agriculture and culture. This package has been
    developed based on data collected by "Proyecto Global de Maíces Nativos México",
    which has conducted exhaustive surveys across the country to document the
    qualitative and quantitative characteristics of different types of native maize.
    The trained model uses a robust and diverse dataset, enabling it to achieve an
    80% accuracy in classifying maize racial complexes. The characteristics included
    in the analysis comprise geographic location, grain and cob colors, as well as
    various physical measurements, such as lengths and widths.
Relocates oversampled data from a specific oversampling method to
    cover area determined by pure and proper class cover catch digraphs (PCCCD). 
    It prevents any data to be generated in class overlapping area.
    For more details, see the corresponding publication: 
    F. Sağlam (2025) <doi:10.1007/s10994-025-06755-8>.
Provides a set of fast, chainable image-processing operations
             which are applicable to images of two, three or four dimensions,
             particularly medical images.
Containerizes cytometry data and allows for S4 class structure to extend slots related to cell morphology, spatial coordinates, phenotype network information, and unique cellular labeling.
Cluster sampling is a valuable approach when constructing a comprehensive list of individual units is challenging. It provides operational and cost advantages. This package is designed to test the efficiency of cluster sampling in terms cluster variance and design effect in context to crop surveys. This package has been developed using the algorithm of Iqbal et al. (2018) <doi:10.19080/BBOAJ.2018.05.555673>.
Index of Multiple Deprivation for UK nations at various 
    geographical levels. In England, deprivation data is for Lower Layer Super
    Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities
    based on data from <https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019>.
    In Wales, deprivation data is for Lower Layer Super Output Areas, Middle 
    Layer Super Output Areas, Wards, and Local Authorities based on data from
    <https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019>.
    In Scotland, deprivation data is for Data Zones, Intermediate Zones, and 
    Council Areas based on data from <https://simd.scot>. In Northern Ireland,
    deprivation data is for Super Output Areas and Local Government Districts
    based on data from <https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017>.
    The 'IMD' package also provides the composite UK index developed by
    <https://github.com/mysociety/composite_uk_imd>.
Provides API access to the <http://imdbapi.net> which maintains metadata
             about movies, games and television shows through a public API.
Implements likelihood based methods for mediation analysis.
A straightforward interface for accessing the IMF 
    (International Monetary Fund) data JSON API, 
    available at <https://data.imf.org/>. This package offers direct access to 
    the primary API endpoints: Dataflow, DataStructure, and CompactData. 
    And, it provides an intuitive interface for exploring available 
    dimensions and attributes, as well as querying individual time-series datasets. 
    Additionally, the package implements a rate limit on API calls to reduce the 
    chances of exceeding service limits (limited to 10 calls every 5 seconds) 
    and encountering response errors.
Provides user-friendly functions for programmatic access to macroeconomic data from the International Monetary Fund's 'SDMX 3.0 IMF Data API' <https://data.imf.org/en/Resource-Pages/IMF-API>.
Tree height is an important dendrometric variable and forms the basis of vertical structure of a forest stand. This package will help to fit and validate various non-linear height diameter models for assessing the underlying relationship that exists between tree height and diameter at breast height in case of conifer trees. This package has been implemented on Naslund, Curtis, Michailoff, Meyer, Power, Michaelis-Menten and Wykoff non linear models using algorithm of  Huang et al.  (1992) <doi:10.1139/x92-172> and Zeide et al. (1993) <doi:10.1093/forestscience/39.3.594>.
Provides tools to download, process, and analyze data from the
    International Monetary Fund's World Economic Outlook (WEO) database 
    <https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases>. 
    Functions support downloading complete WEO releases, accessing specific 
    economic indicators for selected countries, and listing available data.
Provides ability to create color palettes from image files. It 
    offers control over the type of color palette to derive from an image 
    (qualitative, sequential or divergent) and other palette properties.
    Quantiles of an image color distribution can be trimmed. Near-black or 
    near-white colors can be trimmed in RGB color space independent of trimming 
    brightness or saturation distributions in HSV color space. Creating 
    sequential palettes also offers control over the order of HSV color 
    dimensions to sort by. This package differs from other related packages 
    like 'RImagePalette' in approaches to quantizing and extracting colors in 
    images to assemble color palettes and the level of user control over 
    palettes construction.
Provides an interface for image recognition using the 'Google Vision API' <https://cloud.google.com/vision/> .  Converts API data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.
Interpretability methods to analyze the behavior and
    predictions of any machine learning model.  Implemented methods are:
    Feature importance described by Fisher et al. (2018)
    <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley
    (2018) <doi:10.48550/arxiv.1612.08468>, partial dependence plots described by
    Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional
    expectation ('ice') plots described by Goldstein et al.  (2013)
    <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime')
    described by Ribeiro et. al (2016) <doi:10.48550/arXiv.1602.04938>, the Shapley
    Value described by Strumbelj et. al (2014)
    <doi:10.1007/s10115-013-0679-x>, feature interactions described by
    Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.
Send emails using the 'mailgun' api. To use this package you will need an account from <https://www.mailgun.com> .
Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package. 
    See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>)
    for an overview of modeling alternatives.
Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.
Fitting and validation of machine learning algorithms
             for volume prediction of trees, currently for conifer trees based on
			 diameter at breast height and height as explanatory variables.
A comprehensive analytics framework for building reproducible pipelines on T-cell and B-cell immune receptor repertoire data.
    Delivers multi-modal immune profiling (bulk, single-cell, CITE-seq/AbSeq, spatial, immunogenicity data), 
    feature engineering (ML-ready feature tables and matrices), and biomarker discovery workflows
    (cohort comparisons, longitudinal tracking, repertoire similarity, enrichment). 
    Provides a user-friendly interface to widely used AIRR methods — 
    clonality/diversity, V(D)J usage, similarity, annotation, tracking, and many more.
    Think Scanpy or Seurat, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or 
    VDJ sequencing data. A successor to our previously published "tcR" R package (Nazarov 2015).
Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.
Provides a unified data layer for single-cell, spatial and bulk
 T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject,
 but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or 
 VDJ sequencing data.
Provides a literature-derived database of immune cell markers
    formatted as Gene Matrix Transposed (GMT) files. Users can search immune
    cell signatures, retrieve marker lists, export GMT files, create custom
    marker sets, and score gene-by-cell expression matrices with dependency-free
    rank-based or mean-expression methods. Cell subpopulations are distinguished 
    by their source PMIDs. For the core curation of the lung cell atlas, 
    see Travaglini et al. (2020) <doi:10.1038/s41586-020-2922-4>. For the 
    pan-cancer B cell signatures, see Fitzsimons et al. (2024) 
    <doi:10.1016/j.ccell.2024.09.011>.
A comprehensive toolkit for clinical Human Leukocyte Antigen (HLA) informatics, built on 'tidyverse' <https://tidyverse.tidyverse.org/> principles and making use of genotype list string (GL string, Mack et al. (2023) <doi:10.1111/tan.15126>) for storing and computing HLA genotype data.  Specific functionalities include: coercion of HLA data in tabular format to and from GL string; calculation of matching and mismatching in all directions, with multiple output formats; automatic formatting of HLA data for searching within a GL string; truncation of molecular HLA data to a specific number of fields; and reading HLA genotypes in HML files and extracting the GL string. This library is intended for research use. Any application making use of this package in a clinical setting will need to be independently validated according to local regulations.
Provides fast, side-effect free data structures, including
    catenable named lists, priority queues, double-ended queues, ordered
    sequences, and interval indices. Implementation is based on the
    finger-tree data structure of Hinze and Paterson (2006)
    <doi:10.1017/S0956796805005769>.
It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.
Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.
Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.
A non-parametric effect size measure capturing changes in central tendency or shape of data distributions. The package provides the necessary functions to calculate and plot the Impact effect size measure between two groups.
Implements the compartment model from Tokars (2018) 
  <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of 
  population-wide impact of vaccination against vaccine-preventable 
  diseases such as influenza.
Functions to read, process and analyse accelerometer
    data related to mechanical loading variables. This package is
    developed and tested for use with raw accelerometer data from
    triaxial 'ActiGraph' <https://theactigraph.com> accelerometers.
The goal is to improve a permutation-based approach (R package: 'pARI') for simultaneous inference on the true discovery proportion by a branch-and-bound algorithm. It is designed to return a list with a bracketing for the true discovery proportion, rather than a single lower bound from 'pARI'. For more details see Andreella. A (2023) <doi:10.1002/sim.9725>.
Pull data from the 'Impect' Customer API <https://glossary.impect.com/api-design>. The package can retrieve data such as events or match sums.
Computes the key metrics for assessing the performance of a liquidity provider (LP) position in a weighted multi-asset Automated Market Maker (AMM) pool. Calculates the nominal and percentage impermanent loss (IL) by comparing the portfolio value inside the pool (based on the weighted geometric mean of price ratios) against the value of simply holding the assets outside the pool (based on the weighted arithmetic mean). The primary function, `impermanent_loss()`, incorporates the effect of earned trading fees to provide the LP's net profit and loss relative to a holding strategy, using a methodology derived from Tiruviluamala, N., Port, A., and Lewis, E. (2022) <doi:10.48550/arXiv.2203.11352>.
Imputing blockwise missing data by imprecise imputation,
    featuring a domain-based, variable-wise, and case-wise strategy. 
    Furthermore, the estimation of lower and upper bounds for 
    unconditional and conditional probabilities based on the obtained
    imprecise data is implemented.
    Additionally, two utility functions are supplied: one to check 
    whether variables in a data set contain set-valued observations;
    and another to merge two already imprecisely imputed data. 
    The method is described in a technical report by Endres, Fink and
    Augustin (2018, <doi:10.5282/ubm/epub.42423>).
Support for implicit expansion of arrays in operations involving
    arrays of mismatching sizes. This pattern is known as "broadcasting" in
    'Python' and  "implicit expansion" in 'Matlab' and is explained for example in
    the article "Array programming with NumPy" by C. R. Harris et al. (2020)
    <doi:10.1038/s41586-020-2649-2>.
Convert between bookmaker odds and probabilities. Eight different
    algorithms are available, including basic normalization, Shin's method 
    (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.
'SQL' back-end to 'dplyr' for Apache Impala, the massively
    parallel processing query engine for Apache 'Hadoop'. Impala enables
    low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed
    File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple 
    Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', 
    and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more
    information about Impala.
Alternative mechanism for importing objects from packages
    and R modules. The syntax allows for importing multiple objects with a single
    command in an expressive way. The import package bridges some of the gap
    between using library (or require) and direct (single-object) imports.
    Furthermore the imported objects are not placed in the current environment.
The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) <http://cjascience.com/index.php/CJAS/article/view/1009/1350>. This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) <DOI: 10.1590/1519-6984.253215>.
Interfaces for choosing important predictors in supervised
    regression, classification, and censored regression models. Permuted
    importance scores (Biecek and Burzykowski (2021)
    <doi:10.1201/9780429027192>) can be computed for 'tidymodels' model
    fits.
Import and export data from the most common statistical formats by using 
	     R functions that guarantee the least loss of the data information, giving special
	     attention to the date variables and the labelled ones.
Download and manage data sets of statistical projects and geographic data created by Instituto Nacional de Estadistica y Geografia (INEGI). See <https://www.inegi.org.mx/>.
Provides functions to simulate baseline characteristics,
    reconstruct overall survival data from published Kaplan-Meier curves,
    and generate publication-ready tables and forest plots reproducing
    the IMpower133 clinical trial results (Horn et al., 2018,
    <doi:10.1056/NEJMoa1809064>). The IPD reconstruction method is based
    on Liu et al.(2021, <doi:10.1186/s12874-021-01308-8>).
An imprecise inference presented in the study of Walley (1996) <doi:10.1111/j.2517-6161.1996.tb02065.x> is one of the statistical reasoning methods when prior information is unavailable. Functions and utils needed for illustrating this inferential paradigm are implemented for classroom teaching and further comprehensive research. Two imprecise models are demonstrated using multinomial data and 2x2 contingency table data. The concepts of prior ignorance and imprecision are discussed in lower and upper probabilities. Representation invariance principle, hypothesis testing, decision-making, and further generalization are also illustrated.
Provides color palettes from Impressionism and post-Impressionism artworks. This package allows to select colors combinations while looking at the original paintings where colors were sampled from.
Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.
A variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) <ISBN: 978-0-471-56375-4>.
Missing values often occur in financial data due to a variety 
    of reasons (errors in the collection process or in the processing stage, 
    lack of asset liquidity, lack of reporting of funds, etc.). However, 
    most data analysis methods expect complete data and cannot be employed 
    with missing values. One convenient way to deal with this issue without 
    having to redesign the data analysis method is to impute the missing 
    values. This package provides an efficient way to impute the missing 
    values based on modeling the time series with a random walk or an 
    autoregressive (AR) model, convenient to model log-prices and log-volumes 
    in financial data. In the current version, the imputation is 
    univariate-based (so no asset correlation is used). In addition,
    outliers can be detected and removed.
    The package is based on the paper:
    J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of 
    Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on 
    Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.
The general workflow of most imputation methods is quite
    similar. The aim of this package is to provide parts of this general
    workflow to make the implementation of imputation methods easier. The
    heart of an imputation method is normally the used model. These models
    can be defined using the 'parsnip' package or customized
    specifications. The rest of an imputation method are more technical
    specification e.g. which columns and rows should be used for
    imputation and in which order. These technical specifications can be
    set inside the imputation functions.
A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
Imputation of longitudinal categorical covariates. We use a methodological framework which ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. See Mamouris (2023) <doi:10.1002/sim.9919> for an overview.
Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Set of functions to impute missing rare earth data, calculate La 
    and Pr concentrations and Ce anomalies in zircons based on the
    Chondrite-Onuma and Chondrite-Lattice of Carrasco-Godoy and 
    Campbell (2023) <doi:10.1007/s00410-023-02025-9> and the Logarithmic regression from  
    Zhong et al. (2019) <doi:10.1007/s00710-019-00682-y>.
Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Provides a test bench for the comparison of missing data imputation 
    methods in uni-variate time series. Imputation methods are compared using 
    different error metrics. Proposed imputation methods and alternative error 
    metrics can be used.
Imputation (replacement) of missing values 
             in univariate time series. 
             Offers several imputation functions
             and missing data plots. 
             Available imputation algorithms include: 
            'Mean', 'LOCF', 'Interpolation', 
            'Moving Average', 'Seasonal Decomposition', 
            'Kalman Smoothing on Structural Time Series models',
            'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) 
            <doi:10.32614/RJ-2017-009>.
This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.
Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.
A toolkit for causal inference in experimental and observational 
    studies. Implements various simple Bayesian models including linear, 
    negative binomial, and logistic regression for impact estimation. 
    Provides functionality for randomization and checking baseline equivalence 
    in experimental designs. The package aims to simplify the process of 
    impact measurement for researchers and analysts across different fields. 
    Examples and detailed usage instructions are available at 
    <https://book.martinez.fyi>.
Implementation of the information matrix test for generalized partial credit models.
Estimate the orientation of an inertial measurement unit 
    (IMU) with a 3-axis accelerometer and a 3-axis gyroscope using a 
    complementary filter. 'imuf' takes an IMU's accelerometer and gyroscope 
    readings, time duration, its initial orientation, and a gain factor as 
    inputs, and returns an estimate of the IMU's final orientation.
Computes the 'InterModel Vigorish' ('IMV'), a metric for comparing
    the predictive accuracy of two models for binary outcomes. The 'IMV' is derived
    from the expected value of a bettor using one model's predicted probabilities
    against those of a competing model, and is estimated via k-fold cross-validation.
    Methods are provided for generalized linear models, mixed-effects models ('lme4'),
    and item response theory models ('mirt'). See
    <doi:10.1371/journal.pone.0316491>.
Volume prediction is one of challenging task in forestry research. This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) <doi:10.13140/RG.2.2.33786.62407>.
Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting the algorithms. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the centers of clusters, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.
A framework for analysing inbreeding and heterozygosity-fitness
    correlations (HFCs) based on microsatellite and SNP markers.
Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. These functions are useful for census-weights adjustments, survey calibration, or for performing linear regression with integer parameters <https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/New_Integer_Calibration_%20Procedure_2016.pdf>. This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy.
Offers a pipe-friendly alternative to the 'dplyr' functions
    case_when() and if_else(), as well as a number of user-friendly
    simplifications for common use cases.  These functions accept a vector
    as an optional first argument, allowing conditional statements to be
    built using the 'magrittr' dot operator.  The functions also coerce
    all outputs to the same type, meaning you no longer have to worry
    about using specific typed variants of NA or explicitly declaring
    integer outputs, and evaluate outputs somewhat lazily, so you don't
    waste time on long operations that won't be used.
The Dynamic Time Warping (DTW) distance measure for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. IncDTW is characterized by (1) the incremental calculation of DTW (reduces runtime complexity to a linear level for updating the DTW distance) - especially for life data streams or subsequence matching, (2) the vector based implementation of DTW which is faster because no matrices are allocated (reduces the space complexity from a quadratic to a linear level in the number of observations) - for all runtime intensive DTW computations, (3) the subsequence matching algorithm runDTW, that efficiently finds the k-NN to a query pattern in a long time series, and (4) C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>. For details about this package, Dynamic Time Warping and Incremental Dynamic Time Warping please see "IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping" by Leodolter et al. (2021) <doi:10.18637/jss.v099.i09>.
An efficient and incremental approach for calculating 
    the differences in orbit counts when performing single edge modifications 
    in a network. Calculating the differences in orbit counts is much more efficient than
    recalculating all orbit counts from scratch for each time point.
Provides functions and classes to compute, handle and visualise
  incidence from dated events for a defined time interval. Dates can be provided
  in various standard formats. The class 'incidence' is used to store computed
  incidence and can be easily manipulated, subsetted, and plotted. In addition,
  log-linear models can be fitted to 'incidence' objects using 'fit'. This
  package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit
  for outbreak analysis.
Provides functions and classes to compute, handle and visualise 
  incidence from dated events for a defined time interval. Dates can be 
  provided in various standard formats. The class 'incidence2' is used to store
  computed incidence and can be easily manipulated, subsetted, and plotted.
Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort.
Make empirical Bayes incidence curves from reported case data using a specified delay distribution.
Functions to generate incidence matrices and bipartite graphs that have (1) a fixed fill rate, (2) given marginal sums, (3) marginal sums that follow given distributions, or (4) represent bill sponsorships in the US Congress <doi:10.31219/osf.io/ectms>. It can also generate an incidence matrix from an adjacency matrix, or bipartite graph from a unipartite graph, via a social process mirroring team, group, or organization formation <doi:10.48550/arXiv.2204.13670>, or examine the space of binary matrices with fixed marginals.
Implements a variety of nonparametric and parametric methods that are commonly used when the data set is a mixture of paired observations and independent samples. The package also calculates and returns values of different tests with their corresponding p-values.
    Bhoj, D. S. (1991) <doi:10.1002/bimj.4710330108> "Testing equality of means in the presence of correlation and missing data".
    Dubnicka, S. R., Blair, R. C., and Hettmansperger, T. P. (2002) <doi:10.22237/jmasm/1020254460> "Rank-based procedures for mixed paired and two-sample designs".
    Einsporn, R. L. and Habtzghi, D. (2013) <https://pdfs.semanticscholar.org/89a3/90bafeb2bc41ed4414533cfd5ab84a6b54b6.pdf> "Combining paired and two-sample data using a permutation test".
    Ekbohm, G. (1976) <doi:10.1093/biomet/63.2.299> "On comparing means in the paired case with incomplete data on both responses".
    Lin, P. E. and Stivers, L. E. (1974) <doi:10.1093/biomet/61.2.325> On difference of means with incomplete data".
    Maritz, J. S. (1995) <doi:10.1111/j.1467-842x.1995.tb00649.x> "A permutation paired test allowing for missing values".
Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.
Fit parametric models for time-to-event data that show an initial
    'incubation period', i.e. a variable delay phase where no events occur.
    The delayed Weibull distribution serves as the foundational data model.
    For parameter estimation, different flavours of maximum likelihood
    estimation ('MLE') and the method of maximum product of spacings estimation
    ('MPSE') are implemented. Bootstrap confidence intervals for parameters and
    significance tests in a two group setting are provided.
Implements Inductive Node-Splitting Cross-Validation (INCV) for
    selecting the number of communities in stochastic block models. Provides
    f-fold and random-split node-level cross-validation, along with
    competing methods including CROISSANT, Edge Cross-Validation (ECV), and
    Node Cross-Validation (NCV). Supports both SBM and Degree-Corrected
    Block Models (DCBM), with multiple loss functions (L2, binomial
    deviance, AUC). Includes network simulation utilities for SBM, RDPG,
    and latent space models.
A 'shiny' app that supports both dual and bulk RNA-seq, with the dual
    RNA-seq functionality offering the flexibility to perform either a
    sequential approach (where reads are mapped separately to each genome)
    or a combined approach (where reads are aligned to a single merged
    genome). The user-friendly interface automates the analysis process,
    providing step-by-step guidance, making it easy for users to navigate
    between different analysis steps, and download intermediate results
    and publication-ready plots.
Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.
Estimates weights to make a continuous-valued exposure statistically independent of a vector of pre-treatment covariates using the method proposed in Huling, Greifer, and Chen (2021) <arxiv:2107.07086>.
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.
Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages.
Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.
We provide an R tool for teaching in Social Sciences. It allows the computation of index numbers. It is a measure of the evolution of a fixed magnitude for only a product of for several products. It is very useful in Social Sciences. Among others, we obtain simple index numbers (in chain or in serie), index numbers for not only a product or weighted index numbers as the Laspeyres index (Laspeyres, 1864), the Paasche index (Paasche, 1874) or the Fisher index (Lapedes, 1978).
A set of utilities for manipulating index numbers series including chain-linking, re-referencing, and computing growth rates.
Computes bilateral and multilateral index numbers. 
    It has support for many standard bilateral indexes as well as
    multilateral index number methods such as GEKS, GEKS-Tornqvist 
    (or CCDI), Geary-Khamis and the weighted time product dummy
    (for details on these methods see Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>). 
    It also supports updating of multilateral indexes using 
    several splicing methods.
Helps with the thoughtful saving, reading, and management of result
  files (using 'rds' files). The core functions take a list of parameters that are
  used to generate a unique hash to save results under. Then, the same
  parameter list can be used to read those results back in. This
  is helpful to avoid clunky file naming when running a large number of
  simulations. Additionally, helper functions are available for compiling a flat
  file of parameters of saved results, monitoring result usage, and cleaning up
  unwanted or unused results. For more information, visit the 'indexr' homepage 
  <https://lharris421.github.io/indexr/>.
Quick indexation of any type of vector or of any combination of those. Indexation turns a vector into an integer vector going from 1 to the number of unique elements. Indexes are important building blocks for many algorithms. The method is described at <https://github.com/lrberge/indexthis/>.
Allows the construction selection indices based on estimated breeding values in animal and plant breeding and to calculate several analytic measures around to assess its impact on genetic and phenotypic progress. The methodology thereby allows to analyze genetic gain of traits in the breeding goal which are not part of the actual index and automatically computes several analytic measures. It further allows to retrospectively derive realized economic weights from observed genetic trends. The framework is described in Simianer, H., Heise, J., Rensing, S., Pook, T. Geibel, J. and Reimer, C. (2023) <doi:10.1186/s12711-023-00807-0>.
Computation of test statistics of independence between  (continuous) innovations of time series. They can be used with stochastic volatility models and Hidden Markov Models (HMM). This improves the results in Duchesne, Ghoudi & Remillard (2012) <doi:10.1002/cjs.11141>.
Set of routines for influence diagnostics by using case-deletion in ordinary least 
    squares, nonlinear regression [Ross (1987). <doi:10.2307/3315198>], ridge estimation [Walker and Birch (1988). <doi:10.1080/00401706.1988.10488370>] 
    and least absolute deviations (LAD) regression [Sun and Wei (2004). <doi:10.1016/j.spl.2003.08.018>].
Provides functions to access data from public RESTful APIs including 
    'World Bank API', and 'REST Countries API', retrieving real-time or historical 
    data related to India, such as economic indicators, and international 
    demographic and geopolitical indicators. Additionally, the package includes one of the largest 
    curated collections of open datasets focused on India, covering topics such as population, economy, weather, politics, health, biodiversity, sports, agriculture, cybercrime, infrastructure, and more. 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: 
    'World Bank API' <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, 
    'REST Countries API' <https://restcountries.com/>.
Different functions includes constructing composite indicators, imputing missing data, and evaluating imputation techniques. Additionally, different tools for data normalization. Detailed methodologies of  'Indicator' package are: OECD/European Union/EC-JRC (2008), "Handbook on Constructing Composite Indicators: Methodology and User Guide", OECD Publishing, Paris, <DOI:10.1787/533411815016>, Matteo Mazziotta & Adriano Pareto, (2018) "Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices" <DOI:10.1007/s11205-017-1577-5> and De Muro P., Mazziotta M., Pareto A. (2011), "Composite Indices  of Development and Poverty: An Application to MDGs" <DOI:10.1007/s11205-010-9727-z>.
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].
Simplifies the generation of customized R Markdown PDF templates.
    A template may include an individual logo, typography, geometry or color
    scheme. The package provides a skeleton with detailed instructions for
    customizations. The skeleton can be modified by changing defaults in the
    'YAML' header, by adding additional 'LaTeX' commands or by applying dynamic
    adjustments in R. Individual corporate design elements, such as a title page, can be added as R functions that produce 'LaTeX' code.
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
Provides tools to scrape, clean, and analyze football player data
    from Indonesian leagues and perform similarity-based scouting
    analysis using standardized numeric features. The similarity
    approach follows common vector-space methods as described in
    Manning et al. (2008, ISBN:9780521865715) and Salton et al. (1975,
    <doi:10.1145/361219.361220>).
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 Indonesia, such as holidays, economic indicators, and international 
    demographic and geopolitical indicators. The package also includes a curated collection of 
    open datasets focused on Indonesia, covering topics such as consumer prices, poverty 
    probability, food prices by region, tourism destinations, and minimum wage statistics. 
    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/>.
It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence,  and functions for generating and analysing  different types of dependence.
Companion package to the book "industRial data science", 
    J.Ramalho (2021) <https://j-ramalho.github.io/industRial/>. 
    Provides data sets and functions to complete the case studies and contains 
    the book original Rmd files and tutorials.
Get open statistical data and metadata disseminated by the
    National Statistics Institute of Spain (INE). The functions return
    data frames with the requested information thanks to calls to
    the 'INE' API <https://www.ine.es/dyngs/DAB/index.htm?cid=1100>.
Provides access to granular socioeconomic indicators from the
    Spanish Statistical Office (INE) Household Income Distribution Atlas.
    The package downloads and processes data from a companion 'GitHub'
    repository (<https://github.com/pablogguz/ineAtlas.data/>) which
    contains processed versions of the official INE Atlas data. Functions
    are provided to fetch data at multiple geographic levels
    (municipalities, districts, and census tracts), including income
    indicators, demographic characteristics, and inequality metrics. The
    data repository is updated every year when new releases are published
    by INE.
It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecología AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.
An R6-based client to facilitate interaction with the Statistics
    Portugal (Instituto Nacional de Estatistica - INE) API
    (<https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_api&INST=322751522&xlang=en>).
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Decomposition of income inequality by groups formed of individuals 
    possessing similar characteristics (e.g., sex, education, age) and their 
    income sources at the same time.
    Decomposition of the Theil index is based on Giammatteo, M. (2007) 
    <https://www.lisdatacenter.org/wps/liswps/466.pdf>.
    Decomposition of the squared coefficient of variation is based on
    Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.
Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.
Tools for measuring income and wealth inequality. Computes the
    Gini coefficient with bootstrap or asymptotic confidence intervals
    following Davidson (2009) <doi:10.1016/j.jeconom.2008.09.011>, the
    extended S-Gini family, Theil T and L indices (generalised entropy
    family), the Atkinson index, the Kolm absolute inequality index, Palma
    ratio, Hoover index, percentile ratios, and Lorenz curves. Supports
    between-within group decomposition following Bourguignon (1979)
    <doi:10.2307/1914138>, income share tabulation, concentration indices
    for health inequality with Erreygers (2009) correction, Kakwani tax
    progressivity and Reynolds-Smolensky redistribution indices,
    Foster-Greer-Thorbecke poverty measures including the Sen index, growth
    incidence curves following Ravallion and Chen (2003)
    <doi:10.1016/S0165-1765(02)00205-7>, and Wolfson polarisation. All
    functions accept optional survey weights and work with data from any
    source.
Estimates quantile-based inequality indicators from complex
    survey data, including the quantile ratio index (QRI), quintile share
    Ratio (QSR), Palma ratio, and percentile ratios, together with the Gini
    coefficient. Influence functions are provided for linearization and
    variance estimation, along with a rescaled bootstrap for complex sampling
    designs. Estimation from grouped data is also supported.
    See Scarpa et al. (2025) <doi:10.1093/jssam/smaf024> for details.
Performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the 'hdi' package.
It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" <doi:10.1007/s00180-024-01536-8>.
Provides simple functions to compute and plot two types 
    (sample-size- and coverage-based) rarefaction and extrapolation curves for species
    diversity (Hill numbers) based on individual-based abundance data or sampling-unit-
    based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent
    theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution)
    for an introduction of the R package.
Biodiversity is a multifaceted concept covering different levels of organization from
            genes to ecosystems. 'iNEXT.3D' extends 'iNEXT' to include three dimensions (3D) 
            of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional 
            diversity (FD). This package provides functions to compute standardized 3D diversity estimates 
            with a common sample size or sample coverage. A unified framework based on Hill numbers 
            and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates 
            are in the same units of species/lineage equivalents and can be meaningfully compared. 
            The package features size- and coverage-based rarefaction and extrapolation sampling 
            curves to facilitate rigorous comparison of 3D diversity across individual assemblages. 
            Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) 
            <doi:10.1111/2041-210X.13682> for more details.
Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis:
       STEP 1: Assessment of sample completeness profiles.
       STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to 
       determine whether the asymptotic diversity can be accurately estimated. 
       STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. 
       STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves.
       STEP 4: Assessment of evenness profiles. 
       The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.
As a sequel to 'iNEXT', the 'iNEXT.beta3D' package provides functions to compute 
              standardized taxonomic, phylogenetic, and functional diversity (3D) estimates 
              with a common sample size (for alpha and gamma diversity) or sample coverage 
              (for alpha, beta, gamma diversity as well as dissimilarity or turnover indices). 
              Hill numbers and their generalizations are used to quantify 3D and to make 
              multiplicative decomposition (gamma = alpha x beta). The package also features 
              size- and coverage-based rarefaction and extrapolation sampling curves to 
              facilitate rigorous comparison of beta diversity across datasets.  
              See Chao et al. (2023) <doi:10.1002/ecm.1588> for more details.
Provides functions to access real-time infectious disease data from the 'disease.sh API',
    including COVID-19 global, US states, continent, and country statistics, vaccination coverage,
    influenza-like illness data from the Centers for Disease Control and Prevention (CDC), and more. 
    Also includes curated datasets on a variety of infectious diseases such as influenza, measles, dengue, 
    Ebola, tuberculosis, meningitis, AIDS, and others. The package supports epidemiological research 
    and data analysis by combining API access with high-quality historical and survey datasets on infectious diseases. 
    For more details on the 'disease.sh API', see <https://disease.sh/>.
The objective of this package is to perform inference using
    an expressive statistical grammar that coheres with the tidy design
    framework.
An R package for inferring cell-type specific gene regulatory network from single-cell RNA-seq data.
Functions to make inference about the 
        standardized mortality ratio (SMR) when evaluating the
        effect of a screening program. The package is
        based on methods described in Sasieni (2003) 
        <doi: 10.1097/00001648-200301000-00026> and 
        Talbot et al. (2011) <doi: 10.1002/sim.4334>.
Provides methods for estimating causal effects in the presence of interference described in  B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Variance-aware Michaelis-Menten estimation, model screening,
    grouped enzyme-kinetic analyses, and clustered repeated-measurement
    workflows. The package implements profile-score estimators under
    working variance functions, together with a lightweight cluster-aware
    working-covariance extension, Wald and bootstrap confidence intervals,
    prediction utilities, and simulation helpers. Related methodology is
    discussed by Kim and Ma (2012) <doi:10.1007/s10463-011-0332-y>, Kim
    (2023) <doi:10.1002/sta4.606>, and Ma and Genton (2010)
    <doi:10.1111/j.1467-9868.2010.00741.x>.
Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of
    statistical tests provided in 'stats' package by including additional data types as inputs, expanding and
    restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
A set of functions for the modeling of data derived from the
    Minidisc Infiltrometer device.  It calculates cumulative infiltration
    and square root of time. Also, it calculates the A parameter based on
    soil physical properties.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
Contains a number of infix binary operators that may be useful in day to day practices.
Infix functions in R are those that comes between its
  arguments such as %in%, +, and *. These are useful in R 
  programming when manipulating data, performing logical operations, 
  and making new functions. 'infixit' extends the infix functions 
  found in R to simplify frequent tasks, such as finding elements
  that are NOT in a set, in-line text concatenation, augmented assignment
  operations, additional logical and control flow operators, and 
  identifying if a number or date lies between two others.
Convert historical monetary values into their present-day equivalents
    using bundled CPI (Consumer Price Index) and GDP deflator data sourced from the
    World Bank Development Indicators. Supports British pounds (GBP), Australian
    dollars (AUD), US dollars (USD), Euro (EUR), Canadian dollars (CAD), Japanese
    yen (JPY), Chinese yuan (CNY), Swiss francs (CHF), New Zealand dollars (NZD),
    Indian rupees (INR), South Korean won (KRW), Brazilian reais (BRL), and
    Norwegian krone (NOK). Currency codes and country names are both accepted as
    input.
Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.
Tools for analysing inflation dynamics. Computes weighted
    contributions of price index components, core inflation measures
    (trimmed mean, weighted median, exclusion-based) following Bryan and
    Cecchetti (1994) <doi:10.1016/0304-3932(94)90030-2>, inflation
    persistence via sum-of-AR-coefficients, diffusion indices, Phillips
    curve estimation, breakeven inflation, and trend inflation using the
    Beveridge-Nelson decomposition and Hodrick-Prescott filter. All
    functions are pure computation and work with price data from any
    source.
Implementation of methods Extremum Surface Estimator (ESE) and 
   Extremum Distance Estimator (EDE) to identify the inflection point of a curve .
   Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .
   Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
Analyzes raw abundance data from a cellular thermal shift experiment and calculates melt temperatures and melt shifts for each protein in the experiment. 
    McCracken (2022) <doi:10.1101/2022.12.30.522131>.
Provides methods for detecting influential subjects in
    longitudinal data, particularly when observations are collected at
    irregular time points. The package identifies subjects whose response
    trajectories deviate substantially from population-level patterns,
    helping to diagnose anomalies and undue influence on model estimates.
Provides a collection of tools for
        detecting influential cases in generalized mixed effects
        models. It analyses models that were estimated using 'lme4'. The
        basic rationale behind identifying influential data is that
        when single units are omitted from the data, models
        based on these data should not produce substantially different
        estimates. To standardize the assessment of how influential a
        (single group of) observation(s) is, several measures of
        influence are common practice, such as Cook's Distance. 
        In addition, we provide a measure of percentage change of the fixed point 
        estimates and a simple procedure to detect changing levels of significance.
A set of tools for evaluating several measures of case influence for structural equation models.
Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.
Implements the adaptive influence-based borrowing framework 
    proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper 
    ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing 
    of External Controls" <doi:10.48550/arXiv.2604.13973> for augmenting Randomized Controlled 
    Trials (RCTs) with External Control (EC) data. This package provides a 
    comprehensive workflow to: (1) quantify the comparability of external control 
    samples using influence scores approximated via the influence function of the 
    M-estimator; (2) construct candidate borrowing subsets and select the optimal 
    subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic
    differences in external outcomes using R-learner methods implemented via 
    Ordinary Least Squares or Kernel Ridge Regression.
Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of constraint and effective network size by Burt (2000) <doi:10.1016/S0191-3085(00)22009-1>; algorithm 
    to identify key players by Borgatti (2006) <doi:10.1007/s10588-006-7084-x>; and the 
    bridging algorithm by Valente and Fujimoto (2010) <doi:10.1016/j.socnet.2010.03.003>. 
    On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.
Provides functions for the identification, classification, and
    ranking of influential nodes and candidate features from network and omics
    data. The package implements the Integrated Value of Influence (IVI) for
    integrative network centrality analysis, the SIR-based Influence Ranking
    (SIRIR) model for unsupervised influence ranking, and the Experimental
    data-based Integrative Ranking (ExIR) model for prioritizing candidate
    driver, biomarker, and mediator features from experimental omics data.
    Functions are provided for network reconstruction from adjacency matrices
    and data frames, topological analysis, centrality calculation, assessment
    of associations between centrality measures, and conditional probability
    analysis. ExIR supports bulk and single-cell omics data, including matrices,
    sparse matrices, data frames, tibbles, and Seurat objects.
An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
Methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>.
Download official election results for Spain at polling station,
    municipality and province level, format them and import them to the R
    environment. Data are provided by the Spanish Ministry of the Interior
    (<https://infoelectoral.interior.gob.es/es/inicio/>).
Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).
Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    described in <DOI:10.1002/sim.6274> and Risk Score Imputation
    described in <DOI:10.1002/sim.3480>.
Calculation of informative simultaneous confidence intervals
    for graphical described multiple test procedures and given information
    weights. Bretz et al. (2009) <doi:10.1002/sim.3495> and Brannath et
    al. (2024) <doi:10.48550/arXiv.2402.13719>. Furthermore, exploration of the
    behavior of the informative bounds in dependence of the information
    weights.  Comparisons with compatible bounds are possible.
    Strassburger and Bretz (2008) <doi:10.1002/sim.3338>.
Estimation of the most-left informative set of gross returns 
             (i.e., the informative set).
             The procedure to compute the informative set adjusts the method 
             proposed by 
             Mariani et al. (2022a) <doi:10.1007/s11205-020-02440-6> 
             and 
             Mariani et al. (2022b) <doi:10.1007/s10287-022-00422-2> 
             to gross returns of financial assets. 
             This is accomplished through an adaptive algorithm
             that identifies sub-groups of gross returns in 
             each iteration by approximating their distribution with a
             sequence of two-component log-normal mixtures. 
             These sub-groups emerge when a significant change
             in the distribution occurs below the median of the 
             financial returns, with their boundary termed as
             the “change point" of the mixture. 
             The process concludes when no further change points are detected.
             The outcome encompasses parameters of the leftmost mixture 
             distributions and change points of the
             analyzed financial time series.
             The functionalities of the INFOSET package include: (i) modelling asset distribution
             detecting the parameters which describe left tail behaviour (infoset function), (ii) clustering, (iii) labeling of the financial
             series for predictive and classification purposes through a Left Risk measure based on the first change point (LR_cp function)
             (iv) portfolio construction (ptf_construction function).
             The package also provide a specific function to construct rolling windows of different length size and overlapping time.
Implements the Information Matrix test for regression models following Cameron, A. C., & Trivedi, P. K. (1990) <https://cameron.econ.ucdavis.edu/research/imtest_impliedalternatives_ucdwp372.pdf> Decomposes the test into components for heteroscedasticity, skewness, and kurtosis to diagnose specific forms of misspecification. Provides both overall and component-wise statistics for model assessment.
Implements various measures of information theory based on several entropy estimators.
Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) <doi:10.1103/PhysRevE.69.066138>, knockoff conditional mutual information described in Zhang & Chen (2025) <doi:10.1126/sciadv.adu6464>, synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>, allowing detection of complex and diverse relationships among variables.
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated, as described in Rousset et al. (2026) <doi:10.24072/pcjournal.721>.
Collection of tools for assessment of feature importance and feature effects.
    Key functions are:
    feature_importance() for assessment of global level feature importance,
    ceteris_paribus() for calculation of the what-if plots,
    partial_dependence() for partial dependence plots,
    conditional_dependence() for conditional dependence plots,
    accumulated_dependence() for accumulated local effects plots,
    aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles,
    generic print() and plot() for better usability of selected explainers,
    generic plotD3() for interactive, D3 based explanations, and
    generic describe() for explanations in natural language.
    The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.
Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.
R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.
Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.
Sports Injury Data analysis aims to identify and describe the
    magnitude of the injury problem, and to gain more insights (e.g.
    determine potential risk factors) by statistical modelling approaches.
    The 'injurytools' package provides standardized routines and utilities
    that simplify such analyses. It offers functions for data preparation,
    informative visualizations and descriptive and model-based analyses.
A professional R interface to download and analyze spatial 
    development indicators from the 'BBSR' 'INKAR' (Indikatoren und Karten zur Raum- 
    und Stadtentwicklung) database. Features a bilingual interactive wizard, 
    fuzzy search, multi-indicator downloads with automatic tidy merging (long/wide), 
    robust disk caching, and premium 'ggplot2' themes for regional mapping.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <https://www.r-inla.org>.
Facilitates spatial and general latent Gaussian modeling using
  integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>).
  Additionally, extends the GAM-like model class to more general nonlinear predictor
  expressions, and implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data.
  Model components are specified with general inputs and mapping methods to the
  latent variables, and the predictors are specified via general R expressions,
  with separate expressions for each observation likelihood model in
  multi-likelihood models. A prediction method based on fast Monte Carlo sampling
  allows posterior prediction of general expressions of the latent variables.
  Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019)
  <doi:10.1111/2041-210X.13168>.
Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the 'INLA' package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).
Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.
Prepare objects to implement models over spatial and 
  spacetime domains with the 'INLA' package (<https://www.r-inla.org>).
  These objects contain data to for the 'cgeneric' interface in
  'INLA', enabling fast parallel computations.
  We implemented the spatial barrier model, see Bakka et. al. (2019) 
  <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal 
  models proposed in Lindgren et. al. (2024) 
  <https://raco.cat/index.php/SORT/article/view/428665>. 
  Details are provided in the available vignettes and from the URL bellow.
Contain code to work with a C struct, in short cgeneric, to
    define a Gaussian Markov random (GMRF) model.  The cgeneric contain
    code to specify GMRF elements such as the graph and the precision
    matrix, and also the initial and prior for its parameters, useful for
    model inference.  It can be accessed from a C program and is the
    recommended way to implement new GMRF models in the 'INLA' package
    (<https://www.r-inla.org>).  The 'INLAtools' implement functions to
    evaluate each one of the model specifications from R.  The implemented
    functionalities leverage the use of 'cgeneric' models and provide a
    way to debug the code as well to work with the prior for the model
    parameters and to sample from it.  The `generic0` can be used to
    implement intrinsic models with the scaling as proposed in Sørbye &
    Rue (2014) <doi:10.1016/j.spasta.2013.06.004>, and the required
    constraints.  A very useful functionality is the Kronecker product
    method that creates a new model from multiple cgeneric models.  It
    also works with the rgeneric, the R version of the cgeneric intended
    to easy try implementation of new GMRF models.  The Kronecker between
    two cgeneric models where each one needs a constraint, such as
    spatio-temporal intrinsic interaction models, the needed constraints
    are automatically set.
Implements approximate Bayesian inference for Structural
    Equation Models (SEM) using a custom adaptation of the Integrated
    Nested Laplace Approximation (Rue et al., 2009)
    <doi:10.1111/j.1467-9868.2008.00700.x> as described in Jamil and Rue
    (2026a) <doi:10.48550/arXiv.2603.25690>. Provides a computationally
    efficient alternative to Markov Chain Monte Carlo (MCMC) for Bayesian
    estimation, allowing users to fit latent variable models using the
    'lavaan' syntax. See also the companion paper on implementation and
    workflows, Jamil and Rue (2026b) <doi:10.48550/arXiv.2604.00671>.
A collection of functions for creating color schemes.
    Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory Project Office.
Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling
 conventions.
Generates Rd files from R source code with comments.
 The main features of the default syntax are that
 (1) docs are defined in comments near the relevant code,
 (2) function argument names are not repeated in comments, and
 (3) examples are defined in R code, not comments.
 It is also easy to define a new syntax.
Contains bibliographic information for the U.S. Geological Survey
    (USGS) Idaho National Laboratory (INL) Project Office.
Interpretation methods for analyzing the behavior and individual
    predictions of modern neural networks in a three-step procedure: Converting 
    the model, running the interpretation method, and visualizing the results. 
    Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004)
    <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance
    propagation ('LRP') described by Bach et al. (2015)
    <doi:10.1371/journal.pone.0130140>, deep learning important features
    ('DeepLIFT') described by Shrikumar et al.  (2017) <doi:10.48550/arXiv.1704.02685>
    and gradient-based methods like 'SmoothGrad' described by Smilkov et
    al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' 
    or 'Vanilla Gradient'.
    Details can be found in the accompanying scientific paper: Koenen & Wright 
    (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).
Analysis of the initialization for numerical optimization of 
    real-valued functions, particularly likelihood functions of statistical 
    models. See <https://loelschlaeger.de/ino/> for more details.
Infix operators to detect, subset, and replace the elements matched by a given condition.
  The functions have several variants of operator types, including subsets, ranges, regular expressions and others.
  Implemented operators work on vectors, matrices, and lists.
Three-stage pipeline for detecting inattention episodes in
    long psychophysical tasks (200+ trials). Uses accuracy residuals and
    response pattern signals to locate, sharpen, and formally test
    candidate inattention regions at trial-level precision.
A set of functions to analyse and compare texts, using classical 
  text mining	functions, as well as those from theoretical ecology.
A set of functions for performing null hypothesis testing on
    samples of persistence diagrams using the theory of permutations. Currently,
    only two-sample testing is implemented. Inputs can be either samples of
    persistence diagrams themselves or vectorizations. In the former case, they
    are embedded in a metric space using either the Bottleneck or Wasserstein
    distance. In the former case, persistence data becomes functional data and
    inference is performed using tools available in the 'fdatest' package. Main
    reference for the interval-wise testing method: Pini A., Vantini S. (2017)
    "Interval-wise testing for functional data" <doi:10.1080/10485252.2017.1306627>.
    Main reference for inference on populations of networks: Lovato, I., Pini, A.,
    Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued
    data" <doi:10.1016/j.csda.2019.106896>.
It provides in-place operators for R 
    that are equivalent to '+=', '-=', '*=', '/=' in C++. 
    Those can be applied on integer|double vectors|matrices.
    You have also access to sweep operations (in-place).
Non-parametric resampling-based inference tests for ExPosition.
Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data).
For environmental chemists, ecologists, researchers and agricultural scientists to understand the dissipation kinetics, calculate the half-life periods and rate constants of compounds, pesticides, contaminants in different matrices.
A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) 
    assays in pancreatic beta cells or islets.
    The package allows the user to import several sets of experiments from different spreadsheets 
    and to perform subsequent steps: summarise in a tidy format, visualise data quality 
    and compare experimental conditions without omitting to account for technical confounders 
    such as the date of the experiment or the technician.
    Together, insane is a comprehensive method that optimises pre-processing and analyses of 
    GSIS experiments in a friendly-user interface.
    The Shiny App was initially designed for EndoC-betaH1 cell line following method described 
    in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).
Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.
Streamlines the creation of high-quality labels for insect pinning. By taking a dataset as input, the package allow to generate printable labels in 'LaTeX' and PDF format, helping researchers and entomologists maintain accurate and standardized specimen records. Requires a compatible installation of 'pdflatex' (e.g. <https://www.tug.org/texlive/>). For enhanced accessibility, the package includes a user-friendly 'shiny' application (accessible online <https://nicolas-moiroux.shinyapps.io/InsectLabelR/>), which provides a graphical interface for generating labels without requiring programming expertise.
Using embedded sdmx queries, get the data of more than 150 000 insee series from 'bdm' macroeconomic database.
Tools for easily and flexibly creating 'ggplot2' maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code.
Provides a basic set of compact widgets for 'shiny' apps which
 occupy less space and can appear inline with surrounding text.
A tool to provide an easy, intuitive and consistent
    access to information contained in various R models, like model
    formulas, model terms, information about random effects, data that was
    used to fit the model or data from response variables. 'insight'
    mainly revolves around two types of functions: Functions that find
    (the names of) information, starting with 'find_', and functions that
    get the underlying data, starting with 'get_'. The package has a
    consistent syntax and works with many different model objects, where
    otherwise functions to access these information are missing.
Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
A 'shiny' application to assist in phytosanitary inspections. It generates a diagram of pallets in a lot, highlights the units to be sampled, and documents them based on the selected sampling method (simple random or systematic sampling).
Utility functions that implement and automate common sets of validation tasks. 
    These functions are particularly useful to validate inputs, intermediate objects and output 
    values in user-defined functions, resulting in tidier and less verbose functions.
Collect  marketing data from Instagram Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
The function 'install_load' checks the local R library(ies) to see
    if the required package(s) is/are installed or not. If the package(s)
    is/are not installed, then the package(s) will be installed along with
    the required dependency(ies). This function pulls source or
    binary packages from the Posit/RStudio-sponsored CRAN mirror. Lastly, the
    chosen package(s) is/are loaded. The function 'load_package' simply loads
    the provided package(s). If this package does not fit your needs, then you
    may want to consider these other R packages: 'needs', 'easypackages',
    'pacman', 'pak', 'anyLib', and/or 'librarian'.
R is great for installing software.  Through the 'installr'
    package you can automate the updating of R (on Windows, using updateR())
    and install new software. Software installation is initiated through a
    GUI (just run installr()), or through functions such as: install.Rtools(),
    install.pandoc(), install.git(), and many more. The updateR() command
    performs the following: finding the latest R version, downloading it,
    running the installer, deleting the installation file, copy and updating
    old packages to the new R installation.
Similar to 'rstantools' for 'rstan',
  the 'instantiate' package builds pre-compiled
  'CmdStan' models into CRAN-ready statistical modeling R packages.
  The models compile once during installation,
  the executables live inside the file systems of their respective packages,
  and users have the full power and convenience of
  'cmdstanr' without any additional compilation after package installation.
  This approach saves time and helps R package developers
  migrate from 'rstan' to the more modern 'cmdstanr'.
  Packages 'rstantools', 'cmdstanr', 'stannis', and
  'stanapi' are similar Stan clients with different objectives.
Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.
Calculates insulin secretion rates from C-peptide values
    based on the methods described in Van Cauter et al. (1992)
    <doi:10.2337/diab.41.3.368>. Includes functions to calculate estimated
    insulin secretion rates using linear or cubic spline interpolation of
    c-peptide values (see Eaton et al., 1980 <doi:10.1210/jcem-51-3-520>
    and Polonsky et al., 1986 <doi:10.1172/JCI112308>) and to calculate
    estimates of input coefficients (volume of distribution, short half life,
    long half life, and fraction attributed to short half life) as described by
    Van Cauter. Although the generated coefficients are specific to insulin
    secretion, the two-compartment secretion model used here is useful for
    certain applications beyond insulin.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
Provides actuarial tools and building blocks for analysing,
    modelling, refining, and validating insurance rating models. Designed to
    support common GLM-based pricing tasks and the translation of statistical
    model output into practical tariff structures. The package supports the
    construction of insurance tariff classes using a data-driven approach, based
    on the methodology of Antonio and Valdez
    (2012) <doi:10.1007/s10182-011-0152-7>.
Facilitates the calculation of 40 different insulin sensitivity
    indices based on fasting, oral glucose tolerance test (OGTT), lipid
    (adipose), tracer (palmitate and glycerol rate), and DXA (fat mass)
    measurement values. Enables easy and accurate assessment of insulin
    sensitivity, critical for understanding and managing metabolic disorders
    like diabetes and obesity. Indices calculated are described in
    Gastaldelli (2022) <doi:10.1002/oby.23503>,
    Suleman (2024) <doi:10.1210/clinem/dgae275>, and
    Lorenzo (2010) <doi:10.1210/jc.2010-1144>.
Provides two- and three-way Johnson-Neyman-(Krause) plots for easier interpretation of interactions. It extends the classic framework of Johnson and Neyman (1936) and Johnson and Fay (1950) <doi:10.1007/BF02288864> to Bayesian models and three-way interactions. The functions have dedicated routines for classic lm()/glm() models, as well as 'lme4' models and 'RSiena' results. However, the package can also be used model agnostic and thus extends the availability of JN(K)-plots beyond what is currently available. A detailed introduction can be found in Krause (2026) <doi:10.48550/arXiv.2604.22051>.
Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
The methods in this package adds to the functionality of  the 'intamap' package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.
The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.
Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi{10.1002/sim.7350} and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi{10.1093/biostatistics/kxaa052}. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.
Generates a 'Graphviz' graph of the most significant 3-way 
    interaction gains (i.e. conditional information gains) based on a provided 
    discrete data frame. Various output formats are supported ('Graphviz', SVG, 
    PNG, PDF, PS). For references, see the webpage of Aleks Jakulin 
    <http://stat.columbia.edu/~jakulin/Int/>.
Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
The integrity package implements the IPD Integrity Tool, a
    structured and transparent framework for evaluating the integrity of
    individual participant data (IPD) from randomised trials (see Hunter et
    al. (2024) <doi:10.1002/jrsm.1738> and <doi:10.32614/RJ-2017-008>). It
    supports users to identify potential issues, such as unusual data
    patterns, implausible values, lack of expected correlations, date
    violations, and inconsistencies. The package provides reproducible
    workflows for screening, documenting and summarising integrity concerns,
    and may be applied by evidence synthesists, editors, and others to
    determine whether a randomised trial may be considered sufficiently
    trustworthy to contribute to the evidence base that informs policy and
    practice.
Currently used CI method has its limitation when the test statistics are asymmetrical (chi-square test, F-test) or the model functions are non-linear. It can be overcome by using the likelihood functions for the interval estimation. 'inteli' package now supports interval estimation for the mean, variance, variance ratio, binomial distribution, Poisson distribution, odds ratio, risk difference, relative risk and their likelihood function plots. Testing functions are also provided.
Four datasets are provided here from the 'Intendo' game
    'Super Jetroid'. It is data from the 2015 year of operation and it comprises
    a revenue table ('all_revenue'), a daily users table ('users_daily'), a user
    summary table ('user_summary'), and a table with data on all user sessions
    ('all_sessions'). These core datasets come in different sizes, and, each of
    them has a variant that was intentionally made faulty (totally riddled with
    errors and inconsistencies). This suite of tables is useful for testing with
    packages that focus on data validation and data documentation.
Electricity is not made equal and it vary in its carbon footprint (or carbon intensity) 
    depending on its source. This package enables to access and query data provided by the 
    Carbon Intensity API (<https://carbonintensity.org.uk/>). National Grid’s Carbon Intensity API 
    provides an indicative trend of regional carbon intensity of the electricity system in Great Britain.
Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. 
    This package is based on the following research: Eckardt and Mateu (2018) <doi:10.1080/10618600.2017.1391695>. Eckardt and Mateu (2021) <doi:10.1007/s11749-020-00720-4>.
An R implementation of Matthew Thomas's 'Python' library 'inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) <doi:10.1145/321150.321157>. Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) <doi:10.48550/arXiv.2106.08496>. Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) <doi:10.1137/0706034>.
Power analysis for regression models which test the interaction of
    two or three independent variables on a single dependent variable. Includes options 
    for correlated interacting variables and specifying variable reliability. 
    Two-way interactions can include continuous, binary, or ordinal variables.
    Power analyses can be done either analytically or via simulation.  Includes 
    tools for simulating single data sets and visualizing power analysis results.
    The primary functions are power_interaction_r2() and power_interaction() for two-way
    interactions, and power_interaction_3way_r2() for three-way interactions. The function 
    run_pos_power_search() provides a stability analysis for two-way interactions.
    Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
    Olino TM (2023). "Tutorial: Power analyses for interaction effects in 
    cross-sectional regressions." <doi:10.1177/25152459231187531>. 
    If you use the stability analyses, please cite: Castillo A, Miller JD, Vize C, 
    Baranger DAA, Lynam DR. "When Do Interaction/Moderation Effects Stabilize in
    Linear Regression?"<doi:10.1177/25152459251407860>.
Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>]. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).
A tool to calculate and plot estimates from models 
    in which an interaction between the main predictor and a continuous covariate has been specified.
    Methods used in the package refer to Harrell Jr FE (2015, ISBN:9783319330396); 
    Durrleman S, Simon R. (1989) <doi:10.1002/sim.4780080504>; Greenland S. (1995) <doi:10.1097/00001648-199507000-00005>.
A suite of functions for conducting and interpreting analysis 
  of statistical interaction in regression models that was formerly part of the 
  'jtools' package. Functionality includes visualization of two- and three-way
  interactions among continuous and/or categorical variables as well as 
  calculation of "simple slopes" and Johnson-Neyman intervals (see e.g., 
  Bauer & Curran, 2005 <doi:10.1207/s15327906mbr4003_5>). These
  capabilities are implemented for generalized linear models in addition to the 
  standard linear regression context.
Implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.
Chi-square tests are computed with corrections.
Various functions and a Shiny app to enrich the results of Multiple Correspondence Analysis with interpretive axes and planes (see Moschidis, Markos, and Thanopoulos, 2022; <doi:10.1108/ACI-07-2022-0191>).
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Provides a runtime type system, allowing users to define and implement interfaces, enums, typed data.frame/data.table, as well as typed functions. This package enables stricter type checking and validation, improving code structure, robustness and reliability.
Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.
A dataframe validation framework for package builders who use
  dataframes as function parameters. It performs checks on column names, coerces
  data-types, and checks grouping to make sure user inputs conform to a
  specification provided by the package author. It provides a mechanism for
  package authors to automatically document supported dataframe inputs and
  selectively dispatch to functions depending on the format of a dataframe much
  like S3 does for classes. It also contains some developer tools to make
  working with and documenting dataframe specifications easier. It helps package
  developers to improve their documentation and simplifies parameter validation
  where dataframes are used as function parameters.
Performs estimation, diagnostics, and visualization of conditional marginal effects and group average treatment effects of a treatment on an outcome across different values of a moderator. Optionally integrates with the 'mlr3extralearners' package for additional machine learning backends compatible with the double machine learning estimators. 'mlr3extralearners' is not on CRAN but can be obtained from <https://github.com/mlr-org/mlr3extralearners>.
Functions implemented in this package allow to coerce (i.e.
	convert) network data between classes provided by other R packages.
	Currently supported classes are those defined in packages: network and
	igraph.
Allows the simulation of the recruitment and both the event and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.
Allows an interactive assessment of the timing of interim analyses. The algorithm simulates both the recruitment and treatment/event phase of a clinical trial based on the package 'interim'.
Converts matrices and lists of matrices into a single vector by interleaving 
    their values. That is, each element of the result vector is filled from the input 
    matrices one row at a time. This is the same as transposing a matrix, then removing the 
    dimension attribute, but is designed to operate on matrices in nested list structures.
Interlinearized glossed texts (IGT) are used in descriptive linguistics for
 representing a morphological analysis of a text through a morpheme-by-morpheme gloss.
 'InterlineaR' provide a set of functions that targets several popular formats of IGT
 ('SIL Toolbox', 'EMELD XML') and that turns an IGT into a set of data frames following
 a relational model (the tables represent the different linguistic units: texts,
 sentences, word, morphems).
 The same pieces of software ('SIL FLEX', 'SIL Toolbox') typically produce dictionaries
 of the morphemes used in the glosses. 'InterlineaR' provide a function for turning
 the LIFT XML dictionary format into a set of data frames following a relational model
 in order to represent the dictionary entries, the sense(s) attached to the entries,
 the example(s) attached to senses, etc.
Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. <doi:10.1016/j.hazadv.2023.100325> and Paul and Yeasin <doi:10.1371/journal.pone.0272999>.
Bivariate data interpolation on regular and irregular
  grids, either linear or using splines are the main part of this
  package.  It is intended to provide FOSS replacement functions for
  the ACM licensed akima::interp and tripack::tri.mesh functions.
  Linear interpolation is implemented in 
  interp::interp(..., method="linear"), this corresponds to the call 
  akima::interp(..., linear=TRUE) which is the default setting and 
  covers most of akima::interp use cases in depending packages.  
  A re-implementation of Akimas irregular grid spline
  interpolation (akima::interp(..., linear=FALSE)) is now also
  available via interp::interp(..., method="akima").
  Estimators for partial derivatives are now also available in 
  interp::locpoly(), these are a prerequisite for the spline interpolation.  
  The basic part is a GPLed triangulation algorithm (sweep hull 
  algorithm by David Sinclair) providing the starting point for the
  irregular grid interpolator. As side effect this algorithm is also
  used to provide replacements for almost all functions of the tripack
  package which also suffers from the same ACM license restrictions.  
  All functions are designed to be backward compatible with their 
  akima / tripack counterparts.
Plots the conditional coefficients ("marginal effects") of
    variables included in multiplicative interaction terms.
Spatial interpolation toolkit designed for environmental and geospatial applications. It includes a range of methods, from traditional techniques to advanced machine learning approaches, ensuring accurate and efficient estimation of values in unobserved locations.
Some interpolation methods taken from 'Boost': barycentric
    rational interpolation, modified Akima interpolation, PCHIP (piecewise
    cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom
    splines.
Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
Estimate confidence intervals for mean, proportion, mean difference 
   for unpaired and paired samples and proportion difference. Plot the confidence 
   intervals. Generate documents explaining the statistical result step by step.
High resolution mass spectrometry yields often large data sets of 
    spectra from compounds which are not present in available libraries. These 
    spectra need to be annotated and interpreted.
    'InterpretMSSpectrum' provides a set of functions to perform such tasks for 
    Electrospray-Ionization and Atmospheric-Pressure-Chemical-Ionization derived 
    data in positive and negative ionization mode.
Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Provides access to low-level operating system mechanisms for
    performing atomic operations on shared data structures. Mutexes provide
    shared and exclusive locks. Semaphores act as counters. Message queues
    move text strings from one process to another. All these interprocess
    communication (IPC) tools can optionally block with or without a timeout.
    Implemented using the cross-platform 'boost' 'C++' library
    <https://www.boost.org/doc/libs/release/libs/interprocess/>.
A set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1017/pan.2021.19>.
Generates three inter-related genomic datasets: methylation, gene expression and protein expression having user specified cluster patterns. The simulation utilizes the realistic inter- and intra- relationships from real DNA methylation, mRNA expression and protein expression data from the TCGA ovarian cancer study, Chalise (2016) <doi:10.1016/j.cmpb.2016.02.011>.
Provides an R version of the 'InterVA4' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Provides an R version of the 'InterVA5' software (<http://www.byass.uk/interva/>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
Perform fast and memory efficient time-weighted averaging of values 
    measured over intervals into new arbitrary intervals. 
	This package is useful in the context of data measured or represented
	as constant values over intervals on a one-dimensional discrete axis 
	(e.g. time-integrated averages of a curve over defined periods). 
	This package was written specifically to deal with air 
	pollution data recorded or predicted as averages over sampling periods. 
	Data in this format often needs to be shifted to non-aligned periods 
	or averaged up to periods of longer duration (e.g. averaging data measured over 
	sequential non-overlapping periods to calendar years).
Implements the Interval Consensus Model (ICM) for analyzing continuous bounded interval-valued responses in psychometrics using 'Stan' for 'Bayesian' estimation. Provides functions for transforming interval data to simplex representations, fitting item response theory (IRT) models with isometric log-ratio (ILR) and sum log-ratio (SLR) link functions, and visualizing results. The package enables aggregation and analysis of interval-valued response data commonly found in psychological measurement and related disciplines. Based on Kloft et al. (2024) <doi:10.31234/osf.io/dzvw2>.
A user-friendly toolbox for doing the statistical analysis of
  interval-valued responses in questionnaires measuring intrinsically
  imprecise human attributes or features (attitudes, perceptions, opinions,
  feelings, etc.). In particular, this package provides S4 classes, methods,
  and functions in order to compute basic arithmetic and statistical operations
  with interval-valued data; prepare customized plots; associate each
  interval-valued response to its equivalent Likert-type and visual analogue
  scales answers through the minimum theta-distance and the mid-point criteria;
  analyze the reliability of respondents' answers from the internal consistency
  point of view by means of Cronbach's alpha coefficient; and simulate
  interval-valued responses in this type of questionnaires. The package also
  incorporates some real-life data that can be used to illustrate its working
  with several non-trivial reproducible examples. The methodology used in this
  package is based in many theoretical and applied publications from
  SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and
  Comparison of Distributions of Random Elements) Research Group
  (<https://bellman.ciencias.uniovi.es/smire+codire/>)
  from the University of Oviedo (Spain).
Tools for working with and comparing sets of points and intervals.
Manipulate integer-bounded intervals including finding overlaps, piling and merging.
Performs hypothesis testing
    using the interval estimates (e.g., confidence intervals). The
    non-overlapping interval estimates indicates the statistical
    significance. References to these procedures can be found at
    Noguchi and Marmolejo-Ramos (2016) <doi:10.1080/00031305.2016.1200487>,
    Bonett and Seier (2003) <doi:10.1198/0003130032323>, and
    Lemm (2006) <doi:10.1300/J082v51n02_05>.
Creation of tables of summary statistics or counts for clinical data (for 'TLFs'). 
  These tables can be exported as in-text table (with the 'flextable' package) for a Clinical Study Report 
  (Word format) or a 'topline' presentation (PowerPoint format), 
  or as interactive table (with the 'DT' package) to an html document for clinical data review.
The 'inti' package is part of the 'inkaverse' project for developing 
    different procedures and tools used in plant science and experimental designs. 
    The mean aim of the package is to support researchers during the planning of
    experiments and data collection (tarpuy()), data analysis and graphics (yupana())
    , and scientific writing. 
    Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
An interval-valued extension of ordinary and simple kriging.
    Optimization of the function is based on a generalized interval distance.
    This creates a non-differentiable cost function that requires a
    differentiable approximation to the absolute value function. This
    differentiable approximation is optimized using a Newton-Raphson algorithm
    with a penalty function to impose the constraints. Analyses in the package
    are driven by the 'intsp' and 'intgrd' 
    classes, 
    which are interval-valued extensions of
    'SpatialPointsDataFrame' and 'SpatialPixelsDataFrame' respectively. 
    The package includes several wrappers to functions in the 
    'gstat' and 'sp' packages.
Provides a key-value store data structure. The keys are
    integers and the values can be any R object. This is like a list but
    indexed by a set of integers, not necessarily contiguous and possibly
    negative. The implementation uses a 'R6' class. These containers are 
    not faster than lists but their usage can be more convenient for 
    certain situations.
Implementing the interventional effects for mediation analysis for up to 3 mediators.
    The methods used are based on VanderWeele, Vansteelandt and Robins (2014) <doi:10.1097/ede.0000000000000034>, 
    Vansteelandt and Daniel (2017) <doi:10.1097/ede.0000000000000596> and Chan and Leung (2020; unpublished manuscript, available on request from the author of this package).
    Linear regression, logistic regression and Poisson regression are used for continuous, binary 
    and count mediator/outcome variables respectively.
Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.
Models, analyzes, and forecasts financial intraday signals. This package
    currently supports a univariate state-space model for intraday trading volume provided
    by Chen (2016) <doi:10.2139/ssrn.3101695>.
Performs Goodness of Fit for regression models 
  using Integrated Regression method. Works for several 
  different fitting techniques.
Estimate the proportions of the null and the reproducibility and
    non-reproducibility of the signal group for the input data set. The Bayes factor
    calculation and EM (Expectation Maximization) algorithm procedures are also
    included.
Provides functions to estimate the intrinsic dimension of a dataset 
  via likelihood-based approaches. Specifically, the package implements the 
  'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. 
  In addition, the first reference contains an extended vignette on the usage of 
  the 'TWO-NN' and 'Hidalgo' models. References: 
    Denti (2023, <doi:10.18637/jss.v106.i09>); 
    Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); 
    Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>);
    Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); 
    Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
Functions for evaluating and testing asset pricing models, including
    estimation and testing of factor risk premia, selection of "strong" risk 
    factors (factors having nonzero population correlation with test asset
    returns), heteroskedasticity and autocorrelation robust covariance matrix
    estimation and testing for model misspecification and identification. 
    The functions for estimating and testing factor risk 
    premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass 
    approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) 
    <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk
    premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The 
    functions for selecting the "strong" risk factors are based on the Oracle
    estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the 
    factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. 
    The functions for evaluating model misspecification implement the HJ
    model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>,
    which is a modification of the prominent Hansen-Jagannathan (1997)
    <doi:10.1111/j.1540-6261.1997.tb04813.x> distance.
    The functions for testing model identification 
    specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> 
    and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test 
    to the regression coefficient matrix of test asset returns on risk factors.
    Finally, the function for heteroskedasticity and autocorrelation robust 
    covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912>
    covariance estimator.
Kappa statistics is one of the most used methods to evaluate the effectiveness of inpsections based on attribute assessments in industry. However, its estimation by available methods does not provide its "real" or "intrinstic" value. This package provides functions for the computation of the intrinsic kappa value as it is described in: Rafael Sanchez-Marquez, Frank Gerhorst and David Schindler (2023) "Effectiveness of quality inspections of attributive characteristics – A novel and practical method for estimating the “intrinsic” value of kappa based on alpha and beta statistics." <doi:10.1016/j.cie.2023.109006>.
Evaluating if values
  of vectors are within different open/closed intervals
  (`x %[]% c(a, b)`), or if two closed
  intervals overlap (`c(a1, b1) %[]o[]% c(a2, b2)`).
  Operators for negation and directional relations also implemented.
Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.
Integration of disparate datasets is needed in order to make efficient use of all available data and thereby address the issues currently threatening biodiversity.
   Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset.
   We therefore introduce the package, 'intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online.
   An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
Contains implementations of
    the integrative Cox model with uncertain event times
    proposed by Wang, et al. (2020) <doi:10.1214/19-AOAS1287>,
    the regularized Cox cure rate model with uncertain event status
    proposed by Wang, et al. (2023) <doi:10.1007/s12561-023-09374-w>,
    and other survival analysis routines including
    the Cox cure rate model proposed by
    Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531>
    via an EM algorithm proposed by
    Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>,
    the regularized Cox cure rate model with elastic net penalty following
    Masud et al. (2018) <doi:10.1177/0962280216677748>.
Provides tools for importing, merging, and analysing data from 
  international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).
Enum-type representation of vectors and representation
  of intervals, including a method of coercing variables in data frames.
Provides an up-to-date version of the 'InvaCost' database 
    (<doi:10.6084/m9.figshare.12668570>) in R, and
    several functions to analyse the costs of invasive alien species 
    (<doi:10.1111/2041-210X.13929>).
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
Vector operations between grapes: An infix-only package! The 'invctr' functions perform common and less common operations on vectors, data frames matrices and list objects:
    - Extracting a value (range), or, finding the indices of a value (range).
    - Trimming, or padding a vector with a value of your choice.
    - Simple polynomial regression.
    - Set and membership operations.
    - General check & replace function for NAs, Inf and other values.
Simulate inventory policies with and without forecasting, facilitate inventory analysis calculations such as  stock levels and re-order points,pricing and promotions calculations. 
  The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations.
    The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization.
  The functions are referenced from :
  1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 
  2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition.
  3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 
  4-Ballou, R.H. Business Logistics Management. 
  5-MIT Micromasters Program. 
  6- Columbia University  course for supply and demand analysis.
  8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W
  For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.
Reverse engineer a regular expression pattern for the characters
    contained in an R object. Individual characters can be categorised into
    digits, letters, punctuation or spaces and encoded into run-lengths. This
    can be used to summarise the structure of a dataset or identify non-standard
    entries. Many non-character inputs such as numeric vectors and data frames
    are supported.
Provides composable invertible transforms for
    (sparse) matrices.
Functions to facilitate inverse estimation (e.g., calibration) in
    linear, generalized linear, nonlinear, and (linear) mixed-effects models. A
    generic function is also provided for plotting fitted regression models with
    or without confidence/prediction bands that may be of use to the general
    user. For a general overview of these methods, see Greenwell and Schubert 
    Kabban (2014) <doi:10.32614/RJ-2014-009>.
Light weight implementation of the standard distribution
  functions for the inverse gamma distribution, wrapping those for the gamma
  distribution in the stats package.
Computes the log likelihood for an inverse gamma stochastic volatility model using a closed form expression of the likelihood. The details of the computation of this closed form expression are given in Gonzalez and Majoni (2023) <http://rcea.org/RePEc/pdf/wp23-11.pdf> .  
    The closed form expression is obtained for a stationary inverse gamma stochastic volatility model by marginalising out the volatility. This allows the user to obtain the maximum likelihood estimator for this non linear non Gaussian state space model. In addition, the user can obtain the estimates of the smoothed volatility using the exact smoothing distributions.
Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
A collection of datasets containing a variety of in vitro toxicokinetic 
             measurements including -- but not limited to -- chemical fraction unbound 
             in the presence of plasma (f_up), intrinsic hepatic clearance (Clint,
             uL/min/million hepatocytes), and membrane permeability for
             oral absorption (Caco2). The datasets provided by the package were 
             processed and analyzed with the companion 'invitroTKstats' package.
A set of tools for processing and analyzing in vitro toxicokinetic
             measurements in a standardized and reproducible pipeline. The package
             was developed to perform frequentist and Bayesian estimation on a
             variety of in vitro toxicokinetic measurements including -- but not
             limited to -- chemical fraction unbound in the presence of plasma
             (f_up), intrinsic hepatic clearance (Clint,
             uL/min/million hepatocytes), and membrane permeability for
             oral absorption (Caco2). The methods provided
             by the package were described in Wambaugh et al. (2019) <doi:10.1093/toxsci/kfz205>.
Takes in vivo toxicokinetic concentration-time data and fits
    parameters of 1-compartment and 2-compartment models for each
    chemical. These methods are described in detail in "Informatics for Toxicokinetics" (2025).
Contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019)  <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.
Interaction and analysis of multiple response data,
    along with other tools for analysing these types of data including
    missing value analysis and calculation of standard errors for
    a range of covariance matrix results (proportions, multinomial,
    independent samples, and multiple response).
Simple plotting function(s) for exploratory data analysis with flexible options allowing for easy plot customisation. The goal is to make it easy for beginners to start exploring a dataset through simple R function calls, as well as provide a similar interface to summary statistics and inference information. Includes functionality to generate interactive HTML-driven graphs. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.
Provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the 'tidyverse' code used to obtain the result in an effort to bridge the gap between GUI and coding.
Provides a collection of functions for working with time series data, including functions for drawing, decomposing, and forecasting. Includes capabilities to compare multiple series and fit both additive and multiplicative models. Used by 'iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Holt (1957) <doi:10.1016/j.ijforecast.2003.09.015>, Winters (1960) <doi:10.1287/mnsc.6.3.324>, Cleveland, Cleveland, & Terpenning (1990) "STL: A Seasonal-Trend Decomposition Procedure Based on Loess".
One function to read files. One function to write files. One
    function to direct plots to screen or file. Automatic file format inference
    and directory structure creation.
Calculates fundamental IO matrices (Leontief, Wassily W. (1951) <doi:10.1038/scientificamerican1051-15>); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) <doi:10.1080/09535319200000013>, Blair and Miller (2009) <ISBN:978-0-521-73902-3>, Antras et al (2012) <doi:10.3386/w17819>, Hummels, Ishii, and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) <doi:10.1080/09535319300000017>, Sonis, Hewings, and Guo (2006) <doi:10.1080/09535319600000002>); and a variety of table operators.
Provides six modules for tumor microenvironment (TME)
    analysis based on multi-omics data. These modules cover data
    preprocessing, TME estimation, TME infiltrating patterns, cellular
    interactions, genome and TME interaction, and visualization for TME
    relevant features, as well as modelling based on key features. It
    integrates multiple microenvironmental analysis algorithms and
    signature estimation methods, simplifying the analysis and downstream
    visualization of the TME. In addition to providing a quick and easy
    way to construct gene signatures from single-cell RNA-seq data, it
    also provides a way to construct a reference matrix for TME
    deconvolution from single-cell RNA-seq data. The analysis pipeline and
    feature visualization are user-friendly and provide a comprehensive
    description of the complex TME, offering insights into tumour-immune
    interactions (Zeng D, et al. (2024)
    <doi:10.1016/j.crmeth.2024.100910>.  Fang Y, et al. (2025)
    <doi:10.1002/mdr2.70001>).
Addresses the 'log of zero' by developing a new family of 
    estimators called iterated Ordinary Least Squares. 
    This family nests standard approaches such as log-linear and 
    Poisson regressions, offers several computational advantages, 
    and corresponds to the correct way to perform the popular 
    log(Y + 1) transformation. For more details about how to use it, 
    see the notebook at: <https://www.davidbenatia.com/>.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.
Network functionalities specialized for data generated from input-output tables.
Pre-processing and basic analytical tasks for working with 
    Eurostat's symmetric input–output tables, and basic input–output 
    economics calculations. Part of rOpenGov <https://ropengov.github.io/> 
    for open source open government initiatives.
Routines and tools for assessing the quality of content
    analysis on the basis of the Iota Reliability Concept. The concept is
    inspired by item response theory and can be applied to any kind of
    content analysis which uses a standardized coding scheme and discrete
    categories.  It is also applicable for content analysis conducted by
    artificial intelligence.  The package provides reliability measures
    for a complete scale as well as for every single category. Analysis of
    subgroup-invariance and error corrections are implemented. This
    information can support the development process of a coding scheme and
    allows a detailed inspection of the quality of the generated data.
    Equations and formulas working in this package are part of Berding et
    al. (2022)<doi:10.3389/feduc.2022.818365> and Berding and Pargmann
    (2022) <doi:10.30819/5581>.
Basic I/O tools for streaming and data parsing.
Enables the user to find the country, region, district, city, coordinates, zip code, time zone, ISP, domain name, connection type, area code, weather, Mobile Country Code, Mobile Network Code, mobile brand name, elevation, usage type, address type, IAB category and Autonomous system information that any IP address or hostname originates from. Supported IPv4 and IPv6.
        Please visit <https://www.ip2location.com> to learn more. You may also want to visit <https://lite.ip2location.com> for free database download.
        This package requires 'IP2Location Python' module. At the terminal, please run 'pip install IP2Location' to install the module.
A system for submitting multiple IP information queries to 'IP2Location.io'’s IP Geolocation API and storing the resulting data in a dataframe. You provide a vector of IP addresses and your 'IP2Location.io' API key. The package returns a dataframe with one row per IP address and a column for each available data field (data fields not included in your API plan will contain NAs). This is the second submission of the package to CRAN.
Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too.
    This package uses the 'IP2Location.io' API to query this data. To get started with a free API key, sign up here <https://www.ip2location.io/sign-up?ref=1>.
Enable user to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits.
    The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit <https://lite.ip2location.com> for free database download.
Easily implement the checking of 'WHOIS' information for a particular domain. 'IP2WHOIS' supports the query for 1113 Top-level Domains(TLDs) and 634 Country Code Top-level Domains(ccTLDs).
    To get started with a free API key, you may sign up at here <https://www.ip2whois.com/register>.
Converts character vectors between phonetic
    representations.  Supports IPA (International Phonetic Alphabet),
    X-SAMPA (Extended Speech Assessment Methods Phonetic Alphabet), and
    ARPABET (used by the CMU Pronouncing Dictionary).
Classes and functions for working with IP (Internet Protocol)
    addresses and networks, inspired by the Python 'ipaddress' module.
    Offers full support for both IPv4 and IPv6 (Internet Protocol versions
    4 and 6) address spaces. It is specifically designed to work well with
    the 'tidyverse'.
A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) <doi:10.1101/2020.03.21.001206>.
Provides a collection of intuitive and user-friendly functions
    for computing confidence intervals for common statistical tasks, including
    means, differences in means, proportions, and odds ratios. The package also
    includes tools for linear regression analysis and several real-world datasets
    intended for teaching and applied statistical inference.
Read data from 'LimeSurvey'
    (<https://www.limesurvey.org/>)
    in a comfortable way.
    Heavily inspired by 'limer'
    (<https://github.com/cloudyr/limer/>),
    which lacked a few comfort features for me.
An R client for the 'ipbase.com' IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://ipbase.com/docs> .
Provides tools for passing messages between R processes. 
    Shiny examples are provided showing how to perform useful tasks such as: 
    updating reactive values from within a future, progress bars for long running 
    async tasks, and interrupting async tasks based on user input.
We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.
Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2025) <doi:10.1093/bioinformatics/btaf055>.
An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. ’s digitize package (2011)  <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as 'DigitizeIt' (for MAC or windows) or 'ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.
Functions are provided to interpolate geo-referenced point data via
    Inverse Path Distance Weighting. Useful for coastal marine applications where
    barriers in the landscape preclude interpolation with Euclidean distances.
Provides direct access to the macroeconomic, financial,
    and regional database maintained by the Institute for
    Applied Economic Research (Ipea) via the Ipeadata API.
    For more information, see <https://www.ipeadata.gov.br/>.
Convenient functions to create 'ggplot2' graphics following the 
             editorial guidelines of the  Institute for Applied Economic 
             Research (Ipea).
Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.
Contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.
Starting from user-supplied institutional data, these scripts 
    transform, aggregate, and reshape the information to produce 
    key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) 
    through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes.  
    Final files are saved locally to a location of the user's choice. 
    User-friendly readable files can also be produced for purposes of data review and validation.
A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.
Provides methods to evaluate predictive performance of models that 
    estimate risks under hypothetical intervention scenarios 
    (interventional/causal/counterfactual predictions) with observational data 
    subject to treatment-outcome confounding. Inverse probability of treatment 
    weighting (IPTW) is used to construct a pseudopopulation in which all 
    individuals receive a specified intervention, enabling assessment of 
    agreement between predicted risks under the intervention and observed 
    outcomes in the pseudo-population corresponding to that intervention. 
    Package supports binary and time-to-event outcomes under binary 
    interventions made at a single time point. Performance measures supported 
    are AUC (Area Under the receiving operating characteristic Curve), Brier 
    score, observed-expected ratio, and calibration plots. Methods implemented 
    in this package are based on work by Keogh and Van Geloven (2024) 
    <DOI:10.1097/EDE.0000000000001713>.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.
A fast (C) implementation of the iterative proportional fitting
    procedure.
Performs iterative proportional updating given a seed table and
  an arbitrary number of marginal distributions. This is commonly used in
  population synthesis, survey raking, matrix rebalancing, and other
  applications. For example, a household survey may be weighted to match the
  known distribution of households by size from the census. An origin/
  destination trip matrix might be balanced to match traffic counts.
  The approach used by this package is based on a paper from
  Arizona State University (Ye, Xin, et. al. (2009)
  <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>).
  Some enhancements have been made to their work including primary and 
  secondary target balance/importance, general marginal agreement, and weight 
  restriction.
Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.
Provides functions to query the
    'IPGeolocation.io IP Location API'
    (<https://ipgeolocation.io/documentation/ip-location-api.html>).
    Supports retrieval of IP location, ASN, network, currency,
    timezone, abuse, and security data. Response filtering is
    supported using 'fields' and 'excludes' parameters (dot notation
    supported), and optional objects can be requested via the
    'include' parameter. Returns parsed API responses as R objects.
When you want to install R package or download file from GitHub, 
    but you can't access GitHub, this package helps you install R packages or 
    download file from GitHub via the proxy website <https://gh-proxy.com/> 
    or <https://ghfast.top/>, which is in real-time sync with GitHub.
Combining genomic prediction with Monte Carlo simulation, three different 
    strategies are implemented to select parental lines for multiple traits in plant 
    breeding. The selection strategies include (i) GEBV-O considers only genomic 
    estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O 
    considers only genomic diversity (GD) of the candidate individuals; and (iii) 
    GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY, 
    Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best 
    linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate 
    GEBVs of the target traits, and then a selection index is adopted to evaluate 
    the composite performance of an individual.
An R client for the 'iplookupapi.com' IP Lookup API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://iplookupapi.com/docs> .
Provides functions and data sets to accompany the  book 'Integrated Population Models: Theory and Ecological Applications with R and JAGS' by Michael Schaub and Marc Kéry (ISBN: 9780128205648).
Flexibly implements Integral Projection Models using a
  mathematical(ish) syntax. This package will not help with the vital rate
  modeling process, but will help convert those regression models into an
  IPM. 'ipmr' handles density dependence and environmental stochasticity, with a
  couple of options for implementing the latter. In addition, provides functions
  to avoid unintentional eviction of individuals from models. Additionally, 
  provides model diagnostic tools, plotting functionality, 
  stochastic/deterministic simulations, and analysis tools.
  Integral projection models are described in depth by Easterling et al. (2000) 
  <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) 
  <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>,
  and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. 
  Williams et al. (2012) <doi:10.1890/11-2147.1> discuss the problem of 
  unintentional eviction.
Computes intervention in prediction measure for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.
Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. 
    Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc.
    'ipolygrowth' takes time series data from individual biological samples (with technical replicates) or multiple samples.
Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.
Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.
Improved predictive models by indirect classification and
  bagging for classification, regression and survival problems 
  as well as resampling based estimators of prediction error.
Provides methods to perform and analyse I-prior regression models.
    Estimation is done either via direct optimisation of the log-likelihood or 
    an EM algorithm.
Immunotherapy has revolutionized cancer treatment, but predicting patient
    response remains challenging. Here, we presented Intelligent Predicting
    Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a
    novel network-based model that integrates multiple data types to predict
    immunotherapy outcomes. It incorporates gene expression, biological
    functional network, tumor microenvironment characteristics, immune-related
    pathways, and clinical data to provide a comprehensive view of factors
    influencing immunotherapy efficacy. By identifying key genetic and
    immunological factors, it provides an insight for more personalized
    treatment strategies and combination therapies to overcome resistance
    mechanisms.
Provides tools for estimating uncertainty in individual 
    polygenic risk scores (PRSs) using both sampling-based and analytical 
    methods, as well as the Best Linear Unbiased Estimator (BLUE). 
    These methods quantify variability in PRS estimates for both binary 
    and quantitative traits. 
    See Henderson (1975) <doi:10.2307/2529430> for more details.
Functions that wrap popular phylogenetic software for sequence
    alignment, masking of sequence alignments, and estimation of phylogenies and
    ancestral character states.
Estimates the density of a spatially distributed animal population 
  sampled with an array of passive detectors, such as traps. Models incorporating 
  distance-dependent detection are fitted by simulation and inverse prediction 
  as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.
Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.
Contains data sets, programmes and illustrations 
    discussed in the book, "Introduction to Probability, Statistics and R:
    Foundations for Data-Based Sciences."  Sahu (2024, isbn:9783031378645)
    describes the methods in detail.
An easy way to work with census, survey, and geographic data
    provided by IPUMS in R. Generate and download data through the IPUMS
    API and load IPUMS files into R with their associated metadata to
    make analysis easier. IPUMS data describing 1.4 billion individuals
    drawn from over 750 censuses and surveys is available free of charge
    from the IPUMS website <https://www.ipums.org>.
Generate plots based on the Item Pool Visualization concept for
    latent constructs. Item Pool Visualizations are used to display the
    conceptual structure of a set of items (self-report or psychometric).
    Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.
Functions to estimate the probability to receive the observed treatment, based on
    individual characteristics. The inverse of these probabilities can be used as weights when
	estimating causal effects from observational data via marginal structural models. Both point
	treatment situations and longitudinal studies can be analysed. The same functions can be used to
	correct for informative censoring.
Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or  modelled  using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.
An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.
An implementation of the MaxLFQ algorithm by
    Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive
    pipeline for processing proteomics data in data-independent acquisition mode
    (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>;
    Pham et al. 2026 <doi:10.1021/acs.jproteome.5c01038>).
    It offers additional options for protein quantification using
    the N most intense fragment ions, using all fragment ions, 
    the median polish algorithm by Tukey (1977, ISBN:0201076160), 
    and a robust linear model. In general, the tool can be used to integrate
    multiple proportional observations into a single quantitative value.
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Functions to import and handle infrared spectra (import from '.csv' and
  Thermo Galactic's '.spc', baseline correction, binning, clipping,
  interpolating, smoothing, averaging, adding, subtracting, dividing, 
  multiplying, atmospheric correction, 'tidyverse' methods, plotting).
Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
             M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, T. Stützle,
             and M. Birattari (2016) <doi:10.1016/j.orp.2016.09.002>.
Graphical visualization tools for analyzing the data produced by 'irace'. The 'iraceplot' package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of 'irace' and their performance. The functions just require the log file generated by 'irace' and, in some cases, they can be used with user-provided data.
Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.
Provides implementation of various correlation coefficients of common use in
  Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
  as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
  <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
  developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
Contains some important regression methods for interval-valued variables. For each method, it is available the fitted values, residuals and some goodness-of-fit measures.
Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
Provides examples of code for analyzing data or accomplishing tasks that may be useful to institutional or educational researchers.
A comprehensive suite of tools for managing, processing, and
    analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports
    quality control, geospatial analysis, and preparation of IFCB data for
    publication in databases like <https://www.gbif.org>,
    <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>,
    <https://shark.smhi.se/en/>, and <https://www.ecotaxa.org>. The package
    integrates with the MATLAB 'ifcb-analysis' tool, which is described in
    Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides
    features for working with raw, manually classified, and machine
    learning–classified image datasets. Key functionalities include image
    extraction, particle size distribution analysis, taxonomic data
    handling, and biomass concentration calculations, essential for
    plankton research.
Fits a double logistic function to NDVI time series and calculates 
             instantaneous rate of green (IRG) according to methods described
             in Bischoff et al. (2012) <doi:10.1086/667590>.
Provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Classes and functions for metrics calculation as part of the
             'EarthScope MUSTANG' project. The functionality in this package 
             builds upon the base classes of the 'IRISSeismic' package.
             Metrics include basic statistics as well as higher level
             'health' metrics that can help identify problematic seismometers.
Provides classes and methods for seismic data analysis. The
             base classes and methods are inspired by the python code found in
             the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and 
             methods support data returned by web services provided by the EarthScope Consortium.
             <https://service.earthscope.org/>.
The R kernel for the 'Jupyter' environment executes R code which the front-end
    ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense
    matrices.
Generalised linear models via the iteratively reweighted least squares algorithm. The functions perform logistic, Poisson and Gamma regression (ISBN:9780412317606), either for a single model or many regression models in a column-wise fashion.
The Row-column designs are widely recommended for experimental situations when there are two well-identified factors that are cross-classified representing known sources of variability. These designs are expected to result a gain in accuracy of estimating treatment comparisons in an experiment as they eliminate the effects of the row and column factors. However, these designs are not readily available when the number of treatments is more than the levels of row and column blocking factors. This package named 'iRoCoDe' generates row-column designs with incomplete rows and columns, by amalgamating two incomplete block designs (D1 and D2). The selection of D1 and D2 (the input designs) can be done from the available incomplete block designs, viz., balanced incomplete block designs/ partially balanced incomplete block designs/ t-designs. (Mcsorley, J.P., Phillips, N.C., Wallis, W.D. and Yucas, J.L. (2005).<doi:10.1007/s10623-003-6149-9>).
Imbalanced domain learning has almost exclusively focused on solving 
    classification tasks, where the objective is to predict cases labelled with a 
    rare class accurately. Such a well-defined approach for regression tasks lacked 
    due to two main factors. First, standard regression tasks assume that each value 
    is equally important to the user. Second, standard evaluation metrics focus on 
    assessing the performance of the model on the most common cases. This package 
    contains methods to tackle imbalanced domain learning problems in regression 
    tasks, where the objective is to predict extreme (rare) values.
    The methods contained in this package are: 1) an automatic and non-parametric 
    method to obtain such relevance functions; 2) visualisation tools; 3) suite of 
    evaluation measures for optimisation/validation processes; 4) the squared-error 
    relevance area measure, an evaluation metric tailored for imbalanced regression tasks.
    More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.
A procedure for seeding R's built in random number
    generators using a variable-length sequence of values.
    Accumulates input entropy into a 256-bit hash digest or "ironseed"
    and is able to generate a variable-length sequence of output seeds
    from an ironseed.
Provides functions to download, clean, and structure open data from the Brazilian Federal Revenue (Receita Federal do Brasil - RFB) regarding Personal Income Tax (IRPF) statements. Includes a data dictionary and categorized metadata for several sections such as assets, rights, debts, and income brackets. More information about the data source can be found at <https://dados.gov.br/dados/conjuntos-dados/grandes-nmeros-do-imposto-de-renda-da-pessoa-fsica>.
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson's A, Kendall's W, Cohen's Kappa, ...
Implements a 'Shiny Item Analysis' module and functions for computing false positive rate 
  and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2024) 
   <doi:10.1111/bmsp.12343>.
Contains a series of R functions for calculating various chance-corrected agreement 
  coefficients (CAC) among 2 or more raters. Among the CAC coefficients covered are Cohen's kappa, Conger's kappa,
  Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple
  sets of weights are proposed for computing weighted analyses. Also included in this package is Bangdiwala's B
  coefficient.
Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).
Calculates various intraclass correlation coefficients used to quantify 
  inter-rater and intra-rater reliability.  The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented
  in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
  Advanced Analytics, LLC.
Provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}.
A collection of Item Response Theory (IRT) and Computerized 
     Adaptive Testing (CAT) functions that are used in psychometrics.
Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory.
Estimates item parameters of the two-parameter logistic (2PL)
    model in Item Response Theory (IRT) using the marginal Bayesian modal
    estimation via the Expectation-Maximization (EM) algorithm. The package
    calibrates item discrimination and difficulty parameters, yielding results
    comparable to software like 'BILOG-MG'.
Applying the family of the Bayesian Expectation-Maximization-Maximization (BEMM) algorithm to estimate: (1) Three parameter logistic (3PL) model proposed by Birnbaum (1968, ISBN:9780201043105); (2) four parameter logistic (4PL) model proposed by Barton & Lord (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x>; (3) one parameter logistic guessing (1PLG) and (4) one parameter logistic ability-based guessing (1PLAG) models proposed by San Martín et al (2006) <doi:10.1177/0146621605282773>. The BEMM family includes (1) the BEMM algorithm for 3PL model proposed by Guo & Zheng (2019) <doi:10.3389/fpsyg.2019.01175>; (2) the BEMM algorithm for 1PLG model and (3) the BEMM algorithm for 1PLAG model proposed by Guo, Wu, Zheng, & Chen (2021) <doi:10.1177/0146621621990761>; (4) the BEMM algorithm for 4PL model proposed by Zheng, Guo, & Kern (2021) <doi:10.1177/21582440211052556>; and (5) their maximum likelihood estimation versions proposed by Zheng, Meng, Guo, & Liu (2018) <doi:10.3389/fpsyg.2017.02302>. Thus, both Bayesian modal estimates and maximum likelihood estimates are available.
Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.
Item response theory (IRT) parameter estimation using marginal maximum likelihood and expectation-maximization algorithm 
    (Bock \& Aitkin, 1981 <doi:10.1007/BF02293801>). 
    Within parameter estimation algorithm, several methods for latent distribution estimation are available.
    Reflecting some features of the true latent distribution, these latent distribution estimation methods can possibly enhance the estimation accuracy and free the normality assumption on the latent distribution.
Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.
IRT-M is a semi-supervised approach based on Bayesian Item Response Theory
    that produces theoretically identified underlying dimensions from input data
    and a constraints matrix. The methodology is fully described in 'Morucci et al. (2024),
    "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. 
    Details are available at <https://www.cambridge.org/core/journals/american-political-science-review/article/measurement-that-matches-theory-theorydriven-identification-in-item-response-theory-models/395DA1DFE3DCD7B866DC053D7554A30B>.
A collection of functions useful in learning and practicing IRT,
    which can be combined into larger programs. Provides basic CTT analysis,
    a simple common interface to the estimation of item
    parameters in IRT models for binary responses with three different programs
    (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible 
    values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord,
    and the complete Hebaera method), and a rich array of parametric and 
    non-parametric (kernel) plots. Estimates and plots Haberman's interaction model
    when all items are dichotomously scored.
Implementation of analytical and sampling-based power analyses for
    the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied
    to item response theory (IRT) models that are fitted using marginal maximum
    likelihood estimation. The methods are described in our paper
    (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).
Fit unidimensional item response theory (IRT) models to test
    data, which includes both dichotomous and polytomous items, calibrate 
    pretest item parameters, estimate examinees' abilities, and examine 
    the IRT model-data fit on item-level in different ways as well as provide 
    useful functions related to IRT analyses such as IRT model-data fit 
    evaluation and differential item functioning analysis. 
    The bring.flexmirt() and write.flexmirt() functions were written by modifying 
    the read.flexmirt() function (Pritikin & Falk (2022) <doi:10.1177/0146621620929431>).
    The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() 
    and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>).
    The bisection() function was written by modifying the bisection() function 
    (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve 
    scoring in the est_score() function was written by modifying the irt.eq.tse() function 
    (González (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted 
    likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions
    of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).
Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.
Provides a pipeline application programming interface (API) for
    Monte Carlo simulation-based sample-size planning in item response theory
    (IRT). Implements the 10-decision framework
    from Schroeders and Gnambs (2025) <doi:10.1177/25152459251314798> as a
    three-step workflow: specify the data-generating model with irt_design(),
    add study conditions with irt_study(), and run simulations with
    irt_simulate(). Supports one-parameter logistic (1PL), two-parameter
    logistic (2PL), and graded response models with missing-completely-at-random
    (MCAR), missing-at-random (MAR), booklet, and linking missingness
    mechanisms. Results include mean squared error (MSE), bias, root mean
    squared error (RMSE), standard error (SE), and coverage criteria with
    summary and plot methods.
The ISA is a biclustering algorithm that finds modules 
  in an input matrix. A module or bicluster is a block of the
  reordered input matrix.
We provide data sets used in the textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2025).
These are data and functions to support quantitative peace science
    research. The data are important state-year information on democracy and
    wealth, which require periodic updates and regular maintenance. The
    functions permit some exploratory and diagnostic assessment of the kinds of
    data in demand by the community, but do not impose many dependencies on the
    user.
ISA is a metadata framework to manage an increasingly diverse set 
    of life science, environmental and biomedical experiments. In isatabr 
    methods for reading, modifying and writing of files in the ISA-Tab format
    are implemented. It also contains methods for processing assay data.
The Inductive Subgroup Comparison Approach ('ISCA') offers a way to compare groups that are internally differentiated and heterogeneous. It starts by identifying the social structure of a reference group against which a minority or another group is to be compared, yielding empirical subgroups to which minority members are then matched based on how similar they are. The modelling of specific outcomes then occurs within specific subgroups in which majority and minority members are matched. 'ISCA' is characterized by its data-driven, probabilistic, and iterative approach and combines fuzzy clustering, Monte Carlo simulation, and regression analysis. ISCA_random_assignments() assigns subjects probabilistically to subgroups. ISCA_clustertable() provides summary statistics of each cluster across iterations. ISCA_modeling() provides Ordinary Least Squares regression results for each cluster across iterations. For further details please see Drouhot (2021) <doi:10.1086/712804>.
Introductory statistics methods to accompany "Investigating
    Statistical Concepts, Applications, and Methods" (ISCAM) by Beth
    Chance & Allan Rossman (2024) <https://rossmanchance.com/iscam4/>.
    Tools to introduce statistical concepts with a focus on simulation
    approaches. Functions are verbose, designed to provide ample output
    for students to understand what each function does. Additionally, most
    functions are accompanied with plots. The package is designed to be
    used in an educational setting alongside the ISCAM textbook.
Implementation of functions to assign corresponding common job prestige scores (SIOPS, ISEI), the official job or group title and the ISCO-88 code to given ISCO-08 codes. ISCO-08 is the latest version of the International Standard Classification of Occupations which is used to organise information on labour and jobs.
Provides tools for evaluating and ranking missing value
    imputation methods using proper scoring rules.
    Implements the Energy-I-Score and the DR-I-Score for the assessment
    of deterministic, stochastic and multiple imputation methods
    for numerical and mixed datasets, following Näf et al. (2022)
    <doi:10.48550/arXiv.2106.03742> and Näf et al. (2025)
    <doi:10.48550/arXiv.2507.11297>.
Provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).
Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files,
    described at <https://www.ncdc.noaa.gov/isd>. Data includes for example,
    wind speed and direction, temperature, cloud data, sea level pressure,
    and more. Includes data from approximately 35,000 stations worldwide,
    though best coverage is in North America/Europe/Australia. Data is stored
    as variable length ASCII character strings, with most fields optional.
    Included are tools for parsing entire files, or individual lines of data.
Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); 
    activity estimation of experimental samples. Implements methods described in:
	Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510>,  
	Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>,
	Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) <doi:10.3390/s19204544>, and
	Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020)  <doi:10.1021/acssensors.9b02133>.
The implement of integrative analysis methods based on a two-part penalization, which realizes dimension reduction analysis and mining the heterogeneity and association of multiple studies with compatible designs. The software package provides the integrative analysis methods including integrative sparse principal component analysis (Fang et al., 2018), integrative sparse partial least squares (Liang et al., 2021) and integrative sparse canonical correlation analysis, as well as corresponding individual analysis and meta-analysis versions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). Integrative sparse principal component analysis. Journal of Multivariate Analysis, <doi:10.1016/j.jmva.2018.02.002>. (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). Integrative sparse partial least squares. Statistics in Medicine, <doi:10.1002/sim.8900>.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
A toolbox for constructing potential landscapes for Ising
    networks. The parameters of the networks can be directly supplied by
    users or estimated by the 'IsingFit' package by van Borkulo and
    Epskamp (2016) <https://CRAN.R-project.org/package=IsingFit> from
    empirical data. The Ising model's Boltzmann distribution is preserved
    for the potential landscape function. The landscape functions can be
    used for quantifying and visualizing the stability of network states,
    as well as visualizing the simulation process.
Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided. Such as transfer matrix for 1D. Utility functions for exact solutions are provided. Example use cases are as follows: Measuring effective ergodicity and power-laws in so called functional-diffusion. Example usage contains parallel runs, fitting power-laws, finite size scaling, computing autocorrelation, uncertainty analysis and plotting utilities.
Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
Develops stochastic models based on the Theory of Island
    Biogeography (TIB) of MacArthur and Wilson (1967) <doi:10.1023/A:1016393430551>
    and extensions. It implements methods to estimate colonization and
    extinction rates (including environmental variables) given presence-absence
    data, simulates community assembly, and performs model selection.
A curated reference list of countries and territories with
    classifications for Small Island Developing States (SIDS),
    sub-national island jurisdictions (SNIJ), World Bank region and
    income group, and political association. Sub-sovereign cases such
    as Aruba, Curacao, Bonaire, Sint Maarten, the French overseas
    territories, and Aaland Islands are represented with disambiguating
    codes that standard country-code packages often collapse or omit.
    Provides predicate helpers and a tidy joiner intended to extend
    rather than replace 'countrycode'. Source data is maintained at
    <https://github.com/University-of-Aruba/island-research-reference-data>
    and licensed CC BY 4.0.
An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.
The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).
Functions to support the computations carried out in
  `An Introduction to Statistical Modeling of Extreme Values' by
  Stuart Coles. The functions may be divided into the following 
  groups; maxima/minima, order statistics, peaks over thresholds
  and point processes.
Paquete creado con el fin de facilitar el cálculo y distribución del índice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodología completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].
A comprehensive toolkit for Interpretive Structural Modelling (ISM) 
    analysis. Provides functions for creating adjacency matrices from various 
    input formats including SSIM (Structural Self-Interaction Matrix), computing 
    reachability matrices using Warshall's algorithm, performing hierarchical 
    level partitioning, MICMAC (Cross-Impact Matrix Multiplication Applied to 
    Classification) analysis, and visualizing ISM structures through both static 
    and interactive diagrams. ISM is a methodology for identifying and summarizing 
    relationships among specific elements which define an issue or problem, 
    as described in Warfield (1974) <doi:10.1109/TSMC.1974.5408524>.
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Check if an 'externalptr' is a null pointer.
    R does currently not have a native function for that purpose.
    This package contains a C function that returns TRUE in case of a null pointer.
Linear order and unimodal order (univariate)
	     isotonic regression; bivariate isotonic regression
	     with linear order on both variables.
Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers.
    The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal 'dot' format, e.g., 3E7.1CBE991A14. These are referred
    to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary
    structure (see <https://en.wikipedia.org/wiki/ISO_11784_and_ISO_11785>). There are two 'flavours' of this format, a left and a right variation. Which flavour
    a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on 
    the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically.
Calculates calorific values (gross and net), density, relative
    density, and Wobbe indices together with their standard uncertainties from
    natural gas composition, implementing the method of ISO 6976:2016
    "Natural Gas — Calculation of calorific values, density, relative density
    and Wobbe indices from composition". Uncertainty propagation follows
    Annex B of that standard.
    Reference: International Organization for Standardization (2016)
    <https://www.iso.org/standard/55842.html>.
Functions to parse strings with ISO8601 dates, times, and
    date-times into R-objects.  Additionally, there are functions to determine
    the type of ISO8601 string and to standardise ISO8601 strings.
A fast C++ implementation to generate contour lines
    (isolines) and contour polygons (isobands) from regularly spaced grids
    containing elevation data.
In classification problems a monotone relation between some
  predictors and the classes may be assumed. In this package 'isoboost' 
  we propose new boosting algorithms, based on LogitBoost, that 
  incorporate this isotonicity information, yielding more accurate 
  and easily interpretable rules.
A set of functions to run simple and composite box-models to describe the 
    dynamic or static distribution of stable isotopes in open 
    or closed systems. The package also allows the sweeping of many 
    parameters in both static and dynamic conditions.
    The mathematical models used in this package are derived from Albarede, 1995, 
    Introduction to Geochemical Modelling, Cambridge University Press,
    Cambridge <doi:10.1017/CBO9780511622960>.
Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology <doi:10.1111/gcb.16407>.
This resource provides tools to create, compare, and post-process 
    spatial isotope assignment models of animal origin. It generates 
    probability-of-origin maps for individuals based on user-provided tissue and 
    environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] 
    <doi:10.1111/2041-210X.12147>) using the framework established in Bowen et al.
    (2010) <doi:10.1146/annurev-earth-040809-152429>). The package 'isocat' can then 
    quantitatively compare and cluster these maps to group individuals by 
    similar origin. It also includes techniques for applying four approaches 
    (cumulative sum, odds ratio, quantile only, and quantile simulation) with 
    which users can summarize geographic origins and probable distance traveled 
    by individuals. Campbell et al. [2020] establishes several of the functions
    included in this package <doi:10.1515/ami-2020-0004>.
A bunch of functions to deal with circular data under order restrictions.
ISO language, territory, currency, script and character codes.
  Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
  currency codes, ISO 15924 script codes, and the ISO 8859 character codes
  as well as the UN M.49 area codes.
Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) 
    measurement data to evaluate isotope ratios (IRs) is a complex process. 
    The 'IsoCor' package facilitates this process and renders it reproducible 
    by providing a function to run a 'Shiny'-App locally in any web browser. 
    In this App the user can upload data files of various formats, select ion 
    traces, apply peak detection and perform calculation of IRs and delta 
    values. Results are provided as figures and tables and can be exported. 
    The App, therefore, facilitates data processing of ICP-MS experiments to 
    quickly obtain optimal processing parameters compared to traditional 'Excel'
    worksheet based approaches. A more detailed description can be found in the 
    corresponding article <doi:10.1039/D2JA00208F>. The most recent version 
    of 'IsoCor' can be tested online at <https://apps.bam.de/shn00/IsoCor/>.
ISO 3166-1 country codes and ISO 4217 currency codes provided
    by the International Organization for Standardization.
A voxel is a representation of a value on a regular, three-dimensional 
   grid; it is the 3D equivalent of a 2D pixel.  Voxel data can be visualised
   with this package using fixed viewpoint isometric cubes for each data point.
   This package also provides
   sample voxel data and tools for transforming the data.
Distributional regression under stochastic order restrictions for
    numeric and binary response variables and partially ordered covariates. See
    Henzi, Ziegel, Gneiting (2020) <arXiv:1909.03725>.
This toolbox makes working with oxygen,
    carbon, and clumped isotope data reproducible and straightforward.
    Use it to quickly calculate isotope fractionation factors,
    and apply paleothermometry equations.
Implementation of Isolation kernel (Qin et al. (2019) <doi:10.1609/aaai.v33i01.33014755>).
API wrapper that contains functions to retrieve data from the 'IsoMemo' partnership databases. Web services for API: <https://isomemodb.com/api/v1/iso-data>.
Deriving isodar-based niche breadth indices from
    abundance data of two or more habitats, including several methods based on
    pairwise isodars, multidimensional isodars, and isodar-adjusted inequality.
Read and process isotopocule data from an Orbitrap Isotope Solutions mass spectrometer. Citation: Kantnerova et al. (Nature Protocols, 2024).
Clustering algorithm developed for use with plot inventories of species. It groups plots by subsets of diagnostic species rather than overall species composition. There is an unsupervised and a supervised mode, the latter accepting suggestions for species with greater weight and cluster medoids.
Ternary plots made simple. This package allows to create
    ternary plots using 'graphics'. It provides functions to display the
    data in the ternary space, to add or tune graphical elements and to
    display statistical summaries. It also includes common ternary
    diagrams which are useful for the archaeologist (e.g. soil texture
    charts, ceramic phase diagram).
Plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using 'York', 'Titterington', 'Ludwig' and Omnivariant Generalised Least-Squares ('OGLS') approaches. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith') plots. Constructs total Pb-U, Pb-Pb, Th-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
Provides a graphical user interface to the 'IsoplotR' package for radiometric geochronology. The GUI runs in an internet browser and can either be used offline, or hosted on a server to provide online access to the 'IsoplotR' toolbox.
Building isoscapes using mixed models and inferring the geographic
  origin of samples based on their isotopic ratios. This package is essentially a
  simplified interface to several other packages which implements a new
  statistical framework based on mixed models. It uses 'spaMM' for fitting and
  predicting isoscapes, and assigning an organism's origin depending on its
  isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for
  plotting the maps produced with 'terra' using 'lattice'.
IsoSpec is a fine structure calculator used for obtaining the most
    probable masses of a chemical compound given the frequencies of the composing
    isotopes and their masses. It finds the smallest set of isotopologues with
    a given probability. The probability is assumed to be that of the product of
    multinomial distributions, each corresponding to one particular element and
    parametrized by the frequencies of finding these elements in nature. These
    numbers are supplied by IUPAC - the International Union of Pure and Applied
    Chemistry. See: Lacki, Valkenborg, Startek (2020) <DOI:10.1021/acs.analchem.0c00959>
    and Lacki, Startek, Valkenborg, Gambin (2017) <DOI:10.1021/acs.analchem.6b01459>
    for the description of the algorithms used.
Nonparametric estimation on survival analysis under order-restrictions.
Contains two main functions: one for
        solving general isotone regression problems using the
        pool-adjacent-violators algorithm (PAVA); another one provides
        a framework for active set methods for isotone optimization
        problems with arbitrary order restrictions. Various types of
        loss functions are prespecified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Fast and multi-threaded implementation of
	isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
	extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>),
	SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
	fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>),
	robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>),
	and customizable variations of them, for isolation-based outlier detection, clustered outlier detection,
	distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>),
	isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>),
	and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>),
	based on random or guided decision tree splitting, and providing different metrics for
	scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>).
	Provides simple heuristics for fitting the model to categorical columns and handling missing data,
	and offers options for varying between random and guided splits, and for using different splitting criteria.
The wiDB...() functions provide an interface to the public API 
    of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: 
    build, check and submit queries, and receive and 
    unpack responses. Data analysis functions support Bayesian 
    inference of the source and source isotope composition of water 
    samples that may have experienced evaporation. Algorithms 
    adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).
This is an substitute for the %V and %u formats which are
        not implemented on Windows. In addition, the package offers
        functions to convert from standard calender format yyyy-mm-dd
        to and from ISO 8601 week format yyyy-Www-d.
A collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain  and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are  incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.
Allows access to data from the Rio de Janeiro Public Security Institute (ISP), such as criminal statistics, data on gun seizures and femicide. The package also contains the spatial data of Pacifying Police Units (UPPs) and Integrated Public Safety Regions, Areas and Circumscriptions.
Provides analysis results and trial simulation functions for the I-SPY Acute Respiratory Disease Syndrome trial based on composite ranked outcomes. The composite ranked outcome is a hierarchical outcome where trial participants are ranked first by 28 day mortality, then ventilator days, then by advanced respiratory support days. A Bayesian win probability approach is used for analysis. Trial design options include group sequential looks for safety, superiority, futility, and adjustment of randomization probabilities.
We use the  ISR to handle with PCA-based missing data with high  correlation,  and the DISR to handle with distributed PCA-based missing data.  The philosophy of the package is described in Guo G. (2024)  <doi:10.1080/03610918.2022.2091779>.
This is the central location for data and tools for the development,
    maintenance, analysis, and deployment of the International Soil Radiocarbon Database
    (ISRaD). ISRaD was developed as a collaboration between the U.S. Geological Survey
    Powell Center and the Max Planck Institute for Biogeochemistry. This R package provides
    tools for accessing and manipulating ISRaD data, compiling local data using the ISRaD
    data structure, and simple query and reporting functions for ISRaD. For more detailed
    information visit the ISRaD website at: <https://soilradiocarbon.org/>.
Methodology for subgroup selection in the context of isotonic regression including methods for sub-Gaussian errors, classification, homoscedastic Gaussian errors and quantile regression. See the documentation of ISS(). Details can be found in the paper by Müller, Reeve, Cannings and Samworth (2023) <arXiv:2305.04852v2>.
Manage a 'GitHub' problem using R: wrangle issues, labels and
    milestones. It includes functions for storing, prioritizing (sorting),
    displaying, adding, deleting, and selecting (filtering) issues based
    on qualitative and quantitative information. Issues (labels and
    milestones) are written in lists and categorized into the S3 class to
    be easily manipulated as datasets in R.
You can access to open data published in Instituto Canario De Estadistica (ISTAC) APIs at <https://datos.canarias.es/api/estadisticas/>.
Download data from ISTAT (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a 'shiny' application called 'shinyIstat' can be used to search, download and filter datasets more easily.
Provides an interface to the 'ISTAT' 'SDMX' RESTful API
    <https://esploradati.istat.it/SDMXWS>. Allows users to discover
    available datasets, explore their structure and dimensions, and retrieve
    statistical data from the Italian National Institute of Statistics. Based
    on the Python 'istatapi' package by Jacopo Attolini.
Launches a 'shiny' based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.
Provides functions to to compute a continuum of information-based measures 
 for quantifying the temporal stability of populations, communities, and ecosystems, 
 as well as their associated synchrony, based on species (or species assemblage)
 biomass or other key variables. When biodiversity data are available, the package
 also enables the assessment of the corresponding diversity–stability relationships.
 All measures are applicable in both temporal and spatial contexts. The theoretical
 and methodological background is detailed in Chao et al. (2025) 
 <doi:10.1101/2025.08.20.671203>.
Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations. See Fox et al. (2024) <doi:10.1016/j.crmeth.2024.100884> for details.
Uses Independent Component Analysis to perform feature selection in the presence of unknown confounders.
Data sets and scripts for text examples and exercises in 
  P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Provides two record linkage data sets on the Italian Survey on Household and Wealth, 2008 and 2010, a sample survey conducted by the Bank of Italy every two years. The 2010 survey covered 13,702 individuals, while the 2008 survey covered 13,734 individuals. The following categorical variables are included in this data set: year of birth, working status, employment status, branch of activity, town size, geographical area of birth, sex, whether or not Italian national, and highest educational level obtained. Unique identifiers are available to assess the accuracy of one’s method. Please see Steorts (2015) <DOI:10.1214/15-BA965SI> to find more details about the data set.
Functions for analyzing multiple choice items. These analyses include
  the convertion of student response into binaty data (correct/incorrect),
  the computation of the number of corrected responses and grade for each subject,
  the calculation of item difficulty and discrimination, the computation of the
  frecuency and point-biserial correlation for each distractor and the graphical
  analysis of each item.
Three methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.
The itdr() routine allows  for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) <doi:10.1198/016214506000000140>, convolution transformation methods proposed by Zeng and Zhu (2010) <doi:10.1016/j.jmva.2009.08.004>, and iterative Hessian transformation methods proposed by Cook and Li (2002) <doi:10.1214/aos/1021379861>. Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) <doi:10.5705/ss.202020.0312>, and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) <doi:10.1016/j.csda.2021.107380>.
Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).
Automates the identification and comparative evaluation of 
    item-removal strategies in exploratory factor analysis, producing 
    transparent summaries (explained variance, loading ranges, reliability) 
    to support comfortable, reproducible decisions. The criteria are 
    based on best practices and established heuristics (e.g., Costello & 
    Osborne (2005) <doi:10.7275/jyj1-4868>, Howard (2016) 
    <doi:10.1080/10447318.2015.1087664>). Includes flexible thresholds for 
    factor loadings (min_loading) and cross-loading differences (loading_diff).
Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented.
    ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively.
    For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.
Fits large-scale regression models with a penalty that 
  restricts the maximum number of non-zero regression coefficients
  to a prespecified value.  While Chu et al (2020) <doi:10.1093/gigascience/giaa044>
  describe the basic algorithm, this package uses Cyclops for an efficient implementation.
Know which loop iteration the code execution is up to by including
    a single, convenient function call inside the loop.
Support for iterators, which allow a programmer to traverse
    through all the elements of a vector, list, or other collection of data.
The iterLap (iterated Laplace approximation) algorithm approximates a
             general (possibly non-normalized) probability density on R^p, by repeated
             Laplace approximations to the difference between current approximation 
             and true density (on log scale). The final approximation is a mixture of
             multivariate normal distributions and might be used for example as a
             proposal distribution for importance sampling (eg in Bayesian applications). 
             The algorithm can be seen as a computational generalization of the Laplace 
             approximation suitable for skew or multimodal densities.
A fresh take on iterators in R. Designed to be cross-compatible with the 'iterators' package, but using the 'nextOr' method will offer better performance as well as more compact code. With batteries included: includes a collection of iterator constructors and combinators ported and refined from the 'iterators', 'itertools', and 'itertools2' packages.
Iterator for generating permutations and combinations. They
    can be either drawn with or without replacement, or with distinct/ non-distinct 
    items (multiset). The generated sequences are in lexicographical order 
    (dictionary order). The algorithms to generate permutations and combinations are 
    memory efficient. These iterative algorithms enable users to process all sequences 
    without putting all results in the memory at the same time. 
    The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer
    being maintained. Users are recommended to switch to 'arrangements'.
Various tools for creating iterators, many patterned after
        functions in the Python itertools module, and others patterned
        after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
    looping.
Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
The 'Interactive Tree Of Life' <https://itol.embl.de/> online server can 
    edit and annotate trees interactively. The 'itol.toolkit' package can 
    support all types of annotation templates.
Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).
Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies."  Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>.  The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'.  The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>).  The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.
Implements the Interpolate, Truncate, Project (ITP) root-finding 
    algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>.
    The user provides the function, from the real numbers to the real numbers, 
    and an interval with the property that the values of the function at its 
    endpoints have different signs. If the function is continuous over this 
    interval then the ITP method estimates the value at which the function is 
    equal to zero. If the function is discontinuous then a point of 
    discontinuity at which the function changes sign may be found. 
    The function can be supplied using either an R function or an external 
    pointer to a C++ function. Tuning parameters of the ITP algorithm can be 
    set by the user. Default values are set based on arguments in Oliveira and 
    Takahashi (2021).
Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter 
    scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing 
    XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata. 
    A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified. 
    Multivariate methods are implemented with appropriate data transformation.
The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods.
    See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report.
Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999)
    as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear
    regression analysis which is particularly useful for time course data such as
    EEG, pupil dilation, gaze data (eye tracking), and articulography recordings,
    but also for behavioral data such as reaction times and response data. As time
    course measures are sensitive to autocorrelation problems, GAMMs implements
    methods to reduce the autocorrelation problems. This package includes functions
    for the evaluation of GAMM models (e.g., model comparisons, determining regions
    of significance, inspection of autocorrelational structure in residuals)
    and interpreting of GAMMs (e.g., visualization of complex interactions, and
    contrasts).
Provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/>
    that powers the American Soccer Analysis app.
Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis.
Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the 'iucnrdata' package, available on GitHub <https://github.com/PaulESantos/iucnrdata>. To install the data package, use pak::pak('PaulESantos/iucnrdata').
Implements a suite of sensitivity analysis tools for instrumental
  variable estimates as described in Cinelli and Hazlett (2025)
  <doi:10.1093/biomet/asaf004>.
Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.
Implements tests for the identifying assumptions of
    instrumental variable models, the local exclusion restriction and
    monotonicity conditions required for local average treatment effect
    identification. Covers Kitagawa (2015) <doi:10.3982/ECTA11974>,
    Mourifie and Wan (2017) <doi:10.1162/REST_a_00622>, and Frandsen,
    Lefgren, and Leslie (2023) <doi:10.1257/aer.20201860>. Includes a
    one-shot wrapper that runs all applicable tests on a fitted
    instrumental variable model. Dispatches on 'fixest' and 'ivreg'
    model objects.
A integrated variance correlation is proposed to measure the dependence between a categorical or continuous random variable and a continuous random variable or vector.
    This package is designed to estimate the new correlation coefficient with parametric and nonparametric approaches. 
    Test of independence for different problems can also be implemented via the new correlation coefficient with this package.
Fit mixed-effects location scale models with spike-and-slab priors on the location random effects to identify units with unusual residual variances. The method is described in detail in Carmo, Williams and Rast (2025) <https://osf.io/sh6ne>.
Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <doi:10.48550/arXiv.2103.06328>.
Estimation and diagnostic tools for instrumental variables designs, which implements the guidelines proposed in Lal et al. (2023) <arXiv:2303.11399>, including bootstrapped confidence intervals, effective F-statistic, Anderson-Rubin test, valid-t ratio test, and local-to-zero tests.
Instrumental variable (IV) estimators for homogeneous and
    heterogeneous treatment effects with efficient machine learning instruments.
    The estimators are based on double/debiased machine learning allowing for
    nonlinear and potentially high-dimensional control variables. Details can 
    be found in Scheidegger, Guo and Bühlmann (2025) "Inference for 
    heterogeneous treatment effects with efficient instruments and machine 
    learning" <doi:10.48550/arXiv.2503.03530>.
Uses data and researcher's beliefs on measurement error and
    instrumental variable (IV) endogeneity to generate the space of consistent 
    beliefs across measurement error, instrument endogeneity, and instrumental 
    relevance for IV regressions. 
    Package based on DiTraglia and Garcia-Jimeno (2020) <doi:10.1080/07350015.2020.1753528>.
Provides facilities of general to specific model selection for
    exogenous regressors in 2SLS models. Furthermore, indicator saturation
    methods can be used to detect outliers and structural breaks in the sample.
Carries out instrumental variable
    estimation of causal effects, including power analysis, sensitivity analysis,
    and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.
The marginal treatment effect was introduced by Heckman and
    Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a
    choice-theoretic interpretation to instrumental variables models that
    maintain the monotonicity condition of Imbens and Angrist (1994)
    <doi:10.2307/2951620>. This interpretation can be used to extrapolate from
    the compliers to estimate treatment effects for other subpopulations. This
    package provides a flexible set of methods for conducting this
    extrapolation. It allows for parametric or nonparametric sieve estimation,
    and allows the user to maintain shape restrictions such as monotonicity. The
    package operates in the general framework developed by Mogstad, Santos and
    Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point
    identification or partial identification (bounds). In the partially
    identified case, bounds are computed using either linear programming
    or quadratically constrained quadratic programming. Support for
    four solvers is provided. Gurobi and the Gurobi R API
    can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained
    from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex'
    and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be
    obtained from <https://www.mosek.com/>. The lp_solve library is freely
    available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
    installing its API 'lpSolveAPI', which is available from CRAN.
Nicely formatted frequency tables and contingency tables (1-way, 2-way, 3-way and 4-way tables), that can easily be exported to HTML or 'Office' documents. Designed to work with pipes.
Generate interactive volcano plots for exploring gene expression data. Built with 'ggplot2', the plots are rendered interactive using 'ggiraph', enabling users to hover over points to display detailed information or click to trigger custom actions.
An implementation of the Invariance Partial Pruning (IVPP) approach 
  described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial 
  Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a 
  two-step method that first test for global network structural difference with 
  invariance test and then inspect specific edge difference with partial pruning. 
  The package also allows you to compute centrality measures and use radar chart to 
  plot. Analysis of bridge centralities by community pairs is also possible (e.g.,
  the bridge strength from depression to anxiety, and from depression to panic disorder).
Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.
Provides a library for generic interval manipulations using a
    new interval vector class. Capabilities include: locating various
    kinds of relationships between two interval vectors, merging overlaps
    within a single interval vector, splitting an interval vector on its
    overlapping endpoints, and applying set theoretical operations on
    interval vectors. Many of the operations in this package were inspired
    by James Allen's interval algebra, Allen (1983)
    <doi:10.1145/182.358434>.
Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.
Drawing statistical inference on the coefficients
    of a short- or long-horizon predictive regression with persistent
    regressors by using the IVX method of Magdalinos and Phillips (2009)
    <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and
    Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.
For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. 
    Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than 
    one observation with multiplicity declared in the subtitle.
Calculates irrigation water quality ratios and has functions that could be used to plot several popular diagrams for irrigation water quality classification.
Improved methods based on inverse probability weighting
        and outcome regression for causal inference and missing data
        problems.
Create and view tickets in 'gitea', a self-hosted git service <https://gitea.io>, using an 'RStudio' addin, and use helper functions to publish documentation and use git.
Code to specify, run, and then visualize and analyze the results of
    Ixodidae (hard-bodied ticks) population and infection dynamics models. Such
    models exist in the literature, but the source code to run them is not
    always available. 'IxPopDyMod' provides an easy way for these models to be
    written and shared.
Visualize interactions between multiple experimental factors using
    interactive 3D surface plots powered by 'plotly'. Instead of examining
    combinatorial pairwise interaction plots, map factor combinations to response
    surfaces and use surface crossings as geometric indicators of interaction
    effects. Supports continuous, categorical, and mixed factor designs with
    automatic binning for continuous conditioning variables.
Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and 
    likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) 
    <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate 
    random deviates from zero inflated or hurdle models and obtain  maximum likelihood estimate
    of unknown parameters in these models.
Call the data wrappers for Izmir Metropolitan Municipality's Open Data Portal. This will return all datasets formatted as Excel files (.csv or .xlsx), as well as datasets that require an API key.
An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology (Naduvilezhath et al., 2011 <doi:10.1111/j.1365-294X.2011.05131.x>,
    Mathew et al., 2013 <doi:10.1002/ece3.722>).
    It has optional support for conducting coalescent simulation using
    the 'coala' package.
Provides data about the possible adverse events/reactions
    resulting from being injected with a vaccine/experimental gene therapy.
    Currently, this data set only includes information from six reference
    sources. Refer to the CITATION.cff file for the complete citations of
    the reference sources. For information about vaccination$/immunization$
    hazards, visit <https://www.questionuniverse.com/rethink.html#vaccine>,
    <https://www.ecoccs.com/healing.html#vaccines>,
    <https://www.questionuniverse.com/rethink_current_crisis.html#cov_vaccin>,
    and <https://www.questionuniverse.com/vaccination.html>.
Calculate statistical significance of Jaccard/Tanimoto similarity
    coefficients.
Simply and efficiently
    simulates (i) variants from reference genomes and (ii) reads from both Illumina 
    <https://www.illumina.com/>
    and Pacific Biosciences (PacBio) <https://www.pacb.com/> platforms. 
    It can either read reference genomes from FASTA files or simulate new ones.
    Genomic variants can be simulated using summary statistics, phylogenies, 
    Variant Call Format (VCF) files, and coalescent simulations—the latter of which
    can include selection, recombination, and demographic fluctuations.
    'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, 
    as well as PacBio reads.
    These simulations include sequencing errors, mapping qualities, multiplexing,
    and optical/polymerase chain reaction (PCR) duplicates.
    Simulating Illumina sequencing is based on ART
    by Huang et al. (2012) <doi:10.1093/bioinformatics/btr708>.
    PacBio sequencing simulation is based on 
    SimLoRD  by Stöcker et al. (2016) <doi:10.1093/bioinformatics/btw286>.
    All outputs can be written to standard file formats.
Implements delete-d jackknife resampling for robust statistical estimation. 
    The package provides both weighted (HC3-adjusted) and unweighted versions of 
    jackknife estimation, with parallel computation support. Suitable for biomedical 
    research and other fields requiring robust variance estimation.
Provides method used to check whether data have outlier in efficiency measurement 
  of big samples with data envelopment analysis (DEA). In this jackstrap method, the package 
  provides two criteria to define outliers: heaviside and k-s test. The technique was developed 
  by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting 
  Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.
Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).
Evaluation of the Jacobi theta functions and related
    functions: Weierstrass elliptic function, Weierstrass sigma function,
    Weierstrass zeta function, Klein j-function, Dedekind eta function,
    lambda modular function, Jacobi elliptic functions, Neville theta
    functions, Eisenstein series, lemniscate elliptic functions, elliptic 
    alpha function, Rogers-Ramanujan continued fractions, and Dixon 
    elliptic functions. Complex values of the variable are supported.
Implements the classical Jacobi algorithm for the
    eigenvalues and eigenvectors of a real symmetric matrix, both in 
    pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example 
    for teaching purposes.
Uses the Jaccard similarity index to account for population
    structure in sequencing studies. This method was specifically
    designed to detect population stratification based on rare variants, hence it
    will be especially useful in rare variant analysis.
Contains procedures to estimate the nine condensed Jacquard genetic identity coefficients
              (Jacquard, 1974) <doi:10.1007/978-3-642-88415-3> by constrained least squares
              (Graffelman et al., 2024) <doi:10.1101/2024.03.25.586682> and by the method of moments
	      (Csuros, 2014) <doi:10.1016/j.tpb.2013.11.001>. These procedures require previous estimation of the
	      allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard
	      coefficients, such as individual inbreeding coefficients and kinship coefficients.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
Jade Lizard and Reverse Jade Lizard Option Strategies are presented 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.
    Russell A. Stultz (“The option strategy desk reference: an essential reference for option traders (First edition.)”, 2019, ISBN: 9781949443912).
Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) <doi:10.3109/03014460.2015.1045430>. For details on the conditional correlation correction, see Sgheiza (2022) <doi:10.1016/j.forsciint.2021.111135>. Dental scoring is based on Moorrees, Fanning, and Hunt (1963) <doi:10.1177/00220345630420062701>.
All the data and functions used to produce the book. We do not expect
    most people to use the package for any other reason than to get simple access to the
    'JAGS' model files, the data, and perhaps run some of the simple examples.
    The authors of the book are David Lucy (now sadly deceased) and James Curran. It is 
    anticipated that a manuscript will be provided to Taylor and Francis around February 2020, with
    bibliographic details to follow at that point. Until such time, further information
    can be obtained by emailing James Curran.
Tools are provided to streamline Bayesian analyses in 'JAGS' using 
    the 'jagsUI' package.  Included are functions for extracting output in 
    simpler format, functions for streamlining assessment of convergence, and 
    functions for producing summary plots of output.  Also included is a 
    function that provides a simple template for running 'JAGS' from 'R'.
    Referenced materials can be found at <DOI:10.1214/ss/1177011136>.