It implements functions for simulation and estimation of the ordinal latent block model (OLBM), as described in Corneli, Bouveyron and Latouche (2019).
Fits ordinal regression models with elastic net penalty.
    Supported model families include cumulative probability, stopping ratio, 
    continuation ratio, and adjacent category. These families are a subset of 
    vector glm's which belong to a model class we call the elementwise link 
    multinomial-ordinal (ELMO) class. Each family in this class links a vector 
    of covariates to a vector of class probabilities. Each of these families 
    has a parallel form, which is appropriate for ordinal response data, as 
    well as a nonparallel form that is appropriate for an unordered categorical
    response, or as a more flexible model for ordinal data. The parallel model
    has a single set of coefficients, whereas the nonparallel model has a set of
    coefficients for each response category except the baseline category. It is 
    also possible to fit a model with both parallel and nonparallel terms, which 
    we call the semi-parallel model. The semi-parallel model has the flexibility 
    of the nonparallel model, but the elastic net penalty shrinks it toward the 
    parallel model. For details, refer to Wurm, Hanlon, and Rathouz (2021) 
    <doi:10.18637/jss.v099.i06>.
Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) <doi:10.1007/s00362-013-0536-8> defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) <doi:10.1080/01621459.2016.1164706> are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) <doi:10.1016/j.csda.2022.107472> are also considered.
Implements Bayesian data analyses of balanced repeatability and reproducibility studies with ordinal measurements. Model fitting is based on MCMC posterior sampling with 'rjags'. Function ordinalRR() directly carries out the model fitting, and this function has the flexibility to allow the user to specify key aspects of the model, e.g., fixed versus random effects. Functions for preprocessing data and for the numerical and graphical display of a fitted model are also provided. There are also functions for displaying the model at fixed (user-specified) parameters and for simulating a hypothetical data set at a fixed (user-specified) set of parameters for a random-effects rater population. For additional technical details, refer to Culp, Ryan, Chen, and Hamada (2018) and cite this Technometrics paper when referencing any aspect of this work. The demo of this package reproduces results from the Technometrics paper.
Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included 'shiny' application allows users to simulate outcomes given different ordinal data distributions.
We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.
Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions.
             The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.
The ordered panel methodology (Zezulinski et al 2025 <doi:10.1159/000545366>) provides a structured framework for identifying and organizing sets of biomarkers, such as genetic variants, that distinguish between positive and negative subjects in a study when only a training cohort is available. This approach is particularly useful in situations where an independent validation cohort does not yet exist, rendering conventional performance metrics such as the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) inappropriate or potentially misleading. The methodology emphasizes transparent construction and evaluation of ordered signatures of biomarkers, allowing investigators to examine operating characteristics without establishing predictive performance.
Ordination comprises several multivariate exploratory and
    explanatory techniques with theoretical foundations in 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.
    Greenacre (2010, ISBN:978-84-923846) shows how the most established of
    these, including principal components analysis, correspondence analysis,
    multidimensional scaling, factor analysis, and discriminant analysis,
    rely on eigen-decompositions or singular value decompositions of
    pre-processed numeric matrix data.
    These decompositions give rise to a set of shared coordinates along which
    the row and column elements can be measured. The overlay of their
    scatterplots on these axes, introduced by Gabriel (1971)
    <doi:10.1093/biomet/58.3.453>, is called a biplot.
    'ordr' provides inspection, extraction, manipulation, and visualization
    tools for several popular ordination classes supported by a set of recovery
    methods. It is inspired by and designed to integrate into 'Tidyverse'
    workflows provided by Wickham et al (2019) <doi:10.21105/joss.01686>.
Provides an alternative to R's built-in functionality for handling
    regular expressions, based on the Onigmo library. Offers first-class
    compiled regex objects, partial matching and function-based substitutions,
    amongst other features.
The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>).
An implementation of the Ordered Forest estimator as developed 
    in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly
    estimates the conditional probabilities of models with ordered categorical
    outcomes (so-called ordered choice models). Additionally to common machine 
    learning algorithms the 'orf' package provides functions for estimating
    marginal effects as well as statistical inference thereof and thus provides
    similar output as in standard econometric models for ordered choice. The
    core forest algorithm relies on the fast C++ forest implementation from
    the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>.
Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency.
A framework for organizing R projects with a standardized structure. 
    Most analyses consist of three main components: code, results, and data, 
    each with different requirements such as version control, sharing, and encryption. 
    This package provides tools to set up and manage project directories, handle
    file paths consistently across operating systems, organize results using
    date-based structures, source code from specified directories, and perform
    file operations safely. 
    It ensures consistency across projects while accommodating different 
    requirements for various types of content.
Trains per-horizon probabilistic ensembles from a univariate time series. It supports 'rpart', 'glmnet', and 'kNN' engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons.
Provides functions for quickly creating R and Python scripts, as 
    well as 'Rmarkdown' or Quarto documents with automatically assigned name 
    prefixes. Prefixes are either file counts (e.g. "001") or dates 
    (e.g. "2022-09-26").
A tool for visualizing numerical data (e.g., gene expression, protein abundance) on predefined anatomical maps of human/mouse organs and subcellular organelles. It supports customization of color schemes, filtering by organ systems (for organisms) or organelle types, and generation of optional bar charts for quantitative comparison. The package integrates coordinate data for organs and organelles to plot anatomical/subcellular contours, mapping data values to specific structures for intuitive visualization of biological data distribution.The underlying method was described in the preprint by Zhou et al. (2022) <doi:10.1101/2022.09.07.506938>.
Organic/biological mass spectrometry data analysis.
Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc).  Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects.  Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours.  Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.
Helper functions for Org files (<https://orgmode.org/>):
  a generic function 'toOrg' for transforming R objects into Org
  markup (most useful for data frames; there are also methods for
  Dates/POSIXt) and a function to read Org tables into data frames.
Representations, conversions and display of orientation SO(3) data.
 See the orientlib help topic for details.
A general framework for the application of cross-validation schemes
    to particular functions. By allowing arbitrary lists of results, origami
    accommodates a range of cross-validation applications. This implementation
    was first described by Coyle and Hejazi (2018) <doi:10.21105/joss.00512>.
A visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making.
Automatically adding 'pkg::' to a function, i.e. mutate()
    becomes dplyr::mutate(). It is up to the user to determine which
    packages should be used explicitly, whether to include base R packages
    or use the functionality on selected text, a file, or a complete
    directory. User friendly logging is provided in the 'RStudio' Markers
    pane. Lives in the spirit of 'lintr' and 'styler'. Can also be used
    for checking which packages are actually used in a project.
Functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations.
Obtaining Bayes Expected A Posteriori (EAP) individual score 
    estimates based on linear and non-linear extended Exploratoy Factor Analysis
    solutions that include a correlated-residual structure.
Algorithm of online regularized k-means to deal with online multi(single) view data.
 The philosophy of the package is described in Guo G. (2024) 
 <doi:10.1016/j.ins.2024.121133>.
Objects and methods to handle and solve the min-sum location problem, also known as Fermat-Weber problem. The min-sum location problem search for a point such that the weighted sum of the distances to the demand points are minimized. See "The Fermat-Weber location problem revisited" by Brimberg, Mathematical Programming, 1, pg. 71-76, 1995. <DOI:10.1007/BF01592245>.
	     General global optimization algorithms are used to solve the problem, along with the adhoc Weiszfeld method, see "Sur le point pour lequel la Somme des distances de n points donnes est minimum", by Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pg. 355-386, 1937 or "On the point for which the sum of the distances to n given points is minimum", by E. Weiszfeld and F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
Help and demo in Spanish of the orloca package. Ayuda y demo en espanol del paquete orloca. Objetos y metodos para manejar y resolver el problema de localizacion de suma minima, tambien conocido como problema de Fermat-Weber. El problema de localizacion de suma minima busca un punto tal que la suma ponderada de las distancias a los puntos de demanda se minimice. Vease "The Fermat-Weber location problem revisited" por Brimberg, Mathematical Programming, 1, pag. 71-76, 1995. <DOI: 10.1007/BF01592245>.
	     Se usan algoritmos generales de optimizacion global para resolver el problema, junto con el metodo especifico Weiszfeld, vease "Sur le point pour lequel la Somme des distance de n points donnes est minimum", por Weiszfeld, Tohoku Mathematical Journal, First Series, 43, pag. 355-386, 1937 o "On the point for which the sum of the distances to n given points is minimum", por E. Weiszfeld y F. Plastria, Annals of Operations Research, 167, pg. 7-41, 2009. <DOI:10.1007/s10479-008-0352-z>.
An extension to the Regression Modeling Strategies package that
    facilitates plotting ordinal regression  model predictions together with
    confidence intervals for each dependent variable level. 
    It also adds a functionality to plot the  model summary as a modifiable 
    object.
Data input/output functions for data that conform to the
    Digital Imaging and Communications in Medicine (DICOM) standard, part
    of the Rigorous Analytics bundle.
Functions for the input/output and visualization of
    medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI'
    formats.  This package is part of the Rigorous Analytics bundle.
Image analysis techniques for positron emission tomography
    (PET) that form part of the Rigorous Analytics bundle.
Clinical reports generated by 'Oncomine Reporter' software contain
    critical data in unstructured PDF format, making manual extraction
    time-consuming and error-prone. 'ORscraper' provides a coherent suite of
    functions to automate this process, allowing researchers to parse
    reports, identify key biomarkers, extract genetic variant tables,
    and filter results. It also integrates with the NCBI 'ClinVar' API 
    <https://www.ncbi.nlm.nih.gov/clinvar/> to enrich extracted data.
A data set package with the "Orsi" and "Park/Durand" fronts as
    'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at
    the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' 
    Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details.
Reconstructs plausible 2 by 2 contingency tables from
    published cohort-study summaries when the original cell counts are
    unavailable. Given group sample sizes and an odds ratio with partial
    confidence interval information, the package searches for compatible
    event counts, then derives corresponding relative risks and confidence
    intervals. It implements the methods described in Wang (2013)
    <doi:10.18637/jss.v055.i05> and includes summary and plotting methods
    for reviewing admissible scenarios.
Takes images, imported via 'imager', and converts them into a data
  frame that can be plotted to look like the imported image.  This can be used 
  for creating data that looks like a specific image. Additionally, images with
  color and alpha channels can be converted to grayscale in preparation for 
  converting to the data frame format.
A modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3.
An R Interface to 'Orthanc' DICOM servers for medical imaging workflows.
    'Orthanc' is a lightweight, open-source DICOM server that exposes a comprehensive
    REST API for managing, querying, retrieving, and modifying DICOM resources 
    (<https://www.orthanc-server.com>). The goal of this package is to provide 
    comprehensive and user-friendly access to the 'Orthanc' REST API, designed to 
    align with idiomatic R workflows while preserving the structure and semantics
    of DICOM resources.
Tools to analyze and infer orthology and paralogy relationships between glutamine synthetase proteins in seed plants.
Utilize an orthogonality constrained optimization algorithm of
    Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of
    dimension reduction problems in the semiparametric framework, such as
    Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) 
    <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <DOI:10.1093/biomet/asy064>
    and Zhou, Zhu & Zeng (2021) <DOI:10.1093/biomet/asaa087>. The package also
    implements some existing dimension reduction methods such as hMave by Xia, Zhang,
    & Xu (2010) <DOI:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013)
    <DOI:10.1080/01621459.2012.746065>. It also serves as a general purpose 
    optimization solver for problems with orthogonality constraints, i.e., in Stiefel 
    manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.
Represents the basis functions for B-splines in a simple matrix
    formulation that facilitates, taking integrals, derivatives, and
    making orthogonal the basis functions.
Implements the orthogonal reparameterization
    approach recommended by Lancaster (2002) to estimate dynamic panel
    models with fixed effects (and optionally: panel specific
    intercepts). The approach uses a likelihood-based estimator and
    produces estimates that are asymptotically unbiased as N goes to
    infinity, with a T as low as 2.
A collection of functions to construct sets of orthogonal
        polynomials and their recurrence relations. Additional
        functions are provided to calculate the derivative, integral,
        value and roots of lists of polynomial objects.
Connects to Google cloud vision <https://cloud.google.com/vision> to perform label detection and repurpose this feature for image classification.
Microarray probe ID is not convenient for further enrichment analysis and target gene selection.
    The package is created for the rice microarray probe ID conversion.
    This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research.
    For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes.
    Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.
Offers a streamlined programmatic interface to Ordnance Survey's 
    British National Grid (BNG) index system, enabling efficient spatial 
    indexing and analysis based on grid references. It supports a range of 
    geospatial applications, including statistical aggregation, data 
    visualisation, and interoperability across datasets. Designed for 
    developers and analysts working with geospatial data in Great Britain, 
    'osbng' simplifies integration with geospatial workflows and provides 
    intuitive tools for exploring the structure and logic of the BNG system.
Optimal Subset Cardinality Regression (OSCAR) models offer
    regularized linear regression using the L0-pseudonorm, conventionally
    known as the number of non-zero coefficients. The package estimates an
    optimal subset of features using the L0-penalization via
    cross-validation, bootstrapping and visual diagnostics. Effective
    Fortran implementations are offered along the package for finding
    optima for the DC-decomposition, which is used for transforming the
    discrete L0-regularized optimization problem into a continuous
    non-convex optimization task. These optimization modules include DBDC
    ('Double Bundle method for nonsmooth DC optimization' as described in
    Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited
    Memory Bundle Method for large-scale nonsmooth optimization' as
    in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The
    OSCAR models are comprehensively exemplified in Halkola et al. (2023) 
    <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families
    are supported: Cox, logistic, and Gaussian.
A collection of general optimization routines based on variants of 
    the One Side Cut Accelerated Random Search (OSCARS-II) algorithm 
    (Price et al., 2020, <doi:10.1007/s10898-020-00928-6>). The main function
    , 'oscars()', performs black-box optimization of a general (including nonsmooth or 
    discontinuous) function subject to simple bounds on the unknowns. If all bounds are 
    finite, oscars searches globally. The main method implements a stochastic direct 
    search method and is derivative free.   Testing shows the OSCARS-II algorithm 
    usually finds extrema with fewer function evaluations than similar global 
    derivative-free methods.
The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.
Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.
Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
Ordnance Survey ('OS') is the national mapping agency for Great 
    Britain and produces a large variety of mapping and geospatial products. 
    Much of OS's data is available via the OS Data Hub <https://osdatahub.os.uk/>, 
    a platform that hosts both free and premium data products. 'osdatahub' 
    provides a user-friendly way to access, query, and download these data.
The algorithm first identifies a population of individuals from
    Danish register data with any type of diabetes as individuals with two or
    more inclusion events. Then, it splits this population into individuals with
    either type 1 diabetes or type 2 diabetes by identifying individuals with
    type 1 diabetes and classifying the remainder of the diabetes population as
    having type 2 diabetes.
A suite of functions for the design of case-control and two-phase studies, and the analysis of data that arise from them. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model. For additional detail see: Haneuse S, Saegusa T and Lumley T (2011)<doi:10.18637/jss.v043.i11>.
Provides routines for finding an Optimal System of Distinct Representatives (OSDR),
 as defined by D.Gale (1968) <doi:10.1016/S0021-9800(68)80039-0>.
Methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. 
             See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65–76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646.
An interface for interacting with 'OSF' (<https://osf.io>). 'osfr'
    enables you to access open research materials and data, or create and
    manage your own private or public projects.
In high-dimensional streaming data analysis, extracting core periodic features under real-time constraints remains challenging. Traditional dimension reduction methods fail to adapt to incremental data and yield low accuracy due to irrelevant variables. This package provides the Online Sliced Inverse Regression framework for cosine regression with high-dimensional irrelevant variables. It integrates subspace extraction of sliced inverse regression and incremental learning of online algorithms to efficiently handle periodic streaming data. Cai, Z., Li, R., & Zhu, L. (2020) <doi:10.48550/arXiv.2002.02795>.
Aims to support all features of the system credential store,
    including non-portable ones. Supports 'Keychain' on 'macOS', and
    'Credential Manager' on 'Windows'. See the 'keyring' package if you
    need a portable 'API'.
Implements a moment-targeting normality transformation based on the
    simultaneous optimization of Tukey g-h distribution parameters. The method 
    is designed to minimize both asymmetry (skewness) and excess peakedness 
    (kurtosis) in non-normal data by mapping it to a standard normal distribution
    Cebeci et al (2026) <doi:10.3390/sym18030458>. Optimization is performed 
    by minimizing an objective function derived from the Anderson-Darling 
    goodness-of-fit statistic with Stephens's correction factor, utilizing the
    L-BFGS-B algorithm for robust parameter estimation. This approach provides an 
    effective alternative to power transformations like Box-Cox and Yeo-Johnson, 
    particularly for data requiring precise tail-behavior adjustment.
Function library for the identification and separation of exponentially
    decaying signal components in continuous-wave optically stimulated luminescence measurements.
    A special emphasis is laid on luminescence dating with quartz, which is known for
    systematic errors due to signal components with unequal physical behaviour.
    Also, this package enables an easy to use signal decomposition of
    data sets imported and analysed with the R package 'Luminescence'.
    This includes the optional automatic creation of HTML reports. Further information and tutorials
    can be found at <https://luminescence.de>.
Interface to 'OpenStreetMap API' for fetching and saving data
    from/to the 'OpenStreetMap' database
    (<https://wiki.openstreetmap.org/wiki/API_v0.6>).
Classify Open Street Map (OSM) features into meaningful functional 
    or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> 
    imported as spatial data frames. A classification consists of a list of categories that are related to 
    certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main 
    osm_classify() function returns a classification data table giving, for each feature, the primary and 
    alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. 
    The package also contains a classification of OSM features by economic function/significance, 
    following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.
Download and import of 'OpenStreetMap' ('OSM') data as 'sf'
    or 'sp' objects.  'OSM' data are extracted from the 'Overpass' web
    server (<https://overpass-api.de/>) and processed with very fast 'C++'
    routines for return to 'R'.
Match, download, convert and import Open Street Map data extracts 
    obtained from several providers.
Functionality to handle and project lat-long coordinates, easily download background maps
    and add a correct scale bar to 'OpenStreetMap' plots in any map projection.
A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization.
    To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge
    that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was
    introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri-
    Factorization" which will be submitted to BBRC.
Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package for solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See <doi:10.48550/arXiv.1711.08013> for details.
An interface between R and the 'OSRM' API. 'OSRM' is a routing
    service based on 'OpenStreetMap' data. See <http://project-osrm.org/> for more
    information. This package enables the computation of routes, trips, isochrones and
    travel distances matrices (travel time and kilometric distance).
Install and control 'Open Source Routing Machine' ('OSRM')
    backend executables to prepare routing data and run/stop a local
    'OSRM' server. For computations with the running server use the 'osrm'
    R package (<https://cran.r-project.org/package=osrm>).
Wrapper around the 'Open Source Routing Machine (OSRM)'
        API <http://project-osrm.org/>. 'osrmr' works with API versions
        4 and 5 and can handle servers that run locally as well as the
        'OSRM' webserver.
A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.
Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.
O-statistics, or overlap statistics, measure the degree of community-level trait overlap. 
    They are estimated by fitting nonparametric kernel density functions to each species’ trait 
    distribution and calculating their areas of overlap. For instance, the median pairwise overlap 
    for a community is calculated by first determining the overlap of each species pair 
    in trait space, and then taking the median overlap of each species pair in a community. 
    This median overlap value is called the O-statistic (O for overlap).
    The Ostats() function calculates separate univariate overlap statistics for each trait, 
    while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. 
    O-statistics can be evaluated against null models to obtain standardized effect sizes. 
    'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale 
    drivers of biodiversity across the National Ecological Observatory Network (NEON)." 
    For more information on this project, see the Macrosystems Biodiversity Website 
    (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in
    Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the
    underlying biological concepts at an undergraduate level is described in Grady et al.
    (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.
Inspired by 'S-PLUS' function objects.summary(), provides a
    function with the same name that returns data class, storage mode,
    mode, type, dimension, and size information for R objects in the
    specified environment. Various filtering and sorting options are also
    proposed.
Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (<https://platform.opentargets.org/api>).
Apply unsupervised segmentation algorithms included in 'Orfeo ToolBox' software (<https://www.orfeo-toolbox.org/>), such as mean shift or watershed segmentation.
Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) <doi:10.1002/sam.11418>. Lixiang Zhang, Lin Lin, and Jia Li (2020) <doi:10.1093/bioinformatics/btaa165>.
Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.
High-quality, ubiquitous, and portable telemetry to enable
    effective observability. OpenTelemetry is a collection of tools,
    APIs, and SDKs used to instrument, generate, collect, and export
    telemetry data (metrics, logs, and traces) for analysis in order to
    understand your software's performance and behavior.
    This package implements the OpenTelemetry API:
    <https://opentelemetry.io/docs/specs/otel/>.
    Use this package as a dependency if you want to instrument your R
    package for OpenTelemetry.
OpenTelemetry is a collection of tools,
    APIs, and SDKs used to instrument, generate, collect, and export
    telemetry data (metrics, logs, and traces) for analysis in order to
    understand your software's performance and behavior.
    This package contains the OpenTelemetry SDK, and exporters.
    Use this package to export traces, metrics, logs from instrumented
    R code. Use the otel package to instrument your R code for
    OpenTelemetry.
Sample from the limiting distributions of empirical Wasserstein
    distances under the null hypothesis and under the alternative. Perform a 
    two-sample test on multivariate data using these limiting distributions and 
    binning.
Generating and validating One-time Password based on 
    Hash-based Message Authentication Code (HOTP) 
    and Time Based One-time Password (TOTP)
    according to RFC 4226 <https://datatracker.ietf.org/doc/html/rfc4226> and
    RFC 6238 <https://datatracker.ietf.org/doc/html/rfc6238>.
A wrapper for the 'OpenTripPlanner' <http://www.opentripplanner.org/>
    REST API. Queries are submitted to the relevant 'OpenTripPlanner' API resource, the response
    is parsed and useful R objects are returned.
In the context of data fusion, the package provides a set of functions dedicated to the solving of 'recoding problems' using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.
Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform  distribution covering the whole Euclidean space. Parameters are estimated by  (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.
Provide methods for estimating optimal treatment regimes in survival contexts 
    with Kaplan-Meier-like estimators when no unmeasured confounding assumption is 
    satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) 
    and when no unmeasured confounding assumption fails to hold and a binary instrument 
    is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).
A penalized regression framework that can simultaneously estimate 
    the optimal treatment strategy and identify important variables. 
    Appropriate for either censored or uncensored continuous response.
An implementation of several functions for feature extraction in 
    ordinal time series datasets. Specifically, some of the features proposed by
    Weiss (2019) <doi:10.1080/01621459.2019.1604370> can be computed.  
    These features can be used to perform inferential tasks or to feed machine
    learning algorithms for ordinal time series, among others. The package also includes some
    interesting datasets containing financial time series. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'otsfeatures'.
Tools to segment fire scars and assess severity and vegetation regeneration using 
    'Otsu' thresholding on Relative Burn Ratio (RBR) and differenced Normalized Burn Ratio (dNBR) image composites. 
    Includes support for mosaic handling, polygon metrics, post-fire regeneration detection, day-of-year flagging, 
    and validation against reference datasets. Designed for analysis of fire history in the Iberian Peninsula. 
    Input Landsat composites follow the methodology described in Quintero et al. (2025) <doi:10.2139/ssrn.4929831>.
Provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) <doi:10.1109/TSMC.1979.4310076>, a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score.
An optimal transport (OT) method, which can handle tensors of any order by learning possibly multiple transport plans. For the details of the methods, see Kerdoncuff et al. (2022) <doi:10.1609/aaai.v36i7.20695>.
An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports 
    grading R scripts, R Markdown documents, and R Jupyter Notebooks.
Tools for converting Open-Source Tools for Training Resources
    (OTTR) courses into Leanpub or Coursera courses. 'ottrpal' is for use
    with the OTTR Template repository to create courses.
Summarizes the taxonomic composition, diversity contribution of the rare and abundant community by using OTU (operational taxonomic unit) table which was generated by analyzing pipeline of 'QIIME' or 'mothur'. The rare biosphere in this package is subset by the relative abundance threshold (for details about rare biosphere please see Lynch and Neufeld (2015) <doi:10.1038/nrmicro3400>).
Analyses of OTU tables produced by 16S rRNA gene amplicon sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2017) "Bacterial community composition and dynamics spanning five years in freshwater bog lakes," <doi: 10.1128/mSphere.00169-17>.
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
The Open University Learning Analytics Dataset (OULAD) is available from 
    Kuzilek et al. (2017) <doi:10.1038/sdata.2017.171>.  The 'ouladFormat' 
    package loads, cleans and formats the OULAD for data analysis (each row of the
    returned data set is an individual student). The package’s main function,
    combined_dataset(), allows the user to choose whether the returned
    data set includes assessment, demographics, virtual learning environment (VLE),
    or registration variables etc.
Bayesian reconstruction of disease outbreaks using epidemiological
    and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser
    C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>. Campbell, F,
    Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>.
Empirical or simulated disease outbreak data, provided either as
    RData or as text files.
Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.
Many treatment effect estimators can be written as weighted outcomes.
    These weights have established use cases like checking covariate balancing via packages like 'cobalt'.
    This package takes the original estimator objects and outputs these outcome weights.
    It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>.
    This version is compatible with the 'grf' package and provides an internal implementation of Double Machine Learning.
Provides a random forest based implementation of the method
    described in Chapter 7.1.2 (Regression model based anomaly detection)
    of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as
    follows: Each numeric variable is regressed onto all other variables
    by a random forest. If the scaled absolute difference between observed
    value and out-of-bag prediction of the corresponding random forest is
    suspiciously large, then a value is considered an outlier. The package
    offers different options to replace such outliers, e.g. by realistic
    values found via predictive mean matching. Once the method is trained
    on a reference data, it can be applied to new data.
Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using 
    Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, 
    Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549>
    are also included.
Sequential outlier identification for Gaussian mixture models using
    the distribution of Mahalanobis distances. The optimal number
    of outliers is chosen based on the dissimilarity between the theoretical and
    observed distributions of the scaled squared sample Mahalanobis distances.
    Also includes an extension for Gaussian linear cluster-weighted models using
    the distribution of studentized residuals. 
    Doherty, McNicholas, and White (2025) <doi:10.48550/arXiv.2505.11668>.
A collection of some tests commonly used for identifying
        outliers.
Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.
Provides implementations of some of the most important outlier detection algorithms. 
    Includes a tutorial mode option that shows a description of each algorithm and provides 
    a step-by-step execution explanation of how it identifies outliers from the given data 
    with the specified input parameters. References include the works of Azzedine Boukerche, 
    Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) 
    <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) 
    <doi:10.1002/widm.19>.
Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are HDoutliers() from the package 'HDoutliers', FastPCS() from the package 'FastPCS', mvBACON() from 'robustX', adjOutlyingness() from 'robustbase', DectectDeviatingCells() from 'cellWise', covMcd() from 'robustbase'.
Provides tools for multivariate outlier detection based on
    geometric properties of multivariate data using random directional
    projections. Observation-level outlier scores are computed by jointly
    probing radial magnitude and angular alignment through repeated
    projections onto random directions, with optional robust centering and
    covariance adjustment. In addition to global outlier scoring, the method
    produces dimension-level contribution measures to support interpretation
    of detected anomalies. Visualization utilities are included to summarize
    directional contributions for extreme observations.
Outlier detection method that flags suspicious values within observations,
  constrasting them against the normal values in a user-readable format, potentially
  describing conditions within the data that make a given outlier more rare.
  Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>.
  Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.
Provides functions for detecting outliers in datasets using statistical methods. 
    The package supports identification of anomalous observations in numerical data and 
    is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows.
Create regression tables for publication.
    Currently supports 'lm', 'glm', 'survreg', and 'ivreg' outputs.
Performs outrigger local polynomial regression/ distributional adaptation, using a score-matching spline estimator of the conditional score function.
    Details of the method can be found in Young, Shah and Samworth (2026) <doi:10.48550/arXiv.2603.11282>.
An approach to outlier detection in RNA-seq and related data
    based on five statistics. 'OutSeekR' implements an outlier test by comparing
    the distributions of these statistics in observed data with those of
    simulated null data.
For the problem of indirect treatment comparison with limited subject-level data, this 
    package provides tools for model-based standardisation with several different computation approaches.
        See Remiro‐Azócar A, Heath A, Baio G (2022) ``Parametric G‐computation for compatible
        indirect treatment comparisons with limited individual patient data'',
        Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.
Estimates rates for continuous character evolution under Brownian motion and Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al. (2012).
Conduct sensitivity analysis of omitted variable bias in linear econometric models using the methodology presented in Basu (2025) <doi:10.2139/ssrn.4704246>.
Detection of overdispersion in count data for multiple regression analysis.
    Log-linear count data regression is one of the most popular techniques for predictive 
    modeling where there is a non-negative discrete quantitative dependent variable. In 
    order to ensure the inferences from the use of count data models are appropriate, 
    researchers may choose between the estimation of a Poisson model and a negative binomial
    model, and the correct decision for prediction from a count data estimation is directly
    linked to the existence of overdispersion of the dependent variable, conditional to the 
    explanatory variables. Based on the studies of Cameron and Trivedi (1990)
    <doi:10.1016/0304-4076(90)90014-K> and Cameron and Trivedi (2013, ISBN:978-1107667273), 
    the overdisp() command is a contribution to researchers, providing a fast and secure 
    solution for the detection of overdispersion in count data. Another advantage is that 
    the installation of other packages is unnecessary, since the command runs in the basic 
    R language.
Provides functions to fit kernel density functions to
    data on temporal activity patterns of animals; estimate coefficients
    of overlapping of densities for two species; and calculate bootstrap
    estimates of confidence intervals. As in Ridout and Linkie (2009) <doi:10.1198/jabes.2009.08038>.
Functions for estimating the overlapping area of two or more kernel density estimations from empirical data.
Tests the observed overlapping polygon area in a collection of polygons against a null model of random rotation, as explained in De la Cruz et al. (2017) <doi:10.13140/RG.2.2.12825.72801>.
Provides rectangular elements that can be dragged and resized over
 plots in 'shiny' apps. This may be useful in applications where users need
 to mark regions on the plot for further input or processing.
Overture Maps offers free and open geospatial map data sourced from various providers and standardized to a common schema. This tool allows you to download Overture Maps data for a specific region of interest and convert it to several different file formats. For more information, visit <https://overturemaps.org/download/>.
An integrated R interface to the 'Overture' API 
  (<https://docs.overturemaps.org/>). Allows R users to return 'Overture' data as 
  'dbplyr' data frames or materialized 'sf' spatial data frames.
Makes it easy to display descriptive information on a data
    set.  Getting an easy overview of a data set by displaying and
    visualizing sample information in different tables (e.g., time and
    scope conditions).  The package also provides publishable 'LaTeX' code
    to present the sample information.
Provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386> as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) <doi:10.1007/s10182-025-00545-2>.
This tool was designed to assess the sensitivity of research findings to omitted variables when estimating causal effects using propensity score (PS) weighting. This tool produces graphics and summary results that will enable a researcher to quantify the impact an omitted variable would have on their results. Burgette et al. (2021) describe the methodology behind the primary function in this package, ov_sim. The method is demonstrated in Griffin et al. (2020) <doi:10.1016/j.jsat.2020.108075>.
Open the current working directory (or a given directory path) in your computer's file manager.
Tools for collecting municipal-level data <http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.
An implementation of optimal weight exchange algorithm Yang(2013) <doi:10.1080/01621459.2013.806268> for three models.
    They are Crossover model with subject dropout, crossover model with proportional
    first order residual effects and interference model. You can use it to find either
    A-opt or D-opt approximate designs. Exact designs can be automatically rounded
    from approximate designs and relative efficiency is provided as well.
Evaluates the Owen Q-function for an integer value of the
    degrees of freedom, by applying Owen's algorithm (1965)
    <doi:10.1093/biomet/52.3-4.437>.  It is useful for the calculation of
    the power of equivalence tests.
Retrieve data from the Our World in Data (OWID) Chart API
    <https://docs.owid.io/projects/etl/api/>. OWID provides public access to 
    more than 5,000 charts focusing on global problems such as poverty, 
    disease, hunger, climate change, war, existential risks, and inequality.
Import data from 'Our World in Data', an organisation which publishes research and data on global economic and social issues.
Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service
 (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes,
 and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map 
 Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor 
 Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.
A set of tools that enables using 'OxCal' from within R. 'OxCal' (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.
Calculate the ratio of iron oxides, hematite and goethite, in
    soil using the diffuse reflectance technique. The Kubelka-Munk theory,
    second derivative analysis, and spectral region amplitudes related to
    hematite and goethite content are used for quantification (Torrent,
    J., & Barron, V. (2008) <doi:10.2136/sssabookser5.5.c13>).
    Additionally, the package calculates soil color in the visible
    spectrum using Munsell and RGB color spaces, based on color theory
    (Viscarra et al. (2006) <doi:10.1016/j.geoderma.2005.07.017>).
Provides utilities for processing of Oxy-Bisulfite microarray data 
  (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) 
  with tandem arrays, one using conventional
  bisulfite conversion, the other using oxy-bisulfite conversion.
Collects a list of your third party R packages, and scans
    them with the 'OSS' Index provided by 'Sonatype', reporting back on
    any vulnerabilities that are found in the third party packages you
    use.
Predicts spatial suitability for oyster growth from environmental
    survey data using Analytic Hierarchy Process (AHP) weighted scoring.
    Users supply sensor data from Acoustic Doppler Current Profilers (ADCP),
    Conductivity-Temperature-Depth (CTD) sensors, bathymetric sonar, and
    sidescan sonar, specify a target species, and receive per-location
    suitability scores, a 'GeoTIFF' heatmap for 'QGIS', contour lines, and a
    formatted PDF or HTML report. Supports fourteen species across global
    aquaculture regions, including Ostrea edulis, Magallana gigas, Crassostrea
    virginica, Crassostrea hongkongensis, and ten further species; see
    list_species(). Includes season-aware scoring, tidal height correction,
    Bayesian tolerance parameter updating from field observations, spatial block
    cross-validation (Roberts et al., 2017, <doi:10.1111/ecog.02881>),
    permutation variable importance, wave exposure and sediment stability
    modules, Harmful Algal Bloom (HAB) risk and anthropogenic disturbance
    scoring with optional live International Council for the Exploration of the
    Sea (ICES) data integration, hybrid larval dispersal connectivity scoring
    (union-find Gaussian kernel plus optional 'OpenDrift' or Finite Volume
    Community Ocean Model ('FVCOM') connectivity matrix), and batch
    multi-species comparison.
Functions for plotting Australia's coastline and state
  boundaries.
Data on the most popular baby names by sex and year, and for each state in Australia, as provided by the state and territory governments. The quality and quantity of the data varies with the state.
Maps of Australian coastline and administrative regions. Data 
 can be drawn or accessed directly as simple features objects. Includes
 simple functions for country or state maps of Australia and in-built data
 sets of administrative regions from the Australian Bureau of Statistics 
 <https://www.abs.gov.au/>. Layers include electoral divisions and local 
 government areas, simplified from the original sources but with sufficient 
 detail to allow mapping of a local municipality.
Contains functions for data preparation,
	prediction of transition probabilities,
	estimating semi-parametric regression models
	and for implementing nonparametric estimators
	for other quantities. See Meira-Machado and
	Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.
It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.
Precision agriculture spatial data
 depuration and homogeneous zones (management zone) delineation.
 The package includes functions that performs protocols for data cleaning
 management zone delineation and zone comparison; protocols are described in 
 Paccioretti et al., (2020) <doi:10.1016/j.compag.2020.105556>.
Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.
Investigate (analytically or visually) the inputs and outputs 
    of probabilistic analyses of health economic models using standard
    health economic visualisation and metamodelling methods.
Functions to easily convert data to binary formats other programs/machines can understand.
It provides utility functions for investigating changes within R
    packages. The pkgInfo() function extracts package information such as
    exported and non-exported functions as well as their arguments. The
    pkgDiff() function compares this information for two versions of a package
    and creates a diff file viewable in a browser.
Search for R packages on CRAN directly from the R console, based on the packages' titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', 'or'), view detailed information on any package and keep track of the latest package contributions to CRAN. If you don't want to search from the R console, use the comfortable R Studio add-in.
Provides essential checklists for R package developers, whether
    you're creating your first package or beginning a new project. This tool
    guides you through each step of the development process, including specific
    considerations for submitting your package to the Comprehensive R Archive
    Network (CRAN). Simplify your workflow and ensure adherence to best
    practices with 'packagepal'.
Helper functions for package creation, building and
    maintenance. Designed to work with a build system such as 'GNU make' or
    package 'fakemake' to help you to conditionally work through the stages of
    package development (such as spell checking, linting, testing, before
    building and checking a package).
Compute and visualize package download counts and percentile ranks 
  from Posit/RStudio's CRAN mirror.
Algorithms to find arrangements of non-overlapping circles.
Provides an interactive 'RStudio' gadget for working with an AI
    assistant during package and script development. The gadget can use selected
    editor text, the active source file, package metadata, and uploaded files as
    context for code explanation, code generation, documentation, and review
    workflows. It offers model presets, assistant behavior settings, responsive
    code-focused output, and explicit copy, insert, and replace actions for the
    active source editor. API interactions via the 'httr' package are performed
    asynchronously using 'promises' and 'future' to avoid blocking the R console.
    The backend is configured via the OPENAI_API_KEY environment variable.
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134).
Manage the R packages your project depends on in an isolated,
    portable, and reproducible way.
Tools to more conveniently perform tasks associated with
        add-on packages. pacman conveniently wraps library and package
        related functions and names them in an intuitive and consistent
        fashion.  It seeks to combine functionality from lower level
        functions which can speed up workflow.
Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.
Supplementary utils for CRAN maintainers and R packages developers.
  Validating the library, packages and lock files.
  Exploring a complexity of a specific package like evaluating its size in bytes with all dependencies.
  The shiny app complexity could be explored too.
  Assessing the life duration of a specific package version.
  Checking a CRAN package check page status for any errors and warnings.
  Retrieving a DESCRIPTION or NAMESPACE file for any package version. 
  Comparing DESCRIPTION or NAMESPACE files between different package versions.
  Getting a list of all releases for a specific package.
  The Bioconductor is partly supported.
PACTA (Paris Agreement Capital Transition Assessment) for Banks is
  a tool that allows banks to calculate the climate alignment of their corporate
  lending portfolios. This package is designed to make it easy to install and
  load multiple PACTA for Banks packages in a single step. It also provides
  thorough documentation - the PACTA for Banks cookbook at
  <https://rmi-pacta.github.io/pacta.loanbook/articles/cookbook_overview.html> -
  on how to run a PACTA for Banks analysis. This covers prerequisites for the
  analysis, the separate steps of running the analysis, the interpretation of
  PACTA for Banks results, and advanced use cases.
Run Paris Agreement Capital Transition Assessment ('PACTA') analyses
    on multiple loan books in a structured way. Provides access to standard
    'PACTA' metrics and additional 'PACTA'-related metrics for multiple loan books.
    Results take the form of 'csv' files and plots and are exported to
    user-specified project paths.
Support for a variety of commonly used precision agriculture operations. Includes functions to download and process raw satellite images from Sentinel-2 <https://documentation.dataspace.copernicus.eu/APIs/OData.html>. Includes functions that download vegetation index statistics for a given period of time, without the need to download the raw images <https://documentation.dataspace.copernicus.eu/APIs/SentinelHub/Statistical.html>. There are also functions to download and visualize weather data in a historical context. Lastly, the package also contains functions to process yield monitor data. These functions can build polygons around recorded data points, evaluate the overlap between polygons, clean yield data, and smooth yield maps.
Provides a broad-view perspective on data via
    linear mapping of data onto a radial coordinate system. The package
    contains functions to visualize the residual values of linear
    regression and Cartesian data in the defined radial scheme. See the
    'pacviz' documentation page for more information:
    <https://pacviz.sriley.dev/>.
Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.
A wrapper for 'Paddle' - The Merchant of Record for digital products API (Application Programming Interface) <https://developer.paddle.com/api-reference/overview>. Provides functions to manage and analyze products, customers, invoices and many more.
Given a vector of Taylor series coefficients of sufficient length
    as input, the function returns the numerator and denominator coefficients
    for the Padé approximant of appropriate order (Baker, 1975)
    <ISBN:9780120748556>.
Transforms datetime data into a format ready for analysis.
    It offers two core functionalities; aggregating data to a higher level interval
    (thicken) and imputing records where observations were absent (pad).
Provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.
Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.
We consider the network structure detection for variables Y with auxiliary variables X accommodated, which are possibly subject to measurement error. The following three functions are designed to address various structures by different methods : one is NP_Graph() that is used for handling the nonlinear relationship between the responses and the covariates,  another is Joint_Gaussian() that is used for correction in linear regression models via the Gaussian maximum likelihood, and the other Cond_Gaussian() is for linear regression models via conditional likelihood function.
Use the paged media properties in CSS and the JavaScript
  library 'paged.js' to split the content of an HTML document into discrete
  pages. Each page can have its page size, page numbers, margin boxes, and
  running headers, etc. Applications of this package include books, letters,
  reports, papers, business cards, resumes, and posters.
Quickly and easily add a mini map to your 'rmarkdown' html documents.
A simple way to add page numbers to base/ggplot/lattice graphics.
Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty.
    This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) <doi:10.1016/j.jeconom.2022.12.002>. PAGFL identifies latent group structures and group-specific coefficients in a single step.    
    On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) <doi:10.48550/arXiv.2503.23165>.
Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.
Estimation of the number of colonization events between islands of the same archipelago for a species. It uses rarefaction curves to control for both field and genetic sample sizes as it was described in Coello et al. (2022) <doi:10.1111/jbi.14341>.
XKCD described a supposedly "bad" colormap that it called a "Painbow" (see <https://xkcd.com/2537/>). But simple tests demonstrate that under some circumstances, the colormap can perform very well, and people can find information that is difficult to detect with the ggplot2 default and even supposedly "good" colormaps like viridis. This library let's you use the Painbow in your own ggplot graphs.
Functions for creating color palettes, visualizing
    palettes, modifying colors, and assigning colors for plotting.
Color palettes generated from paintings.
Plots matrices of colours as grids of coloured squares - aka heatmaps, 
	guaranteeing legible row and column names, 
	without transformation of values, 
	without re-ordering rows or columns,
	and without dendrograms.
Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided for analysing paired data with S4 class.
Normalization of numerical matrices by minimizing the mean/median/mode difference between all column pairs.
Creates an interactive scatterplot matrix using the D3 JavaScript
    library. See <https://d3js.org/> for more information on D3.
Improving graphics by ameliorating order effects, using Eulerian tours 
    and Hamiltonian decompositions of  graphs. References for the methods presented 
    here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and 
    C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.
Performs the explicit calculation
    -- not estimation! -- of the Rasch item parameters for dichotomous and
    polytomous item responses, using a pairwise comparison approach. Person
    parameters (WLE) are calculated according to Warm's weighted likelihood
    approach.
Calculation of the parametric, nonparametric confidence intervals
 for the difference or ratio of location parameters, nonparametric confidence interval
 for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial
 proportions for comparison of independent samples. Common wrapper functions to split 
 data sets and apply confidence intervals or tests to these subsets.
 A by-statement allows calculation of CI separately for the levels of further factors. 
 CI are not adjusted for multiplicity.
Provides a unified framework for generating, submitting, and 
    analyzing pairwise comparisons of writing quality using large language 
    models (LLMs). The package supports live and/or batch evaluation workflows 
    across multiple providers ('OpenAI', 'Anthropic', 'Google Gemini', 
    'Together AI', and locally-hosted 'Ollama' models), includes bias-tested 
    prompt templates and a flexible template registry, and offers tools
    for constructing forward and reversed comparison sets to analyze
    consistency and positional bias. Results can be modeled using 
    Bradley–Terry (1952) <doi:10.2307/2334029> or Elo rating methods to derive 
    writing quality scores. For information on the method of pairwise 
    comparisons, see Thurstone (1927) <doi:10.1037/h0070288> and Heldsinger & 
    Humphry (2010) <doi:10.1007/BF03216919>. For information on Elo ratings, see
    Clark et al. (2018) <doi:10.1371/journal.pone.0190393>.
Provides functions for landscape analysis and data retrieval.
    The package allows users to download environmental variables from global
    datasets (e.g., WorldClim, CHELSA, ESA WorldCover, Nighttime Lights), and
    to compute spatial and landscape metrics using a hexagonal grid system
    based on the H3 spatial index. It is useful for ecological modeling,
    biodiversity studies, and spatial data processing in landscape ecology.
    Fick and Hijmans (2017) <doi:10.1002/joc.5086>.
    Zanaga et al. (2022) <doi:10.5281/zenodo.7254221>.
    Uber Technologies Inc. (2022) "H3: Hexagonal hierarchical spatial index".
    Román et al. (2018) <doi:10.1016/j.rse.2018.03.017>.
    Karger et al. (2017) <doi:10.1038/sdata.2017.122>.
    Brun et al. (2022) <doi:10.5194/essd-14-5573-2022>.
The goal of 'pak' is to make package installation faster and
    more reliable. In particular, it performs all HTTP operations in
    parallel, so metadata resolution and package downloads are fast.
    Metadata and package files are cached on the local disk as well. 'pak'
    has a dependency solver, so it finds version conflicts before
    performing the installation. This version of 'pak' supports CRAN,
    'Bioconductor' and 'GitHub' packages as well.
Provides data sets and functions for exploration of Pakistan Population Census 2017 (<http://www.pbscensus.gov.pk/>).
Provides data sets and functions for exploration of Pakistan Population Census 2023 (<https://www.pbs.gov.pk/>).
Provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Household Listing questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household.
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using Probability Proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Children Age 5-17 questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Household questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Maternal Mortality questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
Provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).
References and cites 'R' and 'R' packages on the fly in 'R
    Markdown' and 'Quarto'. 'pakret' provides a minimalist API that
    generates preformatted citations for 'R' and 'R' packages, and adds
    their references to a '.bib' file directly from within your document.
Several tests of quantitative palaeoenvironmental reconstructions 
  from microfossil assemblages, including the null model tests of the 
  statistically significant of reconstructions developed by Telford and Birks
  (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of 
  spatial autocorrelation on transfer function model performance using methods 
  from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and 
  Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with 
  generalized mixed-effect regression from Heegaard et al (2005)
  <doi:10.1191/0959683605hl836rr> are also included.
Provides functionality to support data preparation and exploration for
  palaeobiological analyses, improving code reproducibility and accessibility. The
  wider aim of 'palaeoverse' is to bring the palaeobiological community together 
  to establish agreed standards. The package currently includes functionality for 
  data cleaning, binning (time and space), exploration, summarisation and 
  visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart/>)
  and auxiliary functions are also provided. Details can be found in:
  Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.
Implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).
Implementation of the Partitioned Local Depth (PaLD) 
    approach which provides a measure of local depth and the cohesion of a point 
    to another which  (together with a universal threshold for distinguishing 
    strong and weak ties) may be used to reveal local and global structure in 
    data, based on methods described in Berenhaut, Moore, and Melvin (2022)
    <doi:10.1073/pnas.2003634119>. No extraneous inputs, distributional 
    assumptions, iterative procedures nor optimization criteria are employed. 
    This package includes functions for computing local depths and cohesion as
    well as flexible functions for plotting community networks and displays of 
    cohesion against distance.
Provides functions for fitting abundance distributions over environmental gradients to the species in ecological communities, and tools for simulating the fossil assemblages from those abundance models for such communities, as well as simulating assemblages across various patterns of sedimentary history and sampling. These tools are for particular use with fossil records with detailed age models and abundance distributions used for calculating environmental gradients from ordinations or other indices based on fossil assemblages.
Includes functions to wrap most endpoints of the
    'PaleobioDB' API and to visualize and process the obtained fossil
    data. The API documentation for the Paleobiology Database can be
    found at <https://paleobiodb.org/data1.2/>.
Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.
Contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database <https://paleobiodb.org>, extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy.
Fill missing symmetrical data with mirroring, calculate Procrustes alignments with or without scaling, and compute standard or vector correlation and covariance matrices (congruence coefficients) of 3D landmarks. Tolerates missing data for all analyses.
This extension of the poems pattern-oriented modeling (POM) framework
    provides a collection of modules and functions customized for paleontological
    time-scales, and optimized for single-generation transitions and large populations,
    across multiple generations.
Provides tools for transforming, a posteriori time-scaling, and
    modifying phylogenies containing extinct (i.e. fossil) lineages. In particular,
    most users are interested in the functions timePaleoPhy, bin_timePaleoPhy,
    cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which date cladograms of
    fossil taxa using stratigraphic data. This package also contains a large number
    of likelihood functions for estimating sampling and diversification rates from
    different types of data available from the fossil record (e.g. range data,
    occurrence data, etc). paleotree users can also simulate diversification and
    sampling in the fossil record using the function simFossilRecord, which is a
    detailed simulator for branching birth-death-sampling processes composed of
    discrete taxonomic units arranged in ancestor-descendant relationships. Users
    can use simFossilRecord to simulate diversification in incompletely sampled
    fossil records, under various models of morphological differentiation (i.e.
    the various patterns by which morphotaxa originate from one another), and
    with time-dependent, longevity-dependent and/or diversity-dependent rates of
    diversification, extinction and sampling. Additional functions allow users to
    translate simulated ancestor-descendant data from simFossilRecord into standard
    time-scaled phylogenies or unscaled cladograms that reflect the relationships
    among taxon units.
Facilitates analysis of paleontological sequences of trait values.  
    Functions are provided to fit, using maximum likelihood, simple 
    evolutionary models (including unbiased random walks, directional 
    evolution,stasis, Ornstein-Uhlenbeck, covariate-tracking) and 
    complex models (punctuation, mode shifts).
Hexadecimal codes are typically used to represent colors in R.
    Connecting these codes to their colors requires practice or memorization.
    'palette' provides a 'vctrs' class for working with color palettes, including
    printing and plotting functions. The goal of the class is to place visual
    representations of color palettes directly on or, at least, next to their
    corresponding character representations. Palette extensions also are provided
    for data frames using 'pillar'.
The choices of color palettes in R can be quite overwhelming
    with palettes spread over many packages with many different API's.
    This packages aims to collect all color palettes across the R
    ecosystem under the same package with a streamlined API.
Streamlines the steps for adding colour scales and associated legends 
         when working with base R graphics, especially for interactive use. Popular
         palettes are included and pretty legends produced when mapping a large 
         variety of vector classes to a colour scale. An additional helper for 
         adding axes and grid lines complements the base::plot() work flow.
Provides specialized color palettes representing phenological 
    transitions and biological lifecycles within Philippine landscapes. 
    Rather than abstract gradients, these scales are anchored to 
    topologically grounded states such as agricultural maturity, 
    seasonal vegetation shifts, and environmental readiness. Palettes 
    are indexed against the Biologische Bundesanstalt, Bundessortenamt 
    und CHemische Industrie (BBCH) scale (Meier, 2023) 
    <https://www.openagrar.de/servlets/MCRFileNodeServlet/openagrar_derivate_00010428/BBCH-Skala_en.pdf> 
    for terrestrial vegetation and the Reef Health Index (RHI) framework 
    (McField and Kramer, 2007) <https://www.healthyreefs.org> for marine 
    ecosystems. This ensures scientific interoperability across 
    archipelagic spatial models, aligning with global standards for 
    ecological state-transition modeling (Schwartz, 2013) 
    <doi:10.1007/978-94-007-6925-0>.
Provides a comprehensive library for colour vectors and colour
    palettes using a new family of colour classes (palettes_colour and
    palettes_palette) that always print as hex codes with colour previews.
    Capabilities include: formatting, casting and coercion, extraction and
    updating of components, plotting, colour mixing arithmetic, and colour
    interpolation.
A set of palettes imported from 'Gimp' distributed 
  under GPL3 (<https://www.gimp.org/about/COPYING>), and 'Inkscape' 
  distributed under GPL2 (<https://inkscape.org/about/license/>).
Use Pokemon(R) inspired palettes with additional 'ggplot2' scales.
    Palettes are the colours in each Pokemon's sprite, ordered by how common
    they are in the image. The first 386 Pokemon are currently provided.
Probabilistic framework for the analysis of archaeological
    palimpsests based on the Stratigraphic Entanglement Field (SEF).
    Integrates spatial proximity, stratigraphic depth, chronological
    overlap, and cultural similarity to estimate latent depositional
    phases via diagonal Gaussian mixture Expectation-Maximisation (EM).
    Provides the Stratigraphic Entanglement Index (SEI), Excavation
    Stratigraphic Energy (ESE), and Palimpsest Dissolution Index (PDI)
    for quantifying depositional coherence, detecting intrusive finds,
    and measuring palimpsest formation. Includes simulation, diagnostics,
    phase-count selection, publication-quality plots, and Geographic
    Information System (GIS) export via 'sf'. Methods are described in Cocca (2026)
    <https://github.com/enzococca/palimpsestr>.
R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates.
Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) <DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) <DOI:10.1002/sim.8046>. The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 <DOI:10.1111/biom.12983>). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.
Size measurements, clutch observations, and blood isotope ratios for adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago near Palmer Station, Antarctica. Data were collected and made available by Dr. Kristen Gorman and the Palmer Station Long Term Ecological Research (LTER) Program.
'Google Pathways Language Model 2 (PaLM 2)' as a coding and writing assistant designed for 'R'. With a range of functions, including natural language processing and coding optimization, to assist 'R' developers in simplifying tedious coding tasks and content searching.
This is an implementation of model-based trees with global model
  parameters (PALM trees). The PALM tree algorithm is an extension to the MOB
  algorithm (implemented in the 'partykit' package), where some parameters are
  fixed across all groups. Details about the method can be found in Seibold,
  Hothorn, Zeileis (2016) <arXiv:1612.07498>. The package offers coef(),
  logLik(), plot(), and predict() functions for PALM trees.
Colour palettes for data, based on some well known public data
    sets. Includes helper functions to map absolute values to known palettes, and 
    capture the work of image colour mapping as raster data sets.
A comprehensive collection of color palettes, colormaps, and
    tools to evaluate them. See Kovesi (2015) <doi:10.48550/arXiv.1509.03700>.
Implements the Projected Actor Locations (PALS) method for spatial
    modeling of dyadic interactions between geographically mobile actors, as
    described in Kim, Liu and Desmarais (2023) <doi:10.1017/psrm.2022.6>. PALS
    applies exponential-smoothing weights to the spatiotemporal histories of a
    focal actor and its interaction partners ("alters") to project the location
    of future interactions. The package provides projection, maximum-similarity
    parameter estimation by minimizing great-circle (Haversine) prediction error,
    nonparametric bootstrap with multiple-imputation (Rubin's Rules) pooling,
    dyadic distance covariate construction, visualization, and a simulated
    example dataset of subnational conflict.
Processing Chlorophyll Fluorescence & P700 Absorbance data. Four models are provided for the regression of Pi curves, which can be compared with each other in order to select the most suitable model for the data set. Control plots ensure the successful verification of each regression. Bundled output of alpha, ETRmax, Ik etc. enables fast and reliable further processing of the data.
Functions for easily reading and processing binary data files created by
    'Pamguard' (<https://www.pamguard.org/>). All functions for directly reading the
    binary data files are based on 'MATLAB' code written by Michael Oswald.
Data are partitioned (clustered) into k clusters "around medoids", which is
    a more robust version of K-means implemented in the function pam() in the 'cluster' package.
    The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>.
    Please refer to the pam() function documentation for more references.
    Clustered data is plotted as a split heatmap allowing visualisation of representative
    "group-clusters" (medoids) in the data as separated fractions of the graph while those
    "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.
Simulation functions to assess or explore the power of a dataset to estimates significant random effects (intercept or slope) in a mixed model. The functions are based on the "lme4" and "lmerTest" packages.
A collection of miscellaneous functions for passive acoustics. 
    Much of the content here is adapted to R from code written by other people.
    If you have any ideas of functions to add, please contact Taiki Sakai.
The Piece-wise exponential (Additive Mixed) Model
    (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
    powerful model class for the analysis of survival (or time-to-event) data,
    based on Generalized Additive (Mixed) Models (GA(M)Ms).
    It offers intuitive specification and robust estimation of complex survival
    models with stratified baseline hazards, random effects, time-varying effects,
    time-dependent covariates and cumulative effects (Bender and others (2019)),
    as well as support for left-truncated data as well as competing risks,
    recurrent events and multi-state settings.
    pammtools provides tidy workflow for survival analysis with PAMMs,
    including data simulation, transformation and other functions for data
    preprocessing and model post-processing as well as visualization.
Tools for loading and processing passive acoustic data. Read in data
    that has been processed in 'Pamguard' (<https://www.pamguard.org/>), apply a suite
    processing functions, and export data for reports or external modeling tools. Parameter 
    calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>,
    Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010)
    <doi:10.1121/1.3479549>.
Some functions for sample classification in microarrays.
Implements Profile Analysis via Multidimensional Scaling (PAMS)
    for the identification of population-level core response profiles from
    cross-sectional and longitudinal person-score data. Each person profile
    is decomposed into a level component (the person mean) and a pattern
    component (ipsatized subscores). PAMS uses nonmetric multidimensional
    scaling via the SMACOF algorithm to identify a small number of core
    profiles that represent the central response patterns in a sample of any
    size. Bootstrap standard errors and bias-corrected and accelerated (BCa)
    confidence intervals for individual core profile coordinates are
    estimated, enabling significance testing of coordinates that is not
    available in other profile analysis methods such as cluster profile
    analysis or latent profile analysis. Person-level weights, R-squared
    values, and correlations with core profiles are also estimated, allowing
    individual profiles to be interpreted in terms of the core profile
    structure. PAMS can be applied to both cross-sectional data and
    longitudinal data, where core trajectory profiles describe how response
    patterns change over time. Methods are described in
    Kim and Kim (2024) <doi:10.20982/tqmp.20.3.p230>,
    de Leeuw and Mair (2009) <doi:10.18637/jss.v031.i03>,
    and Kruskal (1964) <doi:10.1007/BF02289565>.
A variety of tools relevant to the analysis
    of marine soundscape data. There are tools for downloading AIS (automatic identification system)
    data from Marine Cadastre <https://hub.marinecadastre.gov>,
    connecting AIS data to GPS coordinates, plotting summaries of various soundscape
    measurements, and downloading relevant environmental variables (wind, swell height) from the
    National Center for Atmospheric Research data server <https://gdex.ucar.edu/datasets/d084001/>.
    Most tools were developed to work well with output from 'Triton' software, but can be adapted
    to work with any similar measurements.
It provides functions and examples for maximum likelihood estimation for
              generalized linear mixed models and Gibbs sampler for multivariate linear
              mixed models with incomplete data, as described in Schafer JL (1997)
              "Imputation of missing covariates under a multivariate linear mixed model".
              Technical report 97-04, Dept. of Statistics, The Pennsylvania State University.
Identification of the most appropriate pharmacotherapy for each 
    patient based on genomic alterations is a major challenge in personalized 
    oncology. 'PANACEA' is a collection of personalized anti-cancer drug 
    prioritization approaches utilizing network methods. The methods utilize 
    personalized "driverness" scores from 'driveR' to rank drugs, mapping these
    onto a protein-protein interaction network. The "distance-based" method 
    scores each drug based on these scores and distances between drugs and genes
    to rank given drugs. The "RWR" method propagates these scores via a 
    random-walk with restart framework to rank the drugs. The methods are 
    described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: 
    network-based methods for pharmacotherapy prioritization in personalized 
    oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.
Provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.
Full dynamic system to describe and forecast the spread and the severity
    of a developing pandemic, based on available data. These data are number of infections, 
    hospitalizations, deaths and recoveries notified each day. The system consists of three 
    transitions, infection-infection, infection-hospital and hospital-death/recovery. 
    The intensities of these transitions are dynamic and estimated using non-parametric local
    linear estimators. The package can be used to provide forecasts and survival indicators 
    such as the median time spent in hospital and the probability that a patient who has been
    in hospital for a number of days can leave it alive. Methods are described in Gámiz, 
    Mammen, Martínez-Miranda, and Nielsen (2024) <doi:10.48550/arXiv.2308.09918> and 
    <doi:10.48550/arXiv.2308.09919>.
A 'shiny' GUI that performs high dimensional cluster analysis. 
  This tool performs data preparation, clustering and visualisation within a dynamic GUI. 
  With interactive methods allowing the user to change settings all without having to to leave the GUI. 
  An earlier version of this package was described in Laa and Valencia (2022) <doi:10.1140/epjp/s13360-021-02310-1>.
Contains some functions catching all messages, 'stdout' and other
    useful information while evaluating R code and other helpers to return user
    specified text elements (like: header, paragraph, table, image, lists etc.)
    in 'pandoc' markdown or several type of R objects similarly automatically
    transformed to markdown format. Also capable of exporting/converting (the
    resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This
    latter reporting feature is supported in brew syntax or with a custom reference
    class with a smarty caching 'backend'.
Provides a set of tools to install, manage and run several
    'Pandoc' versions.
The document converter 'pandoc' <https://pandoc.org/> is widely used
    in the R community. One feature of 'pandoc' is that it can produce and consume
    JSON-formatted abstract syntax trees (AST). This allows to transform a given
    source document into JSON-formatted AST, alter it by so called filters and pass
    the altered JSON-formatted AST back to 'pandoc'. This package provides functions
    which allow to write such filters in native R code. 
    Although this package is inspired by the Python package 'pandocfilters' 
    <https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a 
    report generator. Since 'pandocfilters' inherits most of it's functionality
    from 'pandoc' it can create documents in many formats 
    (for more information see <https://pandoc.org/>) but is also bound to the same
    limitations as 'pandoc'.
A high performance package implementing random effects and/or
    sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2023) <doi:10.2139/ssrn.2702053>.
Provides a comprehensive set of tools for describing and visualizing panel data structures,
             as well as for summarizing and visualizing variables within a panel data context.
Understanding the dynamics of potentially heterogeneous variables is important in statistical applications.
    This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis.
    The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.
Implements a set of methodological tools
	     that enable researchers to apply matching methods to
	     time-series cross-sectional data. Imai, Kim, and Wang
	     (2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> 
	     proposes a nonparametric generalization of the
	     difference-in-differences estimator, which does not rely
	     on the linearity assumption as often done in
	     practice. Researchers first select a method of matching
	     each treated observation for a given unit in a
	     particular time period with control observations from
	     other units in the same time period that have a similar
	     treatment and covariate history. These methods include
	     standard matching methods based on propensity score and
	     Mahalanobis distance, as well as weighting methods. Once 
	     matching and refinement is done,  
	     treatment effects can be estimated with 
	     standard errors. The package also offers diagnostics for researchers to assess the quality 
	     of their results.
Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" <doi:10.1080/01621459.2019.1604367>.
Provides an object type and associated tools for storing and 
  wrangling panel data. Implements several methods for creating regression
  models that take advantage of the unique aspects of 
  panel data. Among other capabilities, automates the "within-between" 
  (also known as "between-within" and "hybrid") panel regression specification
  that combines the desirable aspects of both fixed effects and random effects 
  econometric models and fits them as multilevel models 
  (Allison, 2009 <doi:10.4135/9781412993869.d33>; 
  Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be 
  estimated via generalized estimating equations 
  (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian 
  estimation is (optionally) supported via 'Stan'. 
  Supports estimation of asymmetric effects models via first differences
  (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized
  linear model extension thereof using GEE.
Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) <doi:10.2139/ssrn.5475626> and Peng and Van den Bulte (2024) <doi:10.1287/mnsc.2019.01897>.
Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) <doi:10.18637/jss.v103.i01>) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation.
Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression
             (SUR) systems on unbalanced panel in the one/two-way cases also taking into 
             account the possibility of cross equation restrictions.  Methodological details
             can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni,
             Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.
Pre-testing and diagnostic tools for panel data analysis.
    Researchers should run these tests before any panel regression to
    verify modelling assumptions. The package implements: (1) the Hsiao
    (2014, <ISBN:978-1-107-65763-2>) homogeneity F-tests (F1/F2/F3),
    Swamy (1970) <doi:10.2307/1913012> parameter heterogeneity test, and
    Pesaran (2004) <doi:10.2139/ssrn.572504> cross-sectional dependence
    test via xtpretest(); (2) missing-data detection, mechanism testing,
    and imputation for unbalanced panels via xtmispanel(); (3)
    quantile-regression cross-sectional dependence tests (T_tau and
    T-tilde_tau statistics) of Demetrescu, Hosseinkouchack and Rodrigues
    (2023) <doi:10.1016/j.jeconom.2022.09.001> via xtcsdq(); and (4) the
    panel quantile-regression slope homogeneity S-hat and D-hat
    statistics of Galvao, Juhl, Montes-Rojas and Olmo (2017)
    <doi:10.1080/07350015.2015.1054493> via xtqsh(). Together these
    tests address three fundamental pre-testing questions: (i) are
    slopes homogeneous? (ii) is there cross-sectional dependence? and
    (iii) is the panel balanced and is missingness ignorable?
Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) <doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented.
We extend two general methods of moment estimators to panel vector 
    autoregression models (PVAR) with p lags of endogenous variables, predetermined 
    and strictly exogenous variables. This general PVAR model contains the first 
    difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, 
    Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator 
    by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also 
    provide specification tests (Hansen overidentification test, lag selection 
    criterion and stability test of the PVAR polynomial) and classical structural 
    analysis for PVAR models such as orthogonal and generalized impulse response 
    functions, bootstrapped confidence intervals for impulse response analysis and 
    forecast error variance decompositions.
Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see <doi:10.18637/jss.v107.i07>.
Leading/lagging a panel, creating dummy variables,
             taking panel differences, looking for panel autocorrelations,
             and more. Implemented via a 'data.table' back end.
Tools to interact with the 'Pangaea' Database
    (<https://www.pangaea.de>), including functions for searching for data,
    fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea'
    'OAI-PMH' service.
Provides access to word predictability estimates using large 
             language models (LLMs) based on 'transformer' architectures via 
             integration with the 'Hugging Face' ecosystem 
             <https://huggingface.co/>. The package 
             interfaces with pre-trained neural networks and supports both 
             causal/auto-regressive LLMs (e.g., 'GPT-2') and 
             masked/bidirectional LLMs (e.g., 'BERT') to compute the probability
             of words, phrases, or tokens given their linguistic context. For 
             details on GPT-2 and causal models, see Radford et al. (2019) 
             <https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf>, 
             for details on BERT and masked models, see 
             Devlin et al. (2019) <doi:10.48550/arXiv.1810.04805>. By enabling a
             straightforward estimation of word predictability, the package 
             facilitates research in psycholinguistics, computational 
             linguistics, and natural language processing (NLP).
A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.
Provides a customisable R 'shiny' app for immersively
    visualising, mapping and annotating panospheric (360 degree) imagery.
    The flexible interface allows annotation of any geocoded images using
    up to 4 user specified drop-down menus. The app uses 'leaflet' to
    render maps that display the geo-locations of images and Panellum
    <https://pannellum.org/>, a lightweight panorama viewer for the web,
    to render images in virtual 360 degree viewing mode. Key functions
    include the ability to draw on & export parts of 360 images for
    downstream applications. Users can also draw polygons and points on
    map imagery related to the panoramic images and export them for
    further analysis. Downstream applications include using annotations to
    train Artificial Intelligence/Machine Learning (AI/ML) models and
    geospatial modelling and analysis of camera based survey data.
Shrinkage estimator for polygenic risk prediction (PRS) models based on summary statistics of genome-wide association (GWA) studies. Based upon the methods and original 'PANPRS' package as found in: Chen, Chatterjee, Landi, and Shi (2020) <doi:10.1080/01621459.2020.1764849>.
Sankey diagrams are a powerfull and visually attractive way
  to visualize the flow of conservative substances through a system.
  They typically consists of a network of nodes, and fluxes between them,
  where the total balance in each internal node is 0, i.e. input equals output.
  Sankey diagrams are typically used to display energy systems, material flow accounts etc.
  Unlike so-called alluvial plots, Sankey diagrams also allow for cyclic flows:
  flows originating from a single node can, either direct or indirect, contribute to the input of that same node.
  This package, named after the Greek aphorism Panta Rhei (everything flows),
  provides functions to create publication-quality diagrams, using data in tables (or spread sheets)
  and a simple syntax.
Tools to create dynamic, submission-ready manuscripts, which
  conform to American Psychological Association manuscript guidelines. We
  provide R Markdown document formats for manuscripts (PDF and Word) and
  revision letters (PDF). Helper functions facilitate reporting statistical
  analyses or create publication-ready tables and plots.
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based
	     reports and to prettify the output of various estimated models.
This is a data only package, that provides distances from a paper plane experiment.
A simple interface in the form of R6 classes for executing tasks in
    parallel, tracking their progress, and displaying accurate progress bars.
Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.
Define parameter spaces, constraints and
    dependencies for arbitrary algorithms, to program on such spaces. Also
    includes statistical designs and random samplers. Objects are
    implemented as 'R6' classes.
Creation and selection of PARAllel FACtor Analysis (PARAFAC)
    models of longitudinal microbiome data. You can import your own data with
    our import functions or use one of the example datasets to create your own
    PARAFAC models. Selection of the optimal number of components can be done
    using assessModelQuality() and assessModelStability(). The selected model can
    then be plotted using plotPARAFACmodel(). The Parallel Factor
    Analysis method was originally described by Caroll and Chang (1970)
    <doi:10.1007/BF02310791> and Harshman (1970)
    <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.
A fast parallelized alternative to R's native 'dist' function to
    calculate distance matrices for continuous, binary, and multi-dimensional
    input matrices, which supports a broad variety of 41 predefined distance
    functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user-
    defined functions written in C++. For ease of use, the 'parDist' function
    extends the signature of the 'dist' function and uses the same parameter
    naming conventions as distance methods of existing R packages. The package
    is mainly implemented in C++ and leverages the 'RcppParallel' package to
    parallelize the distance computations with the help of the 'TinyThread'
    library. Furthermore, the 'Armadillo' linear algebra library is used for
    optimized matrix operations during distance calculations. The curiously
    recurring template pattern (CRTP) technique is applied to avoid virtual
    functions, which improves the Dynamic Time Warping calculations while
    the implementation stays flexible enough to support different DTW step
    patterns and normalization methods.
Support for parallel computation with progress bar, and option to stop or proceed on errors. Also provides logging to console and disk,
  and the logging persists in the parallel threads. Additional functions support function call automation with delayed execution (e.g. for executing functions in
  parallel).
Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package.  For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is 'parallel::makeCluster(type = "RPSOCK")', which is backward compatible with 'parallel::makeCluster()' while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.
Unified parallelization framework for multiple
    back-end, designed for internal package and interactive usage.  The
    main operation is parallel mapping over lists.  Supports 'local',
    'multicore', 'mpi' and 'BatchJobs' mode.  Allows tagging of the
    parallel operation with a level name that can be later selected by the
    user to switch on parallel execution for exactly this operation.
See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.:
        "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems,
        vol. 101, p. 101804, (2021). <doi:10.1016/j.is.2021.101804>.
	It uses a binary format for storing and retrieval of matrices developed for the 'jmatrix' package but the functionality of 'jmatrix'
	is included here, so you do not need to install it. Also, it is used by package 'scellpam', so if you have installed it, you do not need
	to install this package.
	PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points.
	It does this with the help of the code developed for other package, 'jmatrix', which allows the matrix not to be loaded in 'R' memory (which
	would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the
	dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part
	of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization
	phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel.
Create a parallel coordinates plot, using `htmlwidgets` package and `d3.js`.
Provides two functions: Group_function() and Parallel_Tree().
    Group_function() applies a given function (e.g., mean()) to input
    variable(s) by group across levels of a multilevel data structure, with
    additional data management options. Parallel_Tree() uses 'ggplot2' to
    create parallel coordinate plots (technically a facsimile of parallel
    coordinate plots in a Cartesian coordinate system). Used in combination,
    these functions can create parallel tree plots, a variant of parallel
    coordinate plots useful for visualizing multilevel data.
To calculate the raw, central and standardized moments
       from distribution parameters. To solve the distribution
       parameters based on user-provided mean, standard deviation,
       skewness and kurtosis. Normal, skew-normal, skew-t and Tukey
       g-&-h distributions are supported, for now.
Allows the user to perform ANOVA tests (in a strict sense: continuous and normally-distributed Y variable and 1 or more factorial/categorical X variable(s)), with the possibility to specify the type of sum of squares (1, 2 or 3), the types of variables (Fixed or Random) and their relationships (crossed or nested) with the sole function of the package (FullyParamANOVA()). The resulting outputs are the same as in 'SAS' software. A dataset (Butterfly) to test the function is also joined.
Calculate parametric mortality and Fertility models, following packages 'BaSTA' in Colchero, Jones and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and 'BaFTA' <https://github.com/fercol/BaFTA>, summary statistics (e.g. ageing rates, life expectancy, lifespan equality, etc.), life table and product limit estimators from census data.
Utilities for processing the parameters of various
    statistical models. Beyond computing p values, CIs, and other indices
    for a wide variety of models (see list of supported models using the
    function 'insight::supported_models()'), this package implements
    features like bootstrapping or simulating of parameters and models,
    feature reduction (feature extraction and variable selection) as well
    as functions to describe data and variable characteristics (e.g.
    skewness, kurtosis, smoothness or distribution).
Allows specification and fitting of some parameter estimation
    examples inspired by time-resolved spectroscopy via a Shiny GUI.
Functions for parameter descriptions and operations in
    black-box optimization, tuning and machine learning. Parameters can be
    described (type, constraints, defaults, etc.), combined to parameter
    sets and can in general be programmed on. A useful OptPath object
    (archive) to log function evaluations is also provided.
Performs statistical tests to compare coefficients and residual 
    variance across models. Also provides graphical methods for 
    assessing heterogeneity in coefficients and residuals. Currently supports 
    linear and generalized linear models.
A convenient framework for aggregating and disaggregating continuously
    varying parameters (for example, case fatality ratio, with age) for proper
    parametrization of lower-resolution compartmental models (for example, with
    broad age categories) and subsequent upscaling of model outputs to high resolution
    (for example, as needed when calculating age-sensitive measures like years-life-lost).
NOTE: 'PARAMLINK' HAS BEEN SUPERSEDED BY THE 'PEDSUITE'
    PACKAGES (<https://magnusdv.github.io/pedsuite/>). 'PARAMLINK' IS
    MAINTAINED ONLY FOR LEGACY PURPOSES AND SHOULD NOT BE USED IN NEW
    PROJECTS. A suite of tools for analysing pedigrees with marker data,
    including parametric linkage analysis, forensic computations,
    relatedness analysis and marker simulations. The core of the package
    is an implementation of the Elston-Stewart algorithm for pedigree
    likelihoods, extended to allow mutations as well as complex
    inbreeding. Features for linkage analysis include singlepoint LOD
    scores, power analysis, and multipoint analysis (the latter through a
    wrapper to the 'MERLIN' software). Forensic applications include
    exclusion probabilities, genotype distributions and conditional
    simulations. Data from the 'Familias' software can be imported and
    analysed in 'paramlink'. Finally, 'paramlink' offers many utility
    functions for creating, manipulating and plotting pedigrees with or
    without marker data (the actual plotting is done by the 'kinship2'
    package).
Parametric linkage analysis of monogenic traits in medical
    pedigrees. Features include singlepoint analysis, multipoint analysis
    via 'MERLIN' (Abecasis et al. (2002) <doi:10.1038/ng786>),
    visualisation of log of the odds (LOD) scores and summaries of linkage
    peaks. Disease models may be specified to accommodate phenocopies,
    reduced penetrance and liability classes. 'paramlink2' is part of the
    'pedsuite' package ecosystem, presented in 'Pedigree Analysis in R'
    (Vigeland, 2021, ISBN:9780128244302).
This function obtains a Random Number Generator (RNG) or collection of RNGs that replicate the required parameter(s) of a distribution for a time series of data. Consider the case of reproducing a time series data set of size 20 that uses an autoregressive (AR) model with phi = 0.8 and standard deviation equal to 1. When one checks the arima.sin() function's estimated parameters, it's possible that after a single trial or a few more, one won't find the precise parameters. This enables one to look for the ideal RNG setting for a simulation that will accurately duplicate the desired parameters.
Run simulations or other functions while easily varying parameters
    from one iteration to the next. Some common use cases would be grid search
    for machine learning algorithms, running sets of simulations (e.g.,
    estimating statistical power for complex models), or bootstrapping under
    various conditions. See the 'paramtest' documentation for more information
    and examples.
An implementation of Horn's technique for numerically and graphically evaluating the components or factors retained in a principle components analysis (PCA) or common factor analysis (FA). Horn's method contrasts eigenvalues produced through a PCA or FA on a number of random data sets of uncorrelated variables with the same number of variables and observations as the experimental or observational data set to produce eigenvalues for components or factors that are adjusted for the sample error-induced inflation. Components with adjusted eigenvalues greater than one are retained. paran may also be used to conduct parallel analysis following Glorfeld's (1995) suggestions to reduce the likelihood of over-retention.
Standardizes and streamlines the processing of parasitological data by integrating descriptive analyses of parasite count distributions, automated calculation of parasitological indices and their dispersion measures, and intuitive visualizations for representing these metrics (Bush et al. 1997 <doi:10.2307/3284227>, Reiczigel et al. 2019 <doi:10.1016/j.pt.2019.01.003>).
Complex graphical representations of data are best explored using interactive elements.
  'parcats' adds interactive graphing capabilities to the 'easyalluvial' package.
  The 'plotly.js' parallel categories diagrams offer a good framework for
  creating interactive flow graphs that allow manual drag and drop sorting of dimensions
  and categories, highlighting single flows and displaying mouse over information. The
  'plotly.js' dependency is quite heavy and therefore is outsourced into a separate package.
An interactive 'shiny' application for Health Technology
    Assessment (HTA) parameter estimation. Converts between rates,
    probabilities, odds, and hazard ratios (HR); extrapolates survival
    curves (Exponential, Weibull, Log-Logistic); fits probabilistic
    sensitivity analysis (PSA) distributions (Beta, Gamma, LogNormal,
    Dirichlet) via the method of moments; calculates incremental
    cost-effectiveness ratios (ICERs), net monetary benefit (NMB),
    value-based pricing, and budget impact; adjusts costs for inflation,
    discounting, and purchasing power parity (PPP) across 30 countries;
    and adjusts background mortality using life-table methods. Designed
    for researchers building cost-effectiveness and budget-impact models
    who need auditable, formula-documented parameter transformations.
    Methods include Zhang and Yu (1998)
    <doi:10.1001/jama.280.19.1690> for odds ratio (OR) to relative
    risk (RR) conversion and Chinn (2000, Statistics in Medicine, 19,
    3127-3131) for effect-size transformations.
Construct parser combinator functions, higher order functions that
  parse input. Construction of such parsers is transparent and easy. Their main
  application is the parsing of structured text files like those generated by
  laboratory instruments. Based on a paper by Hutton (1992)
  <doi:10.1017/S0956796800000411>.
Provides fast, dependency-minimal tools to strip all cell formulas
    from standard Excel (.xlsx) files while preserving evaluated calculated
    values intact. It also supports exporting multi-sheet workbooks into
    standalone single-sheet files. By design, the package extracts only
    cell values and their formatting (styles) to ensure maximum speed and
    safety, discarding complex embedded objects such as images or charts.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Reviews other packages during code review by looking at their
    dependencies, code style, code complexity, and how internally defined
    functions interact with one another.
Utilities for the Pareto, piecewise Pareto and generalized Pareto distribution
    that are useful for reinsurance pricing. In particular, the package provides
    a non-trivial algorithm that can be used to match the expected losses of a 
    tower of reinsurance layers with a layer-independent collective risk model.
    The theoretical background of the matching algorithm and most other methods
    are described in Ulrich Riegel (2018) <doi:10.1007/s13385-018-0177-3>.
Statistical functions to describe a Pareto Positive Stable (PPS) 
    distribution and fit it to real data. Graphical and statistical tools to 
    validate the fits are included.
Fits Parametric Frailty Models by maximum marginal likelihood.
             Possible baseline hazards:
                 exponential, Weibull, inverse Weibull (Fréchet),
                 Gompertz, lognormal, log-skew-normal, and loglogistic.
             Possible Frailty distributions:
                gamma, positive stable, inverse Gaussian and lognormal.
Offers tools to estimate and visualize levels of major pollutants
             (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous
             United States for user-defined time ranges. Provides functions to
             retrieve pollutant data from the U.S. Environmental Protection
             Agency’s 'Air Quality System' (AQS) API service
             <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             interactive visualization through a 'shiny' application, allowing
             users to explore pollutant levels for a given location over time
             relative to the National Ambient Air Quality Standards (NAAQS).
Provides a parallel estimation method for generalized linear
    models without compiling with a multithreaded LAPACK or BLAS.
Computes the All-Resolution Inference method in the permutation framework, i.e., simultaneous lower confidence bounds for the number of true discoveries. <doi:10.1002/sim.9725>.
Palettes inspired by Paris 2024 Olympic and Paralympic Games for 
    data visualizations. Length of color palettes is configurable.
Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.
Parallel estimation of the mutual information based on entropy
    estimates from k-nearest neighbors distances and algorithms for the
    reconstruction of gene regulatory networks
    (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).
Allows for highly flexible and generic function argument matching.
Executes simple parametric models for right-censored 
    survival data.  Functionality emulates capabilities in 'Minitab', including
    fitting right-censored data, assessing fit, plotting survival functions,
    and summary statistics and probabilities.
Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.
Collection of functions to get files in parquet format.
    Parquet is a columnar storage file format <https://parquet.apache.org/>. 
    The files to convert can be of several formats 
    ("csv", "RData", "rds", "RSQLite", 
    "json", "ndjson", "SAS", "SPSS"...).
Implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.
Parse dates automatically, without the need of specifying a
    format. Currently it includes the git date parser.  It can also
    recognize and parse all ISO 8601 formats.
A system to increase the efficiency of dynamic web-scraping with 'RSelenium'
    by leveraging parallel processing. You provide a function wrapper for your 'RSelenium' 
    scraping routine with a set of inputs, and 'parsel' runs it in several browser instances. 
    Chunked input processing as well as error catching and logging ensures seamless 
    execution and minimal data loss, even when unforeseen 'RSelenium' errors occur. You can additionally 
    build safe scraping functions with minimal coding by utilizing constructor functions that act as wrappers 
    around 'RSelenium' methods.
Exports an enhanced version of the tools::parseLatex()
    function to handle 'LaTeX' syntax more accurately.  Also
    includes numerous functions for searching and modifying
    'LaTeX' source.
An implementation of a formal grammar and parser for R Markdown documents
    using the Boost Spirit X3 library. It also includes a collection of high level
    functions for working with the resulting abstract syntax tree.
Functions to load Research Patient Data Registry ('RPDR') text queries from Partners Healthcare institutions into R.
             The package also provides helper functions to manipulate data and execute common procedures
             such as finding the closest radiological exams considering a given timepoint, or creating a DICOM header database
             from the downloaded images. All functionalities are parallelized for fast and efficient analyses.
Perform flexible simulation studies using one or multiple computer
    cores. The package is set up to be usable on high-performance clusters in
    addition to being run locally (i.e., see the package vignettes for more
    information).
A common interface is provided to allow users to specify a
    model without having to remember the different argument names across
    different functions or computational engines (e.g. 'R', 'Spark',
    'Stan', 'H2O', etc).
It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.
A novel tool for generating a piecewise
        constant estimation list of increasingly complex predictors
        based on an intensive and comprehensive search over the entire
        covariate space.
Provides a 'partialised' class that extends the partialising 
    function of 'purrr' by making it easier to change the arguments. This is 
    similar to the function-like object in 'Julia' 
    (<https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects>).
Creates a data frame with the residuals of partial regressions of the main explanatory variable and the variable of interest. This method follows the Frisch-Waugh-Lovell theorem, as explained in Lovell (2008) <doi:10.3200/JECE.39.1.88-91>.
Tests for a comparison of two partially overlapping samples.
  A comparison of means using the partially overlapping samples t-test: 
  See Derrick, Russ, Toher and White (2017), Test 
  statistics for the comparison of means for two samples which include 
  both paired observations and independent observations, Journal of 
  Modern Applied Statistical Methods, 16(1). 
  A comparison of proportions using the partially overlapping samples z-test:
  See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics
  for comparing two proportions with partially overlapping samples.
  Journal of Applied Quantitative Methods, 10(3).
Implements IV-estimator and Bayesian estimator for linear-in-means Spatial Autoregressive (SAR) model (see LeSage, 1997 <doi:10.1177/016001769702000107>; Lee, 2004 <doi:10.1111/j.1468-0262.2004.00558.x>; Bramoullé et al., 2009 <doi:10.1016/j.jeconom.2008.12.021>), while assuming that only a partial information about the network structure is available. Examples are when the adjacency matrix is not fully observed or when only consistent estimation of the network formation model is available (see Boucher and Houndetoungan, 2025 <doi:10.48550/arXiv.2509.08145>).
Implements partial transfer learning (PTL)
  for causal effect estimation using source and target data, with bootstrap-based source detection. 
  Provides data generating processes and nuisance functions for simulation.
A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class are used to run the PSO with methods to easily print, plot and save the result.
Simulating particle movement in 2D space has many
    application. The 'particles' package implements a particle simulator
    based on the ideas behind the 'd3-force' 'JavaScript' library.
    'particles' implements all forces defined in 'd3-force' as well as
    others such as vector fields, traps, and attractors.
Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for
    generating random data, fitting, predicting and check goodness of fit are prepared. The methods are
    developed from the theoretical framework of proportional hazard model and copula functions.
    Using this package, users can now simulate parametric time-dependent ROC and run experiment
    to understand the behavior of the curve under different scenario.
A fast and flexible framework for agglomerative partitioning.
    'partition' uses an approach called Direct-Measure-Reduce to create
    new variables that maintain the user-specified minimum level of
    information. Each reduced variable is also interpretable: the original
    variables map to one and only one variable in the reduced data set.
    'partition' is flexible, as well: how variables are selected to
    reduce, how information loss is measured, and the way data is reduced
    can all be customized.  'partition' is based on the Partition
    framework discussed in Millstein et al. (2020) 
    <doi:10.1093/bioinformatics/btz661>.
A collection of functions that can be used to estimate selection and complementarity effects,
	sensu Loreau & Hector (2001) <doi:10.1038/35083573>, even in cases where data are only available for
	a random subset of species (i.e. incomplete sample-level data). A full derivation and explanation of the 
	statistical corrections used here is available in Clark et al. (2019) <doi:10.1111/2041-210X.13285>.
Provides several measures ((dis)similarity, distance/metric,
    correlation, entropy) for comparing two partitions of the same set of
    objects. The different measures can be assigned to three different
    classes: Pair comparison (containing the famous Jaccard and Rand
    indices), set based, and information theory based.
    Many of the implemented measures can be found in
    Albatineh AN, Niewiadomska-Bugaj M and Mihalko D (2006)
    <doi:10.1007/s00357-006-0017-z> and
    Meila M (2007) <doi:10.1016/j.jmva.2006.11.013>.
    Partitions are represented by vectors of class labels which allow a
    straightforward integration with existing clustering algorithms
    (e.g. kmeans()). The package is mostly based on the S4 object system.
partitionMetric computes a distance between two partitions
        of a set.
Additive partitions of integers.  Enumerates the
  partitions, unequal partitions, and restricted partitions of an
  integer; the three corresponding partition functions are also given.
  Set partitions and now compositions and riffle shuffles are
  included.
Miscellaneous utilities for parallelizing large
   computations.  Alternative to MapReduce.
   File splitting and distributed operations such as sort and aggregate.
   "Software Alchemy" method for parallelizing most statistical methods,
   presented in N. Matloff, Parallel Computation for Data Science,
   Chapman and Hall, 2015.  Includes a debugging aid.
Partitioning the R2 of GLMMs into variation explained by each 
    predictor and combination of predictors using semi-partial (part) R2 and
    inclusive R2. Methods are based on the R2 for GLMMs described in
    Nakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017).
Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.
Datetimes and timestamps are invariably an imprecise notation, with any
    partial representation implying some amount of uncertainty. To handle this,
    'parttime' provides classes for embedding partial missingness as a central
    part of its datetime classes. This central feature allows for more ergonomic
    use of datetimes for challenging datetime computation, including
    calculations of overlapping date ranges, imputations, and more thoughtful
    handling of ambiguity that arises from uncertain time zones. This package was
    developed first and foremost with pharmaceutical applications in mind, but
    aims to be agnostic to application to accommodate general use cases just as
    conveniently.
Visualize the partitions of simple decision trees, involving one or
    two predictors, on the scale of the original data. Provides an intuitive
    alternative to traditional tree diagrams, by visualizing how a decision tree
    divides the predictor space in a simple 2D plot alongside the original data.
    The 'parttree' package supports both classification and regression trees
    from 'rpart' and 'partykit', as well as trees produced by popular frontend
    systems like 'tidymodels' and 'mlr3'. Visualization methods are provided for
    both base R graphics and 'ggplot2'.
A computational toolbox for recursive partitioning.
  The core of the package is ctree(), an implementation of
  conditional inference trees which embed tree-structured 
  regression models into a well defined theory of conditional
  inference procedures. This non-parametric class of regression
  trees is applicable to all kinds of regression problems, including
  nominal, ordinal, numeric, censored as well as multivariate response
  variables and arbitrary measurement scales of the covariates. 
  Based on conditional inference trees, cforest() provides an
  implementation of Breiman's random forests. The function mob()
  implements an algorithm for recursive partitioning based on
  parametric models (e.g. linear models, GLMs or survival
  regression) employing parameter instability tests for split
  selection. Extensible functionality for visualizing tree-structured
  regression models is available. The methods are described in
  Hothorn et al. (2006) <doi:10.1198/106186006X133933>,
  Zeileis et al. (2008) <doi:10.1198/106186008X319331> and 
  Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
Extract political party colors and logos from English Wikipedia
    party pages. Provides functions to scrape party infoboxes for color codes
    (HEX or HTML color names) and logo images. Includes integration with the
    Party Facts database for easy party lookups. Designed for political
    scientists and party researchers working with electoral and party data.
    For Party Facts, see Döring and Regel (2019) <doi:10.1177/1354068818820671>
    and Bederke, Döring, and Regel (2023) <doi:10.7910/DVN/TJINLQ>.
A toolkit with infrastructure for representing, summarizing, and
  visualizing tree-structured regression and classification models. This
  unified infrastructure can be used for reading/coercing tree models from
  different sources ('rpart', 'RWeka', 'PMML') yielding objects that share
  functionality for print()/plot()/predict() methods. Furthermore, new and improved
  reimplementations of conditional inference trees (ctree()) and model-based
  recursive partitioning (mob()) from the 'party' package are provided based
  on the new infrastructure. A description of this package was published
  by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.
Parse messy geographic coordinates from various character formats
    to decimal degree numeric values. Parse coordinates into
    their parts (degree, minutes, seconds); calculate hemisphere
    from coordinates; pull out individually degrees,
    minutes, or seconds; add and subtract degrees, minutes,
    and seconds. C++ code herein originally inspired from code
    written by Jeffrey D. Bogan, but then completely re-written.
An R package for polygenic trait analysis.
Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. Also referred to the following implementation: <https://github.com/rahulrajpl/PyPASAD>.
Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.
Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. Here we 
	present an 'R' package, 'PASSED', that performs power and sample size calculations for 
	the test of two-sample means or ratios with data following beta, 
	gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal, 
	Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and 
	negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.
Simulates judgments of frequency and duration based on
    the Probability Associator Time (PASS-T) model. PASS-T is a memory
    model based on a simple competitive artificial neural network. It 
    can imitate human judgments of frequency and duration, which have
    been extensively studied in cognitive psychology
    (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010)
    <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model
    is an extension of the PASS model (Sedlmeier, 2002,
    ISBN:0198508638). The package provides an easy way to run
    simulations, which can then be compared with empirical data in
    human judgments of frequency and duration.
Create random passwords of letters, numbers
             and punctuation.
Plot both fixed and random effects of linear mixed models, multilevel models in a single spaghetti plot. The package allows to visualize the effect of a predictor on a criterion between different levels of a grouping variable. Additionally, confidence intervals can be displayed for fixed effects. Calculation of predicted values of random effects allows only models with one random intercept and/or one random slope to be plotted. Confidence intervals and predicted values of fixed effects are computed using the 'ggpredict' function from the 'ggeffects' package. Lüdecke, D. (2018) <doi:10.21105/joss.00638>.
A Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0.
Methods to easily extract and manipulate climate
  reconstructions for ecological and anthropological analyses, as described
  in Leonardi et al. (2023) <doi:10.1111/ecog.06481>. The package includes datasets
  of palaeoclimate reconstructions, present observations, and future projections 
  from multiple climate models.
Provides a 'RStudio' addin allowing to paste the content of the clipboard as a comment block or as 'roxygen' lines. This is very useful to insert an example in the 'roxygen' block.
Regularisation, decomposition and analysis of space-time series.
  The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff
  <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.
Functions and data sets for the text Probability and Statistics
    with R.
Functions and data sets for the text Probability and Statistics
    with R, Second Edition.
Functions to patch specials in '.dvi' files,
        or entries in '.synctex' files.  Works with concordance=TRUE 
        in Sweave, knitr or R Markdown to link sources to previews.
This utility eases the debugging of literate documents
	     ('noweb' files) by patching the synchronization information
	     (the '.synctex(.gz)' file) produced by 'pdflatex' with
	     concordance information produced by 'Sweave' or 'knitr' and
	     'Sweave' or 'knitr' ; this allows for bilateral communication
	     between a text editor (visualizing the 'noweb' source) and
	     a viewer (visualizing the resultant 'PDF'), thus bypassing
	     the intermediate 'TeX' file.
The 'ggplot2' package provides a strong API for sequentially 
    building up a plot, but does not concern itself with composition of multiple
    plots. 'patchwork' is a package that expands the API to allow for 
    arbitrarily complex composition of plots by, among others, providing 
    mathematical operators for combining multiple plots. Other packages that try 
    to address this need (but with a different approach) are 'gridExtra' and 
    'cowplot'.
Converts TXT and XML data curated by the United States Patent and
    Trademark Office (USPTO). Allows conversion of bulk data after downloading
    directly from the USPTO bulk data website, eliminating need for users to
    wrangle multiple data formats to get large patent databases in tidy,
    rectangular format. Data details can be found on the USPTO website
    <https://bulkdata.uspto.gov/>. Currently, all 3 formats: 1. TXT data
    (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data
    (2005-current) can be converted to rectangular, CSV format.
    Relevant literature that uses data from USPTO includes Wada (2020)
    <doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008)
    <doi:10.1007/s11192-007-1763-3>.
Provides functions to simplify the 'PatentsView' API
    (<https://search.patentsview.org/docs/docs/Search%20API/SearchAPIReference/#api-query-language>) query language,
    send GET and POST requests to the API's twenty seven endpoints, and parse the data
    that comes back.
R's implementation of the JavaScript library 'path-to-regexp',
 it aims to provide R web frameworks features such as parameter handling 
 among other URL path utilities.
Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.
Provides path_chain class and functions, which facilitates loading and saving 
             directory structure in YAML configuration files via 'config' package. 
             The file structure you created during exploration can be transformed 
             into legible section in the config file, and then easily loaded for further usage.
Provides access to large-scale genomics data from the South Dakota 
    State University's bioinformatics database, a unified platform for pathway 
    analysis of over 13,000 organisms. It includes various gene mappings, gene 
    characteristics, and pathway mapping data from KEGG, GOBP, GOCC, and many 
    more pathway databases. Also provides various helper functions for 
    processing RNA-Seq data for differential expression analysis and pathway 
    enrichment analysis, occasionally sourced from code from Integrated 
    Differential Expression & Pathway analysis (iDEP), developed by Ge, S.X., 
    Son, E.W. & Yao, R. (2018) <doi:10.1186/s12859-018-2486-6>.
Enrichment analysis enables researchers to uncover mechanisms 
    underlying a phenotype. However, conventional methods for enrichment 
    analysis do not take into account protein-protein interaction information, 
    resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment 
    analysis utilizing active subnetworks. The main function identifies active 
    subnetworks in a protein-protein interaction network using a user-provided 
    list of genes and associated p values. It then performs enrichment analyses 
    on the identified subnetworks, identifying enriched terms (i.e. pathways or, 
    more broadly, gene sets) that possibly underlie the phenotype of interest.
    'pathfindR' also offers functionalities to cluster the enriched terms and 
    identify representative terms in each cluster, to score the enriched terms 
    per sample and to visualize analysis results. The enrichment, clustering and 
    other methods implemented in 'pathfindR' are described in detail in 
    Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for 
    Comprehensive Identification of Enriched Pathways in Omics Data Through 
    Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
This is a data-only package, containing data needed to run the CRAN 
    package 'pathfindR', a package for enrichment analysis utilizing active 
    subnetworks. This package contains protein-protein interaction network data, 
    data related to gene sets and example input/output data.
R API for 'Pathling', a tool for querying and transforming electronic health record data that is represented using the 'Fast Healthcare Interoperability Resources' (FHIR) standard - see <https://pathling.csiro.au/docs>.
This wrapper houses PathLit API endpoints for R. The usage of
    these endpoints require the use of an API key which can be obtained at
    <https://www.pathlit.io/docs/cli/>.
Functions for computing fit indices for 
    evaluating the path component of latent variable structural equation models. 
    Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated 
    by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by 
    O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) 
    <doi:10.1177/1094428117736137>. Also included are fit indices described by 
    Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific
  effects are identified under standard ignorability assumptions. This package implements an imputation
  approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis
  (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating 
  path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified 
  using the nonparametric bootstrap.
Tools to import, clean, and visualize movement data,
    particularly from motion capture systems such as Optitrack's 
    'Motive', the Straw Lab's 'Flydra', or from other sources. We provide 
    functions to remove artifacts, standardize tunnel position and tunnel 
    axes, select a region of interest, isolate specific trajectories, fill
    gaps in trajectory data, and calculate 3D and per-axis velocity. For 
    experiments of visual guidance, we also provide functions that use 
    subject position to estimate perception of visual stimuli.
For a given graph containing vertices, edges, and a signal 
    associated with the vertices, the 'PathwaySpace' package performs a convolution 
    operation, which involves a weighted combination of neighboring vertices and 
    their associated signals. The package uses a decay function to project these 
    signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' has various 
    applications, such as visualizing network data in a graphical format that 
    highlights the relationships and signal strengths between vertices. By 
    combining graph theory, signal processing, and visualization, 'PathwaySpace' 
    provides a way of representing graph data on a continuous projection space.
    Based on methods introduced in Tercan et al. (2025) 
    <doi:10.1016/j.xpro.2025.103681> and Ellrott et al. (2025) 
    <doi:10.1016/j.ccell.2024.12.002>.
Performs pathway enrichment analysis using a voting-based framework that integrates CpG–gene regulatory information from expression quantitative trait methylation (eQTM) data. For a grid of top-ranked CpGs and filtering thresholds, gene sets are generated and refined using an entropy-based pruning strategy that balances information richness, stability, and probe bias correction. In particular, gene lists dominated by genes with disproportionately high numbers of CpG mappings are penalized to mitigate active probe bias—a common artifact in methylation data analysis. Enrichment results across parameter combinations are then aggregated using a voting scheme, prioritizing pathways that are consistently recovered under diverse settings and robust to parameter perturbations.
Tools to generate synthetic patient-level test datasets in the
    Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).
    Includes a chat-driven generator backed by large language models and an
    interactive 'shiny' designer for editing CDM test sets.
A user friendly way to create patient level prediction models using
  the Observational Medical Outcomes Partnership Common Data Model. Given a cohort
  of interest and an outcome of interest, the package can use data in the Common
  Data Model to build a large set of features. These features can then be used to
  fit a predictive model with a number of machine learning algorithms. This is
  further described in Reps (2017) <doi:10.1093/jamia/ocy032>.
Identify the characteristics of patients in data mapped to the 
    Observational Medical Outcomes Partnership (OMOP) common data model.
Creation of patient profile visualizations for
  exploration, diagnostic or monitoring purposes during a clinical trial.
  These static visualizations display a patient-specific overview
  of the evolution during the trial time frame of 
  parameters of interest (as laboratory, ECG, vital signs),
  presence of adverse events, exposure to a treatment; 
  associated with metadata patient information, 
  as demography, concomitant medication.
  The visualizations can be tailored for specific domain(s) or endpoint(s) of interest.
  Visualizations are exported into patient profile report(s)
  or can be embedded in custom report(s).
This is an extension of the 'testthat' package that
    lets you add parameters to your unit tests. Parameterized unit tests
    are often easier to read and more reliable, since they follow the DNRY
    (do not repeat yourself) rule.
Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).
Provides a set of functions to efficiently recognize and clean the continuous dorsal pattern of a female brown anole lizard (Anolis sagrei) traced from 'ImageJ', an open platform for scientific image analysis (see <https://imagej.net> for more information), and extract common features such as the pattern sinuosity indices, coefficient of variation, and max-min width.
A comprehensive package for detecting and analyzing causal relationships in complex systems using pattern-based approaches. Key features include state space reconstruction, pattern identification, and causality strength evaluation.
Quantification of variation in organismal color patterns as
    obtained from image data. Patternize defines homology between pattern positions
    across images either through fixed landmarks or image registration. Pattern
    identification is performed by categorizing the distribution of colors using RGB
    thresholds or image segmentation.
Creates aesthetically pleasing and informative pie charts, ring charts, bar charts and box plots with colors, patterns, and images.
A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.
Functions that support a broad range of common tasks in physical
    activity research, including but not limited to creation of Bland-Altman
    plots (<doi:10.1136/bmj.313.7049.106>), metabolic calculations such as basal
    metabolic rate predictions (<https://europepmc.org/article/med/4044297/reloa>),
    demographic calculations such as age-for-body-mass-index percentile
    (<https://www.cdc.gov/growthcharts/cdc_charts.htm>), and analysis of bout
    detection algorithm performance (<https://pubmed.ncbi.nlm.nih.gov/34258524/>).
An open-source toolbox for storing, validating, managing, and
    exploring transportation infrastructure data. Provides a relational data
    model for binders, aggregates, mixtures, and test results, with a
    human-readable file format (.pavdata) aligned with FAIR principles.
A cohesive framework for the spectral and spatial analysis of 
    colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) 
    <doi:10.1111/2041-210X.12069> and Maia, Gruson, Endler & White (2019)
    <doi:10.1111/2041-210X.13174>.
Functions to process, format and store ActiGraph GT1M and GT3X accelerometer data.
Interface to Amazon Web Services <https://aws.amazon.com>,
    including storage, database, and compute services, such as 'Simple
    Storage Service' ('S3'), 'DynamoDB' 'NoSQL' database, and 'Lambda'
    functions-as-a-service.
Interface to 'Amazon Web Services' 'analytics' services,
    including 'Elastic MapReduce' 'Hadoop' and 'Spark' big data service,
    'Elasticsearch' search engine, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' application integration
    services, including 'Simple Queue Service' ('SQS') message queue,
    'Simple Notification Service' ('SNS') publish/subscribe messaging, and
    more <https://aws.amazon.com/>.
Functions for making low-level API requests to Amazon Web Services
    <https://aws.amazon.com>. The functions handle building, signing, and
    sending requests, and receiving responses. They are designed to help build 
    higher-level interfaces to individual services, such as Simple Storage
    Service (S3).
Interface to 'Amazon Web Services' compute services,
    including 'Elastic Compute Cloud' ('EC2'), 'Lambda'
    functions-as-a-service, containers, batch processing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' cost management services,
    including cost and usage reports, budgets, pricing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' customer engagement
    services, including 'Simple Email Service', 'Connect' contact center
    service, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' database services,
    including 'Relational Database Service' ('RDS'), 'DynamoDB' 'NoSQL'
    database, and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' developer tools services,
    including version control, continuous integration and deployment, and
    more <https://aws.amazon.com/products/developer-tools/>.
Interface to 'Amazon Web Services' end user computing
    services, including collaborative document editing, mobile intranet,
    and more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' machine learning services,
    including 'SageMaker' managed machine learning service, natural
    language processing, speech recognition, translation, and more
    <https://aws.amazon.com/machine-learning/>.
Interface to 'Amazon Web Services' management and governance
    services, including 'CloudWatch' application and infrastructure
    monitoring, 'Auto Scaling' for automatically scaling resources, and
    more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' networking and content
    delivery services, including 'Route 53' Domain Name System service,
    'CloudFront' content delivery, load balancing, and more
    <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' security, identity, and
    compliance services, including the 'Identity & Access Management'
    ('IAM') service for managing access to services and resources, and
    more <https://aws.amazon.com/>.
Interface to 'Amazon Web Services' storage services,
    including 'Simple Storage Service' ('S3') and more
    <https://aws.amazon.com/>.
Automated pain scoring from paw withdrawal tracking data. Based on
             Jones et al. (2020) "A machine-vision approach for automated pain
             measurement at millisecond timescales" <doi:10.7554/eLife.57258>.
Parametric bootstrap (PB) has been used for 
             three-way ANOVA model with unequal group variances.
A lightweight package that adds
  progress bar to vectorized R functions
  ('*apply'). The implementation can easily be added
  to functions where showing the progress is
  useful (e.g. bootstrap). The type and style of the
  progress bar (with percentages or remaining time)
  can be set through options.
  Supports several parallel processing backends including mirai and future.
This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk.
Generates a position balanced or nearly position balanced block design with given parameters.  This package can also convert a given proper and equireplicate block design into a position balanced or nearly position balanced block design.
Design and implementation of Percentile-based Shewhart Control Charts for continuous data. Faraz (2019) <doi:10.1002/qre.2384>.
Conducts maximum likelihood analysis and simulation of the
    protracted birth-death model of diversification. See
    Etienne, R.S. & J. Rosindell 2012 <doi:10.1093/sysbio/syr091>;
    Lambert, A., H. Morlon & R.S. Etienne 2014, <doi:10.1007/s00285-014-0767-x>;
    Etienne, R.S., H. Morlon & A. Lambert 2014, <doi:10.1111/evo.12433>.
A simplified, efficient, interface to MPI for HPC clusters. It is
        a derivation and rethinking of the Rmpi package. pbdMPI embraces the
        prevalent parallel programming style on HPC clusters. Beyond the
        interface, a collection of functions for global work with
        distributed data and resource-independent RNG reproducibility is
        included. It is based on S4 classes and methods.
Utilizing scalable linear algebra packages mainly
        including 'BLACS', 'PBLAS', and 'ScaLAPACK' in double precision via
        'pbdMPI' based on 'ScaLAPACK' version 2.0.2.
'ZeroMQ' is a well-known library for high-performance
    asynchronous messaging in scalable, distributed applications.  This
    package provides high level R wrapper functions to easily utilize
    'ZeroMQ'. We mainly focus on interactive client/server programming
    frameworks. For convenience, a minimal 'ZeroMQ' library (4.2.2)
    is shipped with 'pbdZMQ', which can be used if no system installation
    of 'ZeroMQ' is available.  A few wrapper functions compatible with
    'rzmq' are also provided.
The PBIB designs are important type of incomplete block designs
 having wide area of their applications for example in agricultural
 experiments, in plant breeding, in sample surveys etc. This package
 constructs various series of PBIB designs and assists in checking all the
 necessary conditions of PBIB designs and the association scheme on which
 these designs are based on. It also assists in calculating the efficiencies
 of PBIB designs with any number of associate classes. The package also
 constructs Youden-m square designs which are Row-Column designs for the
 two-way elimination of heterogeneity. The incomplete columns of these
 Youden-m square designs constitute PBIB designs. With the present
 functionality, the package will be of immense importance for the researchers
 as it will help them to construct PBIB designs, to check if their PBIB
 designs and association scheme satisfy various necessary conditions for the
 existence, to calculate the efficiencies of PBIB designs based on any
 association scheme and to construct Youden-m square designs for the two-way
 elimination of heterogeneity. R. C. Bose and K. R. Nair (1939)
 <http://www.jstor.org/stable/40383923>.
A set of datasets and functions used in the book
  'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'.
  Datasets either come from real studies or are created to be as similar 
  as possible to real studies.
Safely extracts and coerces values from a Power BI parameter
    table (one row, multiple columns) without string concatenation or
    injection of raw values into scripts.
Make statistical inference on the probability of being in response, 
             the duration of response, and the cumulative response rate up to a given time point. 
             The method can be applied to analyze phase II randomized clinical trials with the endpoints 
             being time to treatment response and time to progression or death.
Provides a vectorized R function for calculating
    probabilities from a standard bivariate normal CDF.
Computes p-values based on (a) Satterthwaite or
    Kenward-Rogers degree of freedom methods and (b) parametric bootstrap
    for mixed effects models as implemented in the 'lme4'
    package. Implements parametric bootstrap test for generalized linear
    mixed models as implemented in 'lme4' and generalized linear
    models. The package is documented in the paper by Halekoh and
    Højsgaard, (2012, <doi:10.18637/jss.v059.i09>).  Please see
    'citation("pbkrtest")' for citation details.
Bivariate additive categorical regression via penalized maximum likelihood. 
             Under a multinomial framework, the method fits bivariate models where both 
             responses are nominal, ordinal, or a mix of the two. Partial proportional 
             odds models are supported, with flexible (non-)uniform association structures. 
             Various logit types and parametrizations can be specified for both marginals 
             and the association, including Dale’s model. The association structure can 
             be regularized using polynomial-type penalty terms. Additive effects are 
             modeled using P-splines. Standard methods such as summary(), residuals(), 
             and predict() are available.
Binding models which are useful when analysing protein-ligand interactions by techniques such as Biolayer Interferometry (BLI) or Surface Plasmon Resonance (SPR). Naman B. Shah, Thomas M. Duncan (2014) <doi:10.3791/51383>. Hoang H. Nguyen et al. (2015) <doi:10.3390/s150510481>. After initial binding parameters are known, binding curves can be simulated and parameters can be varied. The models within this package may also be used to fit a curve to measured binding data using non-linear regression.
A light-weight package helps you track and visualize
  the progress of parallel version of vectorized R functions (mc*apply).
  Parallelization (mc.core > 1) works only on *nix (Linux, Unix such as macOS) system due to
  the lack of fork() functionality, which is essential for mc*apply, on Windows.
Permutation based
    non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper.
Following the method of Bailey et al., computes for a collection
    of candidate models the probability of backtest overfitting, the
    performance degradation and probability of loss, and the stochastic
    dominance.
A profile boosting framework for feature selection in parametric models.
    It offers a unified interface pboost() and several wrapped models, including linear model, generalized linear models, quantile regression, Cox proportional hazards model, beta regression, spatial auto-regressive models.
In short, this package is a locator for cool, refreshing beverages. 
  It will find and return the nearest location where you can get a cold one.
Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.
A collection of software provides R support for 'ADMB'
             (Automatic Differentiation Model Builder) and a 'GUI'
             interface facilitates the conversion of 'ADMB' template
             code to 'C code' followed by compilation to a binary executable.
             Stand-alone functions can also be run by users
             not interested in clicking a 'GUI'.
Functions for solving systems of delay differential equations by
   interfacing with numerical routines written by Simon N. Wood, including
   contributions from Benjamin J. Cairns. These numerical routines first
   appeared in Simon Wood's 'solv95' program. This package includes a vignette
   and a complete user's guide. 'PBSddesolve' originally appeared on CRAN under
   the name 'ddesolve'. That version is no longer supported. The current name
   emphasizes a close association with other 'PBS' packages, particularly
   'PBSmodelling'.
This software has evolved from fisheries research conducted at the
   Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. It
   extends the R language to include two-dimensional plotting features similar
   to those commonly available in a Geographic Information System (GIS).
   Embedded C code speeds algorithms from computational geometry, such as
   finding polygons that contain specified point events or converting between
   longitude-latitude and Universal Transverse Mercator (UTM) coordinates.
   Additionally, we include 'C++' code developed by Angus Johnson for the
   'Clipper' library, data for a global shoreline, and other data sets in the
   public domain. Under the user's R library directory '.libPaths()',
   specifically in './PBSmapping/doc', a complete user's guide is offered and
   should be consulted to use package functions effectively.
Provides software to facilitate the design, testing, and operation
   of computer models. It focuses particularly on tools that make it easy to
   construct and edit a customized graphical user interface ('GUI'). Although our
   simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk'
   package, a user does not need to know 'Tcl/Tk'. Examples illustrate models
   built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. 
   A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package
   effectively.
Computes probabilities of the bivariate normal distribution
    in a vectorized R function (Drezner & Wesolowsky, 1990, 
    <doi:10.1080/00949659008811236>).
Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (2020) <doi:10.1073/pnas.1918269117>, as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) <doi:10.1080/13658816.2025.2581207>, together with a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>.
Methods to detect genetic markers involved in biological
    adaptation. 'pcadapt' provides statistical tools for outlier detection based on
    Principal Component Analysis. Implements the method described in (Luu, 2016)
    <DOI:10.1111/1755-0998.12592> and later revised in (Privé, 2020) 
    <DOI:10.1093/molbev/msaa053>.
A suite of non-parametric, visual tools for assessing differences in data structures
    for two datasets that contain different observations of the same variables. These tools are all 
    based on Principal Component Analysis (PCA) and thus effectively address differences in the structures
    of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, 
    intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue
    (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The
    angle plot presents the information loss when moving from the PCA decomposition of one dataset to the 
    PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby
    presenting the relative weighting and importance of the variables from the original dataset.
Calibrate p-values under a robust perspective using the methods developed
    by Sellke, Bayarri, and Berger (2001) <doi:10.1198/000313001300339950> and obtain 
    measures of the evidence provided by the data in favor of point null hypotheses 
    which are safer and more straightforward to interpret.
Implementations of several methods for principal component analysis 
        using the L1 norm.  The package depends on COIN-OR Clp version >= 
        1.17.4.  The methods implemented are 
        PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, 
        L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, 
        L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, 
        L1-PCAhp (Visentin, Prestwich and Armagan 2016) 
                 <DOI:10.1007/978-3-319-46227-1_37>, 
        wPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>,
        PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and
        SharpEl1-PCA (Brooks and Dula, submitted).
Functions for causal structure
  learning and causal inference using graphical models. The main algorithms
  for causal structure learning are PC (for observational data without hidden
  variables), FCI and RFCI (for observational data with hidden variables),
  and GIES (for a mix of data from observational studies
  (i.e. observational data) and data from experiments
  involving interventions (i.e. interventional data) without hidden
  variables). For causal inference the IDA algorithm, the Generalized
  Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC)
  and some related functions are implemented. Functions for incorporating
  background knowledge are provided.
Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.
Matches cases to controls based on genotype principal components (PC). 
      In order to produce better results, matches are based on the weighted 
      distance of PCs where the weights are equal to the % variance explained 
      by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987)
      <DOI:10.1016/0031-3203(87)90066-5>) is used to determine matches.
Implements principal component analysis, orthogonal rotation and multiple
    factor analysis for a mixture of quantitative and qualitative variables.
Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2023) <https://genome.cshlp.org/content/33/9/1599>.
Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The 'PAM50' classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The 'PCAPAM50' package leverages principal component analysis and iterative 'PAM50' calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.
Provides functions for robust PCA by projection pursuit.
    The methods are described in Croux et al. (2006) <doi:10.2139/ssrn.968376>,
    Croux et al. (2013) <doi:10.1080/00401706.2012.727746>,
    Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>.
Provides an R interface to the 'PCATS' API
    <https://pcats.research.cchmc.org/api/__docs__/>,
    allowing R users to submit tasks and retrieve results.
Creates, fits and samples Pair-Copula Bayesian networks (PCBN)
    under some restrictions on the underlying Directed Acyclic Graph (DAG),
    that is, no active cycles nor interfering v-structures, following 
    Derumigny, Horsman and Kurowicka (2025) <doi:10.48550/arXiv.2510.03518>.
A system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: <https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.
An implementation of the pediatric complex chronic conditions (CCC)
    classification system using R and C++.
Implements the Principal Components Difference-in-Differences estimators as described in Chan, M. K., & Kwok, S. S. (2022) <doi:10.1080/07350015.2021.1914636>.
Implements methods to automate the Auer-Gervini graphical
  Bayesian approach for determining the number of significant
  principal components. Automation uses clustering, change points, or
  simple statistical models to distinguish "long" from "short" steps
  in a graph showing the posterior number of components as a function
  of a prior parameter. See <doi:10.1101/237883>.
Contains the functions for construction and visualization of various families 
    of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2),
    for computing the graph invariants for testing the patterns of segregation and association against complete spatial randomness (CSR)
    or uniformity in one, two and three dimensional cases.
    The package also has tools for generating points from these spatial patterns.
    The graph invariants used in testing spatial point data are the domination number (Ceyhan (2011)
    <doi:10.1080/03610921003597211>) and arc density (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>;
    Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). The PCD families considered are Arc-Slice PCDs,
    Proportional-Edge PCDs, and Central Similarity PCDs.
Contains the functions for construction and visualization of underlying and reflexivity graphs of 
            the three families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2),
            and for computing the edge density of these PCD-based graphs which are then
            used for testing the patterns of segregation and association against complete spatial randomness (CSR))
            or uniformity in one and two dimensional cases. 
            The PCD families considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>) 
            and Central Similarity PCDs (Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). 
            See also (Ceyhan (2016) <doi:10.1016/j.stamet.2016.07.003>) for edge density of the underlying and 
            reflexivity graphs of PE-PCDs.
            The package also has tools for visualization of PCD-based graphs for one, two, and three dimensional data.
Functions for generating progressively Type-II censored data in
    a mixture structure and fitting models using a constrained EM algorithm. It can also
    create a progressive Type-II censored version of a given real dataset to be considered for
    model fitting.
Principal component of explained variance (PCEV) is a statistical
    tool for the analysis of a multivariate response vector. It is a dimension-
    reduction technique, similar to Principal component analysis (PCA), that seeks
    to maximize the proportion of variance (in the response vector) being explained
    by a set of covariates.
Provides convenience functions and pre-programmed
    Stan models related to the paired comparison factor model. Its purpose
    is to make fitting paired comparison data using Stan easy. This
    package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.
We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm,
             with specific conditional independence tests and modified orientation rules. pcgen extends 
			 the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of 
			 direct genetic effects.
Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
Piecewise constant hazard models for survival data. 
    The package allows for right-censored, left-truncated, and interval-censored data.
Bayesian network learning using the PCHC, FEDHC, MMHC and variants of these algorithms. PCHC stands for PC Hill-Climbing, a new hybrid algorithm that uses PC to construct the skeleton of the BN and then 
			 applies the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. 
			 The relevant papers are:
			 a) Tsagris M. (2021). "A new scalable Bayesian network learning algorithm with applications to economics". Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. 
			 b) Tsagris M. (2022). "The FEDHC Bayesian Network Learning Algorithm". Mathematics 2022, 10(15): 2604. <doi:10.3390/math10152604>.
			 c) Sevinc V. and Tsagris M. (2024). "On the Hyperparameters of PCTABU and PCHC Bayesian Network Learning Algorithms". <doi:10.21203/rs.3.rs-5137132/v1>.
A collection of process capability index functions, such as
  C_p(), C_pk(), C_pm(), and others, along with metadata about each, like
  'LaTeX' equations and 'R' expressions. Its primary purpose is to form a
  foundation for other quality control packages to build on top of, by providing
  basic resources and functions. The indices belong to the field of statistical
  quality control, and quantify the degree to which a manufacturing process is
  able to create items that adhere to a certain standard of quality. For details
  see Montgomery, D. C. (2019, ISBN:978-1-119-39930-8).
Calculates the Proactive Conservation Index, a new tool to prioritize species for conservation, which can incorporate information about future threats.
We fit causal models using proxies. We implement two stage proximal least squares estimator. E.J. Tchetgen Tchetgen, A. Ying, Y. Cui, X. Shi, and W. Miao. (2020). An Introduction to Proximal Causal Learning. arXiv e-prints, arXiv-2009 <arXiv:2009.10982>.
A method for fitting the entire regularization path 
    of the principal components lasso for linear and
    logistic regression models. The algorithm uses cyclic coordinate descent
    in a path-wise fashion. See URL below for more information on the algorithm.
    See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression'
    <arXiv:1810.04651>.
Two protein complex-based group regression models (PCLasso and PCLasso2) for risk protein complex identification. PCLasso is a prognostic model that identifies risk protein complexes associated with survival. PCLasso2 is a classification model that identifies risk protein complexes associated with classes. For more information, see Wang and Liu (2021) <doi:10.1093/bib/bbab212>.
Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio' (2019) <doi:10.5506/APhysPolBSupp.12.25>. The suggested geiger package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take latest version.
Phylogenetic comparative methods represent models of continuous trait 
  data associated with the tips of a phylogenetic tree. Examples of such models 
  are Gaussian continuous time branching stochastic processes such as Brownian 
  motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the 
  tips of the tree as an observed (final) state of a Markov process starting from 
  an initial state at the root and evolving along the branches of the tree. The 
  PCMBase R package provides a general framework for manipulating such models. 
  This framework consists of an application programming interface for specifying 
  data and model parameters, and efficient algorithms for simulating trait evolution 
  under a model and calculating the likelihood of model parameters for an assumed
  model and trait data. The package implements a growing collection of models, 
  which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed 
  Gaussian models, in which different types of the above models can be associated 
  with different branches of the tree. The PCMBase package is limited to 
  trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic 
  models. The PCMFit package provides functionality for inference of 
  these models to tree and trait data. The package web-site 
  <https://venelin.github.io/PCMBase/>
  provides access to the documentation and other resources.
Provides a C++ backend for multivariate phylogenetic comparative
  models implemented in the R-package 'PCMBase'. Can be used in combination
  with 'PCMBase' to enable fast and parallel likelihood calculation. Implements
  the pruning likelihood calculation algorithm described in Mitov et al. (2020) 
  <doi:10.1016/j.tpb.2019.11.005>. Uses the 'SPLITT' C++ library for parallel tree traversal 
  described in Mitov and Stadler (2018) <doi:10.1111/2041-210X.13136>.
Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> .  PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.
Performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes
 (Zhang et al., 2014 <doi:10.1177/1740774513498322>;
  Lin et al., 2017 <doi:10.18637/jss.v080.i05>).
Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) 
             criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019)
             <https://hal.archives-ouvertes.fr/hal-02002275>. 
             It can be used with univariate and multivariate data.
An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.
Allows for nonparametric regression where one assumes that the signal is given by the sum of a piecewise constant function and a smooth function. More precisely, it implements the estimator PCpluS (piecewise constant plus smooth regression estimator) from Pein and Shah (2025) <doi:10.48550/arXiv.2112.03878>.
Implementation of the pattern recognition technique Principal
    Component Pursuit tailored to environmental health data, as described
    in Gibson et al (2022) <doi:10.1289/EHP10479>.
Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).
A collection of functions and data sets that support teaching a quantitative finance MS level course on Portfolio Construction and Risk Analysis, and the writing of a textbook for such a course. The package provides several real-world data sets for problem assignments and student projects, including cross-sections of stock data from the Center for Research on Security Prices, LLC (CRSP), corresponding factor exposures from S&P Global, and several S&P 500 data sets.
Extracts features from amplification curve data of quantitative 
    Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 
    <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper 
    functions prepare the amplification curve data for processing as functional 
    data (e.g., Hausdorff distance) or enable the plotting of amplification 
    curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() 
    functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> 
    can be used to predict amplification curves with an hook effect-like 
    curvature. The pcrfit_single() function can be used to extract features 
    from an amplification curve.
Provides a unified framework for principal component analysis (PCA) and principal component regression (PCR), including standard PCA, sparse PCA, robust PCA, and supervised PCA. The package supports automatic selection of the number of components using cumulative variance and elbow methods and integrates PCA with regression modelling through PCR models. It includes tools for PCA suitability assessment using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. Visualisation utilities such as scree plots and biplots are provided for interpretation. The methods are designed to handle multicollinearity, outliers, and high-dimensional data, making them suitable for applied statistical modelling and data analysis. The methodology is based on established approaches described in Jolliffe (2002) <doi:10.1007/b98835>, Zou et al. (2006) <doi:10.1111/j.1467-9868.2005.00503.x>, and Hubert et al. (2005) <doi:10.1198/004017004000000563>.
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
A function to estimate
        panel-corrected standard errors. Data may contain balanced or
        unbalanced panels.
Parallel Constraint Satisfaction (PCS) models are an increasingly
    common class of models in Psychology, with applications to reading and word
    recognition (McClelland & Rumelhart, 1981;
    \doi{10.1037/0033-295X.88.5.375}), judgment and decision making (Glöckner &
    Betsch, 2008 \doi{10.1017/S1930297500002424}; Glöckner, Hilbig, &
    Jekel, 2014 \doi{10.1016/j.cognition.2014.08.017}), and several other
    fields. In each of these fields, they provide a quantitative model of
    psychological phenomena, with precise predictions regarding choice
    probabilities, decision times, and often the degree of confidence. This
    package provides the necessary functions to create and simulate basic
    Parallel Constraint Satisfaction networks within R.
Defines functions to describe regression models using only
    pre-computed summary statistics (i.e. means, variances, and covariances)
    in place of individual participant data.
    Possible models include linear models for linear combinations, products, 
    and logical combinations of phenotypes.
    Implements methods presented in 
    Wolf et al. (2021) <doi:10.3389/fgene.2021.745901>
    Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and 
    Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.
The Prize-Collecting Steiner Tree problem asks to find a subgraph
    connecting a given set of vertices with the most expensive nodes and least
    expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm
    does not exist. This package provides convenient functionality for obtaining an
    approximate solution to this problem using loopy belief propagation algorithm.
Functions and example data to teach and
  increase the reproducibility of the methods and code underlying 
  the Propensity to Cycle Tool (PCT), a research project and web application 
  hosted at <https://www.pct.bike/>. 
  For an academic paper on the methods,
  see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.
Provides a comprehensive suite of tools for analyzing omics data. It includes
           functionalities for alpha diversity analysis, beta diversity analysis, differential
           abundance analysis, community assembly analysis, visualization of phylogenetic tree, and
           functional enrichment analysis. With a progressive approach, the package offers a range of
           analysis methods to explore and understand the complex communities. It is designed to
           support researchers and practitioners in conducting in-depth and professional omics data
           analysis.
Classes and methods for modelling and simulation of
    periodically correlated (PC) and periodically integrated time
    series.  Compute theoretical periodic autocovariances and related
    properties of PC autoregressive moving average models. Some original
    methods including Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
    <doi:10.1111/j.1467-9892.1996.tb00281.x>.
Offers a range of utilities and functions for everyday programming tasks. 1.Data
           Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File
           Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities
           for generating color palettes, validating color formats, and adding transparency.
           4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing
           corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides
           efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams,
           including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for
           creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.
Implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.
Analyse common types of plant phenotyping data, provide a simplified interface
	to longitudinal growth modeling and select Bayesian statistics,
	and streamline use of 'PlantCV' output.
	Several Bayesian methods and reporting guidelines for Bayesian methods are described in
	Kruschke (2018) <doi:10.1177/2515245918771304>,
	Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.
A collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. <doi:10.1214/14-AOS1255>. Shen C., Panda S. and Vogelstein J. T. (2022). "The Chi-Square Test of Distance Correlation". Journal of Computational and Graphical Statistics, 31(1): 254--262. <doi:10.1080/10618600.2021.1938585>. Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. <ISBN:9781482242744>. Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. <doi:10.48550/arXiv.2506.15659>.
The PDE (Pdf Data Extractor) allows the extraction of 
    information and tables optionally based on search words from 
    PDF (Portable Document Format) files and enables the visualization 
    of the results, both by providing a convenient user-interface.
A nonparametric, multicore-capable plausible naive Bayes classifier based on the Pareto density estimation (PDE), supporting memory sharing within multicore computations and featuring a plausible approach to a pitfall in the Bayesian theorem covering low evidence cases Stier, Q., Hoffmann, J., and Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" (2026),  Machine Learning and Knowledge Extraction (MAKE), <DOI:10.3390/make8010013>.
Data sets for the Panel Data Econometrics with R <doi:10.1002/9781119504641> book.
Cluster analysis via nonparametric density 
   estimation is performed. Operationally, the kernel method is used throughout to estimate
   the density. Diagnostics methods for evaluating the quality of the clustering 
   are available. The package includes also a routine to estimate the 
   probability density function obtained by the kernel method, given a set of
   data with arbitrary dimensions.
A 'shiny' app that supports merging of PDF and/or image files with page selection, removal, or rotation options.
    It is a fast, free, and secure alternative to commercial software or various online websites which require users to sign-up, and it avoids any potential risks associated with uploading files elsewhere.
Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method.  Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.
Download economic and financial time series from public sources, 
  including the St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics, 
  the US Energy Information Administration, the World Bank, Eurostat, the European Central Bank,
  the Bank of England, the UK's Office of National Statistics, Deutsche Bundesbank, and INSEE.
Provides an interface to 'PDFMiner' <https://github.com/pdfminer/pdfminer.six> 
    a 'Python' package for extracting information from 'PDF'-files. 
    'PDFMiner' has the goal to get all information available in a 'PDF'-file, 
    position of the characters, font type, font size and informations about lines. 
    Which makes it the perfect starting point for extracting tables from 'PDF'-files.
    More information can be found in the package 'README'-file.
Includes functions for keyword search of pdf files. There is
    also a wrapper that includes searching of all files within a single
    directory.
Digitally sign PDF documents with a 'PKCS#12' keystore and verify
    their signatures. Signing produces a detached 'PKCS#7' / 'CMS' signature
    ('adbe.pkcs7.detached') over the document and is applied as an incremental
    update, so existing signatures remain valid. The cryptography and PDF
    manipulation are performed by a bundled, pure-'Rust' backend (the
    'pdf_signer' crate); no Java runtime, 'OpenSSL', or external command-line
    tools are required. Visible signature appearances with custom text are
    supported.
Allows the user to convert PDF tables to formats more amenable to
  analysis ('.csv', '.xml', or '.xlsx') by wrapping the PDFTables API.
  In order to use the package, the user needs to sign up for an API account
  on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>).
  The package works by taking a PDF file as input, uploading it to PDFTables,
  and returning a file with the extracted data.
Utilities based on 'libpoppler' <https://poppler.freedesktop.org> for extracting
    text, fonts, attachments and metadata from a PDF file. Also supports high quality rendering
    of PDF documents into PNG, JPEG, TIFF format, or into raw bitmap vectors for further 
    processing in R.
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2021) <doi:10.1016/j.foreco.2021.118948>.
Provides standardised functions for quantifying plant disease intensity and disease development over time. The package implements Percent Disease Index (PDI) for assessing overall disease severity based on categorical ratings, Area Under the Disease Progress Curve (AUDPC) for summarizing disease progression using trapezoidal integration, and Relative AUDPC (rAUDPC) for expressing disease development relative to the maximum possible severity over the observation period. These indices are widely used in plant pathology and epidemiology for comparing treatments, cultivars, and environments.
Computes the euclidean distance between rows of a matrix X
        and rows of another matrix Y.  Previously, this could be done
        by binding the two matrices together and calling 'dist', but
        this creates unnecessary computation by computing the distances
        between a row of X and another row of X, and likewise for Y.
        pdist strictly computes distances across the two matrices, not
        within the same matrix, making computations significantly
        faster for certain use cases.
Measures real distances in pictures. With PDM() function, you can choose one '*.jpg' file, select the measure in mm of scale, starting and and finishing point in the graphical scale, the name of the measure, and starting and and finishing point of the measures. After, ask the user for a new measure.
Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.
Building patient level networks for prediction of 
        medical outcomes and draw the cluster of network.        
        This package is based on paper Personalized disease 
        networks for understanding and predicting cardiovascular 
        diseases and other complex processes 
        (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).
A general framework for constructing partial dependence (i.e.,
        marginal effect) plots from various types machine learning models
        in R.
Create, transform, and summarize custom random
    variables with distribution functions (analogues of 'p*()', 'd*()',
    'q*()', and 'r*()' functions from base R). Two types of distributions
    are supported: "discrete" (random variable has finite number of output
    values) and "continuous" (infinite number of values in the form of
    continuous random variable). Functions for distribution
    transformations and summaries are available. Implemented approaches
    often emphasize approximate and numerical solutions: all distributions
    assume finite support and finite values of density function; some
    methods implemented with simulation techniques.
A collection of tools for approximating the 'PDQ' functions
    (respectively, the cumulative distribution, density, and quantile) of
    probability distributions via classical expansions involving moments and
    cumulants.
Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.
Compute and tune some positive definite and sparse covariance
  estimators.
Interactive shiny application for working with Probability Distributions. Calculations and  Graphs are provided.
An implementation of data analysis tools for samples of symmetric or 
  Hermitian positive definite matrices, such as collections of covariance matrices 
  or spectral density matrices. The tools in this package can be used to perform: (i) 
  intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive 
  definite matrices with applications to dimension reduction, denoising and clustering in the 
  space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference 
  for samples of positive definite matrices by means of intrinsic data depth functions and 
  rank-based hypothesis tests in the space of Hermitian positive definite matrices.
Permutation (randomisation) test for single-case phase design data with 
    two phases (e.g., pre- and post-treatment). Correction for dependency of observations 
    is done through stepwise resampling the time series while varying 
    the distance between observations. The required distance 0,1,2,3.. is determined 
    based on repeated dependency testing while stepwise increasing the distance.
    In preparation: Vroegindeweij et al. "A Permutation distancing test 
    for single-case observational AB phase design data: A Monte Carlo simulation study".
The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. 
	     The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. 
	     Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different 
	     data transformation and modeling tasks such as: 
	     imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and 
	     information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, 
	     area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, 
	     heterogeneity, discriminatory and predictive power of the model.
Conduct simulation-based customized power calculation 
             for clustered time to event data in a mixed crossed/nested design,
             where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power,
             motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2025) <doi:10.51387/25-NEJSDS76>.
             This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for
             within-subject (cell line) correlation while holding different distributional assumptions about the outcome. 
             Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.
An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods.
Linear dynamic panel data modeling based on linear and
    nonlinear moment conditions as proposed by
    Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>,
    Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>,
    and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>.
    Estimation of the model parameters relies on the Generalized
    Method of Moments (GMM) and instrumental variables (IV) estimation,
    numerical optimization (when nonlinear moment conditions are
    employed) and the computation of closed form solutions (when
    estimation is based on linear moment conditions). One-step,
    two-step and iterated estimation is available. For inference
    and specification
    testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005>
    and doubly corrected standard errors
    (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>)
    are available. Additionally, serial correlation tests, tests for
    overidentification, and Wald tests are provided. Functions for
    visualizing panel data structures and modeling results obtained
    from GMM estimation are also available. The plot methods include
    functions to plot unbalanced panel structure, coefficient ranges
    and coefficient paths across GMM iterations (the latter is
    implemented according to the plot shown in
    Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).
    For a more detailed description of the GMM-based functionality,
    please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>.
    For more details on the IV-based estimation routines,
    see Fritsch, Pua, and Schnurbus (WP, 2024) and
    Han and Phillips (2010) <doi:10.1017/S026646660909063X>.
These are useful tools and data sets for the study of quantitative 
    peace science. The goal for this package is to include tools and data sets
    for doing original research that mimics well what a user would have to previously
    get from a software package that may not be well-sourced or well-supported.
    Those software bundles were useful the extent to which they encourage replications 
    of long-standing analyses by starting the data-generating process from scratch. However, 
    a lot of the functionality can be done relatively quickly and more transparently
    in the R programming language.
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series. Citation: Louca, Stilianos and Doebeli, Michael (2015) <doi:10.1890/14-0126.1>.
Chromatin immunoprecipitation DNA sequencing results in genomic
    tracks that show enriched regions or peaks where proteins are bound.
    This package implements fast C code that computes the true and false
    positives with respect to a database of annotated region labels.
When working with big data sets, RAM conservation is critically
    important. However, it is not always enough to just monitor the
    size of the objects created. So-called "copy-on-modify" behavior,
    characteristic of R, means that some expressions or functions may
    require an unexpectedly large amount of RAM overhead. For example,
    replacing a single value in a matrix duplicates that matrix in the
    back-end, making this task require twice as much RAM as that used
    by the matrix itself. This package makes it easy to monitor the total
    and peak RAM used so that developers can quickly identify and
    eliminate RAM hungry code.
Disk-based implementation of
 Functional Pruning Optimal Partitioning with up-down constraints
 <doi:10.18637/jss.v101.i10> for single-sample peak calling
 (independently for each sample and genomic problem),
 can handle huge data sets (10^7 or more).
A quadratic time dynamic programming algorithm
 can be used to compute an approximate solution to the problem of
 finding the most likely changepoints
 with respect to the Poisson likelihood, subject
 to a constraint on the number of segments, and the changes which must
 alternate: up, down, up, down, etc. For more info read
 <http://proceedings.mlr.press/v37/hocking15.html>
 "PeakSeg: constrained optimal segmentation and supervised penalty learning
 for peak detection in count data" by TD Hocking et al,
 proceedings of ICML2015.
Jointly segment several ChIP-seq samples to find the peaks 
 which are the same and different across samples. The fast approximate
 maximum Poisson likelihood algorithm is described in
 "PeakSegJoint: fast supervised peak detection via joint segmentation
 of multiple count data samples"
 <doi:10.48550/arXiv.1506.01286>
 by TD Hocking and G Bourque.
Computes optimal changepoint models using the
 Poisson likelihood for non-negative count data,
 subject to the PeakSeg constraint:
 the first change must be up, second change down, third change up, etc.
 For more info about the models and algorithms,
 read "Constrained Dynamic Programming and Supervised Penalty Learning 
 Algorithms for Peak Detection"
 <https://jmlr.org/papers/v21/18-843.html> by TD Hocking et al.
Supports maximum likelihood inference for the Pearson VII
    distribution with shape parameter 3/2 and free location and scale
    parameters. This distribution is relevant when estimating the velocity of
    processive motor proteins with random detachment.
Implementation of the Pearson distribution system, including full
  support for the (d,p,q,r)-family of functions for probability distributions 
  and fitting via method of moments and maximum likelihood method.
The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.
Provides a probabilistic framework that integrates Data Envelopment
  Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine
  learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the
  (in)efficiency status and the probability of efficiency for decision-making
  units. The approach trains predictive models on DEA-derived efficiency labels
  (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable
  artificial intelligence (XAI) workflows with global and local interpretability
  tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>,
  Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>,
  and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>.
  The framework also supports probability-threshold peer selection and counterfactual
  improvement recommendations for benchmarking and policy evaluation. The probabilistic
  efficiency framework is detailed in González-Moyano et al. (2025)
  "Probability-based Technical Efficiency Analysis through Machine Learning",
  in review for publication.
Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.
A portfolio of tools for economic complexity analysis and
    industrial upgrading navigation. The package implements essential measures
    in international trade and development economics, including the relative
    comparative advantage (RCA), economic complexity index (ECI) and product
    complexity index (PCI). It enables users to analyze export structures,
    explore product relatedness, and identify potential upgrading paths grounded
    in economic theory, following the framework in Hausmann et al. (2014)
    <doi:10.7551/mitpress/9647.001.0001>.
Computes partial eta-squared effect sizes for fixed effects in
    linear mixed models fitted with the 'lme4' package. Supports crossed,
    nested, and mixed (crossed-and-nested) random effects structures with any
    number of grouping factors. Mixed designs handle cases where grouping
    factors are simultaneously crossed with some variables and nested within
    others (e.g., photos nested within models, but both crossed with
    participants). Factor predictors are supported directly, and a single
    factor-level (omnibus) effect size can be obtained for a multi-level factor
    or multi-df interaction. Random slope variances are translated to the
    outcome scale using a variance decomposition approach, correctly accounting
    for predictor scaling and interaction terms. Both general and operative
    effect sizes are provided, with optional parametric bootstrap confidence
    intervals. For correlated predictors, per-predictor effect sizes use unique
    (semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd
    (2020) <doi:10.1016/j.tics.2019.12.009>, Correll, Mellinger, and Pedersen
    (2022) <doi:10.3758/s13428-021-01687-2>, and Rights and Sterba (2019)
    <doi:10.1037/met0000184>.
It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.
Implements entrywise splitting cross-validation (ECV) and its
    penalized variant (pECV) for selecting the number of factors in
    generalized factor models.
Data files and documentation for PEDiatric vALidation oF vAriableS
    in TBI (PEDALFAST).  The data was used in "Functional Status Scale in
    Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett,
    Dixon, et al  (2016) <doi:10.1097/PCC.0000000000000934>.
Data and utilities for estimating pediatric blood pressure
    percentiles by sex, age, and optionally height (stature) as described in
    Martin et al. (2022) <doi:10.1001/jamanetworkopen.2022.36918>.
    Blood pressure percentiles for children under one year of age come from Gemelli
    et al. (1990) <doi:10.1007/BF02171556>.  Estimates of blood pressure
    percentiles for children at least one year of age are informed by
    data from the National Heart, Lung, and Blood Institute (NHLBI) and the
    Centers for Disease Control and Prevention (CDC)
    <doi:10.1542/peds.2009-2107C> or from Lo et al. (2013)
    <doi:10.1542/peds.2012-1292>.  The source-selection flowchart comes from Martin et al. (2022)
    <doi:10.1542/hpeds.2021-005998>.
Reconstruct pedigrees from genotype data, by optimising the
    likelihood over all possible pedigrees subject to given restrictions.
    Tailor-made plots facilitate evaluation of the output. This package is
    part of the 'pedsuite' ecosystem for pedigree analysis. In
    particular, it imports 'pedprobr' for calculating pedigree likelihoods
    and 'forrel' for estimating pairwise relatedness.
Tools for exchanging pedigree data between the 'pedsuite'
    packages and the 'Familias' software for forensic kinship computations
    (Egeland et al. (2000) <doi:10.1016/s0379-0738(00)00147-x>). These
    functions were split out from the 'forrel' package to streamline
    maintenance and provide a lightweight alternative for packages otherwise
    independent of 'forrel'.
Gene-level variant association tests with disease status for pedigree data: kernel and burden association statistics.
Tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients
    and the relationship matrix (includes code for pedigrees from self-pollinated species). 
    The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict 
    breeding values. The relationship matrix between the individuals can be derived from pedigree structure 
    ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.
Generate Mermaid syntax for a pedigree flowchart from a pedigree data frame.
    Mermaid syntax is commonly used to generate plots, charts, diagrams, and flowcharts. It is a textual syntax for creating reproducible illustrations. This package generates Mermaid syntax from a pedigree data frame to visualize a pedigree flowchart. The Mermaid syntax can be embedded in a Markdown or R Markdown file, or viewed on Mermaid editors and renderers. Links' shape, style, and orientation can be customized via function arguments, and nodes' shapes and styles can be customized via optional columns in the pedigree data frame.
A collection of functions for modelling mutations in
    pedigrees with marker data, as used e.g. in likelihood computations
    with microsatellite data. Implemented models include equal,
    proportional and stepwise models, as well as random models for
    experimental work, and custom models allowing the user to apply any
    valid mutation matrix. Allele lumping is done following the
    lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.
An R implementation of methods employed in the field of pedometrics, soil science
    discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil
    using statistical and computational methods. The methods found here include the calibration of
    linear regression models using covariate selection strategies, computation of summary validation
    statistics for predictions, generation of summary plots, evaluation of the local quality of a
    geostatistical model of uncertainty, and so on. Other functions simply extend the
    functionalities of or facilitate the usage of functions from other packages that are commonly
    used for the analysis of soil data. Formerly available versions of suggested packages no longer
    available from CRAN can be obtained from the CRAN archive
    <https://cran.r-project.org/src/contrib/Archive/>.
An implementation of the Elston-Stewart algorithm for
    calculating pedigree likelihoods given genetic marker data (Elston and
    Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is
    extended to allow inbred founders. 'pedprobr' is part of the
    'pedsuite', a collection of packages for pedigree analysis in R. In
    particular, 'pedprobr' depends on 'pedtools' for pedigree
    manipulations and 'pedmut' for mutation modelling. For more
    information, see 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Provides an interface to access public economic and financial data for 
    economic research and quantitative analysis. The data sources including 
    NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative 
    functions for trading strategies based on the 'data.table', 'TTR', 
    'PerformanceAnalytics' and etc packages.
Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes.
   Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>;
   Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.
The 'pedsuite' is a collection of packages for pedigree
    analysis, covering applications in forensic genetics, medical genetics
    and more. A detailed presentation of the 'pedsuite' is given in the
    book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN: 9780128244302).
A comprehensive collection of tools for creating,
    manipulating and visualising pedigrees and genetic marker data.
    Pedigrees can be read from text files or created on the fly with
    built-in functions. A range of utilities enable modifications like
    adding or removing individuals, breaking loops, and merging pedigrees.
    An online tool for creating pedigrees interactively, based on
    'pedtools', is available at <https://magnusdv.shinyapps.io/quickped>.
    'pedtools' is the hub of the 'pedsuite', a collection of packages for
    pedigree analysis. A detailed presentation of the 'pedsuite' is given
    in the book 'Pedigree Analysis in R' (Vigeland, 2021,
    ISBN:9780128244302).
Sensitivity and power analysis,
  for calculating statistics describing pedigrees from wild populations,
  and for visualizing pedigrees. This is a reboot of the methods developed by Morrissey and Wilson (2010) <doi: 10.1111/j.1755-0998.2009.02817.x>.
Peek into published 'shinylive' applications by retrieving their source code.
    Supports downloading and extracting 'shiny' application source from 
    'quarto' documents and standalone 'shinylive' applications.
Provides functions to perform the peer performance
    analysis of funds' returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.
The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.
A lightweight, dependency-free, and simplified implementation of the 
    Pseudo-Expectation Gauss-Seidel (PEGS) algorithm. It fits the multivariate 
    ridge regression model for genomic prediction Xavier and Habier (2022) 
    <doi:10.1186/s12711-022-00730-w> and Xavier et al. (2025) 
    <doi:10.1093/genetics/iyae179>, providing heritability estimates, genetic 
    correlations, breeding values, and regression coefficient estimates for 
    prediction. This package provides an alternative to the 'bWGR' package by 
    Xavier et al. (2019) <doi:10.1093/bioinformatics/btz794> by using 'LAPACK'
    for its algebraic operations.
Functions and mined database from 'UniProt' focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) <doi:10.1002/pmic.202400238>.
Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included.
Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.
Historic Pell grant data as provided by the US Department of Education. 
    This package contains data about how much pell grant was awarded by which institution in 
    which year. 
    This data comes from the US Department of Education. Raw data can be downloaded from here: <https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html>.
An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.
Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper
    (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are
    often between-study differences. These can be coded as moderator variables,
    and controlled for using meta-regression. However, if the number of
    moderators is large relative to the number of studies, such an analysis may
    be overfit. Penalized meta-regression is useful in these cases, because
    it shrinks the regression slopes of irrelevant moderators towards zero.
A system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.
Utility functions for the handling, analysis and visualisation of data from portable emissions
            measurement systems ('PEMS') and other similar mobile activity monitoring devices. The package
            includes a dedicated 'pems' data class that manages many of the quality control, unit handling
            and data archiving issues that can hinder efforts to standardise 'PEMS' research.
We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747."
The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular rank-based estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, Statistics in Medicine <doi:10.1002/sim.9264>.
Fitting possibly high dimensional penalized
        regression models. The penalty structure can be any combination
        of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
        positivity constraint on the regression coefficients. The
        supported regression models are linear, logistic and Poisson
        regression and the Cox Proportional Hazards model.
        Cross-validation routines allow optimization of the tuning
        parameters.
Utilize the CDF penalty function to estimate a penalized linear model. 
  It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. 
  For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.
Implements L1 and L2 penalized conditional logistic regression with penalty 
    factors allowing for integration of multiple data sources. Implements
    stability selection for variable selection.
Support Vector Machine (SVM) classification with simultaneous feature selection using penalty
        functions is implemented. The smoothly clipped absolute deviation (SCAD),
        'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
        SCAD' (SCAD and 'L2-norm') penalties are available. The tuning
        parameters can be found using either a fixed grid or a interval
        search.
Implementations of algorithms from 
 Learning Sparse Penalties for Change-point Detection
 using Max Margin Interval Regression, by
 Hocking, Rigaill, Vert, Bach
 <http://proceedings.mlr.press/v28/hocking13.html>
 published in proceedings of ICML2013.
Computes penalized regression calibration (PRC), a
    statistical method for the dynamic prediction of survival when many
    longitudinal predictors are available. See Signorelli (2024)
    <doi:10.32614/RJ-2024-014> and Signorelli et al. (2021)
    <doi:10.1002/sim.9178> for details.
Different regularization approaches for Cox Frailty Models by penalization methods are provided.
	see Groll et al. (2017) <doi:10.1111/biom.12637> for effects selection.
	See also Groll and Hohberg (2024) <doi:10.1002/bimj.202300020> for classical LASSO approach.
Implements statistical methods for estimating disease penetrance in 
    family-based studies. Penetrance refers to the probability of disease
    manifestation in individuals carrying specific genetic variants. The package 
    provides tools for age-specific penetrance estimation, handling missing data, 
    and accounting for ascertainment bias in family studies.
    Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) <d 10.1093/bioadv/vbaf154>.
Fits single- and multiple-group penalized factor analysis models 
    via a trust-region algorithm with integrated automatic multiple tuning 
    parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). 
    Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.
Offers a comprehensive collection of penguin-related datasets suitable for descriptive statistics, hypothesis testing, and experimental design.
    Derived from open ecological and biological sources such as Palmer Station studies, the package integrates datasets covering adult morphology, clutch size, blood isotope composition, and heart rate.
    It is designed for researchers, students, and educators to explore statistical methods including ANOVA, regression, multivariate analysis, and design of experiments in an accessible and reproducible context.
Currently incorporate the generalized odds-rate model (a type of linear 
    transformation model) for interval-censored data based on penalized monotonic B-Spline. 
    More methods under other semiparametric models such as cure model or additive model will
    be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. 
    (2019) <arXiv:1912.11703>.
Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).
A set of tools that enables efficient estimation of penalized 
    Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models 
    that feature one or more sets of high-dimensional fixed effects. The methodology is based on 
    Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> 
    and takes advantage of the method of alternating projections of Gaure (2013) 
    <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as 
    the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) 
    <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out 
    cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) 
    <doi:10.1080/07350015.2015.1102733>.
Personal wiki engine with a large language model (LLM) as
    research assistant. Supports guided sessions through a 'Claude Code'
    <https://github.com/anthropics/claude-code> skill bundle and
    autonomous research runs from R via autoresearch(). Results land in
    a structured vault of markdown pages with 'YAML' frontmatter and
    wikilinks, ready for hand-editing in your favourite editor
    alongside the LLM. Vaults are seeded with 'CLAUDE.md' and
    'AGENTS.md' so 'Claude Code', 'Codex'
    <https://github.com/openai/codex>, and other agents share the same
    operating instructions. Can adopt an existing 'Obsidian'
    <https://obsidian.md/> vault in place via init_vault(adopt = TRUE).
Robust penalized (adaptive) elastic net S and M estimators for
    linear regression. The adaptive methods are proposed in
    Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the
    non-adaptive methods in
    Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
    (2019) <doi:10.1214/19-AOAS1269>.
    The package implements robust hyper-parameter selection with robust
    information sharing cross-validation according to Kepplinger & Wei (2025)
    <doi:10.1080/00401706.2025.2540970>.
Simulation of continuous, correlated high-dimensional data with 
    time to event or binary response, and parallelized functions for Lasso, 
    Ridge, and Elastic Net penalized regression with repeated starts and 
    two-dimensional tuning of the Elastic Net.
Estimate penalized synthetic control models
    and perform hold-out validation to determine their 
    penalty parameter. This method is based on the work by
    Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>.
    Penalized synthetic controls smoothly interpolate between 
    one-to-one matching and the synthetic control method.
Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".
Data sets for statistical inference modeling related to People Analytics.  
  Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' 
  by Keith McNulty (2026).
Provides a framework for quality-aware analysis of ground-based
    phenological data from the PEP725 Pan-European Phenology Database
    (Templ et al. (2018) <doi:10.1007/s00484-018-1512-8>;
    Templ et al. (2026) <doi:10.1111/nph.70869>) and similar observation
    networks. Implements station-level data quality grading, outlier
    detection, phenological normals (climate baselines), anomaly detection,
    elevation and latitude gradient estimation with robust regression,
    spatial synchrony quantification, partial least squares (PLS) regression
    for identifying temperature-sensitive periods, and sequential
    Mann-Kendall trend analysis. Supports data import from PEP725 files,
    conversion of user-supplied data, and downloadable synthetic datasets
    for teaching without barriers of registration.
    All analysis outputs provide 'print', 'summary', and
    'plot' methods. Interactive spatial visualization is available via
    'leaflet'.
Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) <doi:10.1016/j.ijforecast.2023.08.001> and Akgun et al. (2024) <doi:10.1016/j.ijforecast.2023.02.001>.
Performs Bayesian variable selection under normal linear
          models for the data with the model parameters following as prior distributions either 
          the power-expected-posterior (PEP) or the intrinsic (a special case of the former)
          (Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>,
          Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>).          
          The prior distribution on model space is the uniform over all models
          or the uniform on model dimension (a special case of the beta-binomial prior). 
          The selection is performed by either implementing a full enumeration 
          and evaluation of all possible models or using the Markov Chain 
          Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>). 
          Complementary functions for hypothesis testing, estimation and 
          predictions under Bayesian model averaging, as well as, plotting and 
          printing the results are also provided. The results can be compared to the
          ones obtained under other well-known priors on model parameters and model spaces.
Provides tools for analyzing differential abundance in proteomics experiments. Implements S3
    classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) <doi:10.2307/2344614>), Aligned Rank Transform (ART; Wobbrock et al. (2011) <doi:10.1145/1978942.1978963>), and pairwise test
    methods for statistical analysis. Includes visualization functions for
    Principal Component Analysis (PCA), volcano plots, and heatmaps.
Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor.
Designed for prediction error estimation
        through resampling techniques, possibly accelerated by parallel
        execution on a compute cluster. Newly developed model fitting
        routines can be easily incorporated. Methods used in the package are detailed in
        Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
        and were used, for instance, in
        Porzelius Ch., Schumacher M. and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.
A versatile R visualization package that empowers 
    researchers with comprehensive visualization tools for seamlessly mapping peptides 
    to protein sequences, identifying distinct domains and regions of interest, 
    accentuating mutations, and highlighting post-translational modifications, 
    all while enabling comparisons across diverse experimental conditions. 
    Potential applications of 'PepMapViz' include the visualization of cross-software 
    mass spectrometry results at the peptide level for specific protein and domain 
    details in a linearized format and post-translational modification coverage 
    across different experimental conditions; unraveling insights into disease 
    mechanisms. It also enables visualization of Major histocompatibility 
    complex-presented peptide clusters in different antibody regions predicting 
    immunogenicity in antibody drug development.
Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.
Estimate best fit distributions and do power analysis for hypothesis tests on phosphopeptide abundance data.
A PEP, or Portable Encapsulated Project, is a dataset that
    subscribes to the PEP structure for organizing metadata. It is written using
    a simple YAML + CSV format, it is your one-stop solution to metadata
    management across data analysis environments. This package reads this
    standardized project configuration structure into R.
    Described in Sheffield et al. (2021) <doi:10.1093/gigascience/giab077>.
Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot 'XVG' output files from the 'GROMACS' molecular dynamics package.
This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) <doi:10.32614/RJ-2015-001>). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.
The Pequod colour palette, named after the whaler in
    Herman Melville's Moby-Dick. Provides the full Log base scale
    from warm paper (Log 50) to deep ink (Log 950), eight crew
    accent hues with light and dark variants, and 'ggplot2' scales
    for discrete and continuous mapping. Designed for long-form
    reading and code, with low saturation and a consistent earth-
    pigment register. Full design rationale and accessibility notes
    at <https://tiagojct.eu/projects/pequod/>.
Perform permutation-based hypothesis testing for randomized
    experiments as suggested in Ludbrook & Dudley (1998) <doi:10.2307/2685470>
    and Ernst (2004) <doi:10.1214/088342304000000396>, introduced in Pham et al.
    (2022) <doi:10.1016/j.chemosphere.2022.136736>.
To find the certainty of dominance interactions with indirect
    interactions being considered.
An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.
Calculate (stratified) percentiles on a data.frame
    Stratification will split the data.frame into subgroups and calculate percentiles for each independently.
Several person-fit statistics 
  (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) 
  are offered. These statistics allow assessing whether
  individual response patterns to tests or questionnaires are (im)plausible given 
  the other respondents in the sample or given a specified item response theory model. Some PFSs apply to 
  dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs 
  (personal biserial correlation, caution index, (normed) number of Guttman errors, 
  agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
  extensions of lz, U3, and (normed) number of Guttman errors.
Implementation of different performance measures for classification and ranking tasks  including  Area Under the Receiving Characteristic Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC), precision at a given recall, F-score for single and multiple classes.
Utilities for computing measures to assess model quality,
    which are not directly provided by R's 'base' or 'stats' packages.
    These include e.g. measures like r-squared, intraclass correlation
    coefficient (Nakagawa, Johnson & Schielzeth (2017)
    <doi:10.1098/rsif.2017.0213>), root mean squared error or functions to
    check models for overdispersion, singularity or zero-inflation and
    more. Functions apply to a large variety of regression models,
    including generalized linear models, mixed effects models and Bayesian
    models. References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.
Collection of econometric functions for performance and risk
    analysis. In addition to standard risk and performance metrics, this
    package aims to aid practitioners and researchers in utilizing the latest
    research in analysis of non-normal return streams.  In general, it is most
    tested on return (rather than price) data on a regular scale, but most
    functions will work with irregular return data as well, and increasing
    numbers of functions will work with P&L or price data where possible.
Functions for generating variants of curves:
    restricted cubic spline, periodic restricted cubic spline,
    periodic cubic spline. Periodic splines can be used to model data
    that has periodic nature / seasonality.
Provides a dataset containing properties for chemical elements.
    Helper functions are also provided to access some atomic properties.
An enterprise-targeted scalable and UI-standardized 'shiny' framework 
    including a variety of developer convenience functions with the goal of both 
    streamlining robust application development while assisting with creating a 
    consistent user experience regardless of application or developer.
A framework for building enterprise, scalable and UI-standardized 'shiny' applications.  
    It brings enhanced features such as 'bootstrap' v4 <https://getbootstrap.com/docs/4.0/getting-started/introduction/>,
    additional and enhanced 'shiny' modules, customizable UI features, as well as an enhanced application file
    organization paradigm. This update allows developers to harness the ability to build powerful applications and 
    enriches the 'shiny' developers' experience when building and  maintaining applications.
A Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. 
    Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) <https://www.ssrn.com/abstract=4306129>.
Perform Exact or Asymptotic permutation tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].
This version of the permutational algorithm generates a
        dataset in which event and censoring times are conditional on
        an user-specified list of covariates, some or all of which are
        time-dependent.
Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.
Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Implements an extension of the Chacko chi-square test for
    ordered vectors (Chacko, 1966, <https://www.jstor.org/stable/25051572>).
    Our extension brings the Chacko test to the computer age by implementing
    a permutation test to offer a numeric estimate of the p-value, which is
    particularly useful when the analytic solution is not available.
Provides tools for statistical testing of correlation coefficients through robust 
    permutation method and large sample approximation method. Tailored to different types 
    of correlation coefficients including Pearson correlation coefficient, weighted 
    Pearson correlation coefficient, Spearman correlation coefficient, and Lin's 
    concordance correlation coefficient.The robust permutation test controls type I error
    under general scenarios when sample size is small and two variables are dependent but 
    uncorrelated. The large sample approximation test generally controls type I error when
    the sample size is large (>200).
An add-on to the 'party' package, with a faster implementation 
   of the partial-conditional permutation importance for random forests. The 
   standard permutation importance is implemented exactly the same as in 
   the 'party' package. The conditional permutation importance can be 
   computed faster, with an option to be backward compatible to the 'party' 
   implementation. The package is compatible with random forests fit using the 
   'party' and the 'randomForest' package. The methods are described in
   Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and 
   Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.
Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).
Provides randomization using permutation for applications.  To provide a Quality Control (QC) check, QC samples can be randomized within strata.  A second function allows for the ability to ”switch” samples to meet set requirements and perform a certain amount of minimization on these switches.  The functions are flexible for users by specifying strata size and number of QC samples per strata.  The randomization meets the following requirements
    • QC sample requirements: QC samples not adjacent, QC
    samples from same mother must follow certain patterns.
    • Matched sample sets must be within a single strata, and next
    to each other.
Implements the algorithm of Christensen (2024) <doi:10.1214/22-BA1353> for estimating marginal likelihoods via permutation counting.
The permubiome R package was created to perform a permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in a pairwise manner, after a random shuffling of data into the different groups of study with a prior selection of the microbiome features with the largest variation among groups. Previous to the permutation test itself, data can be normalized according to different methods proposed to handle microbiome data ('proportions' or 'Anders'). The median-based differences between groups resulting from the multiple simulations are fitted to a normal distribution with the aim to calculate their significance. A multiple testing correction based on Benjamini-Hochberg method (fdr) is finally applied to extract the differentially presented features between groups of your dataset. LATEST UPDATES: v1.1 and olders incorporates function to parse COLUMN format; v1.2 and olders incorporates -optimize- function to maximize evaluation of features with largest inter-class variation; v1.3 and olders includes the -size.effect- function to perform estimation statistics using the bootstrap-coupled approach implemented in the 'dabestr' (>=0.3.0) R package. Current v1.3.2 fixed bug with "Class" recognition and updated 'dabestr' functions.
Functions to compute p-values based on permutation tests. Regression, ANOVA and ANCOVA, omnibus F-tests, marginal unilateral and bilateral t-tests are available. Several methods to handle nuisance variables are implemented (Kherad-Pajouh, S., & Renaud, O. (2010) <doi:10.1016/j.csda.2010.02.015> ; Kherad-Pajouh, S., & Renaud, O. (2014) <doi:10.1007/s00362-014-0617-3> ; Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) <doi:10.1016/j.neuroimage.2014.01.060>). An extension for the comparison of signals issued from experimental conditions (e.g. EEG/ERP signals) is provided. Several corrections for multiple testing are possible, including the cluster-mass statistic (Maris, E., & Oostenveld, R. (2007) <doi:10.1016/j.jneumeth.2007.03.024>) and the threshold-free cluster enhancement (Smith, S. M., & Nichols, T. E. (2009) <doi:10.1016/j.neuroimage.2008.03.061>).
Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs.
Manipulates invertible functions from a finite set to
             itself.  Can transform from word form to cycle form and
             back.  To cite the package in publications please use
             Hankin (2020) "Introducing the permutations R package",
             SoftwareX, volume 11 <doi:10.1016/j.softx.2020.100453>.
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Helps you determine the analysis window to use when analyzing densely-sampled
    time-series data, such as EEG data, using permutation testing (Maris & Oostenveld, 2007)
    <doi:10.1016/j.jneumeth.2007.03.024>. These permutation tests can help identify the timepoints
    where significance of an effect begins and ends, and the results can be plotted in various
    types of heatmap for reporting. Mixed-effects models are supported using an implementation of
    the approach by Lee & Braun (2012) <doi:10.1111/j.1541-0420.2011.01675.x>.
Implements permutation tests for any test statistic and randomization scheme and constructs associated confidence intervals as described in Glazer and Stark (2024) <doi:10.48550/arXiv.2405.05238>.
A coding assistant using Perplexity's Large Language Models
    <https://www.perplexity.ai/> API. A set of functions and 'RStudio' add-ins
    that aim to help R developers.
Provides tools for fitting periodic coefficients regression models to data where periodicity plays a crucial role. It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) <doi:10.1080/03610918.2024.2314662>.
Tools that allow developers to write functions for prediction
    error estimation with minimal programming effort and assist users with
    model selection in regression problems.
Examples for integrating package 'perry' for prediction error estimation into regression models.
Tailoring the optimal biomarker(s) for disease screening or diagnosis based on subjects' individual characteristics.
Calculate the optimal vertex partition of a graph using the persistence as objective function.
  These subroutines have been used in Avellone et al. <doi:10.1007/s10288-023-00559-z>.
Automated identification of printed array positions from high content 
    microscopy images and the export of those positions as individual images 
    written to output as multi-layered tiff files.
Provides functions for fitting and validation of models for subgroup
    identification and personalized medicine / precision medicine under the general subgroup
    identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>.
    This package is intended for use for both randomized controlled trials and
    observational studies and is described in detail in Huling and Yu (2021) 
    <doi:10.18637/jss.v098.i05>.
Implements the methodology of Huling, Smith, and 
    Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification 
    for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part 
    modeling framework to handle semi-continuous data by separately modeling the positive part 
    of the outcome and an indicator of whether each outcome is positive, but still results in a 
    single treatment rule. High dimensional data is handled with a cooperative lasso penalty, 
    which encourages the coefficients in the two models to have the same sign.
Functions to setup a personal R package that attaches given
    libraries and exports personal helper functions.
Implements classical and contemporary utility-analysis methods 
    for personnel selection, organised by criterion scale (classification or
    continuous/monetary) and selection structure (compensatory or 
    multiple-hurdle). Methods include Taylor-Russell classification 
    (Taylor and Russell, 1939, <doi:10.1037/h0057079>), 
    Brogden-Cronbach-Gleser monetary utility (Brogden, 1949, 
    <doi:10.1111/j.1744-6570.1949.tb01397.x>), Schmidt-Hunter-Pearlman 
    intervention utility (Schmidt and others, 1979, 
    <doi:10.1037/0021-9010.64.6.609>), Sturman comprehensive cascade 
    (Sturman, 2001, <doi:10.1108/eb029072>), Thomas-Owen-Gunst 
    multivariate classification (Thomas and others, 1977, 
    <doi:10.3102/10769986002001055>), compensatory versus multiple-hurdle 
    simulation (Ock and Oswald, 2018, <doi:10.1027/1866-5888/a000205>), 
    AUC-to-effect-size conversions (Salgado, 2018, 
    <doi:10.5093/ejpalc2018a5>), Pareto frontiers for validity-diversity 
    trade-offs, and Monte Carlo uncertainty propagation.
An R-package-version of an open online science-based personality
    test from <https://openpsychometrics.org/tests/IPIP-BFFM/>,
    providing a better-designed interface and a more detailed report.
    The core command launch_test() opens a personality test in your browser,
    and generates a report after you click "Submit". In this report, your results
    are compared with other people's, to show what these results mean.
    Other people's data is from <https://openpsychometrics.org/_rawdata/BIG5.zip>.
Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.
An 'htmlwidgets' binding for the 'FINOS Perspective'
    <https://perspective-dev.github.io/> library, a high-performance
    'WebAssembly'-powered data visualization engine. Provides interactive pivot
    tables, cross-tabulations, and multiple chart types (bar, line, scatter,
    heatmap, and more) that run entirely in the browser.
    Supports self-service analytics with drag-and-drop column selection,
    group-by/split-by pivoting, filtering, sorting, aggregation, and computed
    expressions. Works in 'RStudio' Viewer, 'R Markdown', 'Quarto', and
    'Shiny' with streaming data updates via proxy interface.
Provides a standardized framework to support the selection
    and evaluation of parametric survival models for time-to-event data.
    Includes tools for visualizing survival data, checking proportional
    hazards assumptions (Grambsch and Therneau, 1994,
    <doi:10.1093/biomet/81.3.515>), comparing parametric (Ishak and
    colleagues, 2013, <doi:10.1007/s40273-013-0064-3>), spline (Royston
    and Parmar, 2002, <doi:10.1002/sim.1203>) and cure models, examining
    hazard functions, and evaluating model extrapolation. Methods are
    consistent with recommendations in the NICE Decision Support Unit
    Technical Support Documents (14 and 21
    <https://sheffield.ac.uk/nice-dsu/tsds/survival-analysis>). Results
    are structured to facilitate integration into decision-analytic
    models, and reports can be generated with 'rmarkdown'. The package
    builds on existing tools including 'flexsurv' (Jackson, 2016,
    <doi:10.18637/jss.v070.i08>)) and 'flexsurvcure' for estimating cure
    models.
Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results.
The perturbR() function incrementally perturbs network edges (using the   rewireR function)and compares the resulting community detection solutions 
    from the rewired networks with the solution found for the original network. 
    These comparisons aid in understanding the stability of the 
    original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.
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 Peru, such as holidays, economic indicators, and international 
    demographic and geopolitical indicators. Additionally, the package includes 
    curated datasets focused on Peru, covering topics such as administrative 
    divisions, electoral data, demographics, biodiversity and educational classifications. 
    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/>.
Provides tools to match plant species names against the official 
    threatened species list of Peru (Supreme Decree 043-2006-AG, 2006).
    Implements a hierarchical matching pipeline with exact, fuzzy, and suffix 
    matching algorithms to handle naming variations and taxonomic changes.
    Supports both the original 2006 nomenclature and updated taxonomic names,
    allowing users to check protection status regardless of nomenclatural changes
    since the decree's publication. Threat categories follow International Union for Conservation of Nature standards 
    (Critically Endangered, Endangered, Vulnerable, Near Threatened).
Provides a curated taxonomic backbone of mammal species recorded in Peru,
    based on the checklist published by Pacheco and collaborators (2021)
    <doi:10.15381/rpb.v28i4.21019>. The package includes standardized species
    data, occurrence records by ecological regions, endemic status, and tools
    for validating and matching scientific names through exact and approximate
    string procedures. It is designed as a lightweight and reliable reference
    for ecological, environmental, biogeographical, and conservation workflows
    that require verified species information for Peruvian mammals.
A friendly API for sequence iteration and set comprehension.
Access the data of the 'Catalogue of the Timber Forest Species of the Peruvian Amazon' Vásquez Martínez, R., & Rojas Gonzáles, R.D.P.(2022)<doi:10.21704/rfp.v37i3.1956>.
Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the 'arules' package by Hahsler et al. (2025)<doi:10.32614/CRAN.package.arules> and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science.
Automatic estimation of number of principal components in PCA
    with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse
    "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood"
    (2017) <doi:10.1080/10618600.2017.1340302>.
Computes the Danish Pesticide Load Indicator as described in Kudsk et al. (2018) <doi:10.1016/j.landusepol.2017.11.010> and Moehring et al. (2019) <doi:10.1016/j.scitotenv.2018.07.287> for pesticide use data. Additionally offers the possibility to directly link pesticide use data to pesticide properties given access to the Pesticide properties database (Lewis et al., 2016) <doi:10.1080/10807039.2015.1133242>.
Set of tools to automatize extraction of data on pests from 'EPPO
    Data Services' and 'EPPO Global Database' and to put them into tables with
    human readable format. Those function use 'EPPO database API', thus you 
    first need to register on <https://data.eppo.int> (free of charge).
    Additional helpers allow to download, check and connect to
    'SQLite EPPO database'.
A collection of R functions that are widely used by the Petersen
    Lab. Included are functions for various purposes, including evaluating the
    accuracy of judgments and predictions, performing scoring of assessments,
    generating correlation matrices, conversion of data between various types,
    data management, psychometric evaluation, extensions related to latent
    variable modeling, various plotting capabilities, and other miscellaneous
    useful functions. By making the package available, we hope to make our
    methods reproducible and replicable by others and to help others perform
    their data processing and analysis methods more easily and efficiently. The
    codebase is provided in Petersen (2025) <doi:10.5281/zenodo.7602890> and on
    'CRAN': <doi: 10.32614/CRAN.package.petersenlab>. The package is described
    in "Principles of Psychological Assessment: With Applied Examples in R"
    (Petersen, 2024, 2025a) <doi:10.1201/9781003357421>,
    <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589> and in "Fantasy
    Football Analytics: Statistics, Prediction, and Empiricism Using R"
    (Petersen, 2025b).
Two-sample power-enhanced mean tests, covariance tests, and simultaneous tests on mean vectors and covariance matrices for high-dimensional data. Methods of these PE tests are presented in 
    Yu, Li, and Xue (2022) <doi:10.1080/01621459.2022.2126781>; 
    Yu, Li, Xue, and Li (2022) <doi:10.1080/01621459.2022.2061354>.
Automates sum coding (also known as effect coding) for 
    Ordinary Least Squares (OLS) regression models. This approach is 
    specifically designed to handle seasonal time series and categorical 
    variables by comparing each group to the grand mean, rather than a 
    single baseline category. This ensures that the intercept represents 
    the unweighted grand mean of the dependent variable. For a comprehensive 
    overview of contrast coding systems, see the UCLA Advanced Research 
    Computing documentation (2021) <https://stats.oarc.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/>.
Reproducible, programmatic retrieval of survey datasets from the
    Pew Research Center.
Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.
Eco-phylogenetic and community phylogenetic analyses.
    Keeps community ecological and phylogenetic data matched up and
    comparable using 'comparative.comm' objects. Wrappers for common
    community phylogenetic indices ('pez.shape', 'pez.evenness',
    'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
    of Cavender-Bares (2004) correlation of phylogenetic and
    ecological matrices ('fingerprint.regression'). Phylogenetic
    Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
    Helmus (2011) and Rafferty & Ives (2013). Simulation of null
    assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').
Implements Penalized Fast Causal Inference (PFCI), a two-stage
    causal structure learning procedure for high-dimensional settings with
    potential latent variables and selection bias. In the first stage,
    neighborhood selection via the Lasso constructs a sparse undirected
    skeleton. In the second stage, the Fast Causal Inference (FCI) algorithm
    orients edges on this reduced graph, producing a Partial Ancestral Graph
    (PAG) that accounts for latent confounders. The method is consistent
    under sparsity assumptions and substantially faster than standard FCI
    and RFCI in high dimensions. See Pal, Ghosh, and Yang (2025)
    <doi:10.48550/arXiv.2507.00173> for the underlying theory.
Implementations of Power Fuzzy Clustering (PFC) and Power Fuzzy
    Cluster-wise Regression (PFCR) for multivariate data. The package supports
    Minkowski distances, with the L1 case solved via iteratively re-weighted
    least squares and the case p > 1 solved via coordinate-wise root finding,
    as well as an adaptive, regularised Mahalanobis distance with per-cluster
    covariance matrices. Both plain fuzzy clustering and cluster-wise linear
    regression are provided. The corresponding paper can be found at Nguyen P.T., Tortora C., and Punzo A. (2026) <doi:10.1109/TFUZZ.2026.3683998>.
Performs smoothed (and non-smoothed) principal/independent components analysis
  of functional data. Various functional pre-whitening approaches are implemented as 
  discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional 
  settings", <doi:10.1002/sta4.516>. Further whitening representations of functional 
  data can be derived in terms of a few principal components, providing an avenue 
  to explore hidden structures in low dimensional settings: see Vidal,
  Rosso and Aguilera (2021) “Bi-smoothed functional independent component 
  analysis for EEG artifact removal”, <doi:10.3390/math9111243>.
Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to 
    Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>,
    Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>,
    Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>,
    Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>,
    Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and
    Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.
Implementation of commonly used penalized functional linear regression models, including the Smooth and Locally Sparse (SLoS) method by Lin et al. (2016) <doi:10.1080/10618600.2016.1195273>, Nested Group bridge Regression (NGR) method by Guan et al. (2020) <doi:10.1080/10618600.2020.1713797>, Functional Linear Regression That's interpretable (FLIRTI) by James et al. (2009) <doi:10.1214/08-AOS641>, and the Penalized B-spline regression method.
Builds and runs 'c++' code for classes that encapsulate state space model, particle filtering algorithm pairs.
    Algorithms include the Bootstrap Filter from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR filter, 
    the Auxiliary Particle Filter from Pitt et al (1999) <doi:10.2307/2670179>, and a variety of Rao-Blackwellized 
    particle filters inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>. For more details on the 'c++' library 
    'pf', see Brown (2020) <doi:10.21105/joss.02599>.
Provides tools to import, clean, filter, and prepare Project
    FeederWatch data for analysis. Includes functions for taxonomic
    rollup, easy filtering, zerofilling, merging in site metadata, and more.
    Project FeederWatch data comes from 
    <https://feederwatch.org/explore/raw-dataset-requests/>.
R tools for reconstructing paleo food webs from species traits and size rules.
Provides access to a high performant random distribution
    sampler for the Polya Gamma Distribution using either 'C++' headers for 
    'Rcpp' or 'RcppArmadillo' and 'R'.
Density, distribution function, quantile function, 
    and random generation function based on Kittipong Klinjan,Tipat Sottiwan and Sirinapa Aryuyuen (2024)<DOI:10.28919/cmbn/8833>.
This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.
Provides tools for designing and analyzing agricultural
    experiments. It includes functions for generating randomized
    treatment layouts for standard experimental designs such as
    Completely Randomized Design (CRD), Randomized Block Design (RBD),
    Latin Square Design (LSD), Factorial Randomized Block Design
    (FRBD), split-plot design, and strip-plot design. The package
    implements one-factor and two-factor analysis of variance (ANOVA)
    and offers multiple comparison procedures, including Least
    Significant Difference (lsd), Tukey, and Duncan tests, to compare
    treatment means in single-factor and factorial experiments. The
    methods follow classical experimental design principles described 
    in Gomez and Gomez (1984, Statistical Procedures for Agricultural Research, John Wiley & Sons, New York).
Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.
Perform simultaneous estimation and variable selection for correlated bivariate
    mixed outcomes (one continuous outcome and one binary outcome per cluster) using
    penalized generalized estimating equations. In addition, clustered Gaussian and binary
    outcomes can also be modeled. The SCAD, MCP, and LASSO penalties are supported.
    Cross-validation can be performed to find the optimal regularization parameter(s).
A thin wrapper over 'PLINK' 2's core libraries which provides an R
    interface for reading .pgen files.  A minimal .pvar loader is also
    included.  Chang et al. (2015) <doi:10.1186/s13742-015-0047-8>.
Set of tools for reading, writing and transforming spatial and seasonal data, model selection and specific statistical tests for ecologists. It includes functions to interpolate regular positions of points between landmarks, to discretize polylines into regular point positions, link distant observations to points and convert a bounding box in a spatial object. It also provides miscellaneous functions for field ecologists such as spatial statistics and inference on diversity indexes, writing data.frame with Chinese characters.
Nonparametric density estimation for (hyper)spherical data by means of a parametrically guided kernel estimator (Alonso-Pena et al. (2024) <doi:10.1111/sjos.12737>. The package also allows the data-driven selection of the smoothing parameter and the representation of the estimated density for circular and spherical data. Estimators of the density without guide can also be obtained.
Estimation of panel models for glm-like models:
             this includes binomial models (logit and probit), count models (poisson and negbin)
	     and ordered models (logit and probit), as described in:
             Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7,
	     Hsiao (2014) Analysis of Panel Data  <doi:10.1017/CBO9781139839327> and
	     Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.
Implements recursive construction methods for balanced incomplete block designs (BIBDs), their second generation, resolvable BIBDs (RBIBDs), and uniform designs (UDs) derived from projective geometries over GF(2). It enables extraction of nested structures in multiple stages and supports recursive resolution processes, as introduced in Boudraa et al. (2013).
Carries out model-based clustering or classification using parsimonious Gaussian mixture models. McNicholas and Murphy (2008) <doi:10.1007/s11222-008-9056-0>, McNicholas (2010) <doi:10.1016/j.jspi.2009.11.006>, McNicholas and Murphy (2010) <doi:10.1093/bioinformatics/btq498>, McNicholas et al. (2010) <doi:10.1016/j.csda.2009.02.011>.
Evaluation of the pdf and the cdf of the univariate,
        noncentral, p-generalized normal distribution. Sampling from
        the univariate, noncentral, p-generalized normal distribution
        using either the p-generalized polar method, the p-generalized
        rejecting polar method, the Monty Python method, the Ziggurat
        method or the method of Nardon and Pianca. The package also
        includes routines for the simulation of the bivariate,
        p-generalized uniform distribution and the simulation of the
        corresponding angular distribution.
Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).
Implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.
Provides functions to aid the identification of probable/possible
    duplicates in Plant Genetic Resources (PGR) collections using
    'passport databases' comprising of information records of each constituent
    sample. These include methods for cleaning the data, creation of a
    searchable Key Word in Context (KWIC) index of keywords associated with
    sample records and the identification of nearly identical records with
    similar information by fuzzy, phonetic and semantic matching of keywords.
Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.
Create PostgreSQL statements/scripts from R, optionally executing the SQL statements.
    Common SQL operations are included, although not every configurable option is available at this time. 
    SQL output is intended to be compliant with PostgreSQL syntax specifications. PostgreSQL documentation is available here
    <https://www.postgresql.org/docs/current/index.html>.
Reproducible cleaning of biomedical laboratory data using visualization, error correction, and transformation methods implemented as interactive R notebooks. A detailed description of the methods ca ben found in Malkusch, S., Hahnefeld, L., Gurke, R. and J. Lotsch. (2021) <doi:10.1002/psp4.12704>.
The purpose of 'PH1XBAR' is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi: 10.1080/00224065.2022.2139783>.
An implementation of Bayesian single-arm phase II design methods for binary outcome based on posterior
  probability (Thall and Simon (1994) <doi:10.2307/2533377>) and predictive probability (Lee and Liu (2008) <doi:10.1177/1740774508089279>).
Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).
Fits right-truncated meta-analysis (RTMA), a bias correction for
  the joint effects of p-hacking (i.e., manipulation of results within studies
  to obtain significant, positive estimates) and traditional publication bias
  (i.e., the selective publication of studies with significant, positive
  results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for
  p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication
  bias alone, p-hacking that favors significant, positive results (termed
  "affirmative") can distort the distribution of affirmative results. To
  bias-correct results from affirmative studies would require strong assumptions
  on the exact nature of p-hacking. In contrast, joint p-hacking and publication
  bias do not distort the distribution of published nonaffirmative results when
  there is stringent p-hacking (e.g., investigators who hack always eventually
  obtain an affirmative result) or when there is stringent publication bias
  (e.g., nonaffirmative results from hacked studies are never published). This
  means that any published nonaffirmative results are from unhacked studies.
  Under these assumptions, RTMA involves analyzing only the published
  nonaffirmative results to essentially impute the full underlying distribution
  of all results prior to selection due to p-hacking and/or publication bias.
  The package also provides diagnostic plots described in Mathur (2022).
Allows for estimation of phylogenetic trees and networks
    using Maximum Likelihood, Maximum Parsimony, distance methods and
    Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, 
    model selection and visualization of phylogenetic networks as described in
    Schliep et al. (2017).
Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.
Enhanced RTF wrapper written in R for use with existing R tables
    packages such as 'Huxtable' or 'GT'. This package fills a gap where tables in
    certain packages can be written out to RTF, but cannot add certain metadata
    or features to the document that are required/expected in a report for a
    regulatory submission, such as multiple levels of titles and footnotes,
    making the document landscape, and controlling properties such as margins.
The 'pharmaverse' is a set of packages that compose multiple pathways 
    through clinical data generation and reporting in the pharmaceutical industry.
    This package is designed to guide users to our work-spaces on 'GitHub', 'Slack' 
    and 'LinkedIn' as well as our website and examples.
    Learn more about the 'pharmaverse' at <https://pharmaverse.org>.
A set of Analysis Data Model (ADaM) datasets constructed
    using the Study Data Tabulation Model (SDTM) datasets contained in the
    'pharmaversesdtm' package and the template scripts from the 'admiral'
    family of packages. ADaM dataset specifications are described in the
    CDISC ADaM implementation guide, accessible by creating a free account
    on <https://www.cdisc.org/>.
A set of Analysis Data Model (ADaM) datasets constructed by 
  modifying the ADaM datasets in the 'pharmaverseadam' package to meet J&J Innovative Medicine's
  standard data structure for Clinical and Statistical Programming.
A set of raw datasets used to create SDTM domains in 
   'pharmaversesdtm' package.
A set of Study Data Tabulation Model (SDTM) datasets from the
    Clinical Data Interchange Standards Consortium (CDISC) pilot project
    used for testing and developing Analysis Data Model (ADaM) datasets
    inside the pharmaverse family of packages. SDTM dataset specifications
    are described in the CDISC SDTM implementation guide, accessible by
    creating a free account on <https://www.cdisc.org/>.
A set of Study Data Tabulation Model (SDTM) datasets constructed by 
  modifying the 'pharmaversesdtm' package to meet J&J Innovative Medicine's
  standard data structure for Clinical and Statistical Programming.
Interface to the 'Pharmpy' 'pharmacometrics' library. The 'Reticulate' package is used to interface Python from R.
Compiles functions to trim, bin, visualise, and analyse activity/sleep time-series data collected from the Drosophila Activity Monitor (DAM) system (Trikinetics, USA). The following methods were used to compute periodograms - Chi-square periodogram: Sokolove and Bushell (1978) <doi:10.1016/0022-5193(78)90022-X>, Lomb-Scargle periodogram: Lomb (1976) <doi:10.1007/BF00648343>, Scargle (1982) <doi:10.1086/160554> and Ruf (1999) <doi:10.1076/brhm.30.2.178.1422>, and Autocorrelation: Eijzenbach et al. (1986) <doi:10.1111/j.1440-1681.1986.tb00943.x>. Identification of activity peaks is done after using a Savitzky-Golay filter (Savitzky and Golay (1964) <doi:10.1021/ac60214a047>) to smooth raw activity data. Three methods to estimate anticipation of activity are used based on the following papers - Slope method: Fernandez et al. (2020) <doi:10.1016/j.cub.2020.04.025>, Harrisingh method: Harrisingh et al. (2007) <doi:10.1523/JNEUROSCI.3680-07.2007>, and Stoleru method: Stoleru et al. (2004) <doi:10.1038/nature02926>. Rose plots and circular analysis are based on methods from - Batschelet (1981) <ISBN:0120810506> and Zar (2010) <ISBN:0321656865>.
Provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including:
  - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) <doi:10.1200/PO.20.00257>),
  - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) <doi:10.1002/pst.1864>),
  - EffTox (Thall, P. F., & Cook, J. D. (2004) <doi:10.1111/j.0006-341X.2004.00218.x>),
  - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) <doi:10.1080/10543406.2020.1818250>),
  - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) <doi:10.1177/0962280220977009>),
  - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) <doi:10.1002/sim.7428>),
  - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>),
  - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) <doi:10.1002/sim.8922>).
 Includes flexible simulation parameters that allow
 researchers to efficiently compute operating characteristics under
 various fixed and random trial scenarios and export the results.
Implements Bayesian phase I repeated measurement design that 
    accounts for multidimensional toxicity endpoints and longitudinal efficacy 
    measure from multiple treatment cycles. The package provides flags to 
    fit a variety of model-based phase I design, including 1 stage models with
    or without individualized dose modification, 3-stage models with or without
    individualized dose modification, etc. Functions are provided to recommend 
    dosage selection based on the data collected in the available patient cohorts 
    and to simulate trial characteristics given design parameters. 
    Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.
Estimation and inference for coefficients of linear EIV models with symmetric measurement errors. 
      The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are 
      based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates, 
      and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available. 
      The details of the method are described in Nghiem and Potgieter (2020) <doi:10.1093/biomet/asaa025> and Nghiem and Potgieter (2025) <doi:10.5705/ss.202022.0331>.
Functions to perform Bayesian inference on absorption time data for
             Phase-type distributions. The methods of Bladt et al (2003)
             <doi:10.1080/03461230110106435> and Aslett (2012)
             <https://www.louisaslett.com/PhD_Thesis.pdf> are provided.
General implementation of core function from phase-type
    theory. 'PhaseTypeR' can be used to model continuous and discrete
    phase-type distributions, both univariate and multivariate. The
    package includes functions for outputting the mean and (co)variance of
    phase-type distributions; their density, probability and quantile
    functions; functions for random draws; functions for
    reward-transformation; and functions for plotting the distributions as
    networks. For more information on these functions please refer to
    Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro
    (2019)
    <https://orbit.dtu.dk/en/publications/order-statistics-and-multivariate-discrete-phase-type-distributio>.
PHATE is a tool for visualizing high dimensional single-cell data
  with natural progressions or trajectories. PHATE uses a novel conceptual framework
  for learning and visualizing the manifold inherent to biological systems in which
  smooth transitions mark the progressions of cells from one state to another.
  To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
  stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology
  at <doi:10.1038/s41587-019-0336-3>.
Clustering analysis for sparse microbiome data, based on a Poisson hurdle model.
Provides permutation methods for testing in high-dimensional linear models.
    The tests are often robust against heteroscedasticity and non-normality 
    and usually perform well under anti-sparsity.
    See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.
A toolkit of functions to help: i) effortlessly transform collected data into a 
  publication ready format, ii) generate insightful visualizations from clinical data, iii) report
  summary statistics in a publication-ready format, iv) efficiently export, save and reload R objects
  within the framework of R projects.
Implementation of heatmaps that offers more control
    over dimensions and appearance.
Implement surrogate-assisted feature extraction (SAFE) and
             common machine learning approaches to train and validate phenotyping models.
             Background and details about the methods can be found at 
             Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>,
             Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and 
             Liao et al. (2015) <doi:10.1136/bmj.h1885>.
To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree.
Functions to calculate commonly used public health statistics and 
    their confidence intervals using methods approved for use in the production  
    of Public Health England indicators such as those presented via Fingertips 
    (<https://fingertips.phe.org.uk/>). It provides functions for the generation 
    of proportions, crude rates, means, directly standardised rates, indirectly 
    standardised rates, standardised mortality ratios, slope and relative index
    of inequality and life expectancy. 
    Statistical methods are referenced in the following publications. 
    Breslow NE, Day NE (1987) <doi:10.1002/sim.4780080614>.
    Dobson et al (1991) <doi:10.1002/sim.4780100317>. 
    Armitage P, Berry G (2002) <doi:10.1002/9780470773666>.
    Wilson EB. (1927) <doi:10.1080/01621459.1927.10502953>.
    Altman DG et al (2000, ISBN: 978-0-727-91375-3).
    Chiang CL. (1968, ISBN: 978-0-882-75200-6).
    Newell C. (1994, ISBN: 978-0-898-62451-9).
    Eayres DP, Williams ES (2004) <doi:10.1136/jech.2003.009654>.
    Silcocks PBS et al (2001) <doi:10.1136/jech.55.1.38>.
    Low and Low (2004) <doi:10.1093/pubmed/fdh175>.
    Fingertips Public Health Technical Guide: <https://fingertips.phe.org.uk/profile/guidance/supporting-information/PH-methods/>.
Generates Weibull-parameterized estimates of phenology for any percentile of 
    a distribution using the framework established in Cooke (1979)
    <doi:10.1093/biomet/66.2.367>. Extensive testing against other 
    estimators suggest the weib_percentile() function is especially useful in 
    generating more accurate and less biased estimates of onset and offset 
    (Belitz et al. 2020) <doi:10.1111/2041-210X.13448>. Non-parametric 
    bootstrapping can be used to generate confidence intervals around those 
    estimates, although this is computationally expensive. Additionally, this 
    package offers an easy way to perform non-parametric bootstrapping to 
    generate confidence intervals for quantile estimates, mean estimates, 
    or any statistical function of interest.
Provides some easy-to-use functions for 
	spatial analyses of (plant-) phenological data 
	sets and satellite observations of vegetation.
Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) <doi:10.1086/676622> to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) <doi:10.1007/BF00275224>, bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares <doi:10.3732/apps.1400104>.
Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) <doi:10.1007/s00484-016-1161-8>.
Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.
Provides some easy-to-use functions for time series
        analyses of (plant-) phenological data sets. These functions
        mainly deal with the estimation of combined phenological time
        series and are usually wrappers for functions that are already
        implemented in other R packages adapted to the special
        structure of phenological data and the needs of phenologists.
        Some date conversion functions to handle Julian dates are also
        provided.
Programmatic interface to the 'PhenoCam' web services (<https://phenocam.nau.edu/webcam>).
    Allows for easy downloading of 'PhenoCam' data directly to your R workspace
    or your computer and provides post-processing routines for consistent and easy
    timeseries outlier detection, smoothing and estimation of phenological transition dates.
    Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.
Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.
The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and 
    growing season dividing method and a practical snow elimination method 
    based on Whittaker were proposed. 7 curve fitting methods and 4 phenology 
    extraction methods were provided. Parameters boundary are considered for 
    every curve fitting methods according to their ecological meaning. 
    And 'optimx' is used to select best optimization method for different 
    curve fitting methods.
    Reference:
    Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction 
    package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>;
    Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains 
    the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness 
    Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. 
    <doi:10.1029/2020JG005636>;
    Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing 
    global MODIS EVI time series on the Google Earth Engine. 
    ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24;
    Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology 
    on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). 
    Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.
Fit a time-series model 
    to a crop phenology data, such as time-series rice canopy height.
    This package returns the model parameters as the summary statistics of crop phenology,
    and these parameters will be useful to characterize the growth pattern of each cultivar and 
    predict manually-measured traits, such as days to heading and biomass.
    Please see Taniguchi et al. (2022) <doi:10.3389/fpls.2022.998803> and 
    Taniguchi et al. (2025) <doi: 10.3389/frai.2024.1477637> for detail.
    This package has been designed for scientific use.  
    Use for commercial purposes shall not be allowed.
Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.
This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.
A collection of functions to process digital images, depict greenness index trajectories and extract relevant phenological stages.
The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.
Provides tools for processing, analyzing, and visualizing spectral data 
    collected from 3D laser-based scanning systems. Supports applications in agriculture, 
    forestry, environmental monitoring, industrial quality control, and biomedical research. 
    Enables evaluation of plant growth, productivity, resource efficiency, disease 
    management, and pest monitoring. Includes statistical methods for extracting insights 
    from multispectral and hyperspectral data and generating publication-ready visualizations. 
    See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and 
    Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.
Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.
Phenotype study cohorts in data mapped to the 
    Observational Medical Outcomes Partnership Common Data Model. Diagnostics 
    are run at the database, code list, cohort, and population level to assess 
    whether study cohorts are ready for research.
Methods to calculate and present 'PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.
Use phenotype risk scores based on linked clinical and genetic data
  to study Mendelian disease and rare genetic variants. See Bastarache et al.
  2018 <doi:10.1126/science.aal4043>.
Provides tools for the test for the comparison of survival curves, the evaluation of the goodness-of-fit and the predictive capacity of the proportional hazards model.
Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) <doi:10.1101/2020.06.15.20131458>.
Computes 'Owen's T' function or the bivariate normal integral
    using one of the following methods: the modified Euler's arctangent
    series, tetrachoric series, or Vasicek's series. For the methodology,
    see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026>, or the revised
    postprint version with corrected contents and improved typography at
    <doi:10.48550/arXiv.2312.00011>. Also see the alternative approach by
    Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.
Analysis of terms in linear, generalized and mixed linear models, 
	on the basis of multiple comparisons of factor contrasts.  Specially suited 
	for the analysis of interaction terms.
Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.
Control Philips Hue smart lighting. Use this package to
    connect to a Hue bridge on your local network (remote authentication
    not yet supported) and control your smart lights through the Philips
    Hue API. All API V1 endpoints are supported. See API documentation at
    <https://developers.meethue.com/>.
Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.
Implements the phinterval vector class for representing time
    spans that may contain gaps (disjoint intervals) or be empty. This class
    generalizes the 'lubridate' package's interval class to support vectorized
    set operations (intersection, union, difference, complement) that always
    return a valid time span, even when disjoint or empty intervals are created.
Perform a differential analysis at pathway level based on 
             metabolite quantifications and information on pathway metabolite 
             composition. The method, described in Guilmineau et al (2025)
             <doi:10.1186/s12859-025-06118-z> is based on a Principal Component 
             Analysis step and on a linear mixed model.
  Automatic query of metabolic pathways is also implemented.
Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as
    described in "Development and Validation of the Phoenix Criteria for
    Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt,
    Russell et al. (2024) <doi:10.1001/jama.2024.0196> (Drs. Sanchez-Pinto
    and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr.
    Russell contributed equally to the manuscript), "International Consensus
    Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson,
    Sorce, Argent, et al. (2024) <doi:10.1001/jama.2024.0179> (Drs Schlapbach,
    Watson, Sorce, and Argent contributed equally) and the application note
    "phoenix: an R package and Python module for calculating the Phoenix
    pediatric sepsis score and criteria" by DeWitt, Russell, Rebull,
    Sanchez-Pinto, and Bennett (2024) <doi:10.1093/jamiaopen/ooae066>.
Convert English letters to numbers or numbers to English
    letters as on a telephone keypad. When converting letters to numbers,
    a character vector is returned with "A," "B," or "C" becoming 2, "D,"
    "E", or "F" becoming 3, etc. When converting numbers to letters, a
    character vector is returned with multiple elements (i.e., "2" becomes
    a vector of "A," "B," and "C").
It provides users with functions to parse International Phonetic
  Alphabet (IPA) transcriptions into individual phones (tokenisation) based on
  default IPA symbols and  optional user specified multi-character phones. The
  tokenised transcriptions can be used for obtaining counts of phones or for
  searching for words matching phonetic patterns.
Provides an interface to the 'GenderAPI.io' Phone Number Validation & Formatter API (<https://www.genderapi.io>) for validating international phone numbers, detecting number type (mobile, landline, Voice over Internet Protocol (VoIP)), retrieving region and country metadata, and formatting numbers to E.164 or national format. Designed to simplify integration into R workflows for data validation, Customer Relationship Management (CRM) data cleaning, and analytics tasks. Full documentation is available at <https://www.genderapi.io/docs-phone-validation-formatter-api>.
There are a lot of different typical tasks that have to be solved during phonetic research and experiments. This includes creating a presentation that will contain all stimuli, renaming and concatenating multiple sound files recorded during a session, automatic annotation in 'Praat' TextGrids (this is one of the sound annotation standards provided by 'Praat' software, see Boersma & Weenink 2020 <https://www.fon.hum.uva.nl/praat/>), creating an html table with annotations and spectrograms, and converting multiple formats ('Praat' TextGrid, 'ELAN', 'EXMARaLDA', 'Audacity', subtitles '.srt', and 'FLEx' flextext). All of these tasks can be solved by a mixture of different tools (any programming language has programs for automatic renaming, and Praat contains scripts for concatenating and renaming files, etc.). 'phonfieldwork' provides a functionality that will make it easier to solve those tasks independently of any additional tools. You can also compare the functionality with other packages: 'rPraat' <https://CRAN.R-project.org/package=rPraat>, 'textgRid' <https://CRAN.R-project.org/package=textgRid>.
Provides a collection of phonetic algorithms including
    Soundex, Metaphone, NYSIIS, Caverphone, and others.  The package is
    documented in <doi:10.18637/jss.v095.i08>.
Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels.
Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.
Use 'Phosphor' icons in 'shiny' applications or 'rmarkdown' documents. Icons are available in
    5 different weights and can be customized by setting color, size, orientation and more.
Definitions of classes, methods, operators and functions for use 
    in photobiology and radiation meteorology and climatology. Calculation of
    effective (weighted) and not-weighted irradiances/doses, fluence rates,
    transmittance, reflectance, absorptance, absorbance and diverse ratios and 
    other derived quantities from spectral data. Local maxima and minima: peaks,
    valleys and spikes. Conversion between energy-and photon-based units. 
    Wavelength interpolation. Colours and vision. This package is part of the 
    'r4photobiology' suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral 'transmittance' data for frequently used filters and 
    similar materials. Plastic sheets and films; photography filters; 
    theatrical gels; machine-vision filters; various types of window glass;
    optical glass and some laboratory plastics and glassware. Spectral 
    reflectance data for frequently encountered materials. Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Functions for reading, and in some cases writing, foreign files 
    containing spectral data from spectrometers and their associated software, 
    output from daylight simulation models in common use, and some spectral 
    data repositories. As well as functions for exchange of spectral data with 
    other R packages. Part of the 'r4photobiology' suite, 
    Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used lamps including 
    bulbs and flashlights based on led emitting diodes (LEDs) but excluding
    LEDs available as electronic components. Original spectral irradiance data 
    for incandescent-, LED- and discharge lamps are included. They are 
    complemented by data on the effect of temperature on the emission by 
    fluorescent tubes. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) 
    <doi:10.19232/uv4pb.2015.1.14>.
Spectral emission data for some frequently used light emitting
    diodes available as electronic components. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Provides functions for quantifying visible (VIS) and ultraviolet
    (UV) radiation in relation to the photoreceptors Phytochromes,
    Cryptochromes, and UVR8 which are present in plants. It also includes data 
    sets on the optical properties of plants. Part of the 'r4photobiology' 
    suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Spectral response data for broadband ultraviolet and visible
    radiation sensors. Angular response data for broadband ultraviolet and visible
    radiation sensors and diffusers used as entrance optics. Data obtained from
    multiple sources were used: author-supplied data from scientific research 
    papers, sensor-manufacturer supplied data, and published sensor 
    specifications. Part of the 'r4photobiology' suite Aphalo P. J. (2015) 
    <doi:10.19232/uv4pb.2015.1.14>.
Data for the extraterrestrial solar spectral irradiance and ground 
    level solar spectral irradiance and irradiance. In addition data for 
    shade light under vegetation and irradiance time series from different
    broadband sensors.  Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Constructors of waveband objects for commonly used biological
    spectral weighting functions (BSWFs) and for different wavebands describing
    named ranges of wavelengths in the ultraviolet (UV), visible (VIS)
    and infrared (IR) regions of the electromagnetic spectrum.  Part of the
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Read, process, fit, and analyze photosynthetic gas exchange
    measurements. Documentation is provided by several vignettes; also see
    Lochocki, Salesse-Smith, & McGrath (2025) <doi:10.1111/pce.15501>.
Features unstructured, structured and reverse geocoding using the
    'photon' geocoding API <https://photon.komoot.io/>.
    Facilitates the setup of local 'photon' instances to enable offline
    geocoding.
Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout,
    as well as receive project information and project data. Further it transforms the received JSON objects
    into a data frame, which can be used for the final data analysis.
Provides functions for modeling, comparing, and visualizing photosynthetic light response curves using established mechanistic and empirical models like the rectangular hyperbola Michaelis-Menton based models ((eq1 (Baly (1935) <doi:10.1098/rspb.1935.0026>)) (eq2 (Kaipiainenn (2009) <doi:10.1134/S1021443709040025>)) (eq3 (Smith (1936) <doi:10.1073/pnas.22.8.504>))), hyperbolic tangent based models ((eq4 (Jassby & Platt (1976) <doi:10.4319/LO.1976.21.4.0540>)) (eq5 (Abe et al. (2009) <doi:10.1111/j.1444-2906.2008.01619.x>))), the non-rectangular hyperbola model (eq6 (Prioul & Chartier (1977) <doi:10.1093/oxfordjournals.aob.a085354>)), exponential based models ((eq8 (Webb et al. (1974) <doi:10.1007/BF00345747>)), (eq9 (Prado & de Moraes (1997) <doi:10.1007/BF02982542>))), and finally the Ye model (eq11 (Ye (2007) <doi:10.1007/s11099-007-0110-5>)). Each of these nonlinear least squares models are commonly used to express photosynthetic response under changing light conditions and has been well supported in the literature, but distinctions in each mathematical model represent moderately different assumptions about physiology and trait relationships which ultimately produce different calculated functional trait values. These models were all thoughtfully discussed and curated by Lobo et al. (2013) <doi:10.1007/s11099-013-0045-y> to express the importance of selecting an appropriate model for analysis, and methods were established in Davis et al. (in review) to evaluate the impact of analytical choice in phylogenetic analysis of the function-valued traits. Gas exchange data on 28 wild sunflower species from Davis et al.are included as an example data set here.
Contains all phrasal verbs listed in <https://www.englishclub.com/ref/Phrasal_Verbs/>
            as data frame. Useful for educational purpose as well as for text mining.
A unified interface to access and manipulate various Philippine statistical classifications. It allows users to retrieve, filter, and harmonize classification data, making it easier to work with Philippine statistical data in R.
A collection of methods for commonly undertaken analytical
    tasks, primarily developed for Public Health Scotland (PHS) analysts,
    but the package is also generally useful to others working in the
    healthcare space, particularly since it has functions for working with
    Community Health Index (CHI) numbers. The package can help to make
    data manipulation and analysis more efficient and reproducible.
Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. <arXiv:2101.09197>.
Extract and interact with data from the Scottish Health and
    Social Care Open Data platform <https://www.opendata.nhs.scot>.
Assessment of habitat selection by means of the permutation-based combination of sign tests (Fattorini et al., 2014 <DOI:10.1007/s10651-013-0250-7>). To exemplify the application of this procedure, habitat selection is assessed for a population of European Brown Hares settled in central Italy.
A low-level package for hosting persistence data. It is part of the
    'TDAverse' suite of packages, which is designed to provide a collection of
    packages for enabling machine learning and data science tasks using
    persistent homology. Implements a class for hosting persistence data, a
    number of coercers from and to already existing and used data structures
    from other packages and functions to compute distances between persistence
    diagrams. A formal definition and study of bottleneck and Wasserstein
    distances can be found in Bubenik, Scott and Stanley (2023)
    <doi:10.1007/s41468-022-00103-8>. Their implementation in 'phutil' relies on
    the 'C++' Hera library developed by Kerber, Morozov and Nigmetov (2017)
    <doi:10.1145/3064175>.
Phylogenetic clustering (phyloclustering) is an evolutionary
        Continuous Time Markov Chain model-based approach to identify
        population structure from molecular data without assuming
        linkage equilibrium. The package phyclust (Chen 2011) provides a
        convenient implementation of phyloclustering for DNA and SNP data,
        capable of clustering individuals into subpopulations and identifying
        molecular sequences representative of those subpopulations. It is
        designed in C for performance, interfaced with R for visualization,
        and incorporates other popular open source programs including
        ms (Hudson 2002) <doi:10.1093/bioinformatics/18.2.337>,
        seq-gen (Rambaut and Grassly 1997)
        <doi:10.1093/bioinformatics/13.3.235>,
        Hap-Clustering (Tzeng 2005) <doi:10.1002/gepi.20063> and
        PAML baseml (Yang 1997, 2007) <doi:10.1093/bioinformatics/13.5.555>,
        <doi:10.1093/molbev/msm088>,
        for simulating data, additional analyses, and searching the best tree.
        See the phyclust website for more information, documentations and
        examples.
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.
A collection of utilities and 'ggplot2' extensions to assist with
  visualisations in genomic epidemiology. This includes the 'phylepic' chart,
  a visual combination of a phylogenetic tree and a matched epidemic curve.
  The included 'ggplot2' extensions such as date axes binned by week are
  relevant for other applications in epidemiology and beyond. The approach is
  described in Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.
The spatial interpolation of genetic distances between
	     samples is based on a modified kriging method that
	     accepts a genetic distance matrix and generates a map of
	     probability of lineage presence. This package also offers
	     tools to generate a map of  potential contact zones
	     between groups with user-defined thresholds in the tree
	     to account for old and recent divergence. Additionally,
	     it has functions for IDW interpolation using genetic data
	     and midpoints.
Provides convenience functions to fetch standardized
    phylogenetic trees and per-tree provenance metadata from the
    'Phylo-Species Atlas'
    <https://github.com/franciscorichter/phylo-species-atlas> directly
    from R. The atlas is a curated collection of empirical
    species-level trees covering Bacteria, Archaea, and Eukaryota,
    organized into 62 partitions of life with tip labels normalized
    against a shared dictionary of standardized species identifiers.
    Functions load any of the standardized trees with species labels
    resolved from the dictionary, list available trees, and inspect
    per-tree provenance.
Provides a base S4 class for comparative methods, incorporating
    one or more trees and trait data.
Create and customize interactive phylogenetic trees using the 'phylocanvas' JavaScript library and the 'htmlwidgets' package. These trees can be used directly from the R console, from 'RStudio', in Shiny apps, and in R Markdown documents.  See <http://phylocanvas.org/>  for more information on the 'phylocanvas' library.
Interface to 'Phylocom' (<https://phylodiversity.net/phylocom/>),
    a library for analysis of 'phylogenetic' community structure and
    character evolution. Includes low level methods for interacting with
    the three executables, as well as higher level interfaces for methods
    like 'aot', 'ecovolve', 'bladj', 'phylomatic', and more.
Implementation of the automatic shift detection method for
    Brownian Motion (BM) or Ornstein–Uhlenbeck (OU) models of trait evolution on
    phylogenies. Some tools to handle equivalent shifts configurations are also
    available. See Bastide et al. (2017) <doi:10.1111/rssb.12206> and
    Bastide et al. (2018) <doi:10.1093/sysbio/syy005>.
Manipulation and analysis of phylogenetically simulated
        data sets and phylogenetically based analyses using GLS.
Contains functions for developing phylogenetic trees as
    deeply-nested lists ("dendrogram" objects).
    Enables bi-directional conversion between dendrogram and
    "phylo" objects
    (see Paradis et al (2004) <doi:10.1093/bioinformatics/btg412>),
    and features several tools for command-line tree
    manipulation and import/export via Newick parenthetic text.
Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.
Conducts hierarchical partitioning to calculate individual contributions of phylogenetic tree and  predictors (groups) towards total R2  for phylogenetic linear regression models.
Provides functions to estimate statistical errors of phylogenetic
    metrics particularly to detect binary trait influence on diversification, as
    well as a function to simulate trees with fixed number of sampled taxa and trait
    prevalence.
Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) <doi:10.1111/j.1558-5646.1989.tb04233.x>, Fitzpatrick (2002) <doi:10.1111/j.0014-3820.2002.tb00860.x>, and Castillo (2007) <doi:10.1002/ece3.3093>.
A comprehensive and easy to use R implementation of confirmatory
    phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer
    (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.
Phylogenetic Diversity (PD, Faith 1992), Evolutionary
    Distinctiveness (ED, Isaac et al. 2007), Phylogenetic Endemism (PE,
    Rosauer et al. 2009; Laffan et al. 2016), and Weighted Endemism (WE,
    Laffan et al. 2016) for presence-absence raster.  Faith, D. P. (1992)
    <doi:10.1016/0006-3207(92)91201-3> Isaac, N. J. et al. (2007)
    <doi:10.1371/journal.pone.0000296> Laffan, S. W. et al. (2016)
    <doi:10.1111/2041-210X.12513> Rosauer, D. et al. (2009)
    <doi:10.1111/j.1365-294X.2009.04311.x>.
Computational infrastructure for biogeography, community ecology,
    and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>.
    It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>.
    The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013>
    on patterns and processes of biogeographical regionalization. Additionally, the package
    contains fast and efficient functions to compute more standard conservation measures
    such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness
    and global endangerment, as well as compositional turnover (e.g., beta diversity).
Implements novel tools for estimating sample sizes needed for phylogenetic studies, including
    studies focused on estimating the probability of true pathogen transmission between two cases given phylogenetic linkage and studies focused on tracking pathogen variants at a population level. Methods described in Wohl, Giles, and Lessler (2021) and in Wohl, Lee, DiPrete, and Lessler (2023).
Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using 
    structural equation models (SEM), extending methods from Thorson et al. (2023) <doi:10.1111/2041-210X.14076>.  
    This implementation includes a minimal set of features, to 
    allow users to easily read all of the documentation and source code.  PCM using SEM 
    includes phylogenetic linear models and structural equation models as nested submodels, 
    but also allows imputation of missing values.  Features and comparison with other packages
    are described in Thorson and van der Bijl (2023) <doi:10.1111/jeb.14234>.
Provides functions for graph-based multiple-sample
    testing and visualization of microbiome data, in particular data
    stored in 'phyloseq' objects. The tests are based on those
    described in Friedman and Rafsky (1979)
    <http://www.jstor.org/stable/2958919>, and the tests are described
    in more detail in Callahan et al. (2016)
    <doi:10.12688/f1000research.8986.1>.
A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.
A unified method, called M statistic, is provided for detecting phylogenetic signals in continuous traits, discrete traits, and multi-trait combinations. Blomberg and Garland (2002) <doi:10.1046/j.1420-9101.2002.00472.x> provided a widely accepted statistical definition of the phylogenetic signal, which is the "tendency for related species to resemble each other more than they resemble species drawn at random from the tree". The M statistic strictly adheres to the definition of phylogenetic signal, formulating an index and developing a method of testing in strict accordance with the definition, instead of relying on correlation analysis or evolutionary models. The novel method equivalently expressed the textual definition of the phylogenetic signal as an inequality equation of the phylogenetic and trait distances and constructed the M statistic. The M statistic implemented in this package is based on the methodology described in Yao and Yuan (2025) <doi:10.1002/ece3.71106>. If you use this method in your research, please cite the paper.
Analyze spatial phylogenetic diversity patterns. 
    Use your data on an evolutionary tree and geographic distributions of the 
    terminal taxa to compute diversity and endemism metrics, test significance 
    with null model randomization, analyze community turnover and biotic 
    regionalization, and perform spatial conservation prioritizations. All 
    functions support quantitative community data in addition to binary data.
Functions to read and write APE-compatible phylogenetic
  trees in NEXUS and Newick formats, while preserving annotations.
Tools for calculating and viewing topological properties of phylogenetic trees.
Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 <doi:10.1128/AEM.00062-07>. The package also provides functions to read in 'FASTA'-formatted sequence data.
Analyzis and filtering of phylogenomics datasets. 
	It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify
	what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization of classical multidimensional scaling to multiple distance matrices.
Implements a phylogeny-aware Bayesian graphical modeling
    framework for microbial network inference using a shrinkage
    precision estimator guided by a phylogenetic kernel, with optional
    hyperparameter-ensemble edge reliability analysis.
A collection of functions to do model-based phylogenetic analysis. 
    It includes functions to calculate community phylogenetic diversity,
    to estimate correlations among functional traits while accounting for 
    phylogenetic relationships, and to fit phylogenetic generalized linear
    mixed models. The Bayesian phylogenetic generalized linear mixed models
    are fitted with the 'INLA' package (<https://www.r-inla.org>).
Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true  sequence of binary splits).
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.  Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.
It provides a function "wearingMarking" for classification of monitor
    wear and nonwear time intervals in accelerometer data collected to assess
    physical activity. The package also contains functions for making plot for 
    accelerometer data and obtaining the summary of various information including 
    daily monitor wear time and the mean monitor wear time during valid days.      
    "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail;
    "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete 
    days and removing low activity days; "markPAI" can categorize physical activity
    intensity level based on user-defined cut-points of accelerometer counts. It also
    supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions.
Screens and sorts phylogenetic trees in both traditional and
    extended Newick format. Allows for the fast and flexible screening (within
    a tree) of Exclusive clades that comprise only the target taxa and/or Non-
    Exclusive clades that includes a defined portion of non-target taxa.
Determine the chlorophyll a (Chl a) concentrations of different 
    phytoplankton groups based on their pigment biomarkers. The method uses
    non-negative matrix factorisation and simulated annealing to minimise error
    between the observed and estimated values of pigment concentrations
    (Hayward et al. (2023) <doi:10.1002/lom3.10541>).
    The approach is similar to the widely used 'CHEMTAX' program
    (Mackey et al. 1996) <doi:10.3354/meps144265>, but is more straightforward,
    accurate, and not reliant on initial guesses for the pigment to Chl a
    ratios for phytoplankton groups.
Provides functions and example datasets for phytosociological 
    analysis, forest inventory, biomass and carbon estimation, and visualization 
    of vegetation data. Includes functions to compute structural parameters 
    [phytoparam(), summary.param(), stats()], estimate above-ground biomass 
    and carbon [AGB()], stratify wood volume by diameter at breast height (DBH) 
    classes [stratvol()], generate collector and rarefaction curves [collector.curve(), 
    rarefaction()], and visualize basal areas on quadrat maps [BAplot(), including 
    rectangular plots and individual coordinates]. Several example datasets are provided 
    to demonstrate the functionality of these tools. For more details see FAO 
    (1981, ISBN:92-5-101132-X) "Manual of forest inventory", IBGE (2012, ISBN:9788524042720) 
    "Manual técnico da vegetação brasileira" and Heringer et al. (2020) "Phytosociology in 
    R: A routine to estimate phytosociological parameters" <doi:10.22533/at.ed.3552009033>.
A wide range of methods for phylogenetic analysis - concentrated in phylogenetic comparative biology, but also including numerous techniques for visualizing, analyzing, manipulating, reading or writing, and even inferring phylogenetic trees. Included among the functions in phylogenetic comparative biology are various for ancestral state reconstruction, model-fitting, and simulation of phylogenies and trait data. A broad range of plotting methods for phylogenies and comparative data include (but are not restricted to) methods for mapping trait evolution on trees, for projecting trees into phenotype space or a onto a geographic map, and for visualizing correlated speciation between trees. Lastly, numerous functions are designed for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. For instance, there are functions for computing consensus phylogenies from a set, for simulating phylogenetic trees and data under a range of models, for randomly or non-randomly attaching species or clades to a tree, as well as for a wide range of other manipulations and analyses that phylogenetic biologists might find useful in their research.
A 'Shiny' application for calculating phytosanitary inspection plans based on risks. 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).
Most price indexes are made with a two-step procedure, where
    period-over-period elementary indexes are first calculated for a collection
    of elementary aggregates at each point in time, and then aggregated according
    to a price index aggregation structure. These indexes can then be chained
    together to form a time series that gives the evolution of prices with
    respect to a fixed base period. This package contains a collection of
    functions that revolve around this work flow, making it easy to build
    standard price indexes, and implement the methods described by
    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>) for bilateral price indexes.
Tools for the processing, segmentation, and analysis of terrestrial laser 
    scanning (TLS and MLS) forest point-cloud data. The package provides fast 
    voxel-based processing, classification of point clouds into forest 
    floor, understory, canopy, and woody components, and algorithms for 
    single-tree analysis and structural characterization. Methods are designed 
    to handle large and dense point-cloud datasets efficiently, supporting 
    applications in forest structure assessment, connectivity analysis, and 
    fire-risk evaluation. Input data are provided as '.xyz', '.txt', '.las', or '.laz' point-cloud files. 
    For methodological details, see Ferrara and Arrizza (2025) 
    <https://hdl.handle.net/20.500.14243/533471> and Ferrara et al. (2018) 
    <doi:10.1016/j.agrformet.2018.04.008>.
Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.
Fast tools for fitting sparse generalized linear models with convex
    penalties (lasso) and concave penalties (smoothly clipped absolute
    deviation and minimax concave penalty). Computation uses multi-stage convex
    relaxation and pathwise coordinate optimization with warm starts,
    active-set updates, and screening rules. Core solvers are implemented in
    C++, and coefficient paths are stored as sparse matrices for memory
    efficiency.
Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.
Provides a 'Shiny' input widget, pasteBoxInput, 
    that allows users to paste images directly into a 'Shiny' 
    application. The pasted images are captured as Base64 
    encoded strings and can be used within the application 
    for various purposes, such as display or further processing. 
    This package is particularly useful for applications that 
    require easy and quick image uploads without the need for 
    traditional file selection dialog boxes.
Performant interactive scatterplot for ~ 1 million points. Zoom, pan,
    and pick points. Includes tooltips, labels, a grid overlay, legend, and
    coupled interactions across multiple plots.
Deduplicates datasets by retaining the most complete and informative records. Identifies duplicated entries based on a specified key column, calculates completeness scores for each row, and compares values within groups. When differences between duplicates exceed a user-defined threshold, records are split into unique IDs; otherwise, they are coalesced into a single, most complete entry. Returns a list containing the original duplicates, the split entries, and the final coalesced dataset. Useful for cleaning survey or administrative data where duplicated IDs may reflect minor data entry inconsistencies.
High Dynamic Range (HDR) images support a large range in luminosity
    between the lightest and darkest regions of an image.  To capture this range,
    data in HDR images is often stored as floating point numbers and in formats
    that capture more data and channels than standard image types.  This package supports
    reading and writing two types of HDR images; PFM (Portable Float Map) 
    and OpenEXR images. HDR images can be converted to lower
    dynamic ranges (for viewing) using tone-mapping.  A number of
    tone-mapping algorithms are included which are based on 
    Reinhard (2002) "Photographic tone reproduction for digital images" 
    <doi:10.1145/566654.566575>.
Computation of predictive information criteria (PIC) from select model object classes 
    for model selection in predictive contexts. In contrast to the more widely used Akaike 
    Information Criterion (AIC), which are derived under the assumption that target(s) of prediction 
    (i.e. validation data) are independently and identically distributed to the fitting data, the PIC
    are derived under less restrictive assumptions and thus generalize AIC to the more practically 
    relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".
Sparse regression and classification via the Pivotal
    Information Criterion (PIC), an alternative to the Bayesian
    Information Criterion (BIC), cross-validation, and Lasso-based
    tuning. The regularization parameter is selected from a pivotal
    null-distribution statistic, eliminating the need for
    cross-validation and yielding sharper support recovery. Provides
    Fast Iterative Shrinkage-Thresholding Algorithm (FISTA)
    optimization for the L1, Smoothly Clipped Absolute Deviation
    (SCAD), and Minimax Concave Penalty (MCP) penalties across six
    response distributions: Gaussian, binomial, Poisson, exponential,
    Gumbel, and Cox. Under standard sparsity assumptions, the
    selector achieves a phase transition for exact support recovery,
    analogous to results in compressed sensing. See Sardy, van Cutsem
    and van de Geer (2026) <doi:10.48550/arXiv.2603.04172>.
A collection of scripts and data files for the statistics text:
 "Process Improvement using Data" <https://learnche.org/pid/> and the online
 course "Experimentation for Improvement" found on Coursera. The package
 contains code for designed experiments, data sets and other convenience
 functions used in the book.
Implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) <doi:10.48550/arXiv.2105.02410>.
Tokenizers break text into pieces that are more usable by
    machine learning models. Many tokenizers share some preparation steps.
    This package provides those shared steps, along with a simple
    tokenizer.
An 'R6' class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.
Provides an implementation of piecewise normalisation techniques useful when dealing with the communication of skewed and highly skewed data. It also provides utilities that recommends a normalisation technique based on the distribution of the data.
Functions to make board game graphics with the 'ggplot2', 'grid', 'rayrender', 'rayvertex', and 'rgl' packages.  Specializes in game diagrams, animations, and "Print & Play" layouts for the 'piecepack' <https://www.ludism.org/ppwiki> but can make graphics for other board game systems.  Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, 'piecepack', playing cards, etc.
Implements piecewise structural equation modeling from a single
    list of structural equations, with new methods for non-linear, latent, and
    composite variables, standardized coefficients, query-based prediction and
    indirect effects. See <http://jslefche.github.io/piecewiseSEM/> for more.
Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. The pie glyphs are independent of the axes and plot dimensions, to prevent distortions when the plot dimensions are changed.
Because larger (> 50 MB) data files cannot easily be committed to git,
  a different approach is required to manage data associated with an analysis in a 
  GitHub repository.  This package provides a simple work-around by allowing larger
  (up to 2 GB) data files to piggyback on a repository as assets attached to individual
  GitHub releases.  These files are not handled by git in any way, but instead are
  uploaded, downloaded, or edited directly by calls through the GitHub API. These
  data files can be versioned manually by creating different releases.  This approach
  works equally well with public or private repositories.  Data can be uploaded
  and downloaded programmatically from scripts. No authentication is required to
  download data from public repositories.
Improves genotype inference and downstream Adaptive Immune Receptor Repertoire Sequence data analysis. Inference of allele similarity clusters, an alternative naming scheme and genotype inference for immunoglobulin heavy chain repertoires.
             The main tools are allele similarity clusters, and allele based genotype. The first tool is designed to reduce the ambiguity within the immunoglobulin heavy chain V alleles. The ambiguity is caused by duplicated or similar alleles which are shared among different genes.
             The second tool is an allele based genotype, that determined the presence of an allele based on a threshold derived from a naive population. See Peres et al. (2023) <doi:10.1093/nar/gkad603>.
Check a data frame for personal information, including names, location, disability status, and geo-coordinates.
A simple implementation of the Predictive Information Index ('PII').
Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
An 'R' interface to 'pikchr' (<https://pikchr.org>, pronounced "picture"), a 'PIC'-like markup language for creating diagrams within technical documentation. Originally developed by Brian Kernighan, 'PIC' has been adapted into 'pikchr' by D. Richard Hipp, the creator of 'SQLite'. 'pikchr' is designed to be embedded in fenced code blocks of Markdown or other documentation markup languages, making it ideal for generating diagrams in text-based formats. This package allows R users to seamlessly integrate the descriptive syntax of 'pikchr' for diagram creation directly within the 'R' environment.
Provides 'pillar' and 'colonnade' generics designed
    for formatting columns of data using the full range of colours
    provided by modern terminals.
Fit a probabilistic index model as described in 
    Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the 
    modeling function has changed in this new version. The old version is
    still available at R-Forge.
An implementation of prediction intervals for random-effects meta-analysis:
  Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017)
  <doi:10.1002/sim.7140>, and Nagashima et al. (2019) <doi:10.1177/0962280218773520>,
  <arXiv:1804.01054>.
Implementation of T. Hailperin's procedure to calculate lower and upper bounds of the probability for a propositional-logic expression, given equality and inequality constraints on the probabilities for other expressions. Truth-valuation is included as a special case. Applications range from decision-making and probabilistic reasoning, to pedagogical for probability and logic courses. For more details see T. Hailperin (1965) <doi:10.1080/00029890.1965.11970533>, T. Hailperin (1996) "Sentential Probability Logic" ISBN:0-934223-45-9, and package documentation. Requires the 'lpSolve' package.
Genomic data for the plant pathogen "Phytophthora infestans." It
    includes a variant file ('VCF'), a sequence file ('FASTA') and an annotation file
    ('GFF'). This package is intended to be used as example data for packages that
    work with genomic data.
To assist you with troubleshooting internet connection issues and
    assist in isolating packet loss on your network. It does this by allowing you to 
    retrieve the top trace route destinations your internet provider uses, and recursively 
    ping each server in series while capturing the results and writing them to a log file. 
    Each iteration it queries the destinations again, before shuffling the sequence of 
    destinations to ensure the analysis is unbiased and consistent across each trace route.
Check if a remote computer is up. It can either just call the
    system ping command, or check a specified TCP port.
Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.
Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.
Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463).
A 'PNAS'-alike style for 'rmarkdown', derived from the 'Proceedings of the
 National Academy of Sciences of the United States of America' ('PNAS') 'LaTeX' style,
 and adapted for use with 'markdown' and 'pandoc'.
Publish data sets, models, and other R objects, making it
    easy to share them across projects and with your colleagues. You can
    pin objects to a variety of "boards", including local folders (to
    share on a networked drive or with 'DropBox'), 'Posit Connect',
    'AWS S3', and more.
Provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI: 10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>).
A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
Collect  marketing data from Pinterest Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
    Use four spaces when indenting paragraphs within the Description.
Provides tools for estimating model-agnostic prediction intervals using conformal   prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2026) <DOI:10.1017/pan.2025.10010>.
Measure productivity and efficiency using Data Envelopment Analysis 
    (DEA). Available methods include DEA under different technology assumptions, 
    bootstrapping of efficiency scores and calculation of the Malmquist 
    productivity index. Analyses can be performed either in the console or with 
    the provided 'shiny' app. See Banker, R.; Charnes, A.; Cooper, W.W. (1984)
    <doi:10.1287/mnsc.30.9.1078>, Färe, R.; Grosskopf, S. (1996) <doi:10.1007/978-94-009-1816-0>.
Provides a simple function to bind a piped object to a placeholder
    symbol to enable complex function evaluation with the base R |> pipe.
To take nested function calls and convert them to a more readable form using pipes from package 'magrittr'.
A lightweight and intuitive framework for building interactive data
    analysis pipelines. You add R functions one by one, and 'pipeflow'
    wires them into a pipeline that stays consistent as you go. Modify,
    remove, or insert steps at any stage, manage all parameters in one
    place, fast execution (C++-powered DAG) for interactive use and Shiny
    backends.
A framework for defining 'pipelines' of functions for applying data transformations, 
  model estimation and inverse-transformations, resulting in predicted value generation (or 
  model-scoring) functions that automatically apply the entire pipeline of functions required to go
  from input to predicted output.
Provides various styles of function chaining methods: Pipe
    operator, Pipe object, and pipeline function, each representing a distinct
    pipeline model yet sharing almost a common set of features: A value can be
    piped to the first unnamed argument of a function and to dot symbol in an
    enclosed expression. The syntax is designed to make the pipeline more
    readable and friendly to a wide range of operations.
Enable users to measure and record the execution time of pipe operations (using |>) with optional logging to dataframes and output to the console.
An interface to compute poverty and inequality 
    indicators for more than 160 countries and regions from the World
    Bank's database of household surveys, through the 
    Poverty and Inequality Portal (PIP).
An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.
Calculate and optimize dynamic performance ratings of association football 
  teams competing in matches, in accordance with the method used in 
  the research paper "Determining the level of ability of football teams by 
  dynamic ratings based on the relative discrepancies in scores between adversaries", 
  by Constantinou and Fenton (2013) 
  <doi:10.1515/jqas-2012-0036>    
  This dynamic rating system has proven to provide superior 
  results for predicting association football outcomes.
Scored responses and responses times from the Canadian subsample of the PISA 2018 assessment, accessible as the "Cognitive items total time/visits data file" by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.
A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating
    Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement
    their own grammars and easily expose them in R packages.
Provides tools for estimating the statistical power of Interrupted
    Time Series (ITS) designs, with a focus on healthcare applications. The
    package supports prospective power calculations before a study begins, and
    retrospective assessments of whether a completed study was adequately powered.
    It includes functions to estimate nuisance parameters (baseline, residual
    standard deviation, autocorrelation) from data observed before the
    intervention, and to
    estimate power via Monte Carlo simulation for single-site and multi-site
    designs. Utility functions for design optimisation sweeps and publication-
    ready plots are also provided.
Collection of pivotal algorithms 
             for: relabelling the MCMC chains in order to undo the label 
             switching problem in Bayesian mixture models;
             fitting sparse finite mixtures;
             initializing the centers of the classical k-means algorithm 
             in order to obtain a better clustering solution. 
             For further details see
             Egidi, Pappadà, Pauli and Torelli (2018b)<ISBN:9788891910233>.
Functions to get prediction intervals and prediction points of future observations from mixture distributions like gamma, beta, Weibull and normal.
'Pivotal Tracker' <https://www.pivotaltracker.com> is a project
    management software-as-a-service that provides a REST API. This package
    provides an R interface to that API, allowing you to query it and work with
    its responses.
Pivot easily by specifying rows, columns, values and split.
Create regular pivot tables with just a few lines of R.  
    More complex pivot tables can also be created, e.g. pivot tables
    with irregular layouts, multiple calculations and/or derived 
    calculations based on multiple data frames.  Pivot tables are
    constructed using R only and can be written to a range of
    output formats (plain text, 'HTML', 'Latex' and 'Excel'), 
    including with styling/formatting.
Run Queries against the API of 'Piwik Pro' <https://developers.piwik.pro/en/latest/custom_reports/http_api/http_api.html>. The result is a tibble.
Data about Disney Pixar films provided by Wikipedia. This
    package contains data about the films, the people involved, and their
    awards.
Contains functions to classify the pixels of an image file by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.
Puzzle game that can be played in the R console.
  Restore the pixel art by shifting rows.
Provides tools to show and draw image pixels using 'HTML' widgets 
  and 'Shiny' applications.  It can be used to visualize the 'MNIST' dataset
  for handwritten digit recognition or to create new image recognition datasets.
The introduction of the 'broom' package has made converting model
    objects into data frames as simple as a single function. While the 'broom'
    package focuses on providing tidy data frames that can be used in advanced
    analysis, it deliberately stops short of providing functionality for reporting
    models in publication-ready tables. 'pixiedust' provides this functionality with
    a programming interface intended to be similar to 'ggplot2's system of layers
    with fine tuned control over each cell of the table. Options for output include
    printing to the console and to the common markdown formats (markdown, HTML, and
    LaTeX). With a little 'pixiedust' (and happy thoughts) tables can really fly.
A pipe-friendly R client for PX-Web statistical APIs.
    Provides a search-then-fetch workflow for discovering and downloading
    data from national statistics agencies (SCB, SSB, Statistics Finland,
    etc.) using a consistent tibble-based interface.
Functions for import, export, visualization and other manipulations of bitmapped images.
Provides a 'tidyverse'-style interface to the Brazilian Central Bank (<https://www.bcb.gov.br>) 'PIX' Open Data 'API' <https://olinda.bcb.gov.br/olinda/servico/Pix_DadosAbertos/versao/v1/aplicacao#!/recursos>. 
    Retrieve statistics on 'PIX' keys, transactions by municipality, and monthly 
    transaction summaries. All functions return 'tibbles' and support 'OData' 
    query parameters for filtering, selecting, and ordering data.
An implementation of chunked, compressed,
  N-dimensional arrays for R. 'Zarr' spec V2 (2024)
  <doi:10.5281/zenodo.11320255>.
Computes the Patient-Reported Outcomes (PROs) Joint Contrast (PJC), 
 a residual-based summary that captures information left over after accounting 
 for the clinical Disease Activity index for Psoriatic Arthritis (cDAPSA). 
 PROs (pain and patient global assessment) and joint counts (swollen and tender) 
 are standardized, then each component is adjusted for standardized cDAPSA using 
 natural spline coefficients that were derived from previously published models. 
 The resulting residuals are standardized and combined using fixed principal 
 component loadings, to yield a continuous PJC score and quartile groupings. 
 This package provides a calculator for applying those published coefficients to new datasets; 
 it does not itself estimate spline models or principal components.
Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events
    and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. 
    Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. 
    Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.
Estimation of pharmacokinetic parameters using non-compartmental theory.
Achieve internal conversions of mass units, molar units,
	and volume units commonly used in pharmacokinetics, 
	as well as conversions between mass units and molar units.
Calculate and compare the prediction probability (PK) values for Anesthetic Depth Indicators.
    The PK values are widely used for measuring the performance of anesthetic depth and were first 
    proposed by the group of Dr. Warren D. Smith in the paper 
    Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1097/00000542-199601000-00005> and
    Warren D. Smith; Robert C. Dutton; Ty N. Smith (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1199::AID-SIM218%3E3.0.CO;2-Y>.
    The authors provided two 'Microsoft Excel' files in xls format for calculating and comparing PK values.
    This package provides an easy-to-use API for calculating and comparing PK values in R.
Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations.
    The package impose regulatory constrains and sanity checking for common bioanalytical procedures. 
    Additionally, 'PKbioanalysis' provides a relational infrastructure for plate management and injection sequence.
Pharmacokinetics is the study of drug absorption, distribution, 
   metabolism, and excretion. The pharmacokinetics model explains that how the 
   drug concentration change as the drug moves through the different compartments
   of the body. For pharmacokinetic modeling and analysis, it is essential
   to understand the basic pharmacokinetic parameters. All parameters are 
   considered, but only some of parameters are used in the model. Therefore, 
   we need to convert the estimated parameters to the other parameters after 
   fitting the specific pharmacokinetic model. This package is developed to help
   this converting work. For more detailed explanation of pharmacokinetic 
   parameters, see "Gabrielsson and Weiner" (2007), "ISBN-10: 9197651001";
   "Benet and Zia-Amirhosseini" (1995) <DOI: 10.1177/019262339502300203>; 
   "Mould and Upton" (2012) <DOI: 10.1038/psp.2012.4>;
   "Mould and Upton" (2013) <DOI: 10.1038/psp.2013.14>.
Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. 
    This package provides functions to standardize infusion and bolus dose data while
    linking it to drug level or concentration data.
Provides functions used to build R packages. Locates
    compilers needed to build R packages on various platforms and ensures
    the PATH is configured appropriately so R can use them.
Metadata and package cache for CRAN-like repositories.  This
    is a utility package to be used by package management tools that want
    to take advantage of caching.
This provides utilities for creating classed error and warning
  conditions based on where the error originated.
Set configuration options on a per-package basis.
    Options set by a given package only apply to that package,
    other packages are unaffected.
Find recursive dependencies of 'R' packages from various
    sources. Solve the dependencies to obtain a consistent set of packages
    to install. Download packages, and install them. It supports packages
    on 'CRAN', 'Bioconductor' and other 'CRAN-like' repositories,
    'GitHub', package 'URLs', and local package trees and files. It caches
    metadata and package files via the 'pkgcache' package, and performs
    all 'HTTP' requests, downloads, builds and installations in parallel.
    'pkgdepends' is the workhorse of the 'pak' package.
Statically determine and visualize the function dependencies
    within and across packages. This may be useful for managing function 
    dependencies across a code base of multiple R packages.
Identifies differences between
    versions of a package.  Specifically, the functions help
    determine if there are breaking changes from one package version
    to the next.  The package also includes a stability assessment,
    to help you determine the overall stability of a package, or even
    an entire repository.
Generate an attractive and useful website from a source
    package.  'pkgdown' converts your documentation, vignettes, 'README',
    and more to 'HTML' making it easy to share information about your
    package online.
Provides support for building 'pkgdown' websites without an
    internet connection. Works by bundling cached dependencies and
    implementing drop-in replacements for key 'pkgdown' functions.
    Enables package documentation websites to be built in environments
    where internet access is unavailable or restricted.
    For more details on generating 'pkgdown' websites, see
    Wickham et al. (2025) <doi:10.32614/CRAN.package.pkgdown>.
Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).
Explore various dependencies of a packages (on the Comprehensive R Archive Network Like repositories). The functions get_neighborhood() and get_dependencies() provide dependencies of packages and as_graph() can be used to convert into a 'igraph' object for further analysis and plotting.
It is often useful when developing an R package to track the relationship between functions in order to appropriately test and track changes. This package generates a graph of the relationship between all R functions in a package. It can also be used on any directory containing .R files which can be very useful for 'shiny' apps or other non-package workflows.
Provides a function kitten() which creates cute little 
 packages which pass R package checks. This sets it apart from 
 package.skeleton() which it calls, and which leaves imperfect files 
 behind. As this is not exactly helpful for beginners, kitten() offers 
 an alternative. Unit test support can be added via the 'tinytest'
 package (if present), and documentation-creation support can be
 added via 'roxygen2' (if present).
A tool, grammar, and standard to represent and exchange
    R package source code as text files. Converts one or more source
    packages to a text file and restores the package structures from the file.
Simulates the process of installing a package and then
    attaching it. This is a key part of the 'devtools' package as it
    allows you to rapidly iterate while developing a package.
Find R packages from CRAN, 'rOpenSci', or Bioconductor corpora.
    Packages can be matched to general text descriptions, to names of installed
    packages, or to local paths to entire source repositories. The package is
    used to list the most similar packages for each new submission to the
    'rOpenSci' software peer-review program ('rOpenSci' authors, 2026;
    <doi:10.5281/zenodo.18885936>).
A new metric named 'dependency heaviness' is proposed that measures the number 
    of additional dependency packages that a parent package brings to its child package 
    and are unique to the dependency packages imported by all other parents.  
    The dependency heaviness analysis is visualized by a customized heatmap. 
    The package is described in <doi:10.1093/bioinformatics/btac449>. 
    We have also performed the dependency heaviness analysis on the CRAN/Bioconductor 
    package ecosystem, described in <doi:10.1016/j.jss.2023.111610>.
Tools from the domain of graph theory can be used to quantify the complexity
             and vulnerability to failure of a software package. That is the guiding philosophy
             of this package. 'pkgnet' provides tools to analyze the dependencies between functions
             in an R package and between its imported packages.  See the pkgnet website for vignettes 
             and other supplementary information.
Read R package news files, regardless of whether or not the package
  is installed.
Search CRAN metadata about packages by keyword, popularity,
    recent activity, package name and more. Uses the 'R-hub' search
    server, see <https://r-pkg.org> and the CRAN metadata database, that
    contains information about CRAN packages. Note that this is _not_ a
    CRAN project.
Makes it easier to create a 'Quarto' website for an 'R'
    package. Converts 'R' documentation ('Rd') files into 'Quarto' Markdown
    documents individually. Supports configuration via the '_quarto.yml'
    file and groups functions using 'roxygen2' family tags.
Static code analyses for R packages using the external code-tagging
    libraries 'ctags' and 'gtags'. Static analyses enable packages to be
    analysed very quickly, generally a couple of seconds at most. The package
    also provides access to a database generating by applying the main function
    to the full 'CRAN' archive, enabling the statistical properties of any package
    to be compared with all other 'CRAN' packages.
Build your own universe of packages similar to the 'tidyverse'
    package <https://tidyverse.org/> with this meta-package creator. Create a
    package-verse, or meta package, by supplying a custom name for the
    collection of packages and the vector of desired package names to include–
    and optionally supply a destination directory, an indicator of whether to
    keep the created package directory, and/or a vector of verbs implement via
    the 'usethis' <http://usethis.r-lib.org/> package.
Public Key Infrastucture functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021)  <arXiv:2109.10150>.
Provides a tidy interface to Pakistan's official administrative
    boundary data from the United Nations Office for the Coordination of
    Humanitarian Affairs (OCHA). Downloads and caches spatial data at
    country, province, district, and tehsil levels as 'sf' objects
    compatible with the 'tidyverse' and geospatial ecosystem. Includes
    utilities for geographic dictionary lookup, coordinate reference
    system selection, spatial measurement, and neighbour structure
    construction for use with 'spdep', 'ggplot2', 'leaflet', and related packages.
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.
Compute standard Non-Compartmental Analysis (NCA) parameters for
    typical pharmacokinetic analyses and summarize them.
Provides a comprehensive framework for model fitting and simulation
    of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD).
    The package implements widely used mechanistic and empirical models for in
    vitro drug release, including zero-order, first-order, Higuchi,
    Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic
    functionality includes linear and nonlinear functions for one- and
    two-compartment models for intravenous bolus and oral administration,
    Michaelis-Menten kinetics, and non-compartmental analysis (NCA).
    Pharmacodynamic and dose-response modeling is supported through Emax-based
    models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax)
    Hill models, four- and five-parameter logistic models, as well as median
    toxic dose (TD50) and lethal dose (LD50) models. The package is intended to
    support parameter estimation, simulation, and model comparison in
    pharmaceutical research, drug development, and pharmacometrics education.
    For more details, see Gabrielsson & Weiner (2000) <ISBN:9186274929>,
    Holford & Sheiner (1981) <doi:10.2165/00003088-198106060-00002>, and
    Manlapaz (2025) <doi:10.32614/CRAN.package.adsoRptionCMF>.
Fits Emax models to pharmacokinetic/pharmacodynamic (PK/PD) data, estimate key parameters, and visualise model fits for multiple PK/PD indices. Methods are described in Macdougall J (2006) <doi:10.1007/0-387-33706-7_9>, Spiess AN, Neumeyer N (2010) <doi:10.1186/1471-2210-10-6>, and Burnham KP, Anderson DR (2004) <doi:10.1177/0049124104268644>.
Simulate dose regimens for pharmacokinetic-pharmacodynamic (PK-PD)
        models described by differential equation (DE) systems. Simulation using 
        ADVAN-style analytical equations is also supported (Abuhelwa et al. (2015) 
        <doi:10.1016/j.vascn.2015.03.004>).
Fitting and testing probabilistic knowledge structures,
  especially the basic local independence model (BLIM, Doignon & Flamagne,
  1999) and the simple learning model (SLM), using the minimum discrepancy
  maximum likelihood (MDML) method (Heller & Wickelmaier, 2013
  <doi:10.1016/j.endm.2013.05.145>).
A tool for inferring kinase activity changes from 
    phosphoproteomics data. 'pKSEA' uses kinase-substrate 
    prediction scores to weight observed changes in 
    phosphopeptide abundance to calculate a phosphopeptide-level 
    contribution score, then sums up these contribution scores by 
    kinase to obtain a phosphoproteome-level kinase activity 
    change score (KAC score). 'pKSEA' then assesses the 
    significance of changes in predicted substrate abundances for 
    each kinase using permutation testing. This results in a 
    permutation score (pKSEA significance score) reflecting the 
    likelihood of a similarly high or low KAC from random chance, 
    which can then be interpreted in an analogous manner to an 
    empirically calculated p-value. 'pKSEA' contains default 
    databases of kinase-substrate predictions from 'NetworKIN' 
    (NetworKINPred_db) <http://networkin.info> 
    Horn, et. al (2014) <doi:10.1038/nmeth.2968>
    and of known kinase-substrate links from 'PhosphoSitePlus' 
    (KSEAdb) <https://www.phosphosite.org/>
    Hornbeck PV, et. al (2015) <doi:10.1093/nar/gku1267>.
Applying the global sensitivity analysis workflow to investigate 
    the parameter uncertainty and sensitivity in physiologically based 
    kinetic (PK) models, especially the physiologically based 
    pharmacokinetic/toxicokinetic model with multivariate outputs. 
    The package also provides some functions to check the convergence 
    and sensitivity of model parameters. The workflow was first mentioned 
    in Hsieh et al., (2018) <doi:10.3389/fphar.2018.00588>, then further 
    refined (Hsieh et al., 2020 <doi:10.1016/j.softx.2020.100609>).
Tools to process legacy format summary redistricting data files
    produced by the United States Census Bureau pursuant to P.L. 94-171. These
    files are generally available earlier but are difficult to work with as-is.
A semi-parametric estimation method for the Cox model
    with left-truncated data using augmented information
    from the marginal of truncation times.
Normalizes city names for EEA countries and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) <https://ec.europa.eu/eurostat/web/nuts>).
Functions to prepare rankings data and fit the Plackett-Luce model
    jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce
    (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized
    to accommodate ties of any order in the ranking. Partial rankings, in which
    only a subset of items are ranked in each ranking, are also accommodated in
    the implementation. Disconnected/weakly connected networks implied by the
    rankings may be handled by adding pseudo-rankings with a hypothetical item.
    Optionally, a multivariate normal prior may be set on the log-worth
    parameters and ranker reliabilities may be incorporated as proposed by
    Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a
    posteriori estimation is used when priors are set. Methods are provided to
    estimate standard errors or quasi-standard errors for inference as well as
    to fit Plackett-Luce trees. See the package website or vignette for further
    details.
Provides methods for plotting potentially large (raster) images
    interactively on a plain HTML canvas. In contrast to package 'mapview'
    data are plotted without background map, but data can be projected to
    any spatial coordinate reference system.
    Supports plotting of classes 'RasterLayer', 'RasterStack', 'RasterBrick'
    (from package 'raster') as well as 'png' files located on disk. 
    Interactivity includes zooming, panning, and mouse location information. 
    In case of multi-layer 'RasterStacks' or 'RasterBricks', RGB image plots 
    are created (similar to 'raster::plotRGB' - but interactive).
Supports the creation of 'burndown' charts and 'gantt' diagrams.
Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge  Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.
It includes functions to download and process the 'Planet NICFI' (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. 'GDAL' (library for raster and vector geospatial data formats) and 'aria2c' (paralleled download utility) must be installed and configured in the user's Operating System.
The goal of 'planets' is to provide of very simple and accessible data containing basic information from all known planets.
Perform flexible and quick calculations for Demand and Supply Planning, such as projected inventories and coverages, as well as replenishment plan. For any time bucket, daily, weekly or monthly, and any granularity level, product or group of products.
Provides access to the 'PlanScore' Application Programming Interface 
    (<https://github.com/PlanScore/PlanScore/blob/main/API.md>) for scoring redistricting
    plans. Allows for upload of plans from block assignment files and shape files. 
    For shapes in memory, such as from 'sf' or 'redist', it processes them to save
    and upload. Includes tools for tidying responses and saving output from the 
    website.
Coupled leaf gas exchange model, A-Ci curve simulation and
    fitting, Ball-Berry stomatal conductance models, 
    leaf energy balance using Penman-Monteith, Cowan-Farquhar
    optimization, humidity unit conversions. 
    See Duursma (2015) <doi:10.1371/journal.pone.0143346>.
Fit linear mixed models dedicated to the genetic study of plant mixtures, such as those based on general and specific mixing abilities (GMA-SMA) as well as direct and social breeding values (DBV-SBV), also known as direct and indirect genetic effects (DGE-IGE). More details in Forst et al (2019, <doi:10.1016/j.fcr.2019.107571>) for GMA-SMA models, and Salomon et al (2026, <doi:10.64898/2026.03.27.714849>) for DBV-SBV models. The package also provides functions to optimize experimental designs, simulate data sets and compute interaction indices.
Functions tailored for scientific and student communities involved in plant science research. Functionalities encompass estimation chlorophyll content according to Arnon (1949) <doi:10.1104/pp.24.1.1>, determination water potential of Polyethylene glycol(PEG)6000 as in Michel and Kaufmann (1973) <doi:10.1104/pp.51.5.914>  and functions related to estimation of yield related indices like Abiotic tolerance index as given by Moosavi et al.(2008)<doi:10.22059/JDESERT.2008.27115>, Geometric mean productivity (GMP) by Fernandez (1992) <ISBN:92-9058-081-X>, Golden Mean by Moradi et al.(2012)<doi:10.14207/ejsd.2012.v1n3p543>, HAM by Schneider et al.(1997)<doi:10.2135/cropsci1997.0011183X003700010007x>,MPI and TOL by Hossain etal., (1990)<doi:10.2135/cropsci1990.0011183X003000030030x>, RDI by Fischer et al. (1979)<doi:10.1071/AR9791001>,SSI by Fisher et al.(1978)<doi:10.1071/AR9780897>, STI by Fernandez (1993)<doi:10.22001/wvc.72511>,YSI by Bouslama & Schapaugh (1984)<doi:10.2135/cropsci1984.0011183X002400050026x>, Yield index by Gavuzzi et al.(1997)<doi:10.4141/P96-130>.
Extracts growth, survival, and local neighborhood density
    information from repeated, fine-scale maps of organism occurrence.
    Further information about this package can be found in our journal
    article, "plantTracker: An R package to translate maps of plant
    occurrence into demographic data" published in 2022 in Methods in
    Ecology and Evolution (Stears, et al., 2022)
    <doi:10.1111/2041-210X.13950>.
Contains tools for supervised analyses of incomplete, overlapping
  multiomics datasets. Applies partial least squares in multiple steps to find
  models that predict survival outcomes. See Yamaguchi et al. (2023)
  <doi:10.1101/2023.03.10.532096>.
Aims at detecting single nucleotide variation
    (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used
    as a surrogate marker for tumor, at each base position of an Next Generation
    Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele
    frequency at each position to the measured PER in control samples.
Provides tools for cross-validated Lasso and Post-Lasso estimation.
  Built on top of the 'glmnet' package by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>,
  the main function plasso() extends the standard 'glmnet' output with coefficient paths
  for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso
  and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) <doi:10.1111/rssa.12623>.
Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.
Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.
Directly pipes raw quantitative PCR (qPCR) machine outputs into downstream 
    analyses using the comparative Ct (Delta-Delta Ct) method described by Livak and 
    Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Streamlines the workflow from 'Excel' 
    export to publication-ready plots. Integrates unique visual quality control by 
    reconstructing 96-well plate heatmaps, allowing users to instantly detect pipetting 
    errors, edge effects, and outliers. Key features include automated error propagation, 
    laboratory master mix calculations, and generation of bar charts and volcano plots.
Design parameters of the optimal two-period multiarm platform design
    (controlling for either family-wise error rate or pair-wise error rate) can be calculated
    using this package, allowing pre-planned deferred arms to be added during the trial. More
    details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J.
    (2022) "An optimal two-period multiarm platform design with new experimental arms added
    during the trial". Manuscript submitted for publication. For additional references: Dunnett,
    C. W. (1955) <doi:10.2307/2281208>.
Data and analysis from an experiment with improving touch 
    typing speed, using the tDCS PlatoWork headset produced by PlatoScience.
It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.
Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) <https://simonfreyaldenhoven.github.io/papers/Plausible_bounds.pdf>. Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands.
Extends the popular 'lavaan' package by adding penalized
    estimation capabilities. It supports penalty on individual parameters
    as well as the difference between parameters.
Games that can be played in the R console.  Includes coin
    flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut
    the box, spelling bee, and 2048.
Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: 'data', a dataframe containing player data for any sports; 'player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; 'name_col', specifying the name of the column in the dataframe that contains the player names; and 'player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.
Implements schemes for estimating player or 
  team skill based on dynamic updating. Implemented methods include 
  Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation 
  of a reproducible analysis using approximately two million chess 
  matches. Also contains an Elo based method for multi-player games
  where the result is a placing or a score. This includes zero-sum
  games such as poker and mahjong.
Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the 'General Framework for Regression with Mismatched Data' developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).
Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.
Provides tools to compute unbiased pleiotropic heritability estimates of complex diseases from genome-wide association studies (GWAS) summary statistics. We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 <doi:10.1038/ng.3406>) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates.
Log-multiplicative association models (LMA) are
    models for cross-classifications of categorical variables
    where interactions are represented by products of category
    scale values and an association parameter. Maximum
    likelihood estimation (MLE) fails for moderate to large
    numbers of categorical variables. The 'pleLMA' package
    overcomes this limitation of MLE by using pseudo-likelihood
    estimation to fit the models to small or large
    cross-classifications dichotomous or multi-category variables.
    Originally proposed by Besag (1974,
    <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
    estimation takes large complex models and breaks it down
    into smaller ones. Rather than maximizing the likelihood
    of the joint distribution of all the variables, a
    pseudo-likelihood function, which is the product likelihoods
    from conditional distributions, is maximized. LMA models can
    be derived from a number of different frameworks including
    (but not limited to) graphical models and uni-dimensional
    and multi-dimensional item response theory models. More
    details about the models and estimation can be found in
    the vignette.
The portmanteau local feature discriminant approach first identifies the local 
    discriminant features and their differential structures, then constructs the 
    discriminant rule by pooling the identified local features together. This method 
    is applicable to high-dimensional matrix-variate data. 
    See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).
Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo (SMC) inference for fully Bayesian
  Gaussian process (GP) regression and classification models by
  particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>.
  The sequential nature of inference
  and the active learning (AL) hooks provided facilitate thrifty 
  sequential design (by entropy) and optimization
  (by improvement) for classification and
  regression models, respectively.
  This package essentially provides a generic
  PL interface, and functions (arguments to the interface) which
  implement the GP models and AL heuristics.  Functions for 
  a special, linked, regression/classification GP model and 
  an integrated expected conditional improvement (IECI) statistic 
  provide for optimization in the presence of unknown constraints.
  Separable and isotropic Gaussian, and single-index correlation
  functions are supported.
  See the examples section of ?plgp and demo(package="plgp") 
  for an index of demos.
Plots with high flexibility and easy handling, including
  informative regression diagnostics for many models.
Tools for both single and batch image manipulation and
    analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and
    phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>).
    The tools can be used for the quantification of leaf area, object
    counting, extraction of image indexes, shape measurement, object
    landmark identification, and Elliptical Fourier Analysis of object
    outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package
    also provides a comprehensive pipeline for generating shapefiles with
    complex layouts and supports high-throughput phenotyping of RGB,
    multispectral, and hyperspectral orthomosaics. This functionality
    facilitates field phenotyping using UAV- or satellite-based imagery.
Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the Power Lindley distribution. Specificity, sensitivity, area under the curve and ROC curve are provided.
Item response theory based methods are used to compute
        linking constants and conduct chain linking of unidimensional
        or multidimensional tests for multiple groups under a common
        item design.  The unidimensional methods include the Mean/Mean,
        Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous
        (1PL, 2PL and 3PL) and/or polytomous (graded response, partial
        credit/generalized partial credit, nominal, and multiple-choice
        model) items.  The multidimensional methods include the least
        squares method and extensions of the Haebara and Stocking-Lord
        method using single or multiple dilation parameters for
        multidimensional extensions of all the unidimensional
        dichotomous and polytomous item response models.  The package
        also includes functions for importing item and/or ability
        parameters from common IRT software, conducting IRT true score
        and observed score equating, and plotting item response
        curves/surfaces, vector plots, information plots, and comparison 
        plots for examining parameter drift.
Reads/write binary genotype file compatible with 'PLINK' <https://www.cog-genomics.org/plink/1.9/input#bed> into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by 'PLINK' <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or 'GCTA' <https://cnsgenomics.com/software/gcta/#MakingaGRM> into/from a R square matrix. It is best used for bringing data produced by 'PLINK' and 'GCTA' into R workflow.
Genotyping arrays enable the direct measurement of an individuals
    genotype at thousands of markers. 'plinkQC' facilitates genotype quality
    control for genetic association studies as described by Anderson and
    colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic
    statistics (e.g. missing genotyping rates per individual, allele frequencies
    per genetic marker) and relationship functions accessible from 'R' and
    generates a per-individual and per-marker quality control report.
    Individuals and markers that fail the quality control can subsequently be
    removed to generate a new, clean dataset. Removal of individuals based on
    relationship status is optimised to retain as many individuals as possible
    in the study. Additionally, there is a trained classifier to predict 
    genomic ancestry of human samples.
A multiple testing procedure for testing several groups of 
        hypotheses is implemented. Linear dependency among the hypotheses 
        within the same group is modeled by using hidden Markov Models. 
        It is noted that a smaller p value does not necessarily imply 
        more significance due to the dependency. A typical application is 
        to analyze genome wide association studies datasets, where SNPs 
        from the same chromosome are treated as a group and exhibit 
        strong linear genomic dependency. See Wei Z, Sun W, Wang K, 
        Hakonarson H (2009) <doi:10.1093/bioinformatics/btp476> for more details.
A set of estimators for models and (robust) covariance matrices, and tests for panel data
             econometrics, including within/fixed effects, random effects, between, first-difference, 
             nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models,
             panel generalized method of moments (GMM) and general FGLS models,
             mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators
             with common factors, variable coefficients and limited dependent variables models.
             Test functions include model specification, serial correlation, cross-sectional dependence,
             panel unit root and panel Granger (non-)causality. Typical references are general econometrics 
             text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>),
             Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), 
             Panel Data Econometrics with R (<doi:10.1002/9781119504641>).
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
    function from 'stats') to estimate (generalized) linear mixed models (GLMM)
    with factor structures using a profile likelihood approach, as outlined in
    Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
    Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
    Factor analysis and item response models can be extended to allow
    for an arbitrary number of nested and crossed random effects,
    making it useful for multilevel and cross-classified models.
Fits penalized linear mixed models that correct for
    unobserved confounding factors. 'plmmr' infers and corrects for the
    presence of unobserved confounding effects such as population
    stratification and environmental heterogeneity. It then fits a linear
    model via penalized maximum likelihood. Originally designed for the
    multivariate analysis of single nucleotide polymorphisms (SNPs)
    measured in a genome-wide association study (GWAS), 'plmmr' eliminates
    the need for subpopulation-specific analyses and post-analysis p-value
    adjustments. Functions for the appropriate processing of 'PLINK'
    files are also supplied. For examples, see the package homepage
    <https://pbreheny.github.io/plmmr/>.
Performs bivariate composite likelihood and full
        information maximum likelihood estimation for polytomous
        logit-normit (graded logistic) item response theory (IRT)
        models.
The Poisson-lognormal model and variants (Chiquet,
    Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be
    used for a variety of multivariate problems when count data are at
    play, including principal component analysis for count data,
    discriminant analysis, model-based clustering and network inference.
    Implements variational algorithms to fit such models accompanied with
    a set of functions for visualization and diagnostic.
A comprehensive framework for planning and executing analyses in R. 
    It provides a structured approach to running the same function multiple times 
    with different arguments, executing multiple functions on the same datasets, 
    and creating systematic analyses across multiple strata or variables. 
    The framework is particularly useful for applying the same analysis across 
    multiple strata (e.g., locations, age groups), running statistical methods 
    on multiple variables (e.g., exposures, outcomes), generating multiple tables 
    or graphs for reports, and creating systematic surveillance analyses. 
    Key features include efficient data management, structured analysis planning, 
    flexible execution options, built-in debugging tools, and hash-based caching.
A method for the quantitative prediction with much predictors. 
    This package provides functions to construct the quantitative prediction 
    model with less overfitting and robust to noise.
Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
An implementation of the ternary plot for interpreting regression
    coefficients of trinomial regression models, as proposed in Santi, Dickson
    and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be
    drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary'
    package (based on standard graphics). The package and its features are
    illustrated in Santi, Dickson, Espa and Giuliani (2022)
    <doi:10.18637/jss.v103.c01>.
Explore the world of R graphics with fun and interesting plot functions! 
    Use make_LED() to create dynamic LED screens, draw interconnected rings with Olympic_rings(), 
    and make festive Chinese couplets with chunlian(). 
    Unleash your creativity and turn data into exciting visuals!
Interactively annotate 'base R graphics' plots with freehand drawing, symbols (points, lines, arrows, rectangles, circles, ellipses), and text. This is useful for teaching, for example to visually explain certain plot elements, and creating quick sketches.
A grammar of graphics framework built on 'base' graphics. It
  provides a 'bbplot' object and a '+' operator to incrementally compose plots
  from data, aesthetic mappings and layers, then render them using the base
  plotting system. The package includes common geometric layers (points, lines,
  segments, bars, histograms, boxplots and tiles), scales for color and other
  aesthetics, legends, faceting, themes, and significance annotations.
Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables.
The 'plotcli' package provides terminal-based plotting in R.
             It supports colored scatter plots, line plots, bar plots, boxplots, 
             histograms, density plots, and more. The 'ggplotcli()' function is a 
             universal converter that renders any 'ggplot2' plot in the terminal 
             using Unicode Braille characters or ASCII. Features include support for 
             15+ geom types, faceting (facet_wrap/facet_grid), automatic theme 
             detection, legends, optimized color mapping, and multiple canvas types.
This function plots a contour line with a user-defined probability and tightness of fit.
Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command,  using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data,  map projections and continental outlines.  Optionally the data can be animated through time using the 'gganmiate' package.
The goal of 'PlotFTIR' is to easily and quickly kick-start the production of journal-quality Fourier Transform Infra-Red (FTIR) spectral plots in R using 'ggplot2'. The produced plots can be published directly or further modified by 'ggplot2' functions.
    L'objectif de 'PlotFTIR' est de démarrer facilement et rapidement la production des tracés spectraux de spectroscopie infrarouge à transformée de Fourier (IRTF) de qualité journal dans R à l'aide de 'ggplot2'. Les tracés produits peuvent être publiés directement ou modifiés davantage par les fonctions 'ggplot2'.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), 
    including both densities and overlaying mixture weight component curves from the fit GMM. The package
    also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram
    of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, 
    which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture 
    component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying 
    the "fun" argument within "stat_function" in a ggplot2 object.
Hidden Markov Models are useful for modeling
 sequential data. This package provides several functions
 implemented in C++ for explaining the algorithms used for
 Hidden Markov Models (forward, backward, decoding,
 learning).
Provides publication‑quality and interactive plots for exploring 
             the posterior output of Latent Space Item Response Models, including 
             Posterior Interaction Profiles, radar charts, 2‑D latent maps, and 
             item‑similarity heat maps. The methods implemented in this package 
             are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>.
Examines the characteristics of a data frame and a formula to
    automatically choose the most suitable type of plot out of the following supported
    options: scatter, violin, box, bar, density, hexagon bin, spine plot, and
    heat map. The aim of the package is to let the user focus on what to plot,
    rather than on the "how" during exploratory data analysis. It also automates
    handling of observation weights, logarithmic axis scaling, reordering of
    factor levels, and overlaying smoothing curves and median lines. Plots are
    drawn using 'ggplot2'.
Create interactive web graphics from 'ggplot2' graphs and/or a custom interface to the (MIT-licensed) JavaScript library 'plotly.js' inspired by the grammar of graphics.
Includes 'JavaScript' files that allow 'plotly' maps to render without an internet connection.
Markov chain Monte Carlo diagnostic plots. The purpose of the
  package is to combine existing tools from the 'coda' and 'lattice' packages,
  and make it easy to adjust graphical details.
Plot marginal effects for interactions estimated
    from linear models.
The main function, plot_mm(), is used for (gg)plotting output from mixture models, 
    including both densities and overlaying mixture weight component curves from the fit models in line with the
    tidy principles. The package includes several additional functions for added plot customization. 
    Supported model objects include: 'mixtools', 'EMCluster', and 'flexmix', with more from each in active dev. 
    Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, 
    logistic regressions, linear regressions, and Poisson regressions.
Plot model surfaces for a wide variety of models
        using partial dependence plots and other techniques.
        Also plot model residuals and other information on the model.
Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis,
  <https://our.oakland.edu/items/c8942577-2562-4d2f-8677-cb8ec0bf6234>.
Produces odds ratio analyses with comprehensive reporting tools. Generates 
    plots, summary tables, and diagnostic checks for logistic regression models 
    fitted with 'glm()' using binomial family. Provides visualisation methods, 
    formatted reporting tables via 'gt', and tools to assess logistic regression 
    model assumptions.
Plot principal component histograms around a bivariate
        scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.
Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.
Provides tools for visual exploratory data analysis with nested
    data. Includes functions for creating bivariate plots, dot plots,
    histograms, and violin plots for each group or unit in nested data.
    Methods are described in Crabtree and Nelson (2017) "Plotrr: Functions for
    making visual exploratory data analysis with nested data easier"
    <doi:10.21105/joss.00190>.
Figures rendered on graphics devices are usually 
 rescaled to fit pre-determined device dimensions. 'plotscale' 
 implements the reverse: desired plot dimensions are specified 
 and device dimensions are calculated to accommodate marginal
 material, giving consistent proportions for plot elements.
 Default methods support grid graphics such as lattice and ggplot.
 See "example('devsize')" and "vignette('plotscale')".
A framework for creating interactive figures for data exploration. 
    All plots are automatically linked and support several kinds of interactive features,
    including selection, zooming, panning, and parameter manipulation. The figures
    can be interacted with either manually, using a mouse and a keyboard, 
    or by running code from inside an active R session.
Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).
Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly.
    It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects.
    It is particularly useful for creating complex plots with multiple layers, facets, and annotations.
    It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees.
    The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem.
    The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.
Annotate plots with legends for continuous variables and colour
  spectra using the base graphics plotting tools; and manipulate irregular
  polygons. Includes palettes for colour-blind viewers.
Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.
Prediction limits for the Poisson distribution
  are produced from both frequentist and Bayesian viewpoints. Limiting results
  are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior
  distributions. More details on the methodology are discussed in Bejleri and
  Nandram (2018) <doi:10.1080/03610926.2017.1373814> and Bejleri, Sartore and
  Nandram (2021) <doi:10.1007/s42952-021-00157-x>.
Power logit regression models for bounded
  continuous data, in which the density generator may be normal, Student-t, 
  power exponential, slash, hyperbolic, sinh-normal, or type II logistic. 
  Diagnostic tools associated with the fitted model, such as the residuals, 
  local influence measures, leverage measures, and goodness-of-fit statistics,
  are implemented. The estimation process follows the maximum likelihood approach
  and, currently, the package supports two types of estimators: the usual maximum 
  likelihood estimator and the penalized maximum likelihood estimator. More details
  about power logit regression models are described in 
  Queiroz and Ferrari (2022) <arXiv:2202.01697>.
Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
Multivariate regression methods
	Partial Least Squares Regression (PLSR), Principal Component
	Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
        for Partial Least Squares (PLS) Regression. Model selection for
        PLS is based on various information criteria (aic, bic, gmdl)
        or on cross-validation. Estimates for the mean and covariance
        of the PLS regression coefficients are available. They allow
        the construction of approximate confidence intervals and the
        application of test procedures (Kramer and Sugiyama 
        2012 <doi:10.1198/jasa.2011.tm10107>).
        Further, cross-validation procedures for Ridge Regression and 
        Principal Components Regression are available.
Routines for PLS-based genomic analyses,
        implementing PLS methods for classification with
        microarray data and prediction of transcription factor
        activities from combined ChIP-chip analysis. The >=1.2-1
        versions include two new classification methods for microarray
        data: GSIM and Ridge PLS. The >=1.3 versions includes a
        new classification method combining variable selection and
        compression in logistic regression context: logit-SPLS; and
        an adaptive version of the sparse PLS.
Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. 
    The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. 
    Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.
Bindings for additional regression models for use with the
    'parsnip' package, including ordinary and spare partial least squares
    models for regression and classification (Rohart et al (2017)
    <doi:10.1371/journal.pcbi.1005752>).
Fit a model with potentially many linear and smooth predictors. Interaction
    effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
    we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
    (generalised additive model).
Partial Least Squares Path Modeling (PLS-PM), Tenenhaus, Esposito Vinzi, Chatelin, Lauro (2005) <doi:10.1016/j.csda.2004.03.005>,
    analysis for both metric and non-metric data, as well as REBUS analysis, Esposito Vinzi, Trinchera, Squillacciotti, and Tenenhaus (2008) <doi:10.1002/asmb.728>.
Provides Partial least squares Regression for (weighted) beta regression models (Bertrand 2013,  <https://ojs-test.apps.ocp.math.cnrs.fr/index.php/J-SFdS/article/view/215>) and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings <doi:10.1093/bioinformatics/btu660>, Bastien, P., Bertrand, F., Meyer N., Maumy-Bertrand, M. (2015), Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Bioinformatics, 31(3):397-404. Cross validation criteria were studied in <doi:10.48550/arXiv.1810.02962>, Bertrand, F., Bastien, Ph. and Maumy-Bertrand, M. (2018), Cross validating extensions of kernel, sparse or regular partial least squares regression models to censored data.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Estimate complex Structural Equation Models (SEMs) by fitting Partial
    Least Squares Structural Equation Modeling (PLS-SEM) and Partial Least
    Squares consistent Structural Equation Modeling (PLSc-SEM) specifications
    that handle categorical data, non-linear relations, and multilevel
    structures. The implementation follows Lohmöller (1989) for the classic PLS-SEM
    algorithm, Dijkstra and Henseler (2015) for consistent PLSc-SEM, Dijkstra et al.,
    (2014) for nonlinear PLSc-SEM, and Schuberth, Henseler, Dijkstra (2018)
    for ordinal PLS-SEM and PLSc-SEM. Additional extensions are under development.
    The MC-OrdPLSc algorithm, used to handle ordinal interaction models is detailed
    in Slupphaug et al., (2026).
    References:
    Lohmöller, J.-B. (1989, ISBN:9783790803002).
      "Latent Variable Path Modeling with Partial Least Squares."
    Dijkstra, T. K., & Henseler, J. (2015).
      <doi:10.1016/j.jmva.2015.06.002>.
      "Consistent partial least squares path modeling."
    Dijkstra, T. K., & Schermelleh-Engel, K. (2014).
      <doi:10.1016/j.csda.2014.07.008>.
      "Consistent partial least squares for nonlinear structural equation models."
    Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018).
      <doi:10.1007/s11135-018-0767-9>.
      "Partial least squares path modeling using ordinal categorical indicators."
    Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026).
      <doi:10.31234/osf.io/fwzj6_v1>.
      "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators."
Interfaces and methods for variable selection in Partial Least
    Squares. The methods include filter methods, wrapper methods and embedded
    methods. Both regression and classification is supported.
Converts English phrases to singular or plural form based on
    the length of an associated vector.  Contains helper functions to
    create natural language lists from vectors and to include the length
    of a vector in natural language.
Estimating treatment probabilities that maximize a primary 
    clinical outcome while controlling for the occurrence of adverse events. 
    The 'PLUCR' package implements methods for constrained policy learning.
Provides a secure and user-friendly interface to interact with the 'Plug' <https://plugbytpf.com.br> 'API'. It enables developers to store and manage tokens securely using the 'keyring' package, retrieve data from 'API' endpoints with the 'httr2' package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with 'Plug' ecosystem.
Kernel density estimation with global bandwidth selection
	     via "plug-in".
Gives the ability to automatically generate and serve an HTTP
    API from R functions using the annotations in the R documentation
    around your functions.
Automatically create a web server from annotated 'R' files or by
    building it up programmatically. Provides automatic 'OpenAPI' documentation,
    input handling, asynchronous evaluation, and plugin support.
Gives the ability to automatically deploy a plumber API
    from R functions on 'DigitalOcean' and other cloud-based servers.
Build 'Plumber' APIs that can be used in 'Tableau' workbooks.
    Annotations in R comments allow APIs to conform to the 'Tableau Analytics
    Extension' specification, so that R code can be used to power 'Tableau'
    workbooks.
The base R data.frame, like any vector, is
    copied upon modification. This behavior is at odds with
    that of GUIs and interactive graphics. To rectify this,
    plumbr provides a mutable, dynamic tabular data model.
    Models may be chained together to form the complex
    plumbing necessary for sophisticated graphical
    interfaces. Also included is a
    general framework for linking datasets; an typical
    use case would be a linked brush.
Handles and formats author information in scientific writing
    in 'R Markdown' and 'Quarto'. 'plume' provides easy-to-use and
    flexible tools for inserting author data in 'YAML' as well as
    generating author and contribution lists (among others) as strings
    from tabular data.
Retrieves a 'pluscode' by inputting latitude and longitude. Includes additional functions to retrieve neighbouring 'pluscodes'.
Generates 'Plus Code' of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by 'Google Inc.' present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about 'Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.
In ancient Roman mythology, 'Pluto' was the ruler of the underworld
  and presides over the afterlife. 'Pluto' was frequently conflated with 
  'Plutus', the god of wealth, because mineral wealth was found underground. 
  When plotting with R, you try once, twice, practice again and again, and finally 
  you get a pretty figure you want. It's a 'plot tour', a tour about repetition 
  and reward. Hope 'plutor' helps you on the tour!
A fully legal chess move generator and game engine
    implemented in C++17 via 'Rcpp'. Provides FEN (Forsyth-Edwards Notation)
    parsing, PGN (Portable Game Notation) replay, position feature enrichment,
    and a multi-game registry backed by a bitboard representation.
A set of tools that solves a common set of problems: you need
    to break a big problem down into manageable pieces, operate on each
    piece and then put all the pieces back together.  For example, you
    might want to fit a model to each spatial location or time point in
    your study, summarise data by panels or collapse high-dimensional
    arrays to simpler summary statistics. The development of 'plyr' has
    been generously supported by 'Becton Dickinson'.
You can use this program for 3 sets of categorical data for propensity score matching.
            Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. 
            The matching will make the differences in the baseline data smaller.
            This method was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.
Performs Penalized Multivariate Analysis: a penalized
        matrix decomposition, sparse principal components analysis,
        and sparse canonical correlation analysis, described in
        Witten, Tibshirani and Hastie (2009)
        <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani
        (2009) Extensions of sparse canonical correlation analysis,
        with applications to genomic data
        <doi:10.2202/1544-6115.1470>.
Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups.  Implements methods described in:  Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>.  Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>.  Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>.  Matzke et al. (2013) <doi:10.1002/pmic.201200269>.  Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>.  Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.
Monte Carlo based model choice for applied phylogenetics of
    continuous traits. Method described in  Carl Boettiger, Graham Coop,
    Peter Ralph (2012) Is your phylogeny informative? Measuring
    the power of comparative methods, Evolution 66 (7)
    2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.
Fit calibrations curves for clinical prediction models and calculate several associated 
  metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model 
  should align with observed probabilities. Calibration curves relate predicted probabilities 
  (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 
  'pmcalibration' allows users to choose between several smoothers (regression splines, generalized 
  additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. 
  See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; 
  Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; 
  Austin et al. (2020) <doi:10.1002/sim.8570>.
Aims to utilize model-based clustering (unsupervised)
        for high dimensional and ultra large data, especially in a distributed
        manner. The code employs 'pbdMPI' to perform a
        expectation-gathering-maximization algorithm
        for finite mixture Gaussian
        models. The unstructured dispersion matrices are assumed in the
        Gaussian models. The implementation is default in the single program
        multiple data programming model. The code can be executed
        through 'pbdMPI' and MPI' implementations such as 'OpenMPI'
        and 'MPICH'.
        See the High Performance Statistical Computing website
	<https://snoweye.github.io/hpsc/>
	for more information, documents and examples.
Note, that the 'PMCMR' package is superset
	     by the novel 'PMCMRplus' package. The 'PMCMRplus'
	     package contains all functions from 
	     'PMCMR' and many more parametric and non-parametric
	     multiple comparison procedures, one-factorial trend
	     tests, as well as improved method functions, 
	     such as print, summary and plot. 
	     The 'PMCMR' package is no longer maintained,
	     but kept for compatibility of reverse depending
	     packages for some time.
For one-way layout experiments the one-way ANOVA can
	     be performed as an omnibus test. All-pairs multiple comparisons 
	     tests (Tukey-Kramer test, Scheffe test, LSD-test) 
	     and many-to-one tests (Dunnett test) for normally distributed 
	     residuals and equal within variance are available. Furthermore,
	     all-pairs tests (Games-Howell test, Tamhane's T2 test, 
	     Dunnett T3 test, Ury-Wiggins-Hochberg test) and many-to-one
	     (Tamhane-Dunnett Test) for normally distributed residuals 
	     and heterogeneous variances are provided. Van der Waerden's normal
	     scores test for omnibus, all-pairs and many-to-one tests is
	     provided for non-normally distributed residuals and homogeneous
	     variances. The Kruskal-Wallis, BWS and Anderson-Darling
	     omnibus test and all-pairs tests
	     (Nemenyi test, Dunn test, Conover test, Dwass-Steele-Critchlow-
	     Fligner test) as well as many-to-one (Nemenyi test, Dunn test,
	     U-test) are given for the analysis of variance by ranks. 
             Non-parametric trend tests (Jonckheere test, Cuzick test,
	     Johnson-Mehrotra test, Spearman test) are included. 
	     In addition, a Friedman-test for one-way ANOVA with repeated 
	     measures on ranks (CRBD) and Skillings-Mack test for unbalanced 
	     CRBD is provided with consequent all-pairs tests (Nemenyi test, 
	     Siegel test, Miller test, Conover test, Exact test)
	     and many-to-one tests (Nemenyi test, Demsar test, Exact test). 
	     A trend can be tested with Pages's test. Durbin's test 
	     for a two-way balanced incomplete block design (BIBD) is given 
	     in this package as well as Gore's test for CRBD with multiple
	     observations per cell is given.  Outlier tests, Mandel's k- and
	     h statistic as well as functions for Type I error and Power 
	     analysis as well as generic summary, print and plot methods 
             are provided.
Paired mass distance (PMD) analysis proposed in Yu, Olkowicz and Pawliszyn (2018) <doi:10.1016/j.aca.2018.10.062> and PMD based reactomics analysis proposed in Yu and Petrick (2020) <doi:10.1038/s42004-020-00403-z> for gas/liquid chromatography–mass spectrometry (GC/LC-MS) based non-targeted analysis. PMD analysis including GlobalStd algorithm and structure/reaction directed analysis. GlobalStd algorithm could found independent peaks in m/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis could be used to find potential relationship among those independent peaks in different retention time groups based on frequency of paired mass distances. Reactomics analysis could also be performed to build PMD network, assign sources and make biomarker reaction discovery. GUIs for PMD analysis is also included as 'shiny' applications.
Calculate Predictive Moran's Eigenvector Maps (pMEM) for
  spatially-explicit prediction of environmental variables, as defined by
  Guénard and Legendre (2024) <doi:10.1111/2041-210X.14413>. pMEM extends
  classical MEM by enabling interpolation and prediction at unsampled locations
  using spatial weighting functions parameterized by range (and optionally
  shape). The package implements multiple pMEM types (e.g., exponential,
  Gaussian, linear) and features a modular architecture that allows programmers
  to define custom weighting functions. Designed for ecologists, geographers,
  and spatial analysts working with spatially-structured data.
Allows to download current and historical METAR weather reports
  extract and parse basic parameters and present main weather information. 
  Current reports are downloaded from Aviation Weather Center 
  <https://aviationweather.gov/data/metar/> and historical reports from
  Iowa Environmental Mesonet web page of Iowa State University
  ASOS-AWOS-METAR <http://mesonet.agron.iastate.edu/AWOS/>.
Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).
The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015)<doi:10.1016/j.apm.2022.09.004> and Debnath et al. (2015) <doi:10.1007/s40710-015-0107-1>.
Conduct internal validation of a clinical prediction model for a binary outcome.
  Produce bias corrected performance metrics (c-statistic, Brier score, calibration intercept/slope)
  via bootstrap (simple bootstrap, bootstrap optimism, .632 optimism) and cross-validation (CV optimism,
  CV average). Also includes functions to assess model stability via bootstrap resampling.
  See Steyerberg et al. (2001) <doi:10.1016/s0895-4356(01)00341-9>;
  Harrell (2015) <doi:10.1007/978-3-319-19425-7>;
  Riley and Collins (2023) <doi:10.1002/bimj.202200302>.
Check available classification and regression data sets from the PMLB repository and download them.
    The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
    These data sets cover a range of applications, and include binary/multi-class classification problems and 
    regression problems, as well as combinations of categorical, ordinal, and continuous features.
    There are currently over 150 datasets included in the PMLB repository.
Implements two-stage pseudo maximum likelihood estimation (PMLE)
    for copula-based regression models with semi-competing risks data. The
    marginal distributions are modeled by semiparametric transformation
    regression models, and the dependence between bivariate event times is
    specified by a parametric copula function. See Arachchige, Chen and Zhou
    (2025) <doi:10.1007/s10985-024-09640-z> for details.
Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.
Estimate spatial autoregressive nonlinear probit models with and without autoregressive disturbances using partial maximum likelihood estimation. Estimation and inference regarding marginal effects is also possible. For more details see Bille and Leorato (2020) <doi:10.1080/07474938.2019.1682314>.
The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <https://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products.
Provides a simple interface for extracting various elements from
  the publicly available PubMed XML files, incorporating PubMed's regular
  updates, and combining the data with the NIH Open Citation Collection. See
  Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
A progression model for repeated measures (PMRM)
  is a continuous-time nonlinear mixed-effects model for
  longitudinal clinical trials in progressive diseases.
  Unlike mixed models for repeated measures (MMRMs),
  which estimate treatment effects as linear combinations
  of additive effects on the outcome scale,
  PMRMs characterize treatment effects
  in terms of the underlying disease trajectory.
  This framing yields clinically interpretable quantities
  such as average time saved
  and percent reduction in decline due to treatment.
  This package implements frequentist PMRMs by
  Raket (2022) <doi:10.1002/sim.9581> using
  'RTMB' by Kristensen (2016) <doi:10.18637/jss.v070.i05>.
Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.
Provides analytic and simulation tools to estimate the minimum
    sample size required for achieving a target prediction mean-squared error
    (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear
    regression models.  Functions implement the criteria of Ma (2023) 
    <https://digital.wpi.edu/downloads/0g354j58c>, support covariance-matrix 
    handling, and include helpers for root-finding and diagnostic plotting.
Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.
Provides an R implementation of the Particle Metropolis within
    Gibbs sampler for model parameter, covariance matrix and random effect
    estimation. A more general implementation of the sampler based on the paper
    by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D.
    (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing
    the package is available at
    <https://university-of-newcastle-research.github.io/samplerDoc/> and
    includes several detailed examples, some background and troubleshooting
    steps.
Tools for the practical management of financial
  portfolios: backtesting investment and trading strategies,
  computing profit/loss and returns, analysing trades,
  handling lists of transactions, reporting, and more.  The
  package provides a small set of reliable, efficient and
  convenient tools for processing and analysing
  trade/portfolio data.  The manual provides all the details;
  it is available from
  <https://enricoschumann.net/R/packages/PMwR/manual/PMwR.html>.
  Examples and descriptions of new features are provided at
  <https://enricoschumann.net/notes/PMwR/>.
Provides a user interface to create or modify pharmacometric
    models for various modeling and simulation software platforms.
Estimate coefficient of variation percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>.
An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as 'Monolix', 'NONMEM', and 'nlmixr2', see Bräm et al. (2024) <doi:10.1007/s10928-023-09886-4> and Bräm et al. (2025) <doi:10.1002/psp4.13265>. The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with 'Monolix', 'pmxNODE' interfaces with 'MonolixSuite' via the 'lixoftConnectors' package. The 'lixoftConnectors' package is distributed with 'MonolixSuite' (<https://monolixsuite.slp-software.com/r-functions/2024R1/package-lixoftconnectors>) and is not available from public repositories.
Generate nicely formatted HTML tables to display estimation results for
  pharmacometric models.
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given 
    times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- 
    and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) 
    <https://www.facm.ucl.ac.be/cooperation/Vietnam/WBI-Vietnam-October-2011/Modelling/Monolix32_PKPD_library.pdf>); parametric simulation from NONMEM-generated parameter estimates 
    and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
Provides tools for downloading, reading and analyzing the Continuous National
  Household Sample Survey - PNADC, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
Identifies reference periods (months, fortnights, and weeks) in
    Brazil's quarterly PNADC (Pesquisa Nacional por Amostra de Domicilios
    Continua) survey data and computes calibrated weights for sub-quarterly
    analysis. The core algorithm uses IBGE (Instituto Brasileiro de Geografia
    e Estatistica) 'Parada Tecnica' (technical break) rules combined with
    respondent birthdates to determine which temporal period each survey
    observation refers to. Period identification follows a nested hierarchy
    enforced by construction: fortnights require months, weeks require
    fortnights. Achieves approximately 97% monthly determination rate with
    the full series (2012-2025). Strict fortnight and week rates are
    approximately 9% and 3% respectively, as they cannot leverage
    cross-quarter panel aggregation. Experimental strategies (probabilistic
    assignment and UPA (Primary Sampling Unit) aggregation) further improve
    these determination rates. The package provides adaptive hierarchical
    weight calibration (4/2/1 cell levels for month/fortnight/week) with
    period-specific smoothing to produce survey weights calibrated to
    SIDRA (Sistema IBGE de Recuperacao Automatica) population totals. Also
    includes a SIDRA mensalization module that converts 86+ official rolling
    quarter series from the IBGE SIDRA API (Application Programming
    Interface) into exact monthly estimates, without requiring access to
    microdata. Hecksher (2020)
    <https://repositorio.ipea.gov.br/handle/11058/9859>.
Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2023). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.
Provides functions for testing phylogenetic niche conservatism, a key prerequisite in community assembly studies. The package integrates global functional trait data across major taxonomic groups and implements methods such as Pagel's Lambda and Blomberg's K to quantify phylogenetic signals in ecological communities. Methods are described in Münkemüller et al. (2012) <doi:10.1111/j.2041-210X.2012.00196.x>.
Numerical derivatives through finite-difference approximations
    can be calculated using the 'pnd' package with parallel capabilities and
    optimal step-size selection to improve accuracy. These functions facilitate
    efficient computation of derivatives, gradients, Jacobians, and Hessians,
    allowing for more evaluations to reduce the mathematical and machine errors.
    Designed for compatibility with the 'numDeriv' package,
    which has not received updates in several years, it introduces advanced features
    such as computing derivatives of arbitrary order, improving
    the accuracy of Hessian approximations by avoiding repeated differencing,
    and parallelising slow functions on Windows, Mac, and Linux.
Implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble).  Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.
Provides tools for downloading, reading and analyzing the National
  Survey of Demographic and Health - PNDS, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
Provides tools for downloading, reading and analyzing the National
  Survey of Health - PNS, a household survey from Brazilian Institute
  of Geography and Statistics - IBGE. The data must be downloaded from the official
  website <https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
This program contains a function to find the peaks and troughs of 
    a data set. It filters the set of peaks to remove noise based on the expected
    height and expected slope of a peak. Peaks that are too short (caused by random 
    noise), or too shallow (part of the background data) are filtered out.
PNW-Inspired Palettes for 'R' data visualizations. Palettes are variable 
	in length and checked for colorblind accessibility from hue, saturation, 
	and lightness value scaling using the 'Chroma.js  Color Palette Helper' 
	<https://gka.github.io/palettes/>.
Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) of Wages, Conaway and O'Quigley (2011) <doi:10.1177/1740774511408748> for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
This tool computes the probability of detection (POD) curve and the limit of detection (LOD), i.e. the number of copies of the target DNA sequence required to ensure a 95 % probability of detection (LOD95). Other quantiles of the LOD can be specified.
    This is a reimplementation of the mathematical-statistical modelling of the validation of qualitative polymerase chain reaction (PCR) methods within a single laboratory as provided by the commercial tool 'PROLab' <http://quodata.de/>. The modelling itself has been described by Uhlig et al. (2015) <doi:10.1007/s00769-015-1112-9>.
Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.
A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.
Estimate large covariance matrices in approximate factor
        models by thresholding principal orthogonal complements.
Bayesian variable selection for regression models of under-reported
    count data as well as for (overdispersed) Poisson, negative binomal and
    binomial logit regression models using spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
Implementation of both the exact and approximation methods for computing the cdf of the Poisson binomial distribution as described in Hong (2013) <doi: 10.1016/j.csda.2012.10.006>. It also provides the pmf, quantile function, and random number generation for the Poisson binomial distribution. The C code for fast Fourier transformation (FFT) is written by R Core Team (2019)<https://www.R-project.org/>, which implements the FFT algorithm in Singleton (1969) <doi: 10.1109/TAU.1969.1162042>.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates 
             and maximum likelihood estimates of the Poisson lognormal 
             distribution and the bivariate Poisson lognormal distribution.
Provides an implementation of a rare variant association test that utilizes protein tertiary structure to increase signal and to identify likely causal variants. Performs structure-guided collapsing, which leads to local tests that borrow information from neighboring variants on a protein and that provide association information on a variant-specific level. For details of the implemented method see West, R. M.,  Lu, W., Rotroff, D. M., Kuenemann, M., Chang, S-M., Wagner M. J., Buse, J. B., Motsinger-Reif, A., Fourches, D., and Tzeng, J-Y. (2019) <doi:10.1371/journal.pcbi.1006722>.
Validate data in data frames, 'tibble' objects, 'Spark'
    'DataFrames', and database tables. Validation pipelines can be made using
    easily-readable, consecutive validation steps. Upon execution of the
    validation plan, several reporting options are available. User-defined
    thresholds for failure rates allow for the determination of appropriate
    reporting actions. Many other workflows are available including an
    information management workflow, where the aim is to record, collect, and
    generate useful information on data tables.
Imports Coral Point Count with Excel extensions (CPCe)
    point-count annotations and related exported tables, standardizes labels
    with a user-supplied crosswalk, creates ecological cover summaries, writes
    quality-control overlays, and exports machine-learning-ready point labels,
    image patches, sparse masks, and train/validation/test splits. The package
    is fully local and does not depend on third-party web platforms, user
    accounts, or other closed services. CPCe methods are described by Kohler
    and Gill (2006) "Coral Point Count with Excel extensions (CPCe): A Visual
    Basic program for the determination of coral and substrate coverage using
    random point count methodology" <doi:10.1016/j.cageo.2005.11.009>.
The function pointdensity returns a density count and the temporal average for
    every point in the original list. The dataframe returned includes four
    columns: lat, lon, count, and date_avg. The "lat" column is the original
    latitude data; the "lon" column is the original longitude data; the "count"
    is the density count of the number of points within a radius of
    radius*grid_size (the neighborhood); and the date_avg column includes the
    average date of each point in the neighborhood.
Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. 
  Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. 
  We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them.
  There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions.
  An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
Estimate specification models for the state-dependent level of an optimal quantile/expectile forecast.
  Wald Tests and the test of overidentifying restrictions are implemented. Plotting of the estimated specification model is possible.
  The package contains two data sets with forecasts and realizations: the daily accumulated precipitation at London, UK from the high-resolution model of the
  European Centre for Medium-Range Weather Forecasts (ECMWF, <https://www.ecmwf.int/>) and GDP growth Greenbook data by the US Federal Reserve.
  See Schmidt, Katzfuss and Gneiting (2015) <arXiv:1506.01917> for more details on the identification and estimation of a directive behind a point forecast.
R has no built-in pointer functionality. The 'pointr' package fills this gap and lets you create pointers to R objects, including subsets of dataframes. This makes your R code more readable and maintainable.
Functions to calculate and plot event and pointer years as well as resilience indices. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Generation of multiple count, binary and continuous variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. 
             The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.
Generation of multiple count, binary and ordinal variables simultaneously 
             given the marginal characteristics and association structure. Throughout the package,
             the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>.
Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>.
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
             The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Functions to create confidence intervals for ratios of Poisson
    rates under misclassification using double sampling. Implementations of the 
    methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). 
    "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way 
    Differential Misclassification Using Double Sampling." Computational 
    Statistics & Data Analysis, 95:122–132.
Computation of robust standard errors of Poisson fixed effects 
    models, following Wooldridge (1999).
Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson
    likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling)
    (Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued).
    Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization
    instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.
Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.
Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
             The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>.
A collection of color palettes inspired by the enormous diversity of skin colors 
    in Neotropical poison frog species. Suitable for use with 'ggplot2' and base R graphics.
Mixtures of Poisson Generalized Linear Models for high dimensional count data clustering. The (multivariate) responses can be partitioned into set of blocks. Three different parameterizations of the linear predictor are considered. The models are estimated according to the EM algorithm with an efficient initialization scheme <doi:10.1016/j.csda.2014.07.005>.
Efficient implementations of multiple exact and approximate methods
  as described in Hong (2013) <doi:10.1016/j.csda.2012.10.006>, Biscarri, Zhao &
  Brunner (2018) <doi:10.1016/j.csda.2018.01.007> and Zhang, Hong & Balakrishnan
  (2018) <doi:10.1080/00949655.2018.1440294> for computing the probability mass,
  cumulative distribution and quantile functions, as well as generating random
  numbers for both the ordinary and generalised Poisson binomial distribution.
Poisson disk sampling is a method of generating blue noise 
    sample patterns where all samples are at least a specified distance 
    apart. Poisson samples may be generated in two or three dimensions 
    with this package. The algorithm used is an 
    implementation of  Bridson's "Fast Poisson disk sampling in 
    arbitrary dimensions" <doi:10.1145%2F1278780.1278807>.
Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.
For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.
Bindings for Poisson regression models for use with the
    'parsnip' package. Models include simple generalized linear models,
    Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber,
    and Jackman (2008) <doi:10.18637/jss.v027.i08>).
Provides tools for fitting piecewise-constant hazard models for survival and competing risks data, including ensemble hazard estimation via the Super Learner framework. The package supports estimation of survival functions and absolute risk predictions from fitted cause-specific hazard models. For the Super Learner framework see van der Laan, Polley and Hubbard (2007) <doi:10.2202/1544-6115.1309>.
Provides a dataset of Pokemon information in 
  both English and Brazilian Portuguese. The dataset contains 949 rows and 
  22 columns, including information such as the Pokemon's name, ID, height, 
  weight, stats, type, and more.
Distributes data from the Polarization in Comparative Attitudes Project. Helper
    functions enable data retrieval in wide and tidy formats for user-defined
    countries and years. Provides support for case-insensitive country names in many
    languages. Mehlhaff (2022) <https://imehlhaff.net/files/Polarization%20and%20Democracy.pdf>.
A 'shiny' application for visualizing high-dimensional data
    using non-linear dimensionality reduction (NLDR) techniques such as
    t-SNE and UMAP. It provides an interactive platform to explore
    high-dimensional datasets, diagnose the quality of the embeddings
    using the 'quollr' package, and compare different NLDR methods.
Create hexagonal shape sticker image.
  'polaroid' can be used in user's web browser.
  'polaroid' can be used in 'shinyapps.io'.
  In both way, user can download created 'hexSticker' as 'PNG' image.
  'polaroid' is built based on 'argonDash', 'colourpicker' and 'hexSticker' R package.
In each odd dimension is a convex body - the polar zonoid - whose generating functions are trigonometric polynomials.  The polar zonoid is a straightforward generalization of the polar zonohedron in dimension 3, as defined by Chilton and Coxeter (1963) <doi:10.2307/2313051>. The package has some applications of the polar zonoid, including the properties of configuration spaces of arcs on the circle and 3x3 rotation matrices.  There is also a root solver for trigonometric polynomials.
Latent class analysis and latent class regression models 
    for polytomous outcome variables. Also known as latent structure analysis.
A 'C++' reimplementation of 'poLCA' - latent class analysis and
    latent class regression models for polytomous outcome variables, also known
    as latent structure analysis. It attempts to reproduce results and be as
    similar as possible to the original code, while running faster, especially
    with multiple repetitions, by utilising multiple threads. Further reading is
    available on the Queen Mary, University of London, IT Services Research blog
    <https://blog.hpc.qmul.ac.uk/speeding_up_r_packages/>.
Wrangle and annotate different types of political texts.
    It also introduces Urgency Analysis,
    a new method for the analysis of urgency in political texts.
Tools for simplifying the creation and management of data structures
    suitable for dealing with policy portfolios, that is, two-dimensional spaces 
    of policy instruments and policy targets. The package also allows to generate measures of
    portfolio characteristics and facilitates their visualization.
Learn optimal policies via doubly robust empirical
 welfare maximization over trees. Given doubly robust reward estimates, this package
 finds a rule-based treatment prescription policy, where the policy takes the form of
 a shallow decision tree that is globally (or close to) optimal.
This is a collection of data and functions for common metrics in political science research. Data measuring ideology, and functions calculating geographical diffusion and ideological diffusion - geog.diffuse() and ideo.dist(), respectively. Functions derived from methods developed in: Soule and King (2006) <doi:10.1086/499908>, Berry et al. (1998) <doi:10.2307/2991759>, Cruz-Aceves and Mallinson (2019) <doi:10.1177/0160323X20902818>, and Grossback et al. (2004) <doi:10.1177/1532673X04263801>.
Bundles the datasets and functions used in the textbook by Philip
    Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis,
    Second Edition.
Set of functions to polish content for Microsoft 'Word' and 'PowerPoint' 
    into 'OOXML'. Polishing is the conversion of the R object into an 'OOXML'
    representation of the object that can then be added to 'Word' or 'PowerPoint' 
    files.
Authentication, user administration, hosting, and additional infrastructure for 'shiny' apps. See
  <https://polished.tech> for additional documentation and examples.
A very small package for more convenient use of 'NaileR'. You provide a data set containing a latent variable you want to understand. It generates a description and an interpretation of this latent variable using a Large Language Model. For perceptual data, it describes the stimuli used in the experiment.
Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.
Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
A toolbox to facilitate the calculation of political system indicators for researchers. 
    This package offers a variety of basic indicators related to electoral systems, party systems, elections,
    and parliamentary studies, as well as others. Main references are:
    Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>;
    Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>;
    Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>;
    Rae (1968) <doi:10.1177/001041406800100305>;
    Hirschmaņ (1945) <ISBN:0-520-04082-1>;
    Kesselman (1966) <doi:10.2307/1953769>;
    Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>;
    Rice (1925) <doi:10.2307/2142407>;
    Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>;
    SANTOS (2002) <ISBN:85-225-0395-8>.
Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2026) <doi:10.18637/jss.v116.i04> for documentation and references.
Supports analysis of aerobiological data. 
    Available features include determination of pollen season limits, 
    replacement of outliers (Kasprzyk and Walanus (2014) <doi:10.1007/s10453-014-9332-8>),
    calculation of growing degree days (Baskerville and Emin (1969) <doi:10.2307/1933912>), 
    and determination of the base temperature for growing degree days
    (Yang et al. (1995) <doi:10.1016/0168-1923(94)02185-M).
Calculate common types of tables for weighted survey data.
    Options include topline and (2-way and 3-way) crosstab tables of
    categorical or ordinal data as well as summary tables of weighted
    numeric variables. Optionally, include the margin of error at
    selected confidence intervals including the design effect. The
    design effect is calculated as described by 
    Kish (1965) <doi:10.1002/bimj.19680100122> beginning
    on page 257. Output takes the form of tibbles (simple data frames).
    This package conveniently handles labelled data, such as that
    commonly used by 'Stata' and 'SPSS.' Complex survey design is 
    not supported at this time.
Package for corpus analysis using the Corpus Workbench 
    ('CWB', <https://cwb.sourceforge.io>) as an efficient back end for indexing
    and querying large corpora. The package offers functionality to flexibly create
    subcorpora and to carry out basic statistical operations (count, co-occurrences
    etc.). The original full text of documents can be reconstructed and inspected at
    any time. Beyond that, the package is intended to serve as an interface to 
    packages implementing advanced statistical procedures. Respective data structures
    (document-term matrices, term-co-occurrence matrices etc.) can be created based 
    on the indexed corpora.
Routines for the polynomial spline fitting routines
  hazard regression, hazard estimation with flexible tails, logspline,
  lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
A 'shiny' app that allows to access and use the 'INVEKOS' API for field polygons in Austria. API documentation is available at <https://gis.lfrz.gv.at/api/geodata/i009501/ogc/features/v1/>.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.  More information on the implementation can be found at Conrad J. Burden (2014) <arXiv:1406.2780>.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
    a Dirichlet distribution conditioned to satisfy a finite set of linear
    equality and inequality constraints (hence to lie in a convex polytope
    that is a subset of the unit simplex).
Tools for creating, viewing, and assessing qualitative
 palettes with many (20-30 or more) colors. See Coombes and colleagues
 (2019) <doi:10.18637/jss.v090.c01>.
R port of Angus Johnson's open source library 'Clipper'. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Computes polychoric and polyserial correlations by quick "two-step" methods or ML, 
  optionally with standard errors; tetrachoric and biserial correlations are special cases.
A polycross is the pollination by natural hybridization of a group of genotypes, generally selected, grown in isolation from other compatible genotypes in such a way to promote random open pollination. A particular practical application of the polycross method occurs in the production of a synthetic variety resulting from cross-pollinated plants. Laying out these experiments in appropriate designs, known as polycross designs, would not only save experimental resources but also gather more information from the experiment. Different experimental situations may arise in polycross nurseries which may be requiring different polycross designs (Varghese et. al. (2015) <doi:10.1080/02664763.2015.1043860>. " Experimental designs for open pollination in polycross trials"). This package contains a function named PD() which generates nine types of polycross designs suitable for various experimental situations.
Numerical integration of continuously differentiable
    functions f(x,y) over simple closed polygonal domains.
    The following cubature methods are implemented:
    product Gauss cubature (Sommariva and Vianello, 2007,
    <doi:10.1007/s10543-007-0131-2>),
    the simple two-dimensional midpoint rule
    (wrapping 'spatstat.geom' functions), and
    adaptive cubature for radially symmetric functions via line
    integrate() along the polygon boundary (Meyer and Held, 2014,
    <doi:10.1214/14-AOAS743>, Supplement B).
    For simple integration along the axes, the 'cubature' package
    is more appropriate.
Provide easy methods to translate pieces of text.
  Functions send requests to translation services online.
Infer the genetic composition of individuals
    in terms of haplotype dosages for a haploblock, based
    on bi-allelic marker dosages, for any ploidy level.
    Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data.
    Submitted to BMC Bioinformatics (2021).
Kernel density estimation on the polysphere, (hyper)sphere, and
    circle. Includes functions for density estimation, regression estimation,
    ridge estimation, bandwidth selection, kernels, samplers, and homogeneity
    tests. Companion package to García-Portugués and Meilán-Vila (2025)
    <doi:10.1080/01621459.2025.2521898> and García-Portugués and Meilán-Vila
    (2023) <doi:10.1007/978-3-031-32729-2_4>.
A wrapper around the C++ library 'polylabel' from 'Mapbox',
    providing an efficient routine for finding the approximate pole of
    inaccessibility of a polygon, which usually serves as an excellent candidate
    for labeling of a polygon.
Creation of linkage maps in polyploid species from marker dosage
    scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, 
    as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0,
    both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) <doi:10.1007/s00122-021-03834-x>.
Includes functions implementing the conditionally optimal matching algorithm, 
    which can be used to generate matched samples in designs with multiple groups. The algorithm
    is described in Nattino, Song and Lu (2022) <doi:10.1016/j.csda.2021.107364>.
Implementation of class "polyMatrix" for storing a matrix of polynomials and implements 
  basic matrix operations; including a determinant and characteristic polynomial.
  It is based on the package 'polynom' and uses a lot of its methods to implement matrix operations.
  This package includes 3 methods of triangularization of polynomial matrices:
  Extended Euclidean algorithm which is most classical but numerically unstable;
  Sylvester algorithm based on LQ decomposition;
  Interpolation algorithm is based on LQ decomposition and Newton interpolation.
  Both methods are described in 
  D. Henrion & M. Sebek, Reliable numerical methods for polynomial matrix triangularization,
  IEEE Transactions on Automatic Control (Volume 44, Issue 3, Mar 1999, Pages 497-508) <doi:10.1109/9.751344>
  and in 
  Salah Labhalla, Henri Lombardi & Roger Marlin, 
  Algorithmes de calcule de la reduction de Hermite d'une matrice a coefficients polynomeaux,
  Theoretical Computer Science (Volume 161, Issue 1-2, July 1996, Pages 69-92) <doi:10.1016/0304-3975(95)00090-9>.
The gradual release of active substances from packaging can enhance food preservation by maintaining high concentrations of polyphenols and antioxidants for a period of 72 hrs. To assess the effectiveness of packaging materials that serve as carriers for antioxidants, it is crucial to model the diffusivity of the active agents. Understanding this diffusivity helps evaluate the packaging's capacity to prolong the shelf life of food items. The process of migration, which encompasses diffusion, dissolution, and reaching equilibrium, facilitates the transfer of low molecular weight compounds from the packaging into food simulants. The rate at which these active compounds are released from the packaging is typically analysed using food simulants under conditions outlined in European food packaging regulations (Ramos et al., 2014).
A collection of functions to implement a class for univariate
  polynomial manipulations.
Implements univariate polynomial operations in R, including
  polynomial arithmetic, finding zeros, plotting, and some operations on
  lists of polynomials.
Functions to perform paternity exclusion via allele
    matching, in autopolyploid species having ploidy 4, 6, or 8. The
    marker data used can be genotype data (copy numbers known) or
    'allelic phenotype data' (copy numbers not known).
Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature.
  Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) <doi:10.1007/s002280050329>.
  Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) <doi:10.1002/pds.1712>.
  Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) <doi:10.1002/pds.966>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2009) <doi:10.1186/1472-6904-9-11>.
  Hovstadius, B., Astrand, B., & Petersson, G. (2010) <doi:10.1002/pds.1921>.
  Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) <doi:10.1345/aph.1A226>.
  Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) <doi:10.1186/s12877-017-0621-2>.
  Narayan, S. W., & Nishtala, P. S. (2015) <doi:10.1007/s40801-015-0020-y>.
  Nishtala, P. S., & Salahudeen, M. S. (2015) <doi:10.1159/000368191>.
  Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) <doi:10.5414/cp202484>.
  Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) <doi:10.1093/fampra/17.3.261>.
Tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.
Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in 
    autopolyploid bi-parental F1 populations. 
    For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated.
    Significance thresholds, exploring QTL allele effects and visualising results are provided. 
    For more background and to reference the package see <doi:10.1093/bioinformatics/btab574>.
Read depth data from genotyping-by-sequencing (GBS) or restriction 
  site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian
  probability estimates of genotypes in polyploids or diploids.  The genotype 
  probabilities, posterior mean genotypes, or most probable genotypes can then
  be exported for downstream analysis.  'polyRAD' is described by Clark et al.
  (2019) <doi:10.1534/g3.118.200913>, and the Hind/He statistic for marker
  filtering is described by Clark et al. (2022) <doi:10.1186/s12859-022-04635-9>.
  A variant calling pipeline for highly duplicated genomes is also included and
  is described by Clark et al. (2020, Version 1) <doi:10.1101/2020.01.11.902890>.
Automate formation and evaluation of polynomial regression models. The motivation for this package is described in 'Polynomial Regression As an Alternative to Neural Nets' by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and Pete Mohanty (<arXiv:1806.06850>).
A collection of tools to handle microsatellite data of
 any ploidy (and samples of mixed ploidy) where allele copy number is not
 known in partially heterozygous genotypes.  It can import and export data in
 ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
 'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
 pairwise distances between individuals using a stepwise mutation model or
 infinite alleles model, with or without taking ploidies and allele frequencies
 into account.  These distances can be used for the calculation of clonal
 diversity statistics or used for further analysis in R.  Allelic diversity
 statistics and Polymorphic Information Content are also available.  polysat can 
 assist the user in estimating the ploidy of samples, and it can estimate allele 
 frequencies in populations, calculate pairwise or global differentiation statistics 
 based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'.  
 Functions are also included for assigning alleles to isoloci in cases where one pair 
 of microsatellite primers amplifies alleles from two or more independently
 segregating isoloci.  polysat is described by Clark and Jasieniuk (2011)
 <doi:10.1111/j.1755-0998.2011.02985.x> and Clark and Schreier (2017)
 <doi:10.1111/1755-0998.12639>.
Perform classic chi-squared tests and Ripol et al(1999)
             binomial confidence interval approach for autopolyploid
             dominant markers. Also, dominant markers may be generated
             for families of offspring where either one or both of the
             parents possess the marker. Missing values and
             misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers in autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al "Bayesian estimation of marker dosage in sugarcane and other autopolyploids" (2010, <doi:10.1007/s00122-010-1283-z>). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <arxiv:2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large.
Routines for flexible functional form estimation via basis
    regression, with model selection via the adaptive LASSO or SCAD to prevent
    overfitting.
Provides functions to compute and plot power levels, minimum detectable effect sizes, and minimum required sample sizes for the test of the overall average effect size in meta-analysis of dependent effect sizes.
POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.
Implements the method described at the UCLA Statistical
    Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/>
    for checking if the proportional odds assumption holds for a 
    cumulative logit model.
Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.
Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.
Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.
Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.
Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models).  The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a versatile platform for implementation of inference methods for general POMP models.
An implementation of a hybrid method of person-oriented method and perturbation on the model. Pompom is the initials of the two methods. The hybrid method will provide a multivariate intraindividual variability metric (iRAM). The person-oriented method used in this package refers to uSEM (unified structural equation modeling, see Kim et al., 2007, Gates et al., 2010 and Gates et al., 2012 for details). Perturbation on the model was conducted according to impulse response analysis introduced in Lutkepohl (2007). 
    Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007) <doi:10.1002/hbm.20259>. 
    Gates, K. M., Molenaar, P. C. M., Hillary, F. G., Ram, N., & Rovine, M. J. (2010) <doi:10.1016/j.neuroimage.2009.12.117>. 
    Gates, K. M., & Molenaar, P. C. M. (2012) <doi:10.1016/j.neuroimage.2012.06.026>. 
    Lutkepohl, H. (2007, ISBN:3540262393).
Inspired by Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569>,
    this methodology expands the idea by including Marks in the point process.
    Using efficient 'C++' code, the estimation is possible and made faster with
    'OpenMP' <https://www.openmp.org/> enabled computers. This package was
    developed under the project PTDC/MAT-STA/28243/2017, supported by
    Portuguese funds through the Portuguese Foundation for Science and
    Technology (FCT).
Code to identify functional enrichments across diverse taxa
    in phylogenetic tree, particularly where these taxa differ in
    abundance across samples in a non-random pattern. The motivation for
    this approach is to identify microbial functions encoded by diverse
    taxa that are at higher abundance in certain samples compared to
    others, which could indicate that such functions are broadly adaptive
    under certain conditions. See 'GitHub' repository for tutorial and
    examples: <https://github.com/gavinmdouglas/POMS/wiki>. Citation: Gavin M. Douglas, Molly G. Hayes, Morgan G. I. Langille, Elhanan Borenstein (2022) <doi:10.1093/bioinformatics/btac655>.
A scalable and accurate tool for Killer-cell Immunoglobulin-like
    Receptor (KIR) genotype imputation directly from SNP array data using
    supervised machine learning models trained across five continental ancestry
    groups. Uses attribute bagging and an ensemble classifier method with
    haplotype inference for SNPs and KIR types. Models are built from global
    populations in the 1000 Genomes Project and validated across diverse
    biobank cohorts. Methods are based on Zheng et al. (2014)
    <doi:10.1016/j.ajhg.2013.12.015> and Sadeeq et al. (2026)
    <https://github.com/NormanLabUCD/PONG2>.
Homogeneity tests of the coefficients in panel data. Currently,
             only the Hsiao test for determining coefficient homogeneity between
             the panel data individuals is implemented, as described in Hsiao
             (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).
Finds equivalence classes corresponding to a symmetric relation
    or undirected graph.  Finds total order consistent with partial order
    or directed graph (so-called topological sort).
Enables the creation of object pools, which make it less
    computationally expensive to fetch a new object. Currently the only
    supported pooled objects are 'DBI' connections.
When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.
Pool dilution is a isotope tracer technique wherein a 
    biogeochemical pool is artifically enriched with its heavy isotopologue 
    and the gross productive and consumptive fluxes of that pool are 
    quantified by the change in pool size and isotopic composition over time. 
    This package calculates gross production and consumption rates from
    closed-system isotopic pool dilution time series data. Pool size 
    concentrations and heavy isotope (e.g., 15N) content are measured over time 
    and the model optimizes production rate (P) and the first order rate 
    constant (k) by minimizing error in the model-predicted total pool size, 
    as well as the isotopic signature. The model optimizes rates by weighting 
    information against the signal:noise ratio of concentration and heavy-
    isotope signatures using measurement precision as well as the magnitude of 
    change over time. The calculations used here are based on von Fischer and 
    Hedin (2002) <doi:10.1029/2001GB001448> with some modifications.
The 2017 American College of Cardiology and American Heart
  Association blood pressure guideline recommends using 10-year predicted 
  atherosclerotic cardiovascular disease risk to guide the decision to 
  initiate or intensify antihypertensive medication. The guideline recommends 
  using the Pooled Cohort risk prediction equations to predict 10-year 
  atherosclerotic cardiovascular disease risk. This package implements the 
  original Pooled Cohort risk prediction equations and also incorporates 
  updated versions based on more contemporary data and statistical methods.
Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
Analyzing genetic data obtained from pooled samples. 
    This package can read in Fragment Analysis output files, process the data, 
    and score peaks, as well as facilitate various analyses, including cluster 
    analysis, calculation of genetic distances and diversity indices, 
    as well as bootstrap resampling for statistical inference. Specifically 
    tailored to handle genetic data efficiently, researchers can explore 
    population structure, genetic differentiation, and genetic relatedness 
    among samples. We updated some functions from Covarrubias-Pazaran et al. 
    (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats 
    and referenced the following to write our genetic analysis functions: 
    Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) 
    <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) 
    <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) 
    <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) 
    <doi:10.1111/j.1541-0420.2008.01010.x>.
Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' and 'SelEstim' format, see the Package vignette for details).
Simulates pooled sequencing data under a variety of conditions. 
    Also allows for the evaluation of the average absolute difference between allele frequencies 
    computed from genotypes and those computed from pooled data. 
    Carvalho et al., (2022) <doi:10.1101/2023.01.20.524733>.
Functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.
An easy-to-use tool for working with presence/absence tests
    on 'pooled' or 'grouped' samples. The primary application is for
    estimating prevalence of a marker in a population based on the results
    of tests on pooled specimens.  This sampling method is often employed
    in surveillance of rare conditions in humans or animals (e.g.
    molecular xenomonitoring). The package was initially conceived as an
    R-based alternative to the molecular xenomonitoring software,
    'PoolScreen' <https://sites.uab.edu/statgenetics/software/>. However,
    it goes further, allowing for estimates of prevalence to be adjusted
    for hierarchical sampling frames, and perform flexible mixed-effect
    regression analyses (McLure et al. Environmental Modelling and
    Software.  <DOI:10.1016/j.envsoft.2021.105158>). The package is
    currently in early stages, however more features are planned or in the
    works: e.g. adjustments for imperfect test specificity/sensitivity,
    functions for helping with optimal experimental design, and functions
    for spatial modelling.
Gene-based association tests using the actual impurity reduction (AIR) variable importance. The function aggregates AIR importance measures from a group of SNPs or probes and outputs a p-value for each gene. The procedures builds upon the method described in <doi:10.1093/Bioinformatics/Bty373> and will be published soon.
A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.
Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) <doi:10.1016/j.ecolmodel.2016.08.012>.
Infers the trends of one or several animal populations over time 
    from series of counts. It does so by accounting for count precision 
    (provided or inferred based on expert knowledge, e.g. guesstimates), 
    smoothing the population rate of increase over time, and accounting for the
    maximum demographic potential of species. Inference is carried out in a 
    Bayesian framework. This work is part of the FRB-CESAB working group
    AfroBioDrivers 
    <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.
Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).
Facilitates population-level analysis of ligand-receptor (LR) interactions 
    using large-scale single-cell transcriptomic data. Identifies significant LR pairs 
    and quantifies their interactions through correlation-based filtering and projection score 
    computations. Designed for large-sample single-cell studies, the package employs 
    statistical modeling, including linear regression, to investigate LR relationships 
    between cell types. It provides a systematic framework for understanding cell-cell 
    communication, uncovering regulatory interactions and signaling mechanisms. 
    Offers tools for LR pair-level, sample-level, and differential interaction analyses, 
    with comprehensive visualization support to aid biological interpretation. 
    The methodology is described in a manuscript currently under review and will be 
    referenced here once published or publicly available.
Tools for modelling populations and demography using matrix projection models,
 with deterministic and stochastic model implementations. Includes population projection,
 indices of short- and long-term population size and growth, perturbation analysis,
 convergence to stability or stationarity, and diagnostic and manipulation tools.
The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.
Optimal experimental designs for both population and individual
    studies based on nonlinear mixed-effect models. Often this is based on a
    computation of the Fisher Information Matrix. This package was developed
    for pharmacometric problems, and examples and predefined models are available
    for these types of systems. The methods are described in Nyberg et al. 
    (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) 
    <doi:10.1016/S0169-2607(03)00073-7>.
Enables computation of epidemiological statistics, including
    those where counts or mortality rates of the reference population are
    used.  Currently supported: excess hazard models (Dickman, Sloggett,
    Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean
    survival times, relative/net survival (in particular the Ederer II
    (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and
    Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and
    standardized incidence and mortality ratios, all of which can be
    easily adjusted for by covariates such as age. Fast splitting and
    aggregation of 'Lexis' objects (from package 'Epi') and other
    computations achieved using 'data.table'.
Estimate commonly used population genomic statistics and generate publication quality figures. 'PopGenHelpR' uses vcf, 'geno' (012), and csv files to generate output.
Provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546).
Provides beginner friendly framework to analyse population genetic
    data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. 
    For detailed information how to use the package refer to the comprehensive
    tutorials or visit <http://www.popgenreport.org/>.
Implementation of assumption-lean and data-adaptive post-prediction inference (POPInf), for valid and efficient statistical inference based on data predicted by machine learning. See Miao, Miao, Wu, Zhao, and Lu (2023) <arXiv:2311.14220>.
Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index).  Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" <doi:10.1109/DICTA.2009.88>.
Population genetic analyses for hierarchical analysis of partially
    clonal populations built upon the architecture of the 'adegenet' package. 
    Originally described in Kamvar, Tabima, and Grünwald (2014) 
    <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and 
    Grünwald (2015) <doi:10.3389/fgene.2015.00208>.
Handle data from evolve and resequence experiments. 
    Measured allele frequencies (e.g., from variants called from high-throughput 
    sequencing data) are compared using an update of the PsiSeq algorithm 
    (Earley, Eric and Corbin Jones (2011) <doi:10.1534/genetics.111.129445>). 
    Functions for saving and loading important files are also included, as well 
    as functions for basic data visualization.
Functions that facilitate the elaboration of population pyramids.
Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
Methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid detection and cluster visualization using starbursts.  For more details see the paper "Improved Interpretability of the Unified Distance Matrix with Connected Components" by Hamel and Brown (2011) in <ISBN:1-60132-168-6>.  The package provides user-friendly access to two models we construct: (a) a SOM model and (b) a centroid based clustering model. The package also exposes a number of quality metrics for the quantitative evaluation of the map, Hamel (2016) <doi:10.1007/978-3-319-28518-4_4>.  Finally, we reintroduced our fast, vectorized training algorithm for SOM with substantial improvements. It is about an order of magnitude faster than the canonical, stochastic C implementation <doi:10.1007/978-3-030-01057-7_60>.
The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models.
    In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best.
    The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).
Functions to estimate and plot smooth or linear population trends, or population indices, 
    from animal or plant count survey data.
Tools for constructing detailed synthetic human populations
     from frequency tables. Add ages based on age groups and sex, create households, add 
     students to education facilities, create employers, add employers to employees, and 
     create interpersonal networks.
Run population simulations using an Individual-Based Model (IBM) compiled in C.
Fit linear splines to species time series to detect population growth scenarios based on Hyndman, R J and Mesgaran, M B and Cousens, R D (2015) <doi:10.1007/s10530-015-0962-8>.
Run simulations to assess the impact of various designs features
    and the underlying biological behaviour on the outcome of a Patient Derived
    Xenograft (PDX) population study. This project can either be deployed to a
    server as a 'shiny' app or installed locally as a package and run the app
    using the command 'populationPDXdesignApp()'.
Given a 
    set of source zone polygons such as
    census tracts or city blocks alongside with population counts and a 
    target zone of incogruent yet superimposed polygon features (such as
    individual buildings) populR transforms population counts from the 
    former to the latter using Areal Interpolation methods.
Perform tasks commonly encountered when
    preparing and analysing demographic data.
    Some functions are intended for end users, and
    others for developers. Includes functions for
    working with life tables.
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, 
    from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic
    data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, 
    or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction 
    accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). 
    A dataset 'think_barley.rda' is included for reference and examples.
The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).
An R implementation of the cross-platform, language-independent "port4me" algorithm (<https://github.com/HenrikBengtsson/port4me>), which (1) finds a free Transmission Control Protocol ('TCP') port in [1024,65535] that the user can open, (2) is designed to work in multi-user environments, (3), gives different users, different ports, (4) gives the user the same port over time with high probability, (5) gives different ports for different software tools, and (6) requires no configuration.
Obtener listado de datos, acceder y extender series del Portal de 
    Datos de Hacienda.Las proyecciones se realizan con 'forecast', 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>. 
    Search, download and forecast time-series from the Ministry of Economy 
    of Argentina. Forecasts are built with the 'forecast' package, 
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>.
Download and generate summaries for the rodent,
    plant, ant, and weather data from the Portal Project. Portal is a
    long-term (and ongoing) experimental monitoring site in the Chihuahuan
    desert. The raw data files can be found at
    <https://github.com/weecology/portaldata>.
Generate port files for foreign function interfaces to C
    libraries by parsing C-family header files with 'CastXML'.
Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.
Classes for analysing and implementing equity portfolios,
    including routines for generating tradelists and calculating
    exposures to user-specified risk factors.
Portfolio optimization and analysis routines and graphics.
Automated backtesting of multiple portfolios over multiple 
    datasets of stock prices in a rolling-window fashion. Intended for 
    researchers and practitioners to backtest a set of different portfolios, 
    as well as by a course instructor to assess the students in their portfolio 
    design in a fully automated and convenient manner, with results conveniently 
    formatted in tables and plots. Each portfolio design is easily defined as a
    function that takes as input a window of the stock prices and outputs the 
    portfolio weights. Multiple portfolios can be easily specified as a list 
    of functions or as files in a folder. Multiple datasets can be conveniently 
    extracted randomly from different markets, different time periods, and 
    different subsets of the stock universe. The results can be later assessed 
    and ranked with tables based on a number of performance criteria (e.g., 
    expected return, volatility, Sharpe ratio, drawdown, turnover rate, return 
    on investment, computational time, etc.), as well as plotted in a number of 
    ways with nice barplots and boxplots.
    See Chapter 8 (Portfolio Backtesting) of the book: Daniel P. Palomar,
    "Portfolio Optimization: Theory and Application", Cambridge University
    Press, 2025.
Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.
Design, backtest, and analyze portfolio strategies using simple,
    English-like function chains. Includes technical indicators, flexible stock
    selection, portfolio construction methods (equal weighting, signal weighting,
    inverse volatility, hierarchical risk parity), and a compact backtesting
    engine for portfolio returns, drawdowns, and summary metrics.
Provides simple methods to extract data portions from various objects. 
    The relative portion size and the way the portion is selected can be chosen.
The functions are designed to find the efficient mean-variance frontier or 
 portfolio weights for static portfolio (called  Markowitz portfolio) analysis in resource 
 economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), 
 this package deals with the quadratic minimization of the variance-covariances without 
 shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) 
 <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: 
 <https://github.com/ysd2004/portn>.
Following Sommer (2022) <https://mediatum.ub.tum.de/1658240>
    portfolio level risk estimates (e.g. Value at Risk, Expected
    Shortfall) are estimated by modeling each asset univariately by an
    ARMA-GARCH model and then their cross dependence via a Vine Copula
    model in a rolling window fashion. One can even condition on
    variables/time series at certain quantile levels to stress test the
    risk measure estimates.
Win ratio approach to partially ordered data, such as multivariate ordinal 
  responses under product (consensus) or prioritized order. Two-sample tests and multiplicative
  regression models are implemented (Mao, 2024, under revision).
Build and manipulate partially ordered sets (posets), to perform some data analysis 
    on them and to implement multi-criteria decision making procedures. Several efficient ways for generating linear extensions are implemented, together with functions for building mutual ranking probabilities, incomparability, dominance and separation scores (Fattore, M., De Capitani, L., Avellone, A., Suardi, A. (2024). A fuzzy posetic toolbox for multi-criteria evaluation on ordinal data systems. ANNALS OF OPERATIONS RESEARCH <doi:10.1007/s10479-024-06352-3>).
Provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.
In linear LS regression, calculate for a given design matrix
  the multiplier K of coefficient standard errors such that the
  confidence intervals [b - K*SE(b), b + K*SE(b)] have a
  guaranteed coverage probability for all coefficient estimates
  b in any submodels after performing arbitrary model selection.
Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.
Post-selection inference in linear regression models,
    constructing simultaneous confidence intervals across a user-specified universe of models.
    Implements the methodology described in Kuchibhotla, Kolassa, and Kuffner (2022) "Post-Selection Inference"
    <doi:10.1146/annurev-statistics-100421-044639> to ensure valid inference after model
    selection, with applications in high-dimensional settings like Lasso selection.
Collection of tutorials for working with 'Positron' 
    and for learning how to apply generative AI when coding with R. 
    Covers scripts, 'Quarto' documents, 'Git', 'GitHub', and 
    'Quarto' websites. Makes extensive use of the tools in the 
    'tutorial.helpers' package.
Personalize drug regimens using individual pharmacokinetic (PK) and 
    pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic 
    drug monitoring (TDM) data with a population model, 'posologyr' offers 
    accurate posterior estimates and helps compute optimal individualized dosing
    regimens. The empirical Bayes estimates are computed following the method 
    described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.
Computes the exact probability density function of X/Y conditioned on positive quadrant for series of  bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.
Calculates, via simulation, power and appropriate stopping
  alpha boundaries (and/or futility bounds) for sequential analyses (i.e.,
  group sequential design) as well as for multiple hypotheses (multiple tests
  included in an analysis), given any specified global error rate. This enables
  the sequential use of practically any significance test, as long as the
  underlying data can be simulated in advance to a reasonable approximation.
  Lukács (2022) <doi:10.21105/joss.04643>.
Conduct power analyses and inference of marginal effects. 
    Uses plug-in estimation and influence functions to perform robust inference,
    optionally leveraging historical data to increase precision with
    prognostic covariate adjustment. The methods are described in
    Højbjerre-Frandsen et al. (2025) <doi:10.48550/arXiv.2503.22284>.
A collection of R Markdown templates for creating simple and easy 
  to personalize single page websites.
Free UK geocoding using data from Office for National Statistics.
    It is using several functions to get information about post codes, outward
    codes, reverse geocoding, nearest post codes/outward codes, validation, or
    randomly generate a post code. API wrapper around <https://postcodes.io>.
Generates simple and beautiful one-page HTML reference manuals 
    with package documentation. Math rendering and syntax highlighting are done 
    server-side in R such that no JavaScript libraries are needed in the
    browser, which makes the documentation portable and fast to load.
Provides useful tools for both users and developers of packages 
  for fitting Bayesian models or working with output from Bayesian models.
  The primary goals of the package are to: 
  (a) Efficiently convert between many different useful formats of
  draws (samples) from posterior or prior distributions.
  (b) Provide consistent methods for operations commonly performed on draws, 
  for example, subsetting, binding, or mutating draws.
  (c) Provide various summaries of draws in convenient formats.
  (d) Provide lightweight implementations of state of the art posterior 
  inference diagnostics. References: Vehtari et al. (2021) 
  <doi:10.1214/20-BA1221>.
An implementation of a non-parametric statistical model using a
    parallelised Monte Carlo sampling scheme. The method implemented in this
    package allows non-parametric inference to be regularized for small sample
    sizes, while also being more accurate than approximations such as
    variational Bayes. The concentration parameter is an effective sample size
    parameter, determining the faith we have in the model versus the data. When
    the concentration is low, the samples are close to the exact Bayesian
    logistic regression method; when the concentration is high, the samples are
    close to the simplified variational Bayes logistic regression. The method is
    described in full in the paper Lyddon, Walker, and Holmes (2018),
    "Nonparametric learning from Bayesian models with randomized objective
    functions" <arXiv:1806.11544>.
Implements a range of facilities for post-hoc analysis and
             summarizing linear models, generalized linear models and 
             generalized linear mixed models, including grouping and clustering 
             via pairwise comparisons using graph representations and efficient
             algorithms for finding maximal cliques of a graph. 
             Includes also non-parametric toos for post-hoc analysis.
             It has S3 methods for printing summarizing, and producing plots, 
             line and barplots suitable for post-hoc analyses.
Functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.
This is a wrapper for the Mercury Parser API. The Mercury Parser is 
    a single API endpoint that takes a URL and gives you back the content reliably 
    and easily. 
    With just one API request, Mercury takes any web article and returns 
    only the relevant content — headline, author, body text, relevant images and 
    more — free from any clutter. It’s reliable, easy-to-use and free.
    See the webpage here: <https://mercury.postlight.com/>.
Provides a suite of statistical tools for post-linkage data analysis (PLDA), designed to account for record linkage errors in downstream modeling. The package implements a familiar, formula-based regression interface that adjusts for linkage uncertainty, accommodating workflows where direct access to unlinked primary files is restricted. It consolidates diverse adjustment methodologies, all of which support generalized linear models (linear, logistic, Poisson, and Gamma). These methodologies include weighting approaches (Chambers (2009) <https://hdl.handle.net/10779/uow.27788247>; Chambers et al. (2023) <doi:10.1002/wics.1596>), mixture modeling (Slawski et al. (2025) <doi:10.1093/jrsssa/qnae083>), and Bayesian mixture modeling (Gutman et al. (2016) <doi:10.1002/sim.6586>). For time-to-event data, both the weighting (Vo et al. (2024) <doi:10.1002/sim.9960>) and mixture modeling approaches accommodate Cox proportional hazards models, while the Bayesian approaches extend to parametric survival analysis. Additionally, the package leverages mixture modeling for contingency table analyses and Bayesian methods to enable the multiple imputation of latent match status.
Provides adds postfix and infix logic operators for
    if, then, unless, and otherwise.
Implements the Phylogeny-Guided Microbiome OTU-Specific Association 
    Test method, which boosts the testing power by adaptively borrowing 
    information from phylogenetically close OTUs (operational taxonomic units)
    of the target OTU. This method
    is built on a kernel machine regression framework and allows for flexible 
    modeling of complex microbiome effects, adjustments for covariates, and 
    can accommodate both continuous and binary outcomes.
The aim of 'postpack' is to provide the infrastructure for a standardized workflow for 'mcmc.list' objects.
    These objects can be used to store output from models fitted with Bayesian inference using
    'JAGS', 'WinBUGS', 'OpenBUGS', 'NIMBLE', 'Stan', or even custom MCMC algorithms. Although the 'coda' R package provides
    some methods for these objects, it is somewhat limited in easily performing post-processing tasks for
    specific nodes. Models are ever increasing in their complexity and the number of tracked nodes, and oftentimes
    a user may wish to summarize/diagnose sampling behavior for only a small subset of nodes at a time
    for a particular question or figure. Thus, many 'postpack' functions support performing tasks on a
    subset of nodes, where the subset is specified with regular expressions. The functions in 'postpack'
    streamline the extraction, summarization, and diagnostics of specific monitored nodes after model fitting.
    Further, because there is rarely only ever one model under consideration, 'postpack' scales efficiently 
    to perform the same tasks on output from multiple models simultaneously, facilitating rapid assessment 
    of model sensitivity to changes in assumptions.
Some functions useful to perform a Peak Over Threshold
        analysis in univariate and bivariate cases, see Beirlant et al. (2004)
        <doi:10.1002/0470012382>. A user guide is available in the vignette.
Provides functions to compute the potential model as defined by
    Stewart (1941) <doi:10.1126/science.93.2404.89>. Several options are available
    to customize the model, such as the possibility to fine-tune the distance
    friction functions or to use custom distance matrices. Some computations are
    parallelized to improve their efficiency.
Builds potentiometric surface products from groundwater monitoring
    data. The package prepares groundwater observations from direct water-level
    measurements or depth-to-water data paired with land-surface elevations,
    interpolates thin-plate spline surfaces by default, supports alternative
    and user-supplied interpolation methods, exports raster and contour
    products, and derives hydraulic-gradient flow arrows. Raster operations use
    methods from Hijmans (2025) <doi:10.32614/CRAN.package.terra>, thin-plate
    spline interpolation uses methods from Nychka et al. (2021)
    <doi:10.5065/D6W957CT>, and geostatistical interpolation uses methods from
    Pebesma (2004) <doi:10.1016/j.cageo.2004.03.012>.
Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies.
Store and retrieve data from options() using syntax derived from
    the 'here' package. 'potions' makes it straightforward to update and 
    retrieve options, either in the workspace or during package development, 
    without overwriting global options.
Translating messages in R packages is managed using the po
    top-level directory and the 'gettext' program. This package provides
    some helper functions for building this support in R packages, e.g.
    common validation & I/O tasks.
Do Markov chain Monte Carlo (MCMC) simulation of Potts models
   (Potts, 1952, <doi:10.1017/S0305004100027419>),
   which are the multi-color generalization of Ising models
   (so, as as special case, also simulates Ising models).
   Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987,
   <doi:10.1103/PhysRevLett.58.86>) so MCMC is fast.
   Do maximum composite likelihood estimation of parameters
   (Besag, 1975, <doi:10.2307/2987782>,
   Lindsay, 1988, <doi:10.1090/conm/080>).
There are three sets of functions. The first produces
             basic properties of a graph and generates samples from
             multinomial distributions to facilitate the simulation
	     functions (they maybe used for other purposes as well).
	     The second provides various simulation functions for a
	     Potts model in Potts, R. B. (1952)
	     <doi:10.1017/S0305004100027419>.
	     The third currently includes only one function which
	     computes the normalizing constant of a Potts model
	     based on simulation results.
Computes the D', Wn, and conditional asymmetric linkage disequilibrium (ALD) measures for pairs of genetic loci. Performs these linkage disequilibrium (LD) calculations on phased genotype data recorded using Genotype List (GL) String or columnar formats. Alternatively, generates expectation-maximization (EM) estimated haplotypes from phased data, or performs LD calculations on EM estimated haplotypes. Performs sign tests comparing LD values for phased and unphased datasets, and generates heat-maps for each LD measure. Described by Osoegawa et al. (2019a) <doi:10.1016/j.humimm.2019.01.010>, and Osoegawa et. al. (2019b) <doi:10.1016/j.humimm.2019.05.018>.
An implementation of the Partition Of variation (POV) method as
    developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in
    1993 for the analysis of semiconductor data for hard drive manufacturing.
    POV is based on sequential sum of squares and is an exact method that
    explains all observed variation. It quantitates both the between and within
    factor variation effects and can quantitate the influence of both continuous
    and categorical factors.
Fast exponentiation when the exponent is an integer.
Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in 'LaTeX' format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) <doi:10.18637/jss.v069.i03>.
Provides functions for estimating statistical power and required sample sizes 
    in differential abundance microbiome studies using negative binomial models. 
    The methods are based on Agronah and Bolker (2025) <doi:10.1371/journal.pone.0318820>.
    The package includes tools for simulation-based power analysis and sample size 
    estimation using generalized additive models (GAMs), and visualization utilities for exploring 
    the relationship between power, effect size, abundance, and sample size.
Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations
    allow for transforming variables with distributions distant from 0 to
    normality. Transformers are implemented as S4 objects. These allow for
    transforming new instances to normality after optimising fitting parameters
    on other data. A test for central normality allows for rejecting 
    transformations that fail to produce a suitably normal distribution, 
    independent of sample number.
Contains functions to obtain the operational characteristics of 
    bioequivalence studies in Two-Stage Designs (TSD) via simulations.
Power analysis and sample size determination
  for moderation, mediation, and moderated mediation in models
  fitted by structural equation modelling using the 'lavaan'
  package by Rosseel (2012) <doi:10.18637/jss.v048.i02> or
  by multiple regression. The package 'manymome' by
  Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>
  is used to specify the indirect paths or conditional
  indirect paths to be tested.
The first goal of this package is to provide a multitude of tree models,
    i.e., functions that generate rooted binary trees with a given number of leaves.
    Second, the package allows for an easy evaluation and comparison of tree shape
    statistics by estimating their power to differentiate between different tree models.
    Please note that this R package was developed alongside the manuscript
    'Tree balance in phylogenetic models' by
    S. J. Kersting, K. Wicke, and M. Fischer (2025) <doi:10.1098/rstb.2023.0303>,
    which provides further background and the respective mathematical definitions.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Education and Research (No. 03WIR4805).
Makes it easy to push data to 'Power BI' using R and the 'Power BI 
    REST APIs' (see <https://docs.microsoft.com/en-us/rest/api/power-bi/>). 
    A set of functions for turning data frames into 'Power BI' datasets and 
    refreshing these datasets are provided. Administrative tasks such as 
    monitoring refresh statuses and pulling metadata about workspaces and users
    are also supported.
Provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of 'brms' and 'INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".
Functions for bootstrapping the power of ANOVA designs
    based on estimated means and standard deviations of the conditions.
    Please refer to the documentation of the boot.power.anova() function
    for further details.
A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.
Power and sample size calculation for bulk tissue and single-cell eQTL analysis
             based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size 
             for testing the association of a SNP to a continuous type phenotype.
             Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.
Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2025) <doi:10.1371/journal.pcbi.1013293> for method details.
Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
Calculate sample size or power for hierarchical endpoints. 
  The package can handle any type of outcomes (binary, continuous, count, ordinal, 
  time-to-event) and any number of such endpoints. It allows users to calculate 
  sample size with a given power or to calculate power with a given sample size 
  for hypothesis testing based on win ratios, win odds, net benefit, or DOOR 
  (desirability of outcome ranking) as treatment effect between two groups for 
  hierarchical endpoints. The methods of this package are described further in 
  the paper by Barnhart, H. X. et al. (2024, <doi:10.1080/19466315.2024.2365629>).
This R package allows the determination of some distributions of the voters' power when passing laws in weighted voting situations.
We extend 'dplyr' and 'fuzzyjoin' join functions with
    features to preprocess the data, apply various data checks, and deal with
    conflicting columns.
An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>. Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.
An implementation of maximum likelihood estimators for a
    variety of heavy tailed distributions, including both the discrete and
    continuous power law distributions. Additionally, a goodness-of-fit
    based approach is used to estimate the lower cut-off for the scaling
    region.
An implementation of the sample size computation method for network
    models proposed by Constantin et al. (2023) <doi:10.1037/met0000555>.
    The implementation takes the form of a three-step recursive algorithm
    designed to find an optimal sample size given a model specification and a
    performance measure of interest. It starts with a Monte Carlo simulation
    step for computing the performance measure and a statistic at various sample
    sizes selected from an initial sample size range. It continues with a
    monotone curve-fitting step for interpolating the statistic across the entire
    sample size range. The final step employs stratified bootstrapping to quantify
    the uncertainty around the fitted curve.
Functions to 
        calculate power and sample size for testing
        (1) mediation effects; 
        (2) the slope in a simple linear regression; 
        (3) odds ratio in a simple logistic regression;
        (4) mean change for longitudinal study with 2 time points;
        (5) interaction effect in 2-way ANOVA; and
        (6) the slope in a simple Poisson regression.
Model-implied simulation-based power estimation (MSPE) for
    nonlinear (and linear) SEM, path analysis and regression analysis. A
    theoretical framework is used to approximate the relation between
    power and sample size for given type I error rates and effect sizes.
    The package offers an adaptive search algorithm to find the optimal N for
    given effect sizes and type I error rates. Plots can be used to visualize
    the power relation to N for different parameters of interest (POI). 
    Theoretical justifications are given in Irmer et al. 
    (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description
    are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.
There are two main functions: (1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.
It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), <doi:10.48550/arXiv.2403.10289>.
Implements conjugate power priors for efficient Bayesian analysis 
    of normal data. Power priors allow principled incorporation of historical 
    information while controlling the degree of borrowing through a discounting 
    parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This 
    package provides closed-form conjugate representations for both univariate 
    and multivariate normal data using Normal-Inverse-Chi-squared and 
    Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. 
    The conjugate framework builds upon standard Bayesian methods described in 
    Gelman et al. (2013, ISBN:978-1439840955).
A set of functions designed to calculate the standardised
    precipitation and standardised precipitation evapotranspiration indices
    using NASA POWER data as described in Blain et al. (2023)
    <doi:10.2139/ssrn.4442843>.  These indices are calculated using a reference
    data source.  The functions verify if the indices' estimates meet the
    assumption of normality and how well NASA POWER estimates represent
    real-world data.  Indices are calculated in a routine mode.  Potential
    evapotranspiration amounts and the difference between rainfall and potential
    evapotranspiration are also calculated.  The functions adopt a basic time
    scale that splits each month into four periods.  Days 1 to 7, days 8 to 14,
    days 15 to 21, and days 22 to 28, 29, 30, or 31, where 'TS=4' corresponds to
    a 1-month length moving window (calculated 4 times per month) and 'TS=48'
    corresponds to a 12-month length moving window (calculated 4 times per
    month).
Functions to calculate power and
                sample size for testing main effect or interaction effect in
                the survival analysis of epidemiological studies
                (non-randomized studies), taking into account the 
                correlation between the covariate of the
                interest and other covariates. Some calculations also take 
                into account the competing risks and stratified analysis. 
                This package also includes
                a set of functions to calculate power and sample size
                for testing main effect in the survival analysis of 
                randomized clinical trials and conditional logistic regression for nested case-control study.
Power and sample size calculations for a variety of study designs and 
    outcomes. Methods include t tests, ANOVA (including tests for interactions, 
    simple effects and contrasts), proportions, categorical data (chi-square tests 
    and proportional odds), linear, logistic and Poisson regression, alternative 
    and coprimary endpoints, power for confidence intervals, correlation 
    coefficient tests, cluster randomized trials, individually randomized group 
    treatment trials, multisite trials, treatment-by-covariate interaction effects 
    and nonparametric tests of location. Utilities are provided for 
    computing various effect sizes. Companion package to the book 
    "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). 
    Further resources available at <https://powerandsamplesize.org/>.
Contains functions to calculate power and sample size for
    various study designs used in bioequivalence studies. Use known.designs() to
    see the designs supported. Power and sample size can be obtained based on
    different methods, amongst them prominently the TOST procedure (two one-sided
    t-tests). See README and NEWS for further information.
Perform user-friendly power analyses for the random
    intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait 
    autoregressive trait state (STARTS) model. The strategy as proposed by 
    Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions 
    include the use of parameter constraints over time, bounded estimation,
    generation of data with skewness and kurtosis, and the option to setup the power 
    analysis for Mplus.
Conduct a priori power analyses via Monte-Carlo style data simulation for linear and generalized linear mixed-effects models (LMMs/GLMMs). Provides a user-friendly workflow with helper functions to easily define fixed and random effects as well as diagnostic functions to evaluate the adequacy of the results of the power analysis.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.
Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. 
    Reference papers: [1] Beavers et al., (2024) <doi:10.1080/10618600.2024.2341896>. 
    [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." <doi:10.48550/arXiv.2312.06465>.
Performs partial principal component analysis of a large sparse matrix. 
  The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally.
  Useful application includes cases where the number of total nonzero entries exceed the capacity of 
  32 bit integers (e.g., with large Single Nucleotide Polymorphism data).
Calculates the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test that was first introduced by 
	     Filliben (1975) <doi: 10.1080/00401706.1975.10489279> 
	     can be performed to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.
In the era of big data, data redundancy and distributed characteristics present novel challenges to data analysis. This package introduces a method for estimating optimal subsets of redundant distributed data, based on PPCDT (Conjunction of Power and P-value in Distributed Settings). Leveraging PPC technology, this approach can efficiently extract valuable information from redundant distributed data and determine the optimal subset. Experimental results demonstrate that this method not only enhances data quality and utilization efficiency but also assesses its performance effectively. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
Implements recently developed projection 
    pursuit algorithms for finding optimal linear cluster
    separators. The clustering algorithms use optimal
    hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>;
    minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>;
    and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Partitioning clustering divides the objects in a data set into non-overlapping subsets or clusters by using the prototype-based probabilistic and possibilistic clustering algorithms. This package covers a set of the functions for Fuzzy C-Means (Bezdek, 1974) <doi:10.1080/01969727308546047>, Possibilistic C-Means (Krishnapuram & Keller, 1993) <doi:10.1109/91.227387>, Possibilistic Fuzzy C-Means (Pal et al, 2005) <doi:10.1109/TFUZZ.2004.840099>, Possibilistic Clustering Algorithm (Yang et al, 2006) <doi:10.1016/j.patcog.2005.07.005>, Possibilistic C-Means with Repulsion (Wachs et al, 2006) <doi:10.1007/3-540-31662-0_6> and the other variants of hard and soft clustering algorithms. The cluster prototypes and membership matrices required by these partitioning algorithms are initialized with different initialization techniques that are available in the package 'inaparc'. As the distance metrics, not only the Euclidean distance but also a set of the commonly used distance metrics are available to use with some of the algorithms in the package.
Calculates partial and semi-partial
        (part) correlations along with p-value.
A clinical decision support system for sub-symptom threshold
    aerobic exercise (SSTAE) prescription in adolescents with persistent
    post-concussion symptoms (PPCS). Implements an evidence-based protocol
    derived from a systematic review of seven studies (Li, 2026; <doi:10.17605/osf.io/kvuf6>),
    encoding safety screening, Buffalo Concussion Treadmill Test (BCTT)-guided
    heart rate prescription, session-level progress tracking, and evidence
    disclosure using the Grading of Recommendations, Assessment, Development
    and Evaluation (GRADE) framework into an open-source tool for athletic
    trainers and clinicians. Designed to support implementation in
    resource-limited settings where BCTT equipment may be unavailable.
    GRADE certainty of evidence: LOW. For clinician use only; not a
    substitute for clinical judgement.
Spatial Analysis for exploration of Pakistan Population Census 2017 (<https://www.pbs.gov.pk/content/population-census>). It uses data from R package 'PakPC2017'.
A suite of diagnostic tools for univariate point processes.
    This includes tools for simulating and fitting both common and more
    complex temporal point processes. We also include functions to 
    visualise these point processes and collect existing diagnostic
    tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and
    Wu et al. (2021) <doi:10.1002/9781119821588.ch7>,
    which can be used to assess the fit of a chosen point process
    model.
Provides an updated database of accepted endemic plant taxa from Peru. The current collection contains over 8,000 taxonomic records at species and infraspecific ranks. Data are derived from Govaerts, R., Nic Lughadha, E., Black, N. et al., 'The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) <doi:10.1038/s41597-021-00997-6>.
Implements projection pursuit forest algorithm for supervised classification.
Methods for fitting point processes with parameters of generalised additive 
      model (GAM) form are provided. For an introduction to point processes see Cox, D.R & 
      Isham, V. (Point Processes, 1980, CRC Press), GAMs see Wood, S.N. (2017) 
      <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & 
      Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>.
Reconstruction of paleoclimate niches using phylogenetic comparative 
    methods and projection reconstructed niches onto paleoclimate maps.
    The user can specify various models of trait evolution or estimate the best fit
    model, include fossils, use one or multiple phylogenies for inference, and make
    animations of shifting suitable habitat through time. This model was first used
    in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and 
    Rivera et al (2020).
    Lawing and Polly (2011) <doi:10.1371/journal.pone.0028554> "Pleistocene climate,
    phylogeny and climate envelope models: An integrative approach to better 
    understand species' response to climate change"
    Lawing et al (2016) <doi:10.1086/687202> "Including fossils in phylogenetic 
    climate reconstructions: A deep time perspective on the climatic niche evolution
    and diversification of spiny lizards (Sceloporus)"
    Rivera et al (2020) <doi:10.1111/jbi.13915> "Reconstructing historical shifts 
    in suitable habitat of Sceloporus lineages using phylogenetic niche modelling.".
Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.
The Poverty Probability Index (PPI) is a poverty measurement tool 
    for organizations and businesses with a mission to serve the poor. The PPI 
    is statistically-sound, yet simple to use: the answers to 10 questions about 
    a household's characteristics and asset ownership are scored to compute the 
    likelihood that the household is living below the poverty line - or above by 
    only a narrow margin. This package contains country-specific lookup data
    tables used as reference to determine the poverty likelihood of a household
    based on their score from the country-specific PPI questionnaire. These
    lookup tables have been extracted from documentation of the PPI found at 
    <https://www.povertyindex.org> and managed by Innovations for Poverty Action 
    <https://poverty-action.org/>.
Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) <doi:10.1007/s00181-025-02779-0>.
Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences.
Linear and nonlinear regression
        methods based on Partial Least Squares and Penalization
        Techniques. Model parameters are selected via cross-validation,
        and confidence intervals ans tests for the regression
        coefficients can be conducted via jackknifing. 
        The method is described and applied to simulated and experimental 
        data in Kraemer et al. (2008) <doi:10.1016/j.chemolab.2008.06.009>.
Implements data processing described in <doi:10.1126/sciadv.abk3283>
    to align modern differentially private data with formatting of older US Census
    data releases. The primary goal is to read in Census Privacy Protected Microdata
    Files data in a reproducible way. This includes tools for aggregating to relevant
    levels of geography by creating geographic identifiers which match the US Census
    Bureau's numbering. Additionally, there are tools for grouping race numeric
    identifiers into categories, consistent with OMB (Office of Management and Budget)
    classifications. Functions exist for downloading and linking to existing
    sources of privacy protected microdata.
An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.
Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <doi:10.48550/arXiv.2303.04754>) and has been found to outperform several other commonly applied estimators.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.
A unified, computation-friendly framework for penalized principal
    machines (P2M), a class of sparse sufficient dimension reduction (SDR)
    estimators for regression and binary classification. Principal machines
    (PM) estimate the central subspace by solving a family of convex-loss
    problems over several cutoffs; their penalized counterparts (P2M) add a
    row-group sparsity penalty so that dimension reduction and variable
    selection are performed simultaneously. All estimators are fitted by a
    single group coordinate descent (GCD) algorithm that accommodates least
    squares, logistic, asymmetric least squares, L2-hinge, hinge (support
    vector machine, SVM) and quantile losses, together with the least absolute
    shrinkage and selection operator (LASSO), the smoothly clipped absolute
    deviation (SCAD) penalty and the minimax concave penalty (MCP). Methods are
    described in Li, Artemiou and Li (2011) <doi:10.1214/11-AOS932>, Shin and
    Artemiou (2017) <doi:10.1016/j.csda.2016.12.003>, Artemiou, Dong and Shin
    (2021) <doi:10.1016/j.patcog.2020.107768> and Breheny and Huang (2015)
    <doi:10.1007/s11222-013-9424-2>.
Provides a suite of functions that fit models that use PPM type priors for partitions.
                Models include hierarchical Gaussian and probit ordinal models with  a (covariate 
                dependent) PPM.  If a covariate dependent product partition model is selected, 
                then all the options detailed in Page, G.L.; Quintana, F.A. (2018) 
                <doi:10.1007/s11222-017-9777-z> are available.  If covariate values are missing, 
                then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) 
                <doi:10.1080/10618600.2021.1999824> is employed.   Also included in the package is 
                a function that fits a Gaussian likelihood spatial product  partition model that is 
                detailed in Page, G.L.; Quintana, F.A. (2016)  <doi:10.1214/15-BA971>, and 
                multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; 
                Castro, L.M. (2023) <doi:10.1214/22-BA1344>. In addition, a function that fits a 
                univariate or bivariate functional data model that employs a PPM or a PPMx to 
                cluster curves based on B-spline coefficients is provided.
Assessment for statistically-based PPQ sampling plan, including calculating the passing probability, optimizing the baseline and high performance cutoff points, visualizing the PPQ plan and power dynamically. The analytical idea is based on the simulation methods from the textbook Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Methods for CMC Applications. In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry (pp. 227-250). Springer, Cham.
Provides functionality for Bayesian analysis of replication studies using power prior approaches (Pawel et al., 2023) <doi:10.1007/s11749-023-00888-5>.
A toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the 'Merge ToolBox' (<https://www.record-linkage.de>) with current privacy-preserving techniques.
Implements linear and generalized linear models for provider profiling,
    incorporating both fixed and random effects. For large-scale providers, the linear 
    profiled-based method and the SerBIN method for binary data 
    reduce the computational burden. Provides post-modeling features, such as 
    indirect and direct standardization measures, hypothesis testing, 
    confidence intervals, and post-estimation visualization. 
    For more information, see Wu et al. (2022) <doi:10.1002/sim.9387>.
Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.
Stochastic block model used for dynamic graphs represented by Poisson processes.
    To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individuals’ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion.
    Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>.
    C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>.
    M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>.
    J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>.
    A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>.
    G. Grégoire (1993). <http://www.jstor.org/stable/4616289>.
    L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>.
    M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>.
    C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>.
    C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>.
    H. Ramlau-Hansen (1983). <doi:10.1214/aos/1176346152>.
    P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.
Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.
This is an implementation of the partial profile score feature 
    selection (PPSFS) approach to generalized linear (interaction) models. 
    The PPSFS is highly scalable even for ultra-high-dimensional feature space. 
    See the paper by Xu, Luo and Chen (2022) <doi:10.4310/21-SII706>.
The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score 
    that can detect linear or non-linear relationships between two variables. 
    The score ranges from 0 (no predictive power) to 1 (perfect predictive power). 
    PPS can be useful for data exploration purposes, in the same way correlation analysis is.
    For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.
Provides functionality for the prior and posterior projected Polya tree for the analysis of circular data 
  (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).
Implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), <doi:10.1214/13-EJS810> and Lee, E-K. (2018) <doi:10.18637/jss.v083.i08>. The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group.
Tools for exploring projection pursuit classification tree using
    various projection pursuit indexes.
Provides functions for point process time series. Autocorrelation functions for spatial and temporal time series, and estimation of trend-plus-seasonality models for temporal and spatial time series. See Gervini (2025) <doi:10.1111/jtsa.70018> and Gervini and Kopischke (2026) <doi:10.48550/arXiv.2605.21884>.
Weighted Deming regression, also known as 'errors-in-variable' 
    regression, is applied with suitable weights. Weights are modeled via a 
    precision profile; thus the methods implemented here are referred to as  
    precision profile weighted Deming (PWD) regression. The package covers 
    two settings – one where the precision profiles are known either from
    external studies or from adequate replication of the X and Y readings, 
    and one in which there is a plausible functional form for the precision 
    profiles but the exact (unknown) function must be estimated from the 
    (generally singlicate) readings.
    The function set includes tools for: estimated standard errors (via 
    jackknifing); standardized-residual analysis function with regression 
    diagnostic tools for normality, linearity and constant variance; and an 
    outlier analysis identifying significant outliers for closer investigation. 
    The following reference provides further information on mathematical 
    derivations and applications. 
    Hawkins, D.M., and J.J. Kraker (2026). 'Precision Profile Weighted
    Deming Regression for Methods Comparison'. 
    The Journal of Applied Laboratory Medicine 11, 379-392
    <doi:10.1093/jalm/jfaf183>.
Provides functions for estimation and data generation for several piecewise lifetime  distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) <doi:10.2307/2528247> for methodological details.
Tools to perform Pearson-Quetelet analysis on two-way contingency
    tables. The package computes absolute and relative frequencies, Quetelet
    indices, Pearson-Quetelet decomposition, apex tables, and chi-square
    summaries for interpreting associations between categorical variables.
In the big data setting, working data sets are often distributed on multiple machines. However, classical statistical methods are often developed to solve the problems of single estimation or inference. We employ a novel parallel quasi-likelihood method in generalized linear models, to make the variances between different sub-estimators relatively similar. Estimates are obtained from projection subsets of data and later combined by suitably-chosen unknown weights. The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.
Bayesian regularized quantile regression utilizing two major classes of shrinkage priors 
    (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian 
    shrinkage estimation, variable selection and valid statistical inference. In this package, 
    we have implemented robust Bayesian variable selection with spike-and-slab priors under 
    high-dimensional linear regression models (Fan et al. (2024) <doi:10.3390/e26090794> and  
    Ren et al. (2023) <doi:10.1111/biom.13670>), and regularized quantile varying
    coefficient models (Zhou et al.(2023) <doi:10.1016/j.csda.2023.107808>). In particular, 
    valid robust Bayesian inferences under both models in the presence of heavy-tailed errors
    can be validated on finite samples. Additional models with spike-and-slab priors include 
    robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) 
    <doi:10.1002/sta4.70078>). Besides, robust sparse Bayesian regression with the horseshoe
    family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented
    and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) 
    <doi:10.1016/j.csda.2026.108358>). The Markov chain Monte Carlo (MCMC) algorithms of 
    the proposed and alternative models are implemented in C++.
Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO.
It aggregates protein panel data and metadata for protein quantitative
 trait locus (pQTL) analysis using 'pQTLtools' (<https://jinghuazhao.github.io/pQTLtools/>).
 The package includes data from affinity-based panels such as 'Olink' (<https://olink.com/>) 
 and 'SomaScan' (<https://somalogic.com/>), as well as mass spectrometry-based panels from
 'CellCarta' (<https://cellcarta.com/>), 'Seer' (<https://seer.bio/>) and 'SWATH-MS'
 (<doi:10.15252/msb.20178126>). The metadata encompasses updated annotations
 and publication details.
Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of  flexibility. Also provides options for embedding audio in figures and animating figures.
Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetic data 
  for species delimitation, nearest neighbor 
  based noise detection. Genetic distances between communities.
  Tests whether various distance-based regressions
  are equal. Try package?prabclus for on overview.
Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.
Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) <doi:10.48550/arXiv.2303.07876>.
Sample size calculations for practical equivalence trial design with a time to event endpoint.
Consider a possibly nonlinear nonparametric regression
   with p regressors. We provide evaluations by 13 methods to rank
   regressors by their practical significance or importance using 
   various methods, including machine learning tools. Comprehensive
   methods are as follows. 
   m6=Generalized partial correlation coefficient or
   GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and
   Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.
   m7= a generalization of psychologists' effect size incorporating 
   nonlinearity and many variables.
   m8= local linear partial (dy/dxi) using the 'np' package for kernel 
   regressions.
   m9= partial (dy/dxi) using the 'NNS' package.
   m10= importance measure using the 'NNS' boost function.
   m11= Shapley Value measure of importance (cooperative game theory).
   m12 and m13= two versions of the random forest algorithm.
   Taraldsen's exact density for sampling distribution of correlations added.
Functions and datasets to support Valliant, Dever, and Kreuter (2018), <doi:10.1007/978-3-319-93632-1>, "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs, sample sizes in two-phase designs, and a stopping rule for ending data collection. A number of example data sets are included.
The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.
Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.
Curated color palettes drawn from India's natural beauty -
    Himalayan snow, Thar dunes, Kerala backwaters, Andaman reefs, Spiti's
    cold desert, Kashmir's autumn chinar, and more. Provides discrete and
    continuous palettes with first-class 'ggplot2' integration through
    scale_color_prakriti() and scale_fill_prakriti(), plus base graphics
    helpers for displaying palettes.
A novel pseudo-value regression approach for the differential co-expression network analysis in expression data, which can incorporate additional clinical variables in the model. This is a direct regression modeling for the differential network analysis, and it is therefore computationally amenable for the most users. The full methodological details can be found in Ahn S et al (2023) <doi:10.1186/s12859-022-05123-w>.
A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
 Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.
Provides functions for generating novel partially residual
    balanced multi-session designs. These designs arrange products over
    sessions and periods under partial balance restrictions and compute
    canonical efficiency factors for direct and residual (carryover)
    effects. For general background on PRBMS and related crossover design
    literature, see 
    Aggarwal and Jha (2006) <doi:10.1080/03610920600695824> and 
    Fardos et al. (2023) <doi:10.18576/jsap/120227>.
A testing workbench to evaluate tools that calculate precision-recall curves.
    Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.
An implementation of the "Design Analysis" proposed by 
    Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines 
    the evaluation of Power-Analysis with other inferential-risks as 
    Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also
    Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and 
    Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.
Derives prediction rule ensembles (PREs). Largely follows the
    procedure for deriving PREs as described in Friedman & Popescu (2008; 
    <DOI:10.1214/07-AOAS148>), with adjustments and improvements described in 
    Fokkema (2020; <DOI:10.18637/jss.v092.i12>) and Fokkema & Strobl 
    (2020; <DOI:10.1037/met0000256>). The main function pre() derives 
    prediction rule ensembles consisting of rules and/or linear terms for 
    continuous, binary, count, multinomial, survival and multivariate 
    continuous responses. Function gpe() derives generalized prediction 
    ensembles, consisting of rules, hinge and linear functions of the 
    predictor variables.
An efficient data integration method is provided for multiple spatial transcriptomics data with non-cluster-relevant effects such as the complex batch effects. It unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, requiring only partially shared cell/domain clusters across datasets. More details can be referred to Wei Liu, et al. (2023) <doi:10.1038/s41467-023-35947-w>.
An open-access tool/framework to download, validate, visualize, and
  analyze multi-source precipitation data. More information and an example of
  implementation can be found in Vargas Godoy and Markonis (2023,
  <doi:10.1016/j.envsoft.2023.105711>).
Estimate sample size based on precision rather than power.
    'precisely' is a study planning tool to calculate sample size based on
    precision. Power calculations are focused on whether or not an
    estimate will be statistically significant; calculations of precision
    are based on the same principles as power calculation but turn the
    focus to the width of the confidence interval. 'precisely' is based on
    the work of 'Rothman and Greenland' (2018).
Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().
Most of the time floating point arithmetic does
  approximately the right thing.  When adding sums or having products
  of numbers that greatly differ in magnitude, the floating point
  arithmetic may be incorrect.  This package implements the Kahan
  (1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
  <doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
  See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
  accuracy), and arbitrary precision sum (adapted from the fsum in
  'Python' ; Shewchuk (1997)
  <https://people.eecs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
  products are changed to long double precision for accuracy, or
  changed into a log-sum for accuracy.
A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.
Useful git hooks for R building on top of the multi-language
    framework 'pre-commit' for hook management. This package provides git
    hooks for common tasks like formatting files with 'styler' or spell
    checking as well as wrapper functions to access the 'pre-commit'
    executable.
Accurate calculations and visualization of precision-recall and ROC (Receiver Operator Characteristics)
    curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Fornece acesso eficiente à malha espacial de precariedade
    viária brasileira. O pacote realiza o download em cache e a
    leitura otimizada (via Apache Arrow) de arquivos Parquet
    particionados, contendo o cruzamento de variáveis de
    infraestrutura do Entorno do Censo Demográfico 2022 (IBGE)
    com a malha viária aberta do Overture Maps.
    [English] Provides efficient access to the spatial network of
    road precariousness in Brazil. The package performs cached
    downloads and optimized reading (via Apache Arrow) of partitioned
    Parquet files. These files contain the intersection of infrastructure
    variables from the 2022 Demographic Census (IBGE) with the open
    street network from Overture Maps. Methodology and datasets are
    detailed in Passos (2026) <doi:10.5281/zenodo.19711448>.
Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. 
  This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package
  is described by Kamiran, Karim and Zhang (2012) <https://ieeexplore.ieee.org/document/6413831/>.
Performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) <doi:10.1534/g3.116.038059>; Xu Y et al (2021) <doi: 10.1111/pbi.13458>).
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM.
             GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine.
             It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
			 A complete manual for this package is provided in the manual folder of the package installation directory. 
			 You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").
Draw 2 dimensional and three dimensional plot for multiple regression models using package 'ggplot2' and 'rgl'.
   Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).
We included functions to assess the performance of
 risk models. The package contains functions for the various measures that are
 used in empirical studies, including univariate and multivariate odds ratios
 (OR) of the predictors, the c-statistic (or area under the receiver operating
 characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
 reclassification table, net reclassification improvement (NRI) and
 integrated discrimination improvement (IDI). Also included are functions
 to create plots, such as risk distributions, ROC curves, calibration plot,
 discrimination box plot and predictiveness curves. In addition to functions
 to assess the performance of risk models, the package includes functions to
 obtain weighted and unweighted risk scores as well as predicted risks using
 logistic regression analysis. These logistic regression functions are
 specifically written for models that include genetic variables, but they
 can also be applied to models that are based on non-genetic risk factors only.
 Finally, the package includes function to construct a simulated dataset with 
 genotypes, genetic risks, and disease status for a hypothetical population, which 
 is used for the evaluation of genetic risk models.
A one-function package containing prediction(), a type-safe alternative to predict() that always returns a data frame. The summary() method provides a data frame with average predictions, possibly over counterfactual versions of the data (à la the margins command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., lm, glm) from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README file or main package documentation page for a complete listing.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a prediction interval). This package has functions that calculate
     the prediction interval for the correlation (i.e., r),
     standardized mean difference (i.e., d-value), and mean.
Functions to get prediction intervals and prediction points of future observations from any continuous distribution.
Enables researchers to visualize the prediction performance of any algorithm on the individual level (or close to it), given that the predicted outcome is either binary or continuous. Visual results are instantly comprehensible.
Providing functions to diagnose and make inferences from various linear models, 
    such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. 
	Inferences include predicted means and standard errors, contrasts, multiple comparisons, 
	permutation tests, adjusted R-square and graphs.
Estimates when and where a model-guided treatment strategy may 
    outperform a treat-all or treat-none approach by Monte Carlo simulation and 
    evaluation of the Net Monetary Benefit. Details can be viewed in 
    Parsons et al. (2023) <doi:10.21105/joss.05328>.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.
Predicts the most common race of a surname and based on U.S. Census
    data, and the most common first named based on U.S. Social Security Administration data.
Methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.
Implements conformal prediction methods for constructing
    prediction intervals (regression) and prediction sets (classification)
    with finite-sample coverage guarantees. Methods include split conformal,
    'CV+' and 'Jackknife+' (Barber et al. 2021) <doi:10.1214/20-AOS1965>,
    'Conformalized Quantile Regression' (Romano et al. 2019)
    <doi:10.48550/arXiv.1905.03222>, 'Adaptive Prediction Sets'
    (Romano, Sesia, Candes 2020) <doi:10.48550/arXiv.2006.02544>,
    'Regularized Adaptive Prediction Sets' (Angelopoulos et al. 2021)
    <doi:10.48550/arXiv.2009.14193>, Mondrian conformal prediction for
    group-conditional coverage (Vovk et al. 2005), weighted conformal
    prediction for covariate shift (Tibshirani et al. 2019), and adaptive
    conformal inference for sequential prediction (Gibbs and Candes 2021).
    All methods are distribution-free and provide calibrated uncertainty
    quantification without parametric assumptions. Works with any model that can
    produce predictions from new data, including 'lm', 'glm', 'ranger',
    'xgboost', and custom user-defined models.
Fetches the 'PREDICTS' database and relevant metadata from the Data
    Portal at the Natural History Museum, London <https://data.nhm.ac.uk>. Data
    were collated from over 400 existing spatial comparisons of local-scale
    biodiversity exposed to different intensities and types of anthropogenic
    pressures, from sites around the world. These data are described in Hudson
    et al. (2013) <doi:10.1002/ece3.2579>.
Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.
An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.
Provides predictive microbiology model functions and convenience
    wrappers for fitting primary growth, microbial inactivation, dynamic,
    omnibus, and cardinal parameter models to experimental data using nonlinear
    least squares and related mixed-effects or time-varying workflows. Includes
    helper functions for extracting fitted values, calculating model diagnostics,
    and comparing fitted models. Implemented model families include those
    described by: Zwietering et al. (1990)
    <doi:10.1128/AEM.56.6.1875-1881.1990>, Baranyi and Roberts (1994)
    <doi:10.1016/0168-1605(94)90157-0>, Baranyi and Roberts (1995)
    <doi:10.1016/0168-1605(94)00121-L>, Buchanan et al. (1997)
    <doi:10.1006/fmic.1997.0125>, Richards (1959)
    <doi:10.1093/jxb/10.2.290>, Fang et al. (2012)
    <doi:10.1111/j.1750-3841.2012.02873.x>, Fang et al. (2013)
    <doi:10.1016/j.fm.2012.12.005>, Huang (2008)
    <doi:10.1111/j.1750-3841.2008.00785.x>, Huang (2009)
    <doi:10.1016/j.jfoodeng.2008.07.011>, Huang (2013)
    <doi:10.1016/j.foodcont.2012.11.019>, Geeraerd et al. (2005)
    <doi:10.1016/j.ijfoodmicro.2004.11.038>, van Boekel (2002)
    <doi:10.1016/S0168-1605(01)00742-5>, Peleg (1999)
    <doi:10.1016/S0963-9969(99)00081-2>, Mafart et al. (2002)
    <doi:10.1016/S0168-1605(01)00624-9>, Albert and Mafart (2005)
    <doi:10.1016/j.ijfoodmicro.2004.10.016>, Rosso et al. (1993)
    <doi:10.1006/jtbi.1993.1099>, Rosso et al. (1995)
    <doi:10.1128/AEM.61.2.610-616.1995>, and Rosso et al. (1996)
    <doi:10.4315/0362-028X-59.9.944>.
Recent years have seen an increased interest in novel methods
    for analyzing quantitative data from experimental psychology. Currently, however, they lack an
    established and accessible software framework. Many existing implementations provide no guidelines,
    consisting of small code snippets, or sets of packages. In addition, the use of existing packages
    often requires advanced programming experience. 'PredPsych' is a user-friendly toolbox based on
    machine learning predictive algorithms. It comprises of multiple functionalities for multivariate
    analyses of quantitative behavioral data based on machine learning models.
An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made
             by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most 
             accurate for your model.
Evaluate the predictive performance of an existing (i.e. previously 
    developed) prediction/ prognostic model given relevant information about the 
    existing prediction model (e.g. coefficients) and a new dataset. Provides a 
    range of model updating methods that help tailor the existing model to the 
    new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques 
    to aggregate multiple existing prediction models on the new data are also 
    provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. 
    (2018) <doi:10.1002/sim.7586>).
Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.
Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.
Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).
Implements the Single Transferable Vote (STV) electoral system, with
    clear explanatory graphics. The core function stv() uses Meek's method, the purest
    expression of the simple principles of STV, but which requires electronic
    counting. It can handle votes expressing equal preferences for subsets of
    the candidates. A function stv.wig() implementing the Weighted Inclusive
    Gregory method, as used in Scottish council elections, is also provided,
    and with the same options, as described in the manual. The required vote
    data format is as an R list: a function pref.data() is provided to transform
    some commonly used data formats into this format.
    References for methodology:
    Hill, Wichmann and Woodall (1987) <doi:10.1093/comjnl/30.3.277>,
    Hill, David (2006) <https://www.votingmatters.org.uk/ISSUE22/I22P2.pdf>,
    Mollison, Denis (2023) <arXiv:2303.15310>,
    (see also the package manual pref_pkg_manual.pdf).
Allows users to derive multi-objective weights from pairwise comparisons, which
    research shows is more repeatable, transparent, and intuitive other techniques. These weights
    can be rank existing alternatives or to define a multi-objective utility function for optimization.
Design and analyze two-stage randomized trials with a continuous
    outcome measure. The package contains functions to compute the required 
    sample size needed to detect a given preference, treatment, and selection 
    effect; alternatively, the package contains functions that can report the 
    study power given a fixed sample size. Finally, analysis functions are 
    provided to test each effect using either summary data (i.e. means, 
    variances) or raw study data <doi:10.18637/jss.v094.c02>.
Convenient structures for creating, sourcing, reading, writing
  and manipulating ordinal preference data. Methods for writing to/from PrefLib
  formats. See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference
  Data" (2013) <doi:10.1007/978-3-642-41575-3_20>.
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
A visualization toolkit for preferential data, such as ranked-choice election results, tournament outcomes,
    and survey responses. The package provides methods to visualise the preference distribution of one contest with
    bar charts and pairwise comparisons of two contestants, as well as methods to visualise multiple contests through
    2D and high-dimensional simplex plots both statically and interactively. HD simplex displays are implemented via projection
    methods using the 'tourr' and 'detourr' packages, enabling dynamic exploration of high-dimensional preference structure.
    For more details on HD simplex projection, see Wickham et al. (2011) <doi:10.21105/joss.03419>.
Provides functionality for calculating pregnancy-related dates and
    tracking medications during pregnancy and fertility treatment. Calculates
    due dates from various starting points including last menstrual period and
    IVF (In Vitro Fertilisation) transfer dates, determines pregnancy progress
    on any given date, and identifies when specific pregnancy weeks are
    reached. Includes medication tracking capabilities for individuals
    undergoing fertility treatment or during pregnancy, allowing users to
    monitor remaining doses and quantities needed over specified time periods.
    Designed for those tracking their own pregnancies or supporting partners
    through the process, making use of options to personalise output messages.
    For details on due date calculations, see
    <https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2017/05/methods-for-estimating-the-due-date>.
Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.
A database containing the names 
  of the babies born in Quebec between 1980 and 2020.
Prepares data for statistical analysis (e.g., analysis of variance
  ;ANOVA) by enabling the user to easily and quickly merge (using the
  file_merge() function) raw data files into one merged table and then
  aggregate the merged table (using the prep() function) into a finalized
  table while keeping track and summarizing every step of the preparation.
  The finalized table contains several possibilities for dependent measures of
  the dependent variable. Most suitable when measuring variables in an
  interval or ratio scale (e.g., reaction-times) and/or discrete values such
  as accuracy. Main functions included are file_merge() and prep(). The
  file_merge() function vertically merges individual data files (in a long
  format) in which each line is a single observation to one single dataset.
  The prep() function aggregates the single dataset according to any
  combination of grouping variables (i.e., between-subjects and
  within-subjects independent variables, respectively), and returns a data
  frame with a number of dependent measures for further analysis for each cell
  according to the combination of provided grouping variables. Dependent
  measures for each cell include among others means before and after rejecting
  all values according to a flexible standard deviation criteria, number of
  rejected values according to the flexible standard deviation criteria,
  proportions of rejected values according to the flexible standard deviation
  criteria, number of values before rejection, means after rejecting values
  according to procedures described in Van Selst & Jolicoeur (1994; suitable
  when measuring reaction-times), standard deviations, medians, means according
  to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data
  frame prep() returns can also be exported as a txt file to be used for
  statistical analysis in other statistical programs.
Early generation breeding trials are to be conducted in multiple 
             environments where it may not be possible to replicate all the 
             lines in each environment due to scarcity of resources. For such 
             situations, partially replicated (p-Rep) designs have wide 
             application potential as only a proportion of the test lines are 
             replicated at each environment. A collection of several utility 
             functions related to p-Rep designs have been developed. Here, the 
             package contains six functions for a complete stepwise analytical 
             study of these designs. Five functions pRep1(), pRep2(), pRep3(), 
             pRep4() and pRep5(), are used to generate five new series of p-Rep 
             designs and also compute average variance factors and canonical 
             efficiency factors of generated designs. A fourth function NCEV() 
             is used to generate incidence matrix (N), information matrix (C), 
             canonical efficiency factor (E) and average variance factor (V). 
             This function is general in nature and can be used for studying the 
             characterization properties of any block design. A construction 
             procedure for p-Rep designs was given by Williams et al.(2011) 
             <doi:10.1002/bimj.201000102> which was tedious and time consuming. 
             Here, in this package, five different methods have been given to 
             generate p-Rep designs easily.
Provides functions for data normalization and transformation in
    preprocessing stages. Implements scaling methods (min-max, Z-score, L2
    normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox
    transformation is described in Box and Cox (1964)
    <doi:10.1111/j.2517-6161.1964.tb00553.x>, Yeo-Johnson transformation in
    Yeo and Johnson (2000) <doi:10.1093/biomet/87.4.954>.
A set of tools to implement the non-parametric bounds and Bayesian methods for assessing post-treatment bias developed in Blackwell, Brown, Hill, Imai, and Yamamoto (2025) <doi:10.1017/pan.2025.3>.
A figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added (points, lines, barplot with add=TRUE and so forth).
Reconcile species names across datasets and phylogenetic trees
    for comparative biology workflows. Identifies mismatches due to formatting
    differences, taxonomic synonymy, and spelling errors. Produces detailed
    reports documenting how each name was resolved, which taxonomic authority
    was used, and what remains unresolved. Supports exact matching, name
    normalisation, synonym resolution via local taxonomic databases, and
    fuzzy matching for likely typos. Detects taxonomic splits and lumps. For
    methodological context, see Nakagawa et al. (2026)
    <doi:10.32942/X2468Z>.
Provides classes to pre-process microarray gene
  expression data as part of the OOMPA collection of packages
  described at <http://oompa.r-forge.r-project.org/>.
Preprocess numeric data matrices into desired transformed representations.
    Standardization, Unitization, Cubitization and adaptive intervals are offered.
In this record linkage package, data preprocessing has been meticulously executed
  to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. 
  This approach facilitates seamless data integration and analysis across various datasets. While users 
  have the flexibility to modify variable names, the system intelligently ensures that 
  changes are only permitted when they do not compromise data consistency or essential variable essence.
An interactive document for preprocessing the dataset using  'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/PREPShiny/>.
Miscellaneous small utilities are provided to mitigate issues with messy, inconsistent or high dimensional data and help for preprocessing and preparing analyses.
Provides a collection of templates to author preregistration documents for scientific studies in PDF format.
Preregistrations, or more generally, registrations, enable
    explicit timestamped and (often but not necessarily publicly) frozen
    documentation of plans and expectations as well as decisions and
    justifications. In research, preregistrations are commonly used to
    clearly document plans and facilitate justifications of deviations from
    those plans, as well as decreasing the effects of publication bias by
    enabling identification of research that was conducted but not published.
    Like reporting guidelines, (pre)registration forms often have specific
    structures that facilitate systematic reporting of important items. The
    'preregr' package facilitates specifying (pre)registrations in R and
    exporting them to a human-readable format (using R Markdown partials or
    exporting to an 'HTML' file) as well as human-readable embedded data
    (using 'JSON'), as well as importing such exported (pre)registration
    specifications from such embedded 'JSON'.
Provides a set of functions useful when evaluating the results of presence-absence models. Package includes functions for calculating threshold dependent measures such as confusion matrices, pcc, sensitivity, specificity, and Kappa, and produces plots of each measure as the threshold is varied. It will calculate optimal threshold choice according to a choice of optimization criteria. It also includes functions to plot the threshold independent ROC curves along with the associated AUC (area under the curve).
Makes output files from select PreSens Fiber Optic Oxygen
    Transmitters easier to work with in R. See <http://www.presens.de> for more
    information about PreSens (Precision Sensing GmbH). Note: this package is
    neither created nor maintained by PreSens.
Consists of custom wrapper functions using packages
    'openxlsx', 'flextable', and 'officer' to create highly formatted MS office friendly output of your data frames.
    These viewer friendly outputs are intended to match expectations of professional looking presentations
    in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data
    frame to have certain properties in order to take advantage of the automated formatting.
Compilation and digitalization of the official registry of victims of state terrorism in Argentina during the last
             military coup. The original data comes from RUVTE-ILID (2019) <https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and <http://basededatos.parquedelamemoria.org.ar/registros/>. The title, presentes, comes from present in spanish.
Computes probability-scale residuals and residual correlations for continuous, ordinal, binary, count, and time-to-event data Qi Liu, Bryan Shepherd, Chun Li (2020) <doi:10.18637/jss.v094.i12>.
Bland (2009) <doi:10.1136/bmj.b3985> recommended to
    base study sizes on the width of the confidence interval rather the power of 
    a statistical test. The goal of 'presize' is to provide functions for such 
    precision based sample size calculations. For a given sample size, the 
    functions will return the precision (width of the confidence interval), and 
    vice versa.
Multi-state models are essential tools in longitudinal data analysis. 
             One primary goal of these models is the estimation of transition 
             probabilities, a critical metric for predicting clinical prognosis 
             across various stages of diseases or medical conditions. Traditionally, 
             inference in multi-state models relies on the Aalen-Johansen (AJ) 
             estimator which is consistent under the Markov assumption. However, 
             in many practical applications, the Markovian nature of the process
             is often not guaranteed, limiting the applicability of the AJ
             estimator in more complex scenarios. This package extends the landmark Aalen-Johansen
             estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>)
             incorporating presmoothing techniques described by Soutinho, 
             Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, 
             offering a robust alternative for estimating transition probabilities
             in non-Markovian multi-state models with multiple states and potential
             reversible transitions.
Provides a function for estimating the transition probabilities in an illness-death model. 
            The transition probabilities can be estimated from the unsmoothed landmark estimators developed 
            by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>.
            Presmoothed estimates can also be obtained through the use of a parametric family of binary
            regression curves, such as logit, probit or cauchit. The additive logistic regression model 
            and nonparametric regression are also alternatives which have been implemented.
            The idea behind the presmoothed landmark estimators is to use the presmoothing techniques 
            developed by  Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the 
            landmark estimation of the transition probabilities.
This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.
Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.
This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.
An application to calculate a patient's pretest probability
    (PTP) for obstructive Coronary Artery Disease (CAD) from a collection
    of guidelines or studies. Guidelines usually comes from the American
    Heart Association (AHA), American College of Cardiology (ACC) or
    European Society of Cardiology (ESC). Examples of PTP scores that
    comes from studies are the 2020 Winther et al. basic, Risk
    Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery
    Calcium Score-weighted Clinical Likelihood (CACS-CL) models
    <doi:10.1016/j.jacc.2020.09.585>, 2019 Reeh et al. basic and clinical
    models <doi:10.1093/eurheartj/ehy806> and 2017 Fordyce et al. PROMISE
    Minimal-Risk Tool <doi:10.1001/jamacardio.2016.5501>.  As diagnosis of
    CAD involves a costly and invasive coronary angiography procedure for
    patients, having a reliable PTP for CAD helps doctors to make better
    decisions during patient management.  This ensures high risk patients
    can be diagnosed and treated early for CAD while avoiding unnecessary
    testing for low risk patients.
Drop-in replacements for standard base graphics
    functions. The replacements are prettier versions of the originals.
Replace the standard print method for functions with one that
    performs syntax highlighting, using ANSI colors, if the terminal
    supports them.
Defines aesthetically pleasing colour palettes.
Creating tiny yet beautiful documents and vignettes from R
    Markdown. The package provides the 'html_pretty' output format as an
    alternative to the 'html_document' and 'html_vignette' engines that
    convert R Markdown into HTML pages. Various themes and syntax highlight
    styles are supported.
Simple and crisp publication-quality graphics for the ExPosition family of packages.
  See An ExPosition of the Singular Value Decomposition in R (Beaton et al 2014) <doi:10.1016/j.csda.2013.11.006>.
Automates the process of creating a scale bar and north arrow in
    any package that uses base graphics to plot in R. Bounding box tools help find
    and manipulate extents. Finally, there is a function to automate the process
    of setting margins, plotting the map, scale bar, and north arrow, and resetting
    graphic parameters upon completion.
Provides 'ggplot2'-based plotting functions for 'PanelMatch'
    (Imai, Kim, Wang (2023) <doi:10.1111/ajps.12685>) results.
    Tidy-and-plot function pairs for three 'PanelMatch' outputs: treatment effect
    estimates (PanelEstimate()), placebo test diagnostics (placebo_test()), and
    covariate balance checks (get_covariate_balance()). Supports multiple models,
    significance indicators, faceted balance grids, and full 'ggplot2' extensibility.
Pretty, human readable formatting of quantities.
    Time intervals: '1337000' -> '15d 11h 23m 20s'.
    Vague time intervals: '2674000' -> 'about a month ago'.
    Bytes: '1337' -> '1.34 kB'.
    Rounding: '99' with 3 significant digits -> '99.0'
    p-values: '0.00001' -> '<0.0001'.
    Colors: '#FF0000' -> 'red'.
    Quantities: '1239437' -> '1.24 M'.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from <https://mcmc-jags.sourceforge.io/>.
Implements the American Heart Association Predicting
    Risk of cardiovascular disease EVENTs (PREVENT) equations from Khan
    SS, Matsushita K, Sang Y, and colleagues (2023)
    <doi:10.1161/CIRCULATIONAHA.123.067626>, with optional comparison 
    with their de facto predecessor, the Pooled Cohort Equations from the 
	  American Heart Association and American College of Cardiology (2013)
	  <doi:10.1161/01.cir.0000437741.48606.98> and the revision to the Pooled 
	  Cohort Equations from Yadlowsky and colleagues (2018) 
	  <doi:10.7326/M17-3011>.
For working with the 'Prevision.io' AI model management platform's API <https://prevision.io/>.
Spatial estimation of a prevalence surface
    or a relative risks surface, using data from a Demographic and Health
    Survey (DHS) or an analog survey, see Larmarange et al. (2011)
    <doi:10.4000/cybergeo.24606>.
Functions to simulate point prevalence studies (PPSs) of healthcare-associated infections (HAIs) and to convert prevalence to incidence in steady state setups. 
    Companion package to the preprint Willrich et al., From prevalence to incidence - a new approach in the hospital setting;
    <doi:10.1101/554725> , where methods are explained in detail.
Estimate False Discovery Rates (FDRs) for importance metrics from
    random forest runs.
Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: Diewert and Fox (2020) 
    <doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.
Price comparisons within or between countries provide an overall measure of the relative difference in prices, often denoted as price levels. This package provides index number methods for such price comparisons (e.g., The World Bank, 2011, <doi:10.1596/978-0-8213-9728-2>). Moreover, it contains functions for sampling and characterizing price data.
Functions to aid in micro and macro economic analysis and handling of price and
    currency data. Includes extraction of relevant inflation and exchange rate data from World Bank
    API, data cleaning/parsing, and standardisation. Inflation adjustment
    calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current
    and historical end of day exchange rates for 171 currencies from the European Central Bank
    Statistical Data Warehouse (2020).
An implementation of the van Westendorp Price
    Sensitivity Meter in R, which is a survey-based approach
	to analyze consumer price preferences and sensitivity
    (van Westendorp 1976, isbn:9789283100386).
Implementation of an oligonucleotide primer and probe design algorithm using a linearly scaling approximation of set coverage. A detailed description available at Smolander and Tamminen, 2021; <doi:10.1101/2021.09.06.459073>.
Implements 'PRIDIT' (Principal Component Analysis applied to 'RIDITs'), an
    unsupervised, nonparametric method for aggregating ordinal, categorical,
    and continuous indicators into a single interpretable composite score.
    Originally proposed by Brockett et al. (2002)
    <doi:10.1111/1539-6975.00027> for insurance fraud detection and
    extended to hospital quality measurement by Lieberthal (2008)
    <doi:10.1111/j.1475-6773.2007.00821.x> and Lieberthal and Comer (2013)
    <doi:10.1111/rmir.12009>.
    The package provides:
    (1) low-level functions ridit(), PRIDITweight(), and PRIDITscore();
    (2) a unified pridit() entry point returning a classed object with
        print, summary, 'autoplot', and 'coef' methods;
    (3) pridit_boot() for bootstrap confidence intervals on scores and weights;
    (4) a step_pridit() recipe step for out-of-sample scoring within the
        'tidymodels' framework; and
    (5) pridit_longitudinal() for panel data, computing cross-period
        stability of scores and weights.
Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.
Provides functions for working with primary
    event censored distributions and 'Stan' implementations for use in Bayesian
    modeling. Primary event censored distributions are useful for modeling
    delayed reporting scenarios in epidemiology and other fields (Charniga et
    al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for
    arbitrary delay distributions, a range of common primary distributions, and
    allows for truncation and secondary event censoring to be accounted for
    (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of
    common distributions also have analytical solutions implemented, allowing
    for faster computation. In addition, it provides multiple methods for
    fitting primary event censored distributions to data via optional
    dependencies.
Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs.
Use Prime Factorization for simplifying computations,
    for instance for ratios of large factorials.
Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.
Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.
Provides data and functions for "A Primer of Ecology with R" by M. Henry H. Stevens (2009) <doi:10.1007/978-0-387-89882-7>.
Identifies potential target sequences for a given
    set of primers and generates phylogenetic trees annotated with the
    taxonomies of the predicted amplification products.
Fast functions for dealing with prime numbers, such as testing
    whether a number is prime and generating a sequence prime numbers.
    Additional functions include finding prime factors and Ruth-Aaron pairs,
    finding next and previous prime numbers in the series, finding or estimating
    the nth prime, estimating the number of primes less than or equal to an
    arbitrary number, computing primorials, prime k-tuples (e.g., twin primes),
    finding the greatest common divisor and smallest (least) common multiple,
    testing whether two numbers are coprime, and computing Euler's totient
    function. Most functions are vectorized for speed and convenience.
Fitting a principal curve to a data matrix in arbitrary dimensions. 
  Hastie and Stuetzle (1989) <doi:10.2307/2289936>.
Construct a principal surface that are two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. For more information on principal surfaces, see Ganey, R. (2019, "https://open.uct.ac.za/items/4e655d7d-d10c-481b-9ccc-801903aebfc8").
A lightweight message system relying purely on base R. Comes with built-in and pre styled message types and provides an easy way to create custom messages. Supports individually styled and colored text as well as timing information. Designed to make console output more informative and visually organized.
Extends the S3 generic function knit_print() in 'knitr'
    to automatically print some objects using an appropriate format such as
    Markdown or LaTeX. For example, data frames are automatically printed as
    tables, and the help() pages can also be rendered in 'knitr' documents.
Quickly visualize 'R' project directory structures with automatic 
    project detection and clean tree output.
Provides methods for reducing the number of features within a data set. See Bauer JO (2021) <doi:10.1145/3475827.3475832> and Bauer JO, Drabant B (2021) <doi:10.1016/j.jmva.2021.104754> for more information on principal loading analysis.
Three-dimensional systematic conservation planning, conducting
             nested prioritization analyses across multiple depth levels and
             ensuring efficient resource allocation throughout the water column.
             It provides a structured workflow designed to address biodiversity
             conservation and management challenges in the 3 dimensions, while
             facilitating users’ choices and parameterization (Doxa et al. 2025
             <doi:10.1016/j.ecolmodel.2024.110919>).
An innovative tool-set that incorporates graph community detection
             methods into systematic conservation planning. It is designed to
             enhance spatial prioritization by focusing on the protection of
             areas with high ecological connectivity. Unlike traditional
             approaches that prioritize individual planning units, 'priorCON'
             focuses on clusters of features that exhibit strong ecological
             linkages. The 'priorCON' package is built upon the 'prioritizr'
             package <doi:10.32614/CRAN.package.prioritizr>, using commercial
             and open-source exact algorithm solvers that ensure optimal
             solutions to prioritization problems.
Translates beliefs into prior information in the form of Beta and Gamma distributions. 
	It can be used for the generation of priors on the prevalence of disease and 
	the sensitivity/specificity of diagnostic tests and any other binomial experiment.
This uses a mixed integer mathematical programming (MIP)
        approach for building and solving multi-action planning problems, 
        where the goal is to find an optimal combination of management actions that
        abate threats, in an efficient way while accounting for spatial aspects. 
        Thus, optimizing the connectivity and conservation effectiveness of 
        the prioritized units and of the deployed actions. The package is capable of 
        handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. 
        Gurobi optimization solver can be installed using comprehensive instructions in 
        the 'gurobi' installation vignette of the prioritizr package (available in 
        <https://prioritizr.net/articles/gurobi_installation_guide.html>). Instead, 'CPLEX'
        optimization solver can be obtain from IBM CPLEX web page (available here 
        <https://www.ibm.com/es-es/products/ilog-cplex-optimization-studio>). Additionally, 
        the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to obtain solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). Methods used in the 
        package refers to Salgado-Rojas et al. (2020) <doi:10.1016/j.ecolmodel.2019.108901>,
        Beyer et al. (2016) <doi:10.1016/j.ecolmodel.2016.02.005>, Cattarino et al. (2015)
        <doi:10.1371/journal.pone.0128027> and Watts et al. (2009) <doi:10.1016/j.envsoft.2009.06.005>. 
        See the prioriactions website for more information, documentations and examples.
Systematic conservation prioritization using mixed integer linear
    programming (MILP). It provides a flexible interface for building and
    solving conservation planning problems. Once built, conservation planning
    problems can be solved using a variety of commercial and open-source exact
    algorithm solvers. By using exact algorithm solvers, solutions can be
    generated that are guaranteed to be optimal (or within a pre-specified
    optimality gap). Furthermore, conservation problems can be constructed to
    optimize the spatial allocation of different management actions or zones,
    meaning that conservation practitioners can identify solutions that benefit
    multiple stakeholders. To solve large-scale or complex conservation
    planning problems, users should install the Gurobi optimization software
    (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
    Gurobi Installation Guide vignette for details). Users can also install the
    IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and
    the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>).
    Additionally, the 'rcbc' R package (available at
    <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions
    using the CBC optimization software (<https://github.com/coin-or/Cbc>). For
    further details, see Hanson et al. (2025) <doi:10.1111/cobi.14376>.
Conservation planning datasets for learning how to use the
    'prioritizr' package <https://CRAN.R-project.org/package=prioritizr>.
Priority-ElasticNet extends the Priority-LASSO method (Klau et al. (2018) <doi:10.1186/s12859-018-2344-6>) by incorporating the ElasticNet penalty, allowing for both L1 and L2 regularization. 
    This approach fits successive ElasticNet models for several blocks of (omics) data with different priorities, using the predicted values from each block as an offset for the subsequent block. 
    It also offers robust options to handle block-wise missingness in multi-omics data, improving the flexibility and applicability of the model in the presence of incomplete datasets.
Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block. Also offers options to deal with block-wise missingness in multi-omics data.
Provides functions for prior and likelihood sensitivity analysis in Bayesian models. Currently it implements methods to determine the sensitivity of the posterior to power-scaling perturbations of the prior and likelihood.
Allows users to access the Oregon State Prism climate data
    (<https://prism.nacse.org/>). Using the web service API data
    can easily downloaded in bulk and loaded into R for spatial analysis.
    Some user friendly visualizations are also provided.
Implements Penalized Regression with Inferred Seasonality Module (PRISM) to generate forecast estimation of weekly unemployment initial claims using 'Google Trends' data. It includes required data and tools for backtesting the performance in 2007-2020.
Loads and processes huge text
    corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>).
    sally acts as a very fast preprocessor which splits the text files into
    tokens or n-grams. These output files can then be read with the PRISMA
    package which applies testing-based token selection and has some
    replicate-aware, highly tuned non-negative matrix factorization and
    principal component analysis implementation which allows the processing of
    very big data sets even on desktop machines.
Systematic reviews should be described in a high degree of 
    methodological detail. The 'PRISMA' Statement calls for a high level of 
    reporting detail in systematic reviews and meta-analyses. An integral part 
    of the methodological description of a review is a flow diagram. 
    This package produces an interactive flow diagram that conforms to the 
    'PRISMA2020' preprint. When made interactive, the reader/user can click 
    on each box and be directed to another website or file online (e.g. a 
    detailed description of the screening methods, or a list of excluded full 
    texts), with a mouse-over tool tip that describes the information linked 
    to in more detail. Interactive versions can be saved as HTML files, 
    whilst static versions for inclusion in manuscripts can be saved as 
    HTML, PDF, PNG, SVG, PS or WEBP files.
Creates 'PRISMA' <http://prisma-statement.org/> diagram from a minimal dataset of included and excluded studies and allows for more custom diagrams. 'PRISMA' diagrams are used to track the identification, screening, eligibility, and inclusion of studies in a systematic review.
Manipulate and visualize colors in a intuitive,
    low-dependency and functional way.
Prism <https://prismjs.com/> is a lightweight, extensible syntax 
    highlighter, built with modern web standards in mind. This package provides 
    server-side rendering in R using 'V8' such that no JavaScript library is
    required in the resulting HTML documents. Over 400 languages are supported.
Enables user to perform the following:
    1. Roll 'n' number of die/dice (roll()).
    2. Toss 'n' number of coin(s) (toss()).
    3. Play the game of Rock, Paper, Scissors.
    4. Choose 'n' number of card(s) from a pack of 52 playing cards (Joker optional).
Tools for anonymizing sensitive patient and research data. Helps
    protect privacy while keeping data useful for analysis. Anonymizes IDs,
    names, dates, locations, and ages while maintaining referential integrity.
    Methods based on: Sweeney (2002) <doi:10.1142/S0218488502001648>,
    Dwork et al. (2006) <doi:10.1007/11681878_14>,
    El Emam et al. (2011) <doi:10.1371/journal.pone.0028071>,
    Fung et al. (2010) <doi:10.1145/1749603.1749605>.
Implements two differentially private algorithms for 
  estimating L2-regularized logistic regression coefficients. A randomized
  algorithm F is epsilon-differentially private (C. Dwork, Differential
  Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if 
     |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
  for any pair D, D' of datasets that differ in exactly one record, any
  measurable set S, and the randomness is taken over the choices F makes.
Estimates adjusted prevalence ratios (PR) and their confidence
    intervals from logistic regression models, addressing the well-known
    limitation of odds ratios (OR) as approximations to PR in cross-sectional
    studies with common outcomes. Supports independent observations (glm()),
    clustered/multilevel data (glmer() from 'lme4'), longitudinal data via
    Generalised Estimating Equations (geeglm() from 'geepack'), and complex
    survey designs (svyglm() from 'survey'). Inference is available via the 
    delta method (conditional and marginal standardisation) and via
    bootstrap (normal-approximation and percentile intervals).
    Continuous covariates are handled through user-specified or
    median-based reference values; flexible baseline specification
    allows any reference category to be chosen for factor predictors.
    Based on the methodology described in Amorim & Ospina (2021)
    <doi:10.1590/0001-3765202120190316>.
Calculates seat allocation using the D-Hondt method, Sainte-Lague method, and Modified Sainte-Lague method, all commonly used in proportional representation electoral systems. For more information on these methods, see Michael Gallagher (1991)<doi:10.1016/0261-3794(91)90004-C>.
Miscellaneous printing of numeric or statistical results in R Markdown or Quarto documents according to guidelines of the "Publication Manual" of the American Psychological Association (2020, ISBN: 978-1-4338-3215-4). These guidelines are usually referred to as APA style (<https://apastyle.apa.org/>) and include specific rules on the formatting of numbers and statistical test results. APA style has to be implemented when submitting scientific reports in a wide range of research fields, especially in the social sciences. The default output of numbers in the R console or R Markdown and Quarto documents does not meet the APA style requirements, and reformatting results manually can be cumbersome and error-prone. This package covers the automatic conversion of R objects to textual representations that meet the APA style requirements, which can be included in R Markdown or Quarto documents. It covers some basic statistical tests (t-test, ANOVA, correlation, chi-squared test, Wilcoxon test) as well as some basic number printing manipulations (formatting p-values, removing leading zeros for numbers that cannot be greater than one, and others). Other packages exist for formatting numbers and tests according to the APA style guidelines, such as 'papaja' (<https://cran.r-project.org/package=papaja>) and 'apa' (<https://cran.r-project.org/package=apa>), but they do not offer all convenience functionality included in 'prmisc'. The vignette has an overview of most of the functions included in the package.
Provides functions for generating pseudo-random numbers that follow a uniform distribution [0,1]. Randomness tests were conducted using the National Institute of Standards and Technology test suite<https://csrc.nist.gov/pubs/sp/800/22/r1/upd1/final>, along with additional tests. The sequence generated depends on the initial values and parameters. The package includes a linear congruence map as the decision map and three chaotic maps to generate the pseudo-random sequence, which follow a uniform distribution. Other distributions can be generated from the uniform distribution using the Inversion Principle Method and BOX-Muller transformation. Small perturbations in seed values result in entirely different sequences of numbers due to the sensitive nature of the maps being used. The chaotic nature of the maps helps achieve randomness in the generator. Additionally, the generator is capable of producing random bits.
Survey sampling using permanent random numbers (PRN's). A solution
    to the problem of unknown overlap between survey samples, which leads to a
    low precision in estimates when the survey is repeated or combined with
    other surveys. The PRN solution is to supply the U(0, 1) random numbers to
    the sampling procedure, instead of having the sampling procedure generate
    them. In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited
    papers, it is shown how this is carried out and how it improves the
    estimates. This package supports two common fixed-size sampling procedures
    (simple random sampling and probability-proportional-to-size sampling) and
    includes a function for transforming the PRN's in order to control the
    sample overlap.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
Automate the detection of gaps and elevations in mapped sequencing 
  read coverage using a 2D pattern-matching algorithm. 'ProActive' detects, characterizes 
  and visualizes read coverage patterns in both genomes and metagenomes. Optionally, 
  users may provide gene annotations associated with their genome or metagenome 
  in the form of a .gff file. In this case, 'ProActive' will generate an additional 
  output table containing the gene annotations found within the detected regions of gapped 
  and elevated read coverage. Additionally, users can search for gene 
  annotations of interest in the output read coverage plots.
A collection of tools to facilitate standardized analysis 
    and graphical procedures when using the National Cancer Institute’s 
    Patient-Reported Outcomes version of the Common Terminology Criteria for 
    Adverse Events (PRO-CTCAE) and other PRO measurements.
Models can be improved by post-processing class
    probabilities, by: recalibration, conversion to hard probabilities,
    assessment of equivocal zones, and other activities. 'probably'
    contains tools for conducting these operations as well as calibration
    tools and conformal inference techniques for regression models.
The 'probaverse' is a suite of packages designed to facilitate
    creating advanced statistical models through probability distributions.
    These packages work best when loaded together because they share a common
    design philosophy and focus on different aspects of developing statistical
    models. Inspired by the 'tidyverse' package, the 'probaverse' package
    makes it easy to load the entire suite of 'probaverse' packages together.
Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).
Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the model’s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided.
Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Hybridization probes for target sequences can be made based on melting temperature value calculated by R package 'TmCalculator' <https://CRAN.R-project.org/package=TmCalculator> and methods extended from Beliveau, B. J.,(2018) <doi:10.1073/pnas.1714530115>, and those hybridization probes can be used to capture specific target regions in fluorescence in situ hybridization and next generation sequence experiments.
Fast estimation of binomial spatial probit regression models with spatial autocorrelation for big datasets.
Computes predicted probabilities and marginal effects for 
  binary & ordinal logit and probit, (partial) generalized ordinal & 
  multinomial logit models estimated with the glm(), clm() (in the 
  'ordinal' package), and vglm() (in the 'VGAM' package) functions.
Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.
Performs elementary probability calculations on finite sample spaces, which may be represented by data frames  or lists.
    This package is meant to rescue some widely used functions from the archived 'prob' package (see <https://cran.r-project.org/src/contrib/Archive/prob/>).
    Functionality includes setting up sample spaces, counting tools, defining probability spaces, performing set algebra, calculating probability and conditional probability, tools for simulation and checking the law of  large numbers, adding random variables, and finding marginal distributions.  Characteristic functions for all base R distributions are included.
It allows the user to determine sample sizes, select probabilistic samples,
 make estimates of different parameters for the total finite population and in studio domains,
 using the main design drawings.
Utilities for multiple hypothesis testing, companion datasets from "Probability and Statistics for Economics and Business: An Introduction Using R" by Jason Abrevaya (MIT Press, under contract).
Implements kernel-based classification Support Vector Machines with reliable estimated probabilities of class membership. Theoretical support for the functions in this package can be found in Duarte Silva (2025) <doi:10.1016/j.cor.2025.107203>.
Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Package for processing downloaded MODIS Calibrated radiances
    Product HDF files. Specifically, MOD02 calibrated radiance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). The package will be
    most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath
    products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and
    adds the directory with the MRTSwath executable to the default R PATH by editing
    ~/.Rprofile.
Provides tools for exploratory process data analysis. Process data refers to the data describing
    participants' problem-solving processes in computer-based assessments. It is often recorded in computer
    log files. This package provides functions to read, process, and write process data. It also implements
    two feature extraction methods to compress the information stored in process data into standard 
    numerical vectors. This package also provides recurrent neural network based models that relate response processes 
    with other binary or scale variables of interest. The functions that involve training and evaluating neural networks 
    are wrappers of functions in 'keras'.
A small, dependency-free way to generate random names. Methods 
    provided include the adjective-surname approach of Docker containers 
    ('<https://github.com/moby/moby/blob/master/pkg/namesgenerator/names-generator.go>'),
    and combinations of common English or Spanish words.
Check compliance of event-data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).
Visualize event logs using directed graphs, i.e. process maps. Part of the 'bupaR' framework.
Functions for constructing dashboards for business process monitoring. Building on the event log objects class from package 'bupaR'. Allows the use to assemble custom shiny dashboards based on process data.
Means to predict process flow, such as process outcome, next activity, next time, remaining time, and remaining trace. Off-the-shelf predictive models based on the concept of Transformers are provided, as well as multiple way to customize the models. This package is partly based on work described in Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network" <doi:10.48550/arXiv.2104.00721>.
Tools to run system processes in the background.  It can
    check if a background process is running; wait on a background process
    to finish; get the exit status of finished processes; kill background
    processes. It can read the standard output and error of the processes,
    using non-blocking connections. 'processx' can poll a process for
    standard output or error, with a timeout. It can also poll several
    processes at once.
Provides functions for simulating from and fitting the latent hidden
    Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. 
    It also includes functions for simulating from and fitting ordinary hidden Markov models.
Portable '/proc/self/maps' as a data frame.
    Determine which library or other region is mapped to a specific
    address of a process. --
    R packages can contain native code, compiled to shared libraries at build or
    installation time.
    When loaded, each shared library occupies a portion of the address space of
    the main process.
    When only a machine instruction pointer is available (e.g. from a backtrace
    during error inspection or profiling), the address space map determines
    which library this instruction pointer corresponds to.
Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.
Contains functions to simulate the most commonly used 
    'SAS®' procedures.  Specifically, the package aims to 
    simulate the functionality of 'proc freq', 'proc means', 'proc ttest',
    'proc reg', 'proc transpose', 'proc sort', and 'proc print'. 
    The simulation will include recreating 
    all statistics with the highest fidelity possible.
A direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.
Implements the methods proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>, Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>, Ackerberg, D.A. and Caves, K. and Frazer, G. (2015) <doi:10.3982/ECTA13408> and Wooldridge, J.M. (2009) <doi:10.1016/j.econlet.2009.04.026> for structural productivity estimation .
Create a project directory structure, along with typical
    files for that project.  This allows projects to be quickly and easily
    created, as well as for them to be standardized. Designed specifically
    with scientists in mind (mainly bio-medical researchers, but likely
    applies to other fields).
Fast and user friendly implementation of nonparametric estimators
    for censored event history (survival) analysis. Kaplan-Meier and
    Aalen-Johansen method.
A set of concise and efficient tools for statistical production. Can also be used for data management.
    In statistical production, you deal with complex data and need to control your process at each step of your work.
    Concise functions are very helpful, because you do not hesitate to use them.
    The following functions are included in the package.
    'dup'  checks duplicates.
    'miss' checks missing values.
    'tac' computes contingency table of all columns.
    'toc' compares two tables, spotting significant deviations.
    'chi2_find' compares columns within a data.frame, spotting related categories of (a more complex function).
Levels and changes of productivity and profitability are measured with various indices.
  The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, 
  Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index.
  Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for 
  multilateral or multitemporal comparison.
  Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are 
  only to be used for binary comparison.
  All indices can also be decomposed into different components, providing insightful information 
  on the sources of productivity and profitability changes.
  In the use of Malmquist productivity index, the technological change index can be further 
  decomposed into bias technological change components.
  The package also allows to prohibit technological regression (negative technological change). In 
  the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont 
  and Lowe indices, it is furthermore possible to rule out technological change. 
  Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as 
  an option, depending on the user's computer configuration. 
  All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and 
  several assumptions regarding returns to scale are available.
  All DEA linear programs are implemented using 'lp_solve'.
Framework for visualising tables of counts, proportions
  and probabilities. The framework is called product plots, alluding to
  the computation of area as a product of height and width, and the
  statistical concept of generating a joint distribution from the
  product of conditional and marginal distributions. The framework,
  with extensions, is sufficient to encompass over 20 visualisations
  previously described in fields of statistical graphics and 'infovis',
  including bar charts, mosaic plots, 'treemaps', equal area plots and
  fluctuation diagrams.
Helpers for preparing ecommerce product photo planning tables,
    prompt sheets, and public ProductShot AI workflow URLs. The package works
    offline and focuses on data-frame preparation for product image batches.
Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations.
    More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.
Programs to determine student grades and create
  examinations from Question banks.  Programs will create numerous
  multiple choice exams, randomly shuffled, for different versions of same question list.
Computes profile extrema functions for arbitrary functions. If the function is expensive-to-evaluate it computes profile extrema by emulating the function with a Gaussian process (using package 'DiceKriging'). In this case uncertainty quantification on the profile extrema can also be computed. The different plotting functions for profile extrema give the user a tool to better locate excursion sets.
Like similar profiling tools,
  the 'proffer' package automatically detects
  sources of slowness in R code.
  The distinguishing feature of 'proffer' is its utilization of
  'pprof', which supplies interactive visualizations
  that are efficient and easy to interpret.
  Behind the scenes, the 'profile' package converts
  native Rprof() data to a protocol buffer
  that 'pprof' understands.
  For the documentation of 'proffer',
  visit <https://r-prof.github.io/proffer/>.
  To learn about the implementations and methodologies of
  'pprof', 'profile', and protocol buffers,
  visit <https://github.com/google/pprof>.
  <https://protobuf.dev>,
  and <https://github.com/r-prof/profile>, respectively.
Defines a data structure for profiler data, and methods to read and
    write from the 'Rprof' and 'pprof' file formats.
Provides tools for profiling a user-supplied log-likelihood 
    function to calculate confidence intervals for model parameters. Speed of 
    computation can be improved by adjusting the step sizes in the profiling 
    and/or starting the profiling from limits based on the approximate large 
    sample normal distribution for the maximum likelihood estimator of a 
    parameter. The accuracy of the limits can be set by the user. A plot method 
    visualises the log-likelihood and confidence interval. Cases where the 
    profile log-likelihood flattens above the value at which a confidence limit
    is defined can be handled, leading to a limit at plus or minus infinity.
    Disjoint confidence intervals will not be found.
Implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes 'RcppArmadillo' and 'RcppDist' for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) <doi:10.48550/arXiv.2510.08304>.
Functional claims reserving methods based on aggregated chain-ladder data, also known as a run-off triangle, implemented in three nonparametric algorithms (PARALLAX, REACT, and MACRAME) proposed in Maciak, Mizera, and Pešta (2022) <doi:10.1017/asb.2022.4>. Additional methods including permutation bootstrap for completed run-off triangles are also provided.
Provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.
A suite of multivariate methods and data visualization 
      tools to implement profile analysis and cross-validation techniques described in 
      Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
      The package includes routines to perform criterion-related profile analysis, profile analysis 
      via multidimensional scaling, moderated profile analysis, profile analysis by group, and a 
      within-person factor model to derive score profiles.
A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).
Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/arXiv.1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
An alternative data structure and visual rendering
    for the profiling information generated by Rprof.
Calculates profile repeatability for replicate stress response 
  curves, or similar time-series data. Profile repeatability is an individual 
  repeatability metric that uses the variances at each timepoint, the maximum 
  variance, the number of crossings (lines that cross over each other), and 
  the number of replicates to compute the repeatability score. 
  For more information see Reed et al. (2019) <doi:10.1016/j.ygcen.2018.09.015>.
Tools for examining Rprof profile output.
Interactive visualizations for profiling R code.
Configurable Progress bars, they may include percentage,
    elapsed time, and/or the estimated completion time. They work in
    terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the
    'macOS' 'R.app'. The package also provides a 'C++' 'API', that works
    with or without 'Rcpp'.
The progressify() function rewrites (transpiles) calls to sequential and parallel map-reduce functions such as base::lapply(), purrr::map(), foreach::foreach(), and plyr::llply() to signal progress updates. By combining this function with R's native pipe operator, you have a straightforward way to report progress on iterative computations with minimal refactoring, e.g. 'lapply(x, fcn) |> progressify()' and 'purrr::map(x, fcn) |> progressify()'. It is compatible with the parallel-processing map-reduce packages 'future.apply', 'furrr', 'crossmap', 'foreach', 'doFuture', and 'futurize'. It also supports domain-specific packages including 'boot', 'fwb', 'lme4', 'partykit', 'sandwich', and 'SimDesign', e.g. 'boot::boot(data, stat, R) |> progressify()'.
A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing.  The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it.  The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. 'lapply(...) |> futurize()' and 'purrr::map(...) |> futurize()', which uses future.apply::future_lapply() and furrr::future_map() internally. The package is compatible with Shiny applications.
A wrapper around the generic coordinate transformation software 'PROJ'
  that transforms coordinates from one coordinate reference system ('CRS')
  to another. This includes cartographic projections as well as geodetic transformations.  The intention is for this
  package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5
  pathways be provided by the 'proj4' package.
A simple interface to lat/long projection and datum
	     transformation of the PROJ.4 cartographic projections library. It
	     allows transformation of geographic coordinates from one projection
	     and/or datum to another.
A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R.
Provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in 'EpiEstim' or in 'earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class 'projections' which extends 'matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.
Provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through 'tidyLPA' (Rosenberg et al., 2018) <doi:10.21105/joss.00978>, LCA through 'poLCA' (Linzer & Lewis, 2011) <doi:10.32614/CRAN.package.poLCA> & 'glca' (Kim & Kim, 2024) <doi:10.32614/CRAN.package.glca>, LTA/IRT via 'mirt' (Chalmers, 2012) <doi:10.18637/jss.v048.i06>, and EFA via 'psych' (Revelle, 2025). SEM and CFA functionalities build upon the 'lavaan' framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming.
Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities' time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.
Provides a project infrastructure with a focus on
    manuscript creation. Creates a project folder with a single command,
    containing subdirectories for specific components, templates for
    manuscripts, and so on.
Provides functions to
    automatically build a directory structure for a new R
    project. Using this structure, 'ProjectTemplate'
    automates data loading, preprocessing, library
    importing and unit testing.
Provides programmatic access to 'GitHub' API with a
    focus on project management.  Key functionality includes
    setting up issues and milestones from R objects or 'YAML' configurations,
    querying outstanding or completed tasks, and generating progress updates
    in tables, charts, and RMarkdown reports. Useful for those using 'GitHub' in personal,
    professional, or academic settings with an emphasis on streamlining
    the workflow of data analysis projects.
Provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.
Performs projection predictive feature selection for generalized linear
    models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>)
    with or without multilevel or additive terms (Catalina, Bürkner, and
    Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for
    some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025,
    <doi:10.1007/s00180-024-01506-0>), and for many other regression models
    (using the latent projection by Catalina, Bürkner, and Vehtari, 2021,
    <doi:10.48550/arXiv.2109.04702>, which can also be applied to most of the
    former models). The package is compatible with the 'rstanarm' and 'brms'
    packages, but other reference models can also be used. See the vignettes and
    the documentation for more information and examples.
Provides functions for calculating and analyzing the proliferative 
    index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, 
    et al. bioRxiv, 2016 <doi:10.1101/063057>.
A user-friendly interface for creating and managing empirical crowd-sourcing studies via API access to <https://www.prolific.co>.
Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.
Estimation for blinding bias in randomized controlled trials with
  a latent continuous outcome, a binary response depending on treatment and the
  latent outcome, and a noisy surrogate subject to possibly response-dependent
  measurement error. Implements EM estimators in R backed by compiled C routines
  for models with and without the restriction delta0 = 0, and Bayesian Stan
  wrappers for the same two models. Functions were added for latent outcome
  models with differential measurement error.
Functions which can be used to support the Multicriteria Decision Analysis (MCDA) process 
    involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).
The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems.
            The method establishes a preference structure between the alternatives, having a preference function for
            each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking),
            PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).
PROMETHEE (Preference Ranking Organisation METHod for Enrichment of
    Evaluations) based method assesses alternatives to obtain partial and complete
    rankings. The package also provides the GLNF (Global Local Net Flow) sorting
    algorithm to classify alternatives into ordered categories, as well as an index
    function to measure the classification quality. Barrera, F., Segura, M., &
    Maroto, C. (2023) <doi:10.1111/itor.13288>. Brans, J.P.; De Smet, Y., (2016) 
    <doi:10.1007/978-1-4939-3094-4_6>.
Provides fundamental abstractions for doing asynchronous
    programming in R using promises. Asynchronous programming is useful
    for allowing a single R process to orchestrate multiple tasks in the
    background while also attending to something else. Semantics are
    similar to 'JavaScript' promises, but with a syntax that is idiomatic
    R.
A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).
Deploy, maintain, and invoke predictive models using the 'Alteryx
    Promote' REST API.  'Alteryx Promote' is available at the URL:
    <https://www.alteryx.com/products/alteryx-promote>.
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017) <doi:10.7287/peerj.preprints.3190v2> and some prespecified variables (e.g. start of a month).
Set the 'R' prompt dynamically, from a function. The package
    contains some examples to include various useful dynamic information
    in the prompt: the status of the last command (success or failure);
    the amount of memory allocated by the current 'R' process; the name of
    the R package(s) loaded by 'pkgload' and/or 'devtools'; various 'git'
    information: the name of the active branch, whether it is dirty,
    if it needs pushes pulls. You can also create your own prompt if you
    don't like the predefined examples.
In 'Shiny' apps, it is sometimes useful to store information
    on a particular item in a tooltip. 'Prompter' allows you to easily 
    create such tooltips, using 'Hint.css'.
Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.
A native 'R' client library for querying the 'Prometheus' 
    time-series database, using the 'PromQL' query language.
Carrying out inferences about any linear combination of proportions and the ratio of two proportions.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 <doi:10.3929/ethz-a-010113668> (first order), Wang & Iyer 2005 <doi:10.1088/0026-1394/42/5/011> (second order) and BIPM Supplement 1 (Monte Carlo) <doi:10.59161/JCGM101-2008>.
Computes two-sample confidence intervals for single, paired and independent proportions.
Implementation of propensity clustering and
        decomposition as described in Ranola et al. (2013) <doi:10.1186/1752-0509-7-21>. 
        Propensity decomposition can be viewed on the
        one hand as a generalization of the eigenvector-based
        approximation of correlation networks, and on the other hand as
        a generalization of random multigraph models and
        conformity-based decompositions.
Calculates propensity score weights for multiple causal
    'estimands' across binary, continuous, and categorical exposures.
    Provides methods for handling extreme propensity scores through
    trimming, truncation, and calibration. Includes inverse probability
    weighted estimators that correctly account for propensity score
    estimation uncertainty.
The Prognostic Regression Offsets with Propagation of
    ERrors (for Treatment Effect Estimation) package facilitates
    direct adjustment for experiments and observational studies that
    is compatible with a range of study designs and covariance
    adjustment strategies. It uses explicit specification of clusters,
    blocks and treatment allocations to furnish probability of
    assignment-based weights targeting any of several average
    treatment effect parameters, and for standard error calculations
    reflecting these design parameters. For covariance adjustment of
    its Hajek and (one-way) fixed effects estimates, it enables
    offsetting the outcome against predictions from a dedicated
    covariance model, with standard error calculations propagating
    error as appropriate from the covariance model.
Allows to parse 'Java' properties files in the context of
  'R Service Bus' applications.
Implements a procedure for forecasting time series data based on
    an additive model where non-linear trends are fit with yearly, weekly, and
    daily seasonality, plus holiday effects. It works best with time series
    that have strong seasonal effects and several seasons of historical data.
    Prophet is robust to missing data and shifts in the trend, and typically
    handles outliers well.
Calculate seat apportionment for legislative bodies with 
    various methods. The algorithms include divisor or highest averages methods
    (e.g. Jefferson, Webster or Adams), largest remainder methods and 
    biproportional apportionment.
    Gaffke, N. & Pukelsheim, F. (2008) <doi:10.1016/j.mathsocsci.2008.01.004>
    Oelbermann, K. F. (2016) <doi:10.1016/j.mathsocsci.2016.02.003>.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
Contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently only supports trials comparing two groups.
An API wrapper around the 'ProPublica' API <https://projects.propublica.org/api-docs/congress-api/> for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters. This package is different from the 'RPublica' package because it is for the 'ProPublica' U.S. Congress data API, and the 'RPublica' package is for the Nonprofit Explorer, Forensics, and Free the Files data APIs.
Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs.
    The Congress API provides near real-time access to legislative data from the House of 
    Representatives, the Senate and the Library of Congress.
    The Campaign Finance API provides data from United States Federal Election Commission 
    filings and other sources. The API covers summary information for candidates and 
    committees, as well as certain types of itemized data.
    For more information about these APIs go to: <https://www.propublica.org/datastore/apis>.
Provides robust functions for proportional allocation of numeric values. It guarantees sum consistency after rounding or integer truncation using one of two adjustment methods: the largest remainder method or max-value adjustment. Handles edge cases like NA weights and vector total values seamlessly.
It offers a wide variety of techniques, such as graphics, recoding, or regression models, for a comprehensive analysis of patient-reported outcomes (PRO). Especially novel is the broad range of regression models based on the beta-binomial distribution useful for analyzing binomial data with over-dispersion in cross-sectional, longitudinal, or multidimensional response studies (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2019) <doi:10.1002/bimj.201700251>).
An extensible repository of accurate, up-to-date functions to
    score commonly used patient-reported outcome (PRO), quality of life
    (QOL), and other psychometric and psychological measures.
    'PROscorer', together with the 'PROscorerTools' package, is a system
    to facilitate the incorporation of PRO measures into research studies
    and clinical settings in a scientifically rigorous and reproducible
    manner.  These packages and their vignettes are intended to help
    establish and promote best practices for scoring PRO and PRO-like 
    measures in research.  The 'PROscorer' Instrument Descriptions vignette 
    contains descriptions of each instrument scored by 'PROscorer', complete 
    with references.  These instrument descriptions are suitable for inclusion 
    in formal study protocol documents, grant proposals, and manuscript Method
    sections.  Each 'PROscorer' function is composed of helper functions
    from the 'PROscorerTools' package, and users are encouraged to
    contribute new functions to 'PROscorer'.  More scoring functions are
    currently in development and will be added in future updates.
Provides a reliable and flexible toolbox to score 
    patient-reported outcome (PRO), Quality of Life (QOL), and other 
    psychometric measures. The guiding philosophy is that scoring errors can 
    be eliminated by using a limited number of well-tested, well-behaved 
    functions to score PRO-like measures. The workhorse of the package is 
    the 'scoreScale' function, which can be used to score most single-scale 
    measures. It can reverse code items that need to be reversed before 
    scoring and pro-rate scores for missing item data. Currently, three 
    different types of scores can be output: summed item scores, mean item 
    scores, and scores scaled to range from 0 to 100. The 'PROscorerTools' 
    functions can be used to write new functions that score more complex 
    measures. In fact, 'PROscorerTools' functions are the building blocks of 
    the scoring functions in the 'PROscorer' package (which is a repository 
    of functions that score specific commonly-used instruments). Users are 
    encouraged to use 'PROscorerTools' to write scoring functions for their 
    favorite PRO-like instruments, and to submit these functions for 
    inclusion in 'PROscorer' (a tutorial vignette will be added soon). The 
    long-term vision for the 'PROscorerTools' and 'PROscorer' packages is to 
    provide an easy-to-use system to facilitate the incorporation of PRO 
    measures into research studies in a scientifically rigorous and 
    reproducible manner. These packages and their vignettes are intended to 
    help establish and promote "best practices" for scoring and describing 
    PRO-like measures in research.
Perform scale linking to establish relationships between instruments
    that measure similar constructs according to the PROsetta Stone methodology, as in Choi, Schalet, Cook, & Cella (2014) <doi:10.1037/a0035768>.
Implementation of penalized regression with second-generation p-values for variable
    selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details
    can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.
Functions to preprocess spectroscopic data 
    and conduct (representative) sample selection/calibration sampling.
An environment to simulate the development of annual plant populations with regard to population dynamics and genetics, especially herbicide resistance. It combines genetics on the individual level (Renton et al. 2011) with a stochastic development on the population level (Daedlow, 2015).
	Renton, M, Diggle, A, Manalil, S and Powles, S (2011) <doi:10.1016/j.jtbi.2011.05.010> 
	Daedlow, Daniel (2015, doctoral dissertation: University of Rostock, Faculty of Agriculture and Environmental Sciences.)
We provide comprehensive draft data for major professional sports leagues, including the National Football League (NFL), National Basketball Association (NBA), and National Hockey League (NHL). It offers access to both historical and current draft data, allowing for detailed analysis and research on player biases and player performance. The package is useful for sports fans and researchers interested in identifying biases and trends within scouting reports. Created by web scraping data from leading websites that cover professional sports player scouting reports, the package allows users to filter and summarize data for analytical purposes. For further details on the methods used, please refer to Wickham (2022) "rvest: Easily Harvest (Scrape) Web Pages" <https://CRAN.R-project.org/package=rvest> and Harrison (2023) "RSelenium: R Bindings for Selenium WebDriver" <https://CRAN.R-project.org/package=RSelenium>.
In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a “pair”, those with equal masses as a “match”, and all the other peaks as a “mismatch”.For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) <doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016) <doi:10.7171/jbt.16-2702-002>.
Read Protein Data Bank (PDB) files, performs its analysis, and 
    presents the result using different visualization types including 3D. The 
    package also has additional capability for handling Virus Report data from 
    the National Center for Biotechnology Information (NCBI) database.
    Nature Structural Biology 10, 980 (2003) <doi:10.1038/nsb1203-980>.
    US National Library of Medicine (2021) <https://www.ncbi.nlm.nih.gov/datasets/docs/reference-docs/data-reports/virus/>.
Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) <doi:10.1007/b98835>, Gabriel (1971) <doi:10.1093/biomet/58.3.453>, Zhang et al. (2024) <doi:10.1038/s41467-024-53239-9>, and Anandan et al. (2022) <doi:10.1038/s41598-022-07781-5>.
Bayesian toolbox for quantitative proteomics. In particular, this 
    package provides functions to generate synthetic datasets, execute Bayesian
    differential analysis methods, and display results as, described in the 
    associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.
Calculates the percentage coefficient of variation (CV) for mass spectrometry-based proteomic data. The CV can be calculated with the traditional formula for raw (non log transformed) intensity data, or log transformed data.
Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.
Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. 
    Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take 
    advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) 
    <DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by 
    this package can be used for protein fold classification, protein structural class prediction, 
    sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features 
    extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, 
    Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.
An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.
Performs minimax linkage hierarchical clustering.  Every cluster
    has an associated prototype element that represents that cluster as
    described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering 
    with Prototypes via Minimax Linkage," The Journal of the American 
    Statistical Association, 106(495), 1075-1084.
Pure C++ implementations for reading and writing several common data 
    formats based on Google protocol-buffers. Currently supports 'rexp.proto' for 
    serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for 
    vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, 
    hence the entire serialization is optimized at compile time. The 'RProtoBuf' 
    package on the other hand uses the protobuf runtime library to provide a general-
    purpose toolkit for reading and writing arbitrary protocol-buffer data in R.
'The Proton Game' is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  You have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  The knowledge of dplyr is not required but may be very helpful.
  This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw.
  It's a part of Beta and Bit series.
  You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.
Shiny app to interactively visualize hierarchical clustering with prototypes. 
    For details on hierarchical clustering with prototypes, see 
    Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.
Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.
Comprehensive toolkit for generating various numerical
    features of protein sequences described in Xiao et al. (2015)
    <DOI:10.1093/bioinformatics/btv042>. For full functionality,
    the software 'ncbi-blast+' is needed, see
    <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html>
    for more information.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive.
'ProTracker' is a popular music tracker to sequence
    music on a Commodore Amiga machine. This package offers the
    opportunity to import, export, manipulate and play 'ProTracker'
    module files. Even though the file format could be considered
    archaic, it still remains popular to this date. This package
    intends to contribute to this popularity and therewith
    keeping the legacy of 'ProTracker' and the Commodore Amiga
    alive. This package is the successor of 'ProTrackR' providing
    better performance.
Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.
Helps with quality checks, visualizations 
    and analysis of mass spectrometry data, coming from proteomics 
    experiments. The package is developed, tested and used at the Functional 
    Genomics Center Zurich <https://fgcz.ch>. We use this package
    mainly for prototyping, teaching, and having fun with proteomics data.
    But it can also be used to do data analysis for small scale data sets.
Tools for Natural Language Processing in French and texts from Marcel Proust's collection 
  "A La Recherche Du Temps Perdu". The novels contained in this collection are 
  "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", 
  "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".
Uses provenance post-execution to help the user understand
    and debug their script by providing functions to look at intermediate steps and
    data values, their forwards and backwards lineage, and to understand the
    steps leading up to warning and error messages. 'provDebugR' uses
    provenance produced by 'rdtLite' (available on CRAN), stored in PROV-JSON format.
Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.
A simple package to grab a Bible proverb corresponding to the
    day of the month.
Inspects provenance collected by the 'rdt' or 'rdtLite' packages, 
    or other tools providing compatible PROV JSON output created by 
    the execution of a script, and find differences between two provenance 
    collections. Factors under examination included the hardware and 
    software used to execute the script, versions of attached libraries, 
    use of global variables, modified inputs and outputs, and changes 
    in main and sourced scripts. Based on detected changes, 'provExplainR' 
    can be used to study how these factors affect the behavior of 
    the script and generate a promising diagnosis of the causes of different 
    script results. More information about 'rdtLite' and associated tools is available 
    at <https://github.com/End-to-end-provenance/> and Barbara Lerner, 
    Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
    Provenance in R, Informatics, <doi:10.3390/informatics5010012>.
Creates and manages a provenance graph corresponding to the 
    provenance created by the 'rdtLite' package, which
    collects provenance from R scripts.  'rdtLite' is available on CRAN.
    The provenance format is an extension of the 
    W3C PROV JSON format (<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>).
    The extended JSON provenance format is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Tools for scraping match statistics and player data from the Athletes Unlimited (UA) website <https://auprosports.com/volleyball/>, the League One Volleyball website <https://lovb.com>, and the Major League (MLV) website <https://provolleyball.com>.
R functions to access provenance information collected by 'rdt' or 
    'rdtLite'.  The information is stored inside a 'ProvInfo' object and can be 
    accessed through a collection of functions that will return the requested 
    data. The exact format of the JSON created by 'rdt' and 'rdtLite' is described 
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.
Reads the provenance collected by the 'rdtLite' or 'rdt' packages,
    or other tools providing compatible PROV JSON output, created by the
    execution of a script or a console session, and provides a human-readable
    summary identifying the input and output files, the scripts used (if any),
    errors and warnings produced, and the environment in which it was executed.
    It can also optionally package all the files into a zip file.  The exact
    format of the PROV JSON file created by 'rdtLite' and 'rdt' is described
    in <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More
    information about 'rdtLite' and associated tools is available at
    <https://github.com/End-to-end-provenance/> and Lerner, Boose, and Perez
    (2018), Using Introspection to Collect Provenance in R, Informatics,
    <doi: 10.3390/informatics5010012>.
Uses provenance collected by 'rdtLite' package or comparable tool
    to display information about input files, output files, and exchanged files
    for a single R script or a series of R scripts.
Displays provenance graphically for provenance collected by the 'rdt' or
  'rdtLite' packages, or other tools providing compatible PROV JSON output. The exact 
  format of the JSON created by 'rdt' and 'rdtLite' is described in 
  <https://github.com/End-to-end-provenance/ExtendedProvJson>.  More information about
  rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> 
  and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect 
  Provenance in R, Informatics, <doi: 10.3390/informatics5010012>.
Provides tools to build quantitative chemometric models and
    applications for near-infrared (NIR) sensors. Chemometric regression
    models are based on partial least squares regression as described by
    Wold (1975) <doi:10.1016/B978-0-12-103950-9.50017-4> and modified
    partial least squares regression as described by Shenk and Westerhaus
    (1991) <doi:10.2135/cropsci1991.0011183X003100020049x>, with further
    discussion by Westerhaus (2014) <doi:10.1255/nirn.1492>.
Functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability.
  The methods for Alpha and Beta irreplaceability were first described in:
    Baisero D., Schuster R. & Plumptre A.J.
    Redefining and Mapping Global Irreplaceability.
    Conservation Biology 2021;1-11.
    <doi:10.1111/cobi.13806>.
Implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Computes proximity between rows or columns of large matrices efficiently in C++.
    Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries.
    Among various built-in similarity/distance measures, computation of correlation,
    cosine similarity, Dice coefficient and Euclidean distance is particularly fast.
Determine minimal protein set explaining
    peptide spectrum matches. Utility functions for creating fasta amino acid databases with decoys and contaminants.
    Peptide false discovery rate estimation for target decoy search results on psm, precursor, peptide and protein
    level. Computing dynamic swath window sizes based on MS1 or MS2 signal distributions.
Utilize the Bayesian prior and posterior predictive checking
   approach to provide a statistical assessment of replication success 
   and failure. The package is based on the methods proposed in 
   Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.
Provides a function to convert 'PRQL' strings to 'SQL' strings.
    Combined with other R functions that take 'SQL' as an argument,
    'PRQL' can be used on R.
Reverse depends for a given package are queued such that multiple
 workers can run the reverse-dependency tests in parallel.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method. References: Davis and Goadrich (2006) <doi:10.1145/1143844.1143874>; Keilwagen et al. (2014) <doi:10.1371/journal.pone.0092209>; Grau et al. (2015) <doi:10.1093/bioinformatics/btv153>.
Tests periodicity in short time series using response surface regression.
Intended for larger-than-memory tabular data, 'prt' objects provide an interface to read row and/or column subsets into memory as data.table objects. Data queries, constructed as 'R' expressions, are evaluated using the non-standard evaluation framework provided by 'rlang' and file-backing is powered by the fast and efficient 'fst' package.
Implementation of Probabilistic Regression Trees (PRTree),
  providing functions for model fitting and prediction, with specific adaptations
  to handle missing values. The main computations are implemented in 'Fortran'
  for high efficiency. The package is based on the PRTree methodology described in
  Alkhoury et al. (2020), "Smooth and Consistent Probabilistic Regression Trees"
  <https://proceedings.neurips.cc/paper_files/paper/2020/file/8289889263db4a40463e3f358bb7c7a1-Paper.pdf>.
  Details on the treatment of missing data and implementation aspects are presented in
  Prass, T.S.; Neimaier, A.S.; Pumi, G. (2025), "Handling Missing Data in Probabilistic 
  Regression Trees: Methods and Implementation in R" <doi:10.48550/arXiv.2510.03634>.
Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.
Data sets and functions used in the polish book 
    "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). 
    See more at <http://biecek.pl/R>. Among others you will find here 
    data about housing prices, cancer patients, running times and many others.
List, query and manipulate all system processes, on
    'Windows', 'Linux' and 'macOS'.
It is often advantageous to test a hypothesis more than once
    in the context of propensity score analysis (Rosenbaum, 2012)
    <doi:10.1093/biomet/ass032>. The functions in this package facilitate
    bootstrapping for propensity score analysis (PSA). By default,
    bootstrapping using two classification tree methods (using 'rpart' and
    'ctree' functions), two matching methods (using 'Matching' and
    'MatchIt' packages), and stratification with logistic regression.  A
    framework is described for users to implement additional propensity
    score methods.  Visualizations are emphasized for diagnosing balance;
    exploring the correlation relationships between bootstrap samples and
    methods; and to summarize results.
A collection of functions that primarily produce graphics to
    aid in a Propensity Score Analysis (PSA).  Functions include: cat.psa
    and box.psa to test balance within strata of categorical and
    quantitative covariates, circ.psa for a representation of the
    estimated effect size by stratum, loess.psa that provides a graphic
    and loess based effect size estimate, and various balance functions
    that provide measures of the balance achieved via a PSA in a
    categorical covariate.
Connects to the API of <https://pushshift.io/> to search for 'Reddit' comments and submissions.
Hybrid control design is a way to borrow information from external controls to augment concurrent controls in a randomized controlled trial and is expected to overcome the feasibility issue when adequate randomized controlled trials cannot be conducted. A major challenge in the hybrid control design is its inability to eliminate a prior-data conflict caused by systematic imbalances in measured or unmeasured confounding factors between patients in the concurrent treatment/control group and external controls. To prevent the prior-data conflict, a combined use of propensity score matching and Bayesian commensurate prior has been proposed in the context of hybrid control design. The propensity score matching is first performed to guarantee the balance in baseline characteristics, and then the Bayesian commensurate prior is constructed while discounting the information based on the similarity in outcomes between the concurrent and external controls. 'psBayesborrow' is a package to implement the propensity score matching and the Bayesian analysis with commensurate prior, as well as to conduct a simulation study to assess operating characteristics of the hybrid control design, where users can choose design parameters in flexible and straightforward ways depending on their own application.
Algorithms to implement various Bayesian penalized survival regression models including: semiparametric proportional hazards models with lasso priors (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and three  other shrinkage and group priors (Lee et al., Stat Anal Data Min, 2015 <doi:10.1002/sam.11266>); parametric accelerated failure time models with group/ordinary lasso prior (Lee et al. Comput Stat Data Anal, 2017 <doi:10.1016/j.csda.2017.02.014>).
Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).
A tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.
Bayesian dynamic borrowing is an approach to incorporating external
   data to supplement a randomized, controlled trial analysis in which
   external data are incorporated in a dynamic way (e.g., based on similarity
   of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview.
   This package implements the hierarchical commensurate prior approach to dynamic borrowing
   as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>.
   There are three main functionalities. First, 'psborrow2' provides a user-friendly
   interface for applying dynamic borrowing on the study results handles the Markov Chain
   Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a
   simulation framework to compare different borrowing parameters (e.g. full borrowing, no
   borrowing, dynamic borrowing) and other trial and borrowing characteristics
   (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides
   a set of functions to generate data for simulation studies, and also allows
   the user to specify their own data generation process. This package is designed to
   use the sampling functions from 'cmdstanr' which can be installed from
   <https://stan-dev.r-universe.dev>.
Allows the comparison of data cohorts (DC) against a Counter 
    Factual Model (CFM) and measures the difference in terms of an efficacy 
    parameter.  Allows the application of Personalised Synthetic Controls.
Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.
Tools for the design of prospective studies using 
    Personalised Synthetic Controls.  Can be used in either single arm or 
    randomised studies.
Bayesian analysis of item-response theory (IRT) models,
  roll call analysis; computing highest density regions; 
  maximum likelihood estimation of zero-inflated and hurdle models for count data;
  goodness-of-fit measures for GLMs;
  data sets used in writing	and teaching; seats-votes curves.
Provides a number of functions to
  simplify and automate the scoring, comparison, and evaluation of
  different ways of creating composites of data.  It is particularly
  aimed at facilitating the creation of physiological composites of
  metabolic syndrome symptom score (MetSSS) and allostatic load (AL).
  Provides a wrapper to calculate the MetSSS on new data using the
  Healthy Hearts formula.
Provide estimation for particular cases of the power series cure rate model 
             <doi:10.1080/03610918.2011.639971>. For the distribution of the concurrent causes the 
             alternative models are the Poisson, logarithmic, negative binomial and Bernoulli (which 
             are includes in the original work), the polylogarithm model 
             <doi:10.1080/00949655.2018.1451850> and the Flory-Schulz <doi:10.3390/math10244643>. 
             The estimation procedure is based on the EM algorithm discussed in 
             <doi:10.1080/03610918.2016.1202276>.
             For the distribution of the time-to-event the alternative models are slash half-normal, 
             Weibull, gamma and Birnbaum-Saunders distributions.
Produces power spectral density estimates through iterative
    refinement of the optimal number of sine-tapers at each frequency. This
    optimization procedure is based on the method of Riedel and Sidorenko
    (1995), which minimizes the Mean Square Error (sum of variance and bias)
    at each frequency, but modified for computational stability. The same
    procedure can now be used to calculate the cross spectrum (multivariate
    analyses).
Presentation of distributions such as: two-piece power normal (TPPN), plasticizing component (PC), DS normal (DSN), expnormal (EN), Sulewski plasticizing component (SPC), easily changeable kurtosis (ECK) distributions. Density, distribution function, quantile function and random generation are presented. For details on this method see: Sulewski (2019) <doi:10.1080/03610926.2019.1674871>, Sulewski (2021) <doi:10.1080/03610926.2020.1837881>, Sulewski (2021) <doi:10.1134/S1995080221120337>, Sulewski (2022) <"New members of the Johnson family of probability dis-tributions: properties and application">, Sulewski, Volodin (2022) <doi:10.1134/S1995080222110270>, Sulewski (2023) <doi:10.17713/ajs.v52i3.1434>.
Functions that allow you to generate and compare power spectral density (PSD) 
	plots given time series data. Fast Fourier Transform (FFT) is used to take a time series 
	data, analyze the oscillations, and then output the frequencies of these oscillations 
	in the time series in the form of a PSD plot.Thus given a time series, the dominant 
	frequencies in the time series can be identified. Additional functions in this package 
	allow the dominant frequencies of multiple groups of time series to be compared with each other. 
	To see example usage with the main functions of this package, please visit
	this site: <https://yhhc2.github.io/psdr/articles/Introduction.html>. 
	The mathematical operations used to generate the PSDs are described in these sites:
	<https://www.mathworks.com/help/matlab/ref/fft.html>.
	<https://www.mathworks.com/help/signal/ug/power-spectral-density-estimates-using-fft.html>.
Various functions for computing pseudo-observations for censored data regression. Computes pseudo-observations for modeling: competing risks based on the cumulative incidence function, survival function based on the restricted mean,  survival function based on the Kaplan-Meier estimator see Klein et al. (2008) <doi:10.1016/j.cmpb.2007.11.017>.
Calculates the lexicogrammatical and functional features described
  by Biber (1985) <doi:10.1515/ling.1985.23.2.337> and widely used for
  text-type, register, and genre classification tasks.
A collection of easy-to-use tools for regression analysis of survival data with a cure fraction proposed in Su et al. (2022) <doi:10.1177/09622802221108579>. The modeling framework is based on the Cox proportional hazards mixture cure model and the bounded cumulative hazard (promotion time cure) model. The pseudo-observations approach is utilized to assess covariate effects and embedded in the variable selection procedure.
Given an arbitrary set of spatial regions and road networks, 
             generate a set of representative points, or pseudohouseholds, that
             can be used for travel burden analysis. Parallel processing is 
             supported.
Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. See Happ et al. (2020, <doi:10.18637/jss.v095.c01>) for details.
Contains the core methods for the evaluation of principal
    surrogates in a single clinical trial. Provides a flexible interface for
    defining models for the risk given treatment and the surrogate, the models
    for integration over the missing counterfactual surrogate responses, and the
    estimation methods. Estimated maximum likelihood and pseudo-score can be used
    for estimation, and the bootstrap for inference. A variety of post-estimation
    summary methods are provided, including print, summary, plot, and testing.
Pattern Sequence Based Forecasting (PSF) takes univariate
    time series data as input and assist to forecast its future values.
    This algorithm forecasts the behavior of time series
    based on similarity of pattern sequences. Initially, clustering is done with the
    labeling of samples from database. The labels associated with samples are then
    used for forecasting the future behaviour of time series data. The further
    technical details and references regarding PSF are discussed in Vignette.
Pooling, backward and forward selection of linear, logistic and Cox regression models in 
	multiply imputed datasets. Backward and forward selection can be done 
	from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and 
	the median p-values method. This is also possible for Mixed models. 
	The models can contain continuous, dichotomous, categorical and restricted 
	cubic spline predictors and interaction terms between	all these type of predictors. 
	The stability of the models	can be evaluated using (cluster) bootstrapping. The package 
	further contains functions to pool model performance measures as ROC/AUC, Reclassification, 
	R-squared, scaled Brier score, H&L test and calibration	plots for logistic regression models. 
	Internal validation can be done across multiply imputed datasets with cross-validation or 
	bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients 
	can be obtained. Backward and forward selection as part of internal validation is possible. 
	A function to externally validate logistic prediction models in multiple imputed 
	datasets is available and a function to compare models. For Cox models a strata variable
	can be included.
	Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
	Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
	Marshall (2009) <doi:10.1186/1471-2288-9-57>.
Provides access to the Philippine Standard Geographic Code (PSGC),
    an official classification system for geographic areas in the Philippines
    published by the Philippine Statistics Authority (PSA). Includes area names,
    geographic levels (Region, Province, City, Municipality, Sub-Municipality,
    and Barangay), and census population figures across multiple PSA publication
    releases. Offers utilities to look up individual codes, filter by geographic
    level, track code changes across releases via a built-in crosswalk, and
    retrieve population data in long or wide format.
Functions to generate ensembles of generalized linear models using
             a greedy projected subset gradient descent algorithm. The sparsity 
             and diversity tuning parameters are selected by cross-validation.
Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>.
Implements projected sparse Gaussian process Kriging ('Ingram et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation ('Barillec et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).
Functions which facilitate harmonization of data from multiple
    different datasets. Data harmonization involves taking data sources with
    differing values, creating coding instructions to create a harmonized
    set of values, then making those data modifications. 'psHarmonize' will
    assist with data modification once the harmonization instructions are
    written. Coding instructions are written by the user to create a
    "harmonization sheet". This sheet catalogs variable names, domains
    (e.g. clinical, behavioral, outcomes), provides R code instructions for
    mapping or conversion of data, specifies the variable name in the
    harmonized data set, and tracks notes. The package will then harmonize
    the source datasets according to the harmonization sheet to create a
    harmonized dataset. Once harmonization is finished, the package also has
    functions that will create descriptive statistics using 'RMarkdown'. Data
    Harmonization guidelines have been described by Fortier I, Raina P,
    Van den Heuvel ER, et al. (2017) <doi:10.1093/ije/dyw075>. Additional
    details of our R package have been described by Stephen JJ, Carolan P,
    Krefman AE, et al. (2024) <doi:10.1016/j.patter.2024.101003>.
In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.
Makes it easy to build panel data in wide format from Panel Survey
    of Income Dynamics (PSID) delivered raw data. Downloads data directly from
    the PSID server using the 'SAScii' package. 'psidR' takes care of merging
    data from each wave onto a cross-period index file, so that individuals can be
    followed over time. The user must specify which years they are interested in,
    and the 'PSID' variable names (e.g. ER21003) for each year (they differ in each
    year). The package offers helper functions to retrieve variable names from different
    waves. There are different panel data designs and sample subsetting criteria
    implemented ("SRC", "SEO", "immigrant" and "latino" samples). More information about 
    the PSID can be obtained at <https://simba.isr.umich.edu/data/data.aspx>.
Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') <https://psidonline.isr.umich.edu> using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis.
The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) <doi:10.1016/j.matdes.2009.11.020>.
Presentation two independence tests for two-way, three-way and four-way contingency tables. These tests are: the modular test and the logarithmic minimum test. For details on this method see: Sulewski (2017) <doi:10.18778/0208-6018.330.04>, Sulewski (2018) <doi:10.1080/02664763.2018.1424122>, Sulewski (2019) <doi:10.2478/bile-2019-0003>, Sulewski (2021) <doi:10.1080/00949655.2021.1908286>.
Considering the singly imputed synthetic data generated via plug-in sampling under the multivariate normal model, draws inference procedures including the generalized variance, the sphericity test, the test for independence between two subsets of variables, and the test for the regression of one set of variables on the other. For more details see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>.
Core architecture for interpreting continuous data streams as
    homotopy types. It evaluates identity paths against the Gnomonic Ratio
    ('Lombardi', 2026) <doi:10.5281/zenodo.20385840> and processes them via a
    dynamic 'Tableau Refutation Tree'. The engine categorizes data into
    necessity (BOX), possibility (DIAMOND), or noise based on deviation
    thresholds from the invariant value. Includes adaptive auto-tuning and
    native high-contrast Cartesian graphics for structural entropy isolation.
Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).
A focused implementation of the Public Suffix List (PSL). Bundles a
    reproducible, pinned PSL snapshot and implements the official prevailing-rule
    algorithm to answer public-suffix (eTLD) and registrable-domain (eTLD+1)
    queries. Distinguishes ICANN and PRIVATE rule sections, accepts Unicode and
    ASCII hostnames via 'punycoder' canonicalization, and supports an explicit,
    validated offline refresh path. The matcher is compiled with 'cpp11' and
    requires no external system library.
Fits and evaluates three-state partitioned survival analyses
    (PartSAs) and Markov models (clock forward or clock reset) to
    progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in
    cost-effectiveness modeling in advanced/metastatic cancer indications.
    Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.
Fit penalized splines mixed-effects models (a special case of additive models) for large longitudinal datasets. The package includes a psme() function that (1) relies on package 'mgcv' for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package 'lme4' for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) <doi:10.7717/peerj.6876> and Bates et al. (2015) <doi:10.18637/jss.v067.i01> for an introduction. Unlike the gamm() function in 'mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations.
Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.
Provides an implementation of particle swarm optimisation consistent with the standard PSO 2007/2011 by Maurice Clerc. Additionally a number of ancillary routines are provided for easy testing and graphics.
Particle swarm optimization - a basic variant.
Estimates principal causal effects under principal stratification
    using a margin-free, conditional odds ratio sensitivity parameter. This
    framework unifies the monotonicity assumption and the counterfactual
    intermediate independence assumption, allowing for robust analysis when
    monotonicity may not hold. Computes point estimates, standard errors, and
    confidence intervals for conditionally doubly robust and debiased machine
    learning estimators. The methodological details are described in Tong,
    Kahan, Harhay, and Li (2025) <doi:10.48550/arXiv.2501.17514>.
Implements an n-dimensional parameter space partitioning algorithm for evaluating the global behaviour of formal computational models as described by Pitt, Kim, Navarro and Myung (2006) <doi:10.1037/0033-295X.113.1.57>.
Estimation and inference of spatial and spatio-temporal 
    semiparametric models including spatial or spatio-temporal non-parametric 
    trends, parametric and non-parametric covariates and, possibly, a spatial 
    lag for the dependent variable and temporal correlation in the noise.
    The spatio-temporal trend can be decomposed in ANOVA way including main and 
    interaction functional terms. Use of SAP algorithm to estimate the spatial 
    or spatio-temporal trend and non-parametric covariates. The methodology of 
    these models can be found in next references
    Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>;
    Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and,
    particularly referred to the focus of the package, Minguez, R., 
    Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.
Spearman's rank correlation test with precomputed exact
        null distribution for n <= 22.
Provides a suite of Propensity Score Predictive Inference (PSPI) methods to generalize treatment effects in trials to target populations. The package includes an existing model Bayesian Causal Forest (BCF) and four PSPI models (BCF-PS, FullBART, SplineBART, DSplineBART). These methods leverage Bayesian Additive Regression Trees (BART) to adjust for high-dimensional covariates and nonlinear associations, while SplineBART and DSplineBART further use propensity score based splines to address covariate shift between trial data and target population.
Smoothing splines with penalties on order m derivatives.
Performs demographic, bifurcation and evolutionary analysis of physiologically structured population models, which is a class of models that consistently translates continuous-time models of individual life history to the population level.
    A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. 
    M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) <doi:10.1142/S0218202501001264>.
    O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) <doi:10.1016/S0040-5809(02)00058-8>.
    A.M. de Roos (2008) <doi:10.1111/j.1461-0248.2007.01121.x>.
Computes sample size and power for causal inference studies that
    use propensity score (PS) weighting. Supports continuous, binary, and
    time-to-event (survival) outcomes under four estimands: average treatment
    effect (ATE), average treatment effect on the treated (ATT), average
    treatment effect on the controls (ATC), and average treatment effect on
    the overlap population (ATO). For continuous and binary outcomes, the
    asymptotic variance of the Hajek inverse probability weighting estimator
    is derived under a logit-normal propensity score model, approximated by a
    Beta distribution matched through the Bhattacharyya overlap coefficient.
    For survival outcomes, the asymptotic variance of the propensity-score-
    weighted partial likelihood estimator is used for randomized trials and
    observational studies. The Schoenfeld formula is also available for
    randomized trial settings.
Provides quasi-Newton methods to minimize partially separable
    functions. The methods are largely described by  
    Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.
Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800).  Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay.  Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book.
Calculate Plant Stress Response Index (PSRI) from time-series
    germination data with optional radicle vigor integration. Built on the
    methodological foundation of the Osmotic Stress Response Index (OSRI)
    framework developed by Walne et al. (2020) <doi:10.1002/agg2.20087>.
    Provides clean, direct PSRI calculations suitable for agricultural research
    and statistical analysis. Note: This package implements methodology
    currently under peer review. Please contact the author before publication
    using this approach.
Implements the softmax aggregation method for calculating Plant 
    Stress Response Index (PSRI) from time-series germination data under 
    environmental stressors including prions, xenobiotics, osmotic stress, 
    heavy metals, and chemical contaminants. Provides zero-robust PSRI 
    computation through adaptive softmax weighting of germination components 
    (Maximum Stress-adjusted Germination, Maximum Rate of Germination, 
    complementary Mean Time to Germination, and Radicle Vigor Score), 
    eliminating the zero-collapse failure mode of the geometric mean approach 
    implemented in 'PSRICalc'. Includes perplexity-based temperature 
    parameter calibration and modular component functions for transparent 
    germination analysis. Built on the methodological foundation of the 
    Osmotic Stress Response Index (OSRI) framework developed by Walne et al. 
    (2020) <doi:10.1002/agg2.20087>. Note: This package implements 
    methodology currently under peer review. Please contact the author 
    before publication using this approach.
    Development followed an iterative human-machine collaboration where all
    algorithmic design, statistical methodologies, and biological validation
    logic were conceptualized, tested, and iteratively refined by Richard A.
    Feiss through repeated cycles of running experimental data, evaluating
    analytical outputs, and selecting among candidate algorithms and approaches.
    AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants
    and analytical sounding boards under continuous human direction. The
    selection of statistical methods, evaluation of biological plausibility,
    and all final methodology decisions were made by the human author. AI
    systems did not independently originate algorithms, statistical approaches,
    or scientific methodologies.
High-quality real-world data can be transformed into scientific
    real-world evidence for regulatory and healthcare decision-making
    using proven analytical methods and techniques. For example, propensity
    score (PS) methodology can be applied to select a subset of real-world
    data containing patients that are similar to those in the current clinical
    study in terms of baseline covariates, and to stratify the selected
    patients together with those in the current study into more homogeneous
    strata. Then, statistical methods such as the power prior approach or
    composite likelihood approach can be applied in each stratum to draw
    inference for the parameters of interest. This package provides
    functions that implement the PS-integrated real-world 
    evidence analysis methods such as
    Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>,
    Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and
    Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.
Test-based Image structural similarity measure and test of independence. 
    This package implements the key functions of two tasks: (1) computing
    image structural similarity measure PSSIM of Wang, Maldonado and
    Silwal (2011) <DOI:10.1016/j.csda.2011.04.021>;
    and (2) test of independence between a response and 
    a covariate in presence of heteroscedastic treatment effects 
    proposed by Wang, Tolos, and Wang (2010) <DOI:10.1002/cjs.10068>.
Returns almost all features that has been extracted from Position Specific 
             Scoring Matrix (PSSM) so far, which is a matrix of L rows (L is protein length) 
             and 20 columns produced by 'PSI-BLAST' which is a program to produce
             PSSM Matrix from multiple sequence alignment of proteins
             see <https://www.ncbi.nlm.nih.gov/books/NBK2590/> for mor details. some 
             of these features are described in Zahiri, J., et al.(2013)
             <DOI:10.1016/j.ygeno.2013.05.006>,
             Saini, H., et al.(2016)
             <DOI:10.17706/jsw.11.8.756-767>,
             Ding, S., et al.(2014)
             <DOI:10.1016/j.biochi.2013.09.013>,
             Cheng, C.W., et al.(2008)
             <DOI:10.1186/1471-2105-9-S12-S6>,
             Juan, E.Y., et al.(2009)
             <DOI:10.1109/CISIS.2009.194>.
Implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive 'placebo structural risk' modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.
A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data 
  with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed 
  Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes 
  include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification 
  of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, 
  and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific
  abnormal subpathways in the cancer dataset with multi-phenotype samples.
Implements propensity score weighting methods for estimating
    counterfactual survival functions, marginal hazard ratios, and
    weighted Kaplan-Meier and cumulative risk curves in observational
    studies with time-to-event outcomes. Supports binary and multiple
    treatment groups with inverse probability of treatment weighting (IPW),
    overlap weighting (OW), and average treatment effect on the treated
    (ATT). Includes symmetric trimming (Crump extension) for extreme
    propensity scores. Variance estimation via analytical M-estimation or
    bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>,
    Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022)
    <doi:10.1093/aje/kwac043>.
Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).
Implements the Panel Smooth Transition Regression (PSTR) framework for nonlinear panel data modelling.
    The modelling procedure consists of three stages: Specification, Estimation and Evaluation.
    The package provides tools for model specification testing,
    to do PSTR model estimation, and to do model evaluation.
    The implemented tests allow for cluster dependence and are heteroskedasticity-consistent.
    The wild bootstrap and wild cluster bootstrap tests are also implemented.
    Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests.
    The package supports parallel computation, which is useful for large-scale bootstrap procedures.
Estimating causal effects in the presence of post-treatment confounding
    using principal stratification. 'PStrata' allows for customized monotonicity
    assumptions and exclusion restriction assumptions, with automatic full Bayesian
    inference supported by 'Stan'. The main workflow is PStrataModel() to specify the
    model, fit() to run MCMC sampling, estimate() to extract potential outcomes, and
    contrast() to compute causal effects. Visualization tools are provided for
    diagnosis and interpretation. See Liu and Li (2023)
    <doi:10.48550/arXiv.2304.02740> for details.
For a data matrix with m rows and n columns (m>=n), the power
	     method is used to compute, simultaneously, the eigendecomposition
	     of a square symmetric matrix. This result is used to obtain the
	     singular value decomposition (SVD) and the principal component
             analysis (PCA) results. Compared to the classical SVD method,
	     the first r singular values can be computed.
A unified and user-friendly framework for applying the principal sufficient dimension reduction methods for both linear and nonlinear cases. The package has an extendable power by varying loss functions for the support vector machine, even for an user-defined arbitrary function, unless those are convex and differentiable everywhere over the support (Li et al. (2011) <doi:10.1214/11-AOS932>). Also, it provides a real-time sufficient dimension reduction update procedure using the principal least squares support vector machine (Artemiou et al. (2021) <doi:10.1016/j.patcog.2020.107768>).
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.
Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.
Kappa, ICC, reliability coefficient, parallel analysis, 
     multi-traits multi-methods, spherical representation of a correlation matrix.
Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.
A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology.  Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis.  Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions  serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.
Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.
The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, 
   to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output 
   of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
Automates common psychometric workflows for applied researchers,
    including item descriptives, inter-item correlations, exploratory and
    confirmatory factor analysis, reliability, multi-group measurement
    invariance, and alignment optimization. Decision heuristics are informed by
    procedures such as parallel analysis (Horn, 1965,
    <doi:10.1007/BF02289447>), multivariate normality diagnostics (Mardia,
    1970, <doi:10.1093/biomet/57.3.519>), measurement-invariance fit-change
    rules (Chen, 2007, <doi:10.1080/10705510701301834>), and alignment
    optimization (Asparouhov and Muthen, 2014,
    <doi:10.1080/10705511.2014.919210>), among others. Results can be returned as structured
    R objects and exported as bilingual reports for transparent analytical
    documentation.
Contains functions useful for correlation theory, 
    meta-analysis (validity-generalization), reliability, 
    item analysis, inter-rater reliability, and classical utility.
Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models
  with different parameterizations of the score distribution (saturated vs. mean/variance specification),
  Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated
  with or without concomitant variables. See Frick et al. (2012) <doi:10.18637/jss.v048.i07> and
  Frick et al. (2015) <doi:10.1177/0013164414536183> for details on the Rasch mixture models.
Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.
Infrastructure for psychometric modeling such as data classes (for
  item response data and paired comparisons), basic model fitting functions (for
  Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit,
  rating scale, multinomial processing tree models), extractor functions for
  different types of parameters (item, person, threshold, discrimination,
  guessing, upper asymptotes), unified inference and visualizations, and various
  datasets for illustration.  Intended as a common lightweight and efficient
  toolbox for psychometric modeling and a common building block for fitting
  psychometric mixture models in package "psychomix" and trees based on
  psychometric models in package "psychotree".
Recursive partitioning based on psychometric models,
  employing the general MOB algorithm (from package partykit) to obtain
  Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
  MPT trees, trees for 1PL, 2PL, 3PL and 4PL models and generalized partial 
  credit models.
Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Implementation of 'PsychroLib'
    <https://github.com/psychrometrics/psychrolib> library which contains
    functions to enable the calculation properties of moist and dry air in both
    metric (SI) and imperial (IP) systems of units. References: Meyer, D. and
    Thevenard, D (2019) <doi:10.21105/joss.01137>.
Support functions,  data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.
An integrative toolbox of word embedding research that provides:
    (1) a collection of 'pre-trained' static word vectors in the '.RData'
    compressed format <https://psychbruce.github.io/WordVector_RData.pdf>;
    (2) a group of functions to process, analyze, and visualize word vectors;
    (3) a range of tests to examine conceptual associations, including
    the Word Embedding Association Test <doi:10.1126/science.aal4230>
    and the Relative Norm Distance <doi:10.1073/pnas.1720347115>,
    with permutation test of significance; and
    (4) a set of training methods to locally train (static) word vectors
    from text corpora, including 'Word2Vec' <doi:10.48550/arXiv.1301.3781>,
    'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <doi:10.48550/arXiv.1607.04606>.
Person fit statistics based on Quality Control measures are provided for questionnaires and tests given a specified IRT model. Statistics based on Cumulative Sum (CUSUM) charts are provided. Options are given for banks with polytomous and dichotomous data.
Data and examples from meta-analyses in psychology research.
Provides functions and data-sets that are helpful
  for teaching statistics and data analysis. It was originally designed for use
  when teaching students in the Psychology Department at Nottingham Trent University.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
Analyses and reports questionnaire and experiment data exported from 'PsyToolkit'. The package reads downloaded study folders, parses questionnaire structure, optionally merges demographic exports from CloudResearch or Prolific, and produces summary overviews of responses and completion times. It also provides helper functions to extract and aggregate experiment measures and survey variables, and to export results to spreadsheet files for further analysis and archiving. See Stoet (2017) <doi:10.1177/0098628316677643> for the 'PsyToolkit' platform.
The constructs used to study the human psychology have
    many definitions and corresponding instructions for eliciting
    and coding qualitative data pertaining to constructs' content and
    for measuring the constructs. This plethora of definitions and
    instructions necessitates unequivocal reference to specific
    definitions and instructions in empirical and secondary research.
    This package implements a human- and machine-readable standard for
    specifying construct definitions and instructions for measurement
    and qualitative research based on 'YAML'. This standard facilitates
    systematic unequivocal reference to specific construct definitions
    and corresponding instructions in a decentralized manner (i.e.
    without requiring central curation; Peters (2020)
    <doi:10.31234/osf.io/xebhn>).
Tabular data from statistical institutes and agencies are mostly 
    confidential and must be protected prior to publications. The cell-key 
    method is a post-tabular Statistical Disclosure Control perturbation 
    technique that adds random noise to tabular data. The statistical 
    properties of the perturbations are defined by some noise probability 
    distributions - also referred to as perturbation tables. 
    This tool can be used to create the perturbation tables based on a maximum 
    entropy approach as described for example in Giessing (2016) 
    <doi:10.1007/978-3-319-45381-1_18>. The perturbation tables created can 
    finally be used to apply a cell-key method to frequency count or magnitude 
    tables.
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
Evaluates the strength of a surrogate marker by estimating the proportion of treatment effect explained (PTE) and relative power(RP) for the optimally-transformed version of the surrogate.  Details available in Wang et al (2022) <arXiv:2209.08414>.
Generic code for estimating treatment effects with panel data.  The idea is to break into separate steps organizing the data, looping over groups and time periods, computing group-time average treatment effects, and aggregating group-time average treatment effects.  Often, one is able to implement a new identification/estimation procedure by simply replacing the step on estimating group-time average treatment effects.  See several different examples of this approach in the package documentation.
Function pip3d() tests whether a point in 3D space is
  within, exactly on, or outside an enclosed surface defined by a triangular mesh.
  Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon.
  For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing
  and determining triangles, Computer-Aided Design 42(12):1143-1150.
Permute treatment labels for taxa and environmental gradients to
    generate an empirical distribution of change points.  This is an extension
    for the 'TITAN2' package <https://cran.r-project.org/package=TITAN2>.
Contains utilities for the analysis of post-translational modifications (PTMs) in proteins, 
    with particular emphasis on the sulfoxidation of methionine residues. Features include the ability to download, 
    filter and analyze data from the sulfoxidation database 'MetOSite'. Utilities to search and characterize S-aromatic motifs in proteins are also provided. 
    In addition, functions to analyze sequence environments around modifiable residues in proteins can be found. 
    For instance, 'ptm' allows to search for amino acids either overrepresented or avoided around the modifiable 
    residues from the proteins of interest. Functions tailored to test statistical hypothesis related to
    these differential sequence environments are also implemented. 
    Further and detailed information regarding the methods in this package can be found in (Aledo (2020) <https://metositeptm.com>).
Fits the Poisson-Tweedie generalized linear mixed model
    described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>).
    Likelihood approximation based on adaptive Gauss Hermite quadrature
    rule.
Functions used for analyzing count data, mostly crime counts. Includes checking difference in two Poisson counts (e-test), checking the fit for a Poisson distribution, small sample tests for counts in bins, Weighted Displacement Difference test (Wheeler and Ratcliffe, 2018) <doi:10.1186/s40163-018-0085-5>, to evaluate crime changes over time in treated/control areas. Additionally includes functions for aggregating spatial data and spatial feature engineering.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Provides tools for analyzing and optimizing PTSD (Post-Traumatic
    Stress Disorder) diagnostic criteria using PCL-5 (PTSD Checklist for DSM-5)
    data. Functions identify optimal subsets of PCL-5 items that maintain
    diagnostic accuracy while reducing assessment burden. Includes tools for
    both hierarchical (cluster-based) and non-hierarchical symptom combinations,
    calculation of diagnostic metrics, and comparison with standard DSM-5
    criteria. Model validation is conducted using holdout and cross-validation
    methods to assess robustness and generalizability of the results. For more
    details see Weidmann et al. (2025) <doi:10.31219/osf.io/6rk72_v1>.
A collection of functions to simulate, estimate and forecast a wide range of regression based dynamic models for positive time series. This package implements the results presented in Prass, T.S.; Pumi, G.; Taufemback, C.G. and Carlos, J.H. (2025). "Positive time series regression models: theoretical and computational aspects". Computational Statistics 40, 1185–1215. <doi:10.1007/s00180-024-01531-z>.
Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere.
	These can be used to estimate the parameters of a stochastic state space model (i.e. a model where
	a time series is observed with error). The goal of this package is to estimate the variability
	around a deterministic process, both in terms of observation error - i.e. variability due to
	imperfect observations that does not influence system state - and in terms of process noise - i.e.
	stochastic variation in the actual state of the process. Unlike classical methods for estimating
	variability, this package does not necessarily assume that the deterministic state is fixed (i.e.
	a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated
	(e.g. stochastic fluctuations during predator-prey dynamics).
An implementation of an S3 class based on a double vector for storing and displaying precision teaching measures, representing a growing or a decaying (multiplicative) change between two frequencies. The main format method allows researchers to display measures (including data.frame) that respect the established conventions in the precision teaching community (i.e., prefixed multiplication or division symbol, displayed number <= 1). Basic multiplication and division methods are allowed and other useful functions are provided for creating, converting or inverting precision teaching measures. For more details, see Pennypacker, Gutierrez and Lindsley (2003, ISBN: 1-881317-13-7).
Access the 'Public Transport Victoria' Timetable API 
    <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>,
    with results returned as familiar R data structures. Retrieve information on
    stops, routes, disruptions, departures, and more.
Parametric Time Warping aligns patterns, i.e., it aims to
        put corresponding features at the same locations. The algorithm
        searches for an optimal polynomial describing the warping. It
        is possible to align one sample to a reference, several samples
        to the same reference, or several samples to several
        references. One can choose between calculating individual
        warpings, or one global warping for a set of samples and one
        reference. Two optimization criteria are implemented: RMS (Root
        Mean Square error) and WCC (Weighted Cross Correlation). Both
	warping of peak profiles and of peak lists are supported. A
	vignette for the latter is contained in the inst/doc directory
	of the source package - the vignette source can be found on
	the package github site. See `citation("ptw")` for more details.
Contains a dataset of words used in 15.000 randomly extracted pages
    from the Portuguese Wikipedia (<https://pt.wikipedia.org/>).
Package to Percentile estimation of fetal weight for twins by chorionicity (dichorionic-diamniotic or monochorionic-diamniotic).
Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the 
             MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS 'QualityControl' tool).
             Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format.
             Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015)
             <doi:10.1021/acs.jproteome.5b00780>.
Bayesian variable selection for linear regression models using hierarchical
  priors. There is a prior that combines information across responses and one
  that combines information across covariates, as well as a standard spike and
  slab prior for comparison. An MCMC samples from the marginal posterior
  distribution for the 0-1 variables indicating if each covariate belongs to the
  model for each response.
All 'PubChem' compounds are downloaded to a local computer, but for each compound, only partial records are used.  The data are organized into small files referenced by 'PubChem' CID.  This package also contains functions to parse the biologically relevant compounds from all 'PubChem' compounds, using biological database sources, pathway presence, and taxonomic relationships. Taxonomy is used to generate a lowest common ancestor taxonomy ID (NCBI) for each biological metabolite, which then enables creation of taxonomically specific metabolome databases for any taxon.
Provides an interface to the 'PubChem' database via the PUG REST <https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest> and 
            PUG View <https://pubchem.ncbi.nlm.nih.gov/docs/pug-view> services. This package allows users to automatically 
            access chemical and biological data from 'PubChem', including compounds, substances, assays, and various other data types. 
            Functions are available to retrieve data in different formats, perform searches, and access detailed annotations.
A toolbox for making R functions and capabilities more
    accessible to students and professionals from Epidemiology and
    Public Health related disciplines. Includes a function to report 
    coefficients and confidence intervals from models using robust
    standard errors (when available), functions that expand 'ggplot2'
    plots and functions relevant for introductory papers in Epidemiology 
    or Public Health. Please note that use of the 
    provided data sets is for educational purposes only.
These are harmonized datasets produced as part of the Clinical 
    Trials Network (CTN) protocol number 0094. This is a US National Institute
    of Drug Abuse (NIDA) funded project; to learn more go to
    <https://ctnlibrary.org/protocol/ctn0094/>. These are datasets which have
    the data harmonized from CTN-0027 (<https://ctnlibrary.org/protocol/ctn0027/>),
    CTN-0030 (<https://ctnlibrary.org/protocol/ctn0030/>), and CTN-0051 
    (<https://ctnlibrary.org/protocol/ctn0051/>).
Engineered features and "helper" functions ancillary to the
    'public.ctn0094data' package, extending this package for ease of use (see 
    <https://CRAN.R-project.org/package=public.ctn0094data>). This
    'public.ctn0094data' package contains harmonized datasets from some of the
    National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN)
    projects. Specifically, the CTN-0094 project is to harmonize and de-identify
    clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for
    opioid use disorder. This current version is built from 'public.ctn0094data'
    v. 1.0.6.
Performs sensitivity analysis for publication bias in meta-analyses
    (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses
    enable statements such as: "For publication bias to shift the observed point
    estimate to the null, 'significant' results would need to be at least
    30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen
    non-null value or shifting the confidence interval. Provides a worst-case
    meta-analytic point estimate under maximal publication bias obtained simply
    by conducting a standard meta-analysis of only the negative and
    "nonsignificant" studies.
Implements a unified interface for benchmarking meta-analytic
    publication bias correction methods through simulation studies (see 
    Bartoš et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides
    1) predefined data-generating mechanisms from the literature, 2) functions
    for running meta-analytic methods on simulated data, 3) pre-simulated
    datasets and pre-computed results for reproducible benchmarks, 4) tools for
    visualizing and comparing method performance.
Tools for Bayesian estimation of meta-analysis models that account
    for publications bias or p-hacking. For publication bias, this package
    implements a variant of the p-value based selection model of Hedges (1992)
    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also
    implements the mixture of truncated normals model for p-hacking described
    in Moss and De Bin (2019) <arXiv:1911.12445>.
A bunch of convenience functions that transform the results of some basic statistical analyses
       into table format nearly ready for publication. This includes descriptive tables, tables of
       logistic regression and Cox regression results as well as forest plots.
Queries the 'NCBI' (National Center for Biotechnology Information)
    Entrez 'E-utilities' API to count pairwise co-occurrences between two sets
    of terms in 'PubMed' or 'PubMed Central'. It returns a matrix-like data
    frame of publication counts and can export hyperlink-enabled results in
    CSV or ODS format. The package also provides heatmap helpers for
    exploratory visualization of overlap patterns. Based on the method described
    in Becker et al. (2003) "PubMatrix: a tool for multiplex literature mining"
    <doi:10.1186/1471-2105-4-61>.
Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.
Easy function for text-mining the 'PubMed' repository based on defined sets of terms.
    The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus)
    is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/>
    A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term),
    a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.
A set of tools to extract bibliographic content from 'PubMed' database
    using 'NCBI' REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.
    It includes functions to search, download, and convert 'PubMed' bibliographic
    records into data frames compatible with the 'bibliometrix' package.
    Features include programmatic query building, batch downloading by PMID,
    citation enrichment via 'NCBI' E-Link, and robust error handling with
    automatic retry logic.
Provides various functions for retrieving and
    interpreting information from 'Pubmed' via the API,
    <https://www.ncbi.nlm.nih.gov/home/develop/api/>.
Create a word cloud using the abstract of publications from 'Pubmed'.
Provides functions to create high-quality, publication-ready plots for numeric and categorical data, including bar plots, violin plots, boxplots, line plots, error bars, correlation plots, linear model plots, odds ratio plots, and normality plots.
Provides function declarations and inline function definitions that
    facilitate cleaning strings in C++ code before passing them to R.
Simulate and run the Gaussian puff forward atmospheric
    model in sensor (specific sensor coordinates) or grid (across the
    grid of a full oil and gas operations site) modes, following Jia, M., 
    Fish, R., Daniels, W., Sprinkle, B. and Hammerling, D. (2024) 
    <doi:10.26434/chemrxiv-2023-hc95q-v3>. Numerous visualization options, 
    including static and animated, 2D and 3D, and a site map generator 
    based on sensor and source coordinates.
Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, (2022) <doi:10.1007/s11634-021-00488-x> and (2020) <doi:10.1007/s11634-020-00400-z>.
Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <doi:10.48550/arXiv.1711.08129>.
R Interface to Pullword Service for natural language processing
    in Chinese. It enables users to extract valuable words from text by deep learning models. 
    For more details please visit the official site (in Chinese) <http://www.pullword.com/>.
Provides a comprehensive and curated collection of datasets related to the lungs, respiratory system, and associated diseases. 
    This package includes epidemiological, clinical, experimental, and simulated datasets on conditions such as lung cancer, asthma, 
    Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, whooping cough, pneumonia, influenza, and other respiratory illnesses. 
    It is designed to support data exploration, statistical modeling, teaching, and research in pulmonary medicine, public health, 
    environmental epidemiology, and respiratory disease surveillance.
A method of clustering functional data using
    subregion information of the curves. It is intended to supplement the
    'fda' and 'fda.usc' packages in functional data object clustering. It
    also facilitates the printing and plotting of the results in a tree
    format and limits the partitioning candidates into a specific set of
    subregions.
Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <doi:10.48550/arXiv.1605.07072>.
Programmatic access to microdata from Colombia's Gran
    Encuesta Integrada de Hogares ('GEIH'), published by 'DANE'. Provides
    a tidy interface to download, parse, and harmonize labor market
    surveys from 2007 to present. R companion to the 'pulso-co' 'Python'
    package.
Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
Estimates power, minimum detectable effect size (MDES) and sample size requirements. The context is multilevel randomized experiments with multiple outcomes. The estimation takes into account the use of multiple testing procedures. Development of this package was supported by a grant from the Institute of Education Sciences (R305D170030). For a full package description, including a detailed technical appendix, see <doi:10.18637/jss.v108.i06>.
High-performance Unicode and Punycode encoding/decoding for 
    internationalized domain names. Provides RFC 3492 compliant conversion
    functions with a focus on URL processing and data analysis workflows.
    Addresses limitations in existing R packages for handling international
    domain names in web scraping and URL parsing applications.
Contains model fitting functions for linear and non-linear adsorption kinetic and diffusion models. Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) <doi:10.1016/j.cis.2009.07.009>. This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model.
Provides a unified pipeline to clean, prepare, plot,
    and run basic analyses on pupillometry experiments.
Pupillometric data collected using SR Research Eyelink eye trackers 
    requires significant preprocessing. This package contains functions for 
    preparing pupil dilation data for visualization and statistical analysis. 
    Specifically, it provides a pipeline of functions which aid in data validation,
    the removal of blinks/artifacts, downsampling, and baselining, among others. 
    Additionally, plotting functions for creating grand average and conditional 
    average plots are provided. See the vignette for samples of the functionality.
    The package is designed for handling data collected with SR Research 
    Eyelink eye trackers using Sample Reports created in SR Research Data 
    Viewer.
Image-based color matching using the "Mycological Colour Chart" by Rayner (1970, ISBN:9780851980263) and its associated fungal pigments. This package will assist mycologists in identifying color during morphological analysis.
Contains sixteen moisture sorption isotherm models, which evaluate the fitness of adsorption and desorption curves for further understanding of the relationship between moisture content and water activity. Fitness evaluation is conducted through parameter estimation and error analysis. Moreover, graphical representation, hysteresis area estimation, and isotherm classification through the equation of Blahovec & Yanniotis (2009) <doi:10.1016/j.jfoodeng.2008.08.007> which is based on the classification system introduced by Brunauer et. al. (1940) <doi:10.1021/ja01864a025> are also included for the visualization of models and hysteresis.
Retrieve and analyze biomedical literature from 'PubMed' and
    the wider 'NIH'/'NLM' data stack through a single, PMID-centered
    interface. A PubMed search resolves to a set of PMIDs, which can be used
    to retrieve article metadata and abstracts, author affiliations,
    'iCite' citation data and links, 'PubTator3' entity and relation
    annotations, and open-access full text from 'PMC'. A local analysis layer
    operates on the retrieved tables, supporting corpus expansion through
    citation links, citation network construction, sentence-level entity
    co-occurrence, inspection of relation evidence, and 'MeSH' descriptor
    keyness.
Proteins reside in either the cell plasma or in the
    cell membrane. A membrane protein goes through the 
    membrane at least once. Given the amino acid sequence of a
    membrane protein, the tool
    'PureseqTM' (<https://github.com/PureseqTM/pureseqTM_package>,
    as described in "Efficient And Accurate Prediction Of Transmembrane 
    Topology From Amino acid sequence only.", Wang, Qing, et al (2019), 
    <doi:10.1101/627307>),
    can predict the topology of a membrane protein. This package
    allows one to use 'PureseqTM' from R.
Enables the removal of training data from fitted R models while
    retaining predict functionality. The purged models are more portable as their
    memory footprints do not scale with the training sample size.
Inbreeding-purging analysis of pedigreed populations, including the computation of the inbreeding coefficient, partial, ancestral and purged inbreeding coefficients, and measures of the opportunity of purging related to the individual reduction of inbreeding load.
    In addition, functions to calculate the effective population size and other parameters relevant to population genetics are included.
    See López-Cortegano E. (2022) <doi:10.1093/bioinformatics/btab599>.
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
Send requests to the 'PurpleAir' Application Programming Interface (API; <https://community.purpleair.com/c/data/api/18>). Check a 'PurpleAir' API key and get information about the related organization. Download real-time data from a single 'PurpleAir' sensor or many sensors by sensor identifier, geographical bounding box, or time since modified. Download historical data from a single sensor. Stream real time data from monitors on a local area network.
Provides tools for retrieving and analyzing air quality data
    from 'PurpleAir' sensors through their API. Functions enable downloading
    historical measurements, accessing sensor metadata, and managing API
    request limitations through chunked data retrieval. For more information 
    about the 'PurpleAir' API, see <https://api.purpleair.com/>.
A complete and consistent functional programming toolkit for
    R.
Some functions at the intersection of 'dplyr' and
    'purrr' that formerly lived in 'purrr'.
Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.
Create sliders from left, right, top and bottom which may include any html or 'Shiny' input or output.
Send push notifications to mobile devices or the desktop
    using 'Pushover' <https://pushover.net>. These notifications can
    display things such as results, job status, plots, or any other text
    or numeric data.
Provides tools for extracting and processing structured annotations from 'R' 
    and 'Python' source files to facilitate workflow visualization. The package scans 
    source files for special 'PUT' annotations that define nodes, connections, and 
    metadata within a data processing workflow. These annotations can then be used 
    to generate visual representations of data flows and processing steps across 
    polyglot software environments. Builds on concepts from literate programming 
    Knuth (1984) <doi:10.1093/comjnl/27.2.97> and utilizes directed acyclic graph (DAG) 
    theory for workflow representation Foraita, Spallek, and Zeeb (2014) 
    <doi:10.1007/978-0-387-09834-0_65>. Diagram generation powered by 'Mermaid' 
    Sveidqvist (2014) <https://mermaid.js.org/>.
To Simplify the time consuming and error prone task of assembling complex data sets for non-linear mixed effects modeling. Users are able to select from different absorption processes such as zero and first order, or a combination of both. Furthermore, data sets containing data from several entities, responses, and covariates can be simultaneously assembled.
Plot malaria parasite genetic data on two or more episodes. 
    Compute per-person posterior probabilities that each
    Plasmodium vivax (Pv) recurrence is a recrudescence, relapse,
    or reinfection (3Rs) using per-person P. vivax genetic data on two or
    more episodes and a statistical model described in 
    Taylor, Foo and White (2022) <doi:10.1101/2022.11.23.22282669>. 
    Plot per-recurrence posterior probabilities.
Likelihood based population viability analysis in the
  presence of observation error and missing data.
  The package can be used to fit, compare, predict,
  and forecast various growth model types using data cloning.
Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; Greenland S. (2019) <doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.
The calculation of p-variation of the finite sample data. 
	This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3>
	The formal definitions and reference into literature are given in vignette.
Implements the estimator of Tugan (2021) <doi:10.1093/ectj/utaa021>
    for panel vector autoregression (VAR) models with interactive fixed effects.
    Provides joint estimation of VAR coefficients, latent common factors, and
    factor loadings via an iterative algorithm that alternates between principal
    component estimation of the factors and least squares estimation of the VAR
    coefficients, following the approach of Bai (2009) <doi:10.3982/ECTA6135>.
    Supports
    impulse response functions under recursive (Cholesky) identification,
    parametric confidence bands from the joint asymptotic distribution of the
    estimator (Theorem 2.3), and a classical residual bootstrap for robustness
    checks.
Implements (1) panel cointegration rank tests, (2) estimators for panel
    vector autoregressive (VAR) models, and (3) identification methods for panel
    structural vector autoregressive (SVAR) models as described in the accompanying vignette.
    The implemented functions allow to account for cross-sectional dependence
    and for structural breaks in the deterministic terms of the VAR processes.
    Among the large set of functions, particularly noteworthy are those that implement
    (1) the correlation-augmented inverse normal test on the cointegration rank
    by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>),
    (2) the two-step estimator for pooled cointegrating vectors
    by Breitung (2005, <doi:10.1081/ETC-200067895>), and
    (3) the pooled identification based on independent component analysis
    by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).
Performs partial verification bias (PVB) correction for binary 
    diagnostic tests, where PVB arises from selective patient verification in 
    diagnostic accuracy studies. Supports correction of important accuracy
    measures -- sensitivity, specificity, positive predictive values and 
    negative predictive value -- under missing-at-random and 
    missing-not-at-random missing data mechanisms. Available methods and 
    references are "Begg and Greenes' methods" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x> and deGroot et al. (2011) 
    <doi:10.1016/j.annepidem.2010.10.004>; "Multiple imputation" in 
    Harel & Zhou (2006) <doi:10.1002/sim.2494>, "EM-based logistic regression" 
    in Kosinski & Barnhart (2003) <doi:10.1111/1541-0420.00019>; 
    "Inverse probability weighting" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x>; "Inverse probability bootstrap 
    sampling" in Nahorniak et al. (2015) <doi:10.1371/journal.pone.0131765> 
    and Arifin & Yusof (2022) <doi:10.3390/diagnostics12112839>; 
    "Scaled inverse probability resampling methods" in Arifin & Yusof (2025) 
    <doi:10.1371/journal.pone.0321440>.
Computes nonparametric p-values for the potential class
        memberships of new observations as well as cross-validated
        p-values for the training data. The p-values are based on
        permutation tests applied to an estimated Bayesian likelihood
        ratio, using a plug-in statistic for the Gaussian model, 'k
        nearest neighbors', 'weighted nearest neighbors' or
        'penalized logistic regression'.
        Additionally, it provides graphical displays and quantitative
        analyses of the p-values.
An implementation of multiscale bootstrap resampling for
             assessing the uncertainty in hierarchical cluster analysis.
             It provides SI (selective inference) p-value, AU (approximately unbiased)
             p-value and BP (bootstrap probability) value for each cluster in a dendrogram.
Enables the manufacturing, analysis and display of pressure volume curves. From the progression of the curves, turgor loss point, osmotic potential and apoplastic fraction can be derived. Methods adapted from Bartlett, Scoffoni and Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.
Tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.
A suite of empirical Bayes methods to use in pharmacovigilance. Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) <doi:10.1002/sim.70195>, <doi:10.48550/arXiv.2512.01057>; Koenker and Mizera (2014) <doi:10.1080/01621459.2013.869224>; Efron (2016) <doi:10.1093/biomet/asv068>.
Simplifies the manufacturing, analysis and display of pressure volume and leaf drying curves. From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x> and Sack, Scoffoni, PrometheusWikiContributors (2011) <http://prometheuswiki.org/tiki-index.php?page=Minimum+epidermal+conductance+%28gmin%2C+a.k.a.+cuticular+conductance%29>.
A suite of likelihood ratio test based methods to use in pharmacovigilance. Contains various testing and post-processing functions.
Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.
Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - 'PvSTATEM', which is an international project aiming for malaria elimination.
Calculates various functions needed for design and monitoring survival trials
    accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual,
    and different censoring distributions between groups. The event time distribution is assumed to be
    piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution.
    As compared with Version 1.2.1, two more types of hybrid crossover are added. 
    A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, 
    density, hazard and cumulative hazard functions. 
    Also, to generate the crossover-adjusted event time random variable,  a more efficient 
    algorithm is used and the output includes crossover indicators.
Efficient algorithm for estimating piecewise exponential hazard models for right-censored data, and is useful for reliable power calculation, study design, and event/timeline prediction for study monitoring.
Price volatility refers to the degree of variation in series over a certain period of time. This volatility is especially noticeable in agricultural commodities, adding uncertainty for farmers, traders, and others in the agricultural supply chain. Commonly and popularly used four volatility models viz, GARCH, Glosten Jagannatan Runkle-GARCH (GJR-GARCH) model, exponentially weighted moving average (EWMA) model and Multiplicative Error Model (MEM) are selected and implemented. PWAVE, weighted ensemble model based on particle swarm optimization (PSO) is proposed to combine the forecast obtained from all the candidate models. This package has been developed using algorithm of Paul et al. <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
Build piecewise exponential survival model for study design (planning) and event/timeline prediction.
A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.
Allows users to find a piecewise linear regression approximation to a given continuous univariate function within a specified error tolerance. Methods based on Warwicker and Rebennack (2025) "Efficient continuous piecewise linear regression for linearising univariate non-linear functions" <doi:10.1080/24725854.2023.2299809>.
Estimates two-level multilevel linear model and two-level multivariate linear multilevel model with weights following Probability Weighted Iterative Generalised Least Squares approach. For details see Veiga et al.(2014) <doi:10.1111/rssc.12020>.
Power analysis functions along the lines of Cohen (1988).
Statistical power analysis for designs including t-tests, correlations, 
    multiple regression, ANOVA, mediation, and logistic regression. Functions accompany 
    Aberson (2019) <doi:10.4324/9781315171500>.
Provides tools for calculating statistical power for experiments 
    analyzed using linear mixed models. It supports standard designs, including 
    randomized block, split-plot, and Latin Square designs, while offering flexibility 
    to accommodate a variety of other complex study designs.
Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
  which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
  unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give 
  biased results due to using the pooled standard deviation, unlike the Welch's t-test.
Provides functions for conducting power analysis in ANOVA designs, including between-, within-, and mixed-factor designs, with full support for both main effects and interactions. The package allows calculation of statistical power, required total sample size, significance level, and minimal detectable effect sizes expressed as partial eta squared or Cohen's f for ANOVA terms and planned contrasts. In addition, complementary functions are included for common related tests such as t-tests and correlation tests, making the package a convenient toolkit for power analysis in experimental psychology and related fields.
Tools for the evaluation of interim analysis plans for sequentially
 monitored trials on a survival endpoint; tools to construct efficacy and 
 futility boundaries, for deriving power of a sequential design at a specified
 alternative, template for evaluating the performance of candidate plans at a 
 set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
Flexible and comprehensive functions for statistical power, minimum required 
  sample size, and minimum detectable effect calculations across a wide range of
  commonly used hypothesis tests in psychological, biomedical, and social sciences.
Conduct dsep tests (piecewise SEM) of a directed, or mixed, acyclic graph 
    without latent variables (but possibly with implicitly marginalized or
    conditioned latent variables that create dependent errors) based on
    linear, generalized linear, or additive modelswith or without a
    nesting structure for the data. Also included are functions to do desp tests
    step-by-step,exploratory path analysis, and Monte Carlo X2 probabilities.
    This package accompanies Shipley, B, (2026).Cause and Correlation in Biology:
     A User's Guide to Path Analysis, StructuralEquations and
     Causal Inference (3rd edition).  Cambridge University Press.
The Penn World Table provides purchasing power parity and
	national income accounts converted to international prices for
	189 countries for some or all of the years 1950-2010.
The Penn World Table 10.x (<https://www.rug.nl/ggdc/productivity/pwt/>) provides information
	on relative levels of income, output, input, and productivity
	for 183 countries between 1950 and 2019.
The Penn World Table 8.x provides information on relative levels of
	income, output, inputs, and productivity for 167 countries
	between 1950 and 2011.
The Penn World Table 9.x (<http://www.ggdc.net/pwt/>) provides information
	on relative levels of income, output, inputs, and productivity
	for 182 countries between 1950 and 2017.
Create PX-files from scratch or read and modify existing ones. 
    Includes a function for every PX keyword, making metadata manipulation
    simple and human-readable.
Provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The
    PX-Web/PC-Axis API is used by organizations such as Statistics Sweden
    and Statistics Finland to disseminate data. The R package can interact
    with all PX-Web/PC-Axis APIs to fetch information about the data
    hierarchy, extract metadata and extract and parse statistics to R
    data.frame format. PX-Web is a solution to disseminate PC-Axis data
    files in dynamic tables on the web.  Since 2013 PX-Web contains an API
    to disseminate PC-Axis files.
Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
 compute a population density estimate based on Tobler's pycnophylactic interpolation
 algorithm. The result is a SpatialGridDataFrame. 
 Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.
Implements the Penalized Youden Index Estimator (PYE) and the Covariate-Adjusted Youden Index Estimator (covYI), providing a novel framework for feature and covariate selection and combination in high-dimensional binary classification problems. Methodologies are based on Salaroli and Pardo (2023) <doi:10.1016/j.chemolab.2023.104786> and an unpublished manuscript by Salaroli and Pardo (2026) under review.
Deterministic Pena-Yohai initial estimator for robust S estimators
    of regression. The procedure is described in detail in
    Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
Allow to run 'pylint' on Python files with a R command or a 'RStudio' addin. The report appears in the RStudio viewer pane as a formatted HTML file.
Provides access to the latest 'Amazon Mechanical Turk' ('MTurk') <https://www.mturk.com> Requester API (version '2017–01–17'), replacing the now deprecated 'MTurkR' package.
Drawing population pyramid using (1) data.frame or (2) vectors.
	 The former is named as pyramid() and the latter pyramids(), as wrapper
	 function of pyramid().  pyramidf() is the function to draw population
	 pyramid within the specified frame.
Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset.
    The package provides an R interface to a number of 'pysd' functions, and can read files in
    'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from 
    that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested  using 'python3'.
It enables 'sparklyr' to integrate with 'Spark Connect', and
    'Databricks Connect' by providing a wrapper over the 'PySpark'
    'python' library.
'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available 
    longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine 
    cross-sectional and time-series data and limitations in data formats and timelines that limit data 
    granularity over extended time periods. 
    This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data.  
    This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work.
    During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data.  
    In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit <https://pypi.org/project/pytrends/>.
Read and write 'GraphPad Prism' '.pzfx' files in R.
Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', '[10, 15]', and so on. Some applications necessitate a detailed (single) age description. Despite the large number of proposed approaches in the literature, only a few methods ensure great performance at both younger and higher ages. For example, the 6-term 'Lagrange' interpolation function is well suited to mortality interpolation at younger ages (with irregular intervals), but not at older ages. The 'Karup-King' method, on the other hand, performs well at older ages but is not suitable for younger ones. Interested readers can find a full discussion of the two stated methods in the book Shryock, Siegel, and Associates (1993).The Q2q package combines the two methods to allow for the interpolation of mortality rates across all age groups. It begins by implementing each method independently, and then the resulting curves are linked using a 5-age averaged error between the two partial curves.
Construct message-passing style objects with types and features. Q7 types uses composition instead of inheritance in creating derived types, allowing defining any code segment as feature and associating any feature to any object. Compared to R6, Q7 is simpler and more flexible, and is more friendly in syntax.
A copula-based measure for quantifying asymmetry in dependence and associations. Documentation and theory about 'qad' is provided
    by the paper by Junker, Griessenberger & Trutschnig (2021, <doi:10.1016/j.csda.2020.107058>), and the paper by Trutschnig (2011, <doi:10.1016/j.jmaa.2011.06.013>).
Implements the Quantile Autoregressive Distributed Lag (QADF)
    unit root test proposed by Koenker and Xiao (2004)
    <doi:10.1198/016214504000001114>. The test examines unit root behaviour
    across the conditional distribution of a time series using quantile
    regression, providing a richer characterisation of persistence than
    standard ADF tests. Critical values follow Hansen (1995)
    <doi:10.1017/S0266466600009713>. Lag order selection is supported via
    AIC, BIC, or the t-statistic sequential testing approach.
The Ensemble Quadratic and Affine Invariant Markov chain Monte 
    Carlo algorithms provide an efficient way to perform Bayesian inference in 
    difficult parameter space geometries. The Ensemble Quadratic Monte Carlo 
    algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. 
    The Ensemble Affine Invariant algorithm was developed by Goodman and Weare 
    (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by 
    Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic 
    Monte Carlo method was shown to perform better than the Affine Invariant 
    method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and 
    the Quadratic Monte Carlo method is the default method used. The Chen-Shao 
    Highest Posterior Density Estimation algorithm is used for obtaining 
    credible intervals and the potential scale reduction factor diagnostic is 
    used for checking the convergence of the chains.
A tool for automatic generation of sibling items from a parent item model defined by
           the user. It is an implementation of the process automatic item generation (AIG) focused
           on generating quantitative multiple-choice type of items (see Embretson, Kingston
           (2018) <doi:10.1111/jedm.12166>).
Implements heuristics for the Quadratic Assignment Problem (QAP). Although, the QAP was introduced as a combinatorial optimization problem for the facility location problem in operations research, it also has many applications in data analysis. The problem is NP-hard and the package implements a simulated annealing heuristic.
Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.
Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.
Implements the Quantile Autoregressive Distributed Lag (QARDL)
    model of Cho, Kim and Shin (2015) <doi:10.1016/j.jeconom.2015.01.003>.
    Estimates quantile-specific long-run (beta), short-run autoregressive (phi),
    and impact (gamma) parameters. Features include BIC-based automatic lag
    selection, Error Correction Model (ECM) parameterization, Wald tests for
    parameter constancy across quantiles, rolling/recursive QARDL estimation,
    Monte Carlo simulation, and publication-ready output tables.
Provides prediction intervals for classical homoscedastic
    autoregressive models (AR(p)) and quantile autoregressive models
    (QAR(p)). The package implements percentile-based and predictive-root-based
    bootstrap procedures for constructing multi-step-ahead prediction
    intervals. For more details, see Novo and Sanchez-Sellero (2025)
    <doi:10.48550/arXiv.2512.22018>.
Fuel economy, size, performance, and price data for cars in Qatar in 2025. Mirrors many of the columns in mtcars, but uses (1) non-US-centric makes and models, (2) 2025 prices, and (3) metric measurements, making it more appropriate for use as an example dataset outside the United States. For more details see Musgrave (2025) <doi:10.1080/15512169.2025.2572320>.
Create quantile binned and conditional plots for Exploratory Data
  Analysis. The package provides several plotting functions that are all based
  on quantile binning. The plots are created with 'ggplot2' and 'patchwork' 
  and can be further adjusted.
Implements the Bayesian quantile regression model for binary longitudinal data 
             (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>.
             The model handles both fixed and random effects and implements both a blocked
             and an unblocked Gibbs sampler for posterior inference.
This R package assists breeders in linking data systems with their analytic pipelines, 
    a crucial step in digitizing breeding processes. It supports querying and retrieving 
    phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 
    'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>,
    'GIGWA' <https://github.com/SouthGreenPlatform/Gigwa2> (using 'BrAPI' <https://brapi.org> calls),
    , and 'Germinate' <https://germinateplatform.github.io/get-germinate/>. 
    Extra helper functions support environmental data sources, including 
    'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 
    'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database.
Programmatically access the 'Quickbase' JSON API <https://developer.quickbase.com>. 
    You supply parameters for an API call, 'qbr' delivers an http request to the 
    API endpoint and returns its response. Outputs follow 'tidyverse' philosophy.
Provides a 'brms'-like interface for fitting Bayesian regression models 
    using 'INLA' (Integrated Nested Laplace Approximations) and 'TMB' (Template Model Builder). 
    The package offers faster model fitting while maintaining familiar 'brms' syntax and 
    output formats. Supports fixed and mixed effects models, multiple probability distributions, 
    conditional effects plots, and posterior predictive checks with summary methods compatible 
    with 'brms'. 'TMB' integration provides fast ordinal regression capabilities. Implements 
    methods adapted from 'emmeans' for marginal means estimation and 'bayestestR' for Bayesian 
    inference assessment. Methods are based on Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>, 
    Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, Lenth (2016) <doi:10.18637/jss.v069.i01>, 
    Bürkner (2017) <doi:10.18637/jss.v080.i01>, Makowski et al. (2019) <doi:10.21105/joss.01541>, 
    and Kruschke (2014, ISBN:9780124058880).
An extensive set of functions to perform Qualitative Comparative Analysis:
       crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA')
       and fuzzy sets ('fsQCA'). 'QCA' is a methodology that bridges
       the qualitative and quantitative divide in social science research.
       It uses a Boolean minimization algorithm, resulting in a minimal causal
       configuration associated with a given phenomenon.
Clustered set-relational data in Qualitative Comparative Analysis
    (QCA) can have a hierarchical structure, a panel structure or repeated cross
    sections. 'QCAcluster' allows researchers to supplement the analysis
    of pooled the data with a differentiated perspective focusing on selected 
    partitions of the data. The pooled data can be partitioned along the 
    dimensions of the clustered data (individual cross sections or time series) 
    to perform partition-specific truth table minimization. Empirical 
    researchers can further calculate the weight that each partition has on the 
    parameters of the pooled solution and the diversity of the cases under 
    analysis within and across partitions 
    (see <https://ingorohlfing.github.io/QCAcluster/>).
Provides functions for assessing and visualizing robustness in
    Qualitative Comparative Analysis (QCA) workflows built with the 'QCA'
    package, including calibration thresholds, inclusion cutoffs, frequency
    cutoffs, case influence, subsample stability, alternative analysis settings,
    theory-specific condition sets, cluster-specific patterns, and solution
    summaries. Methods build on Dusa (2019)
    <doi:10.1007/978-3-319-75668-4> and Ragin (2014, ISBN:9780520280038).
Researchers working with Qualitative Comparative Analysis (QCA)
    can use the package to estimate power of a sufficient term
    using permutation tests. A term can be anything: A condition, conjunction or 
    disjunction of any combination of these. The package further allows users to plot 
    the estimation results and to estimate the number of cases required to achieve a
    certain level of power, given a prespecified null and alternative hypothesis.
    Reference for the article introducing power estimation for QCA is: 
    Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> 
    (ungated version: <doi:10.17605/OSF.IO/PC4DF>).
Quasi-Cauchy quantile regression, proposed by de Oliveira, Ospina, Leiva, Figueroa-Zuniga and Castro (2023) <doi:10.3390/fractalfract7090667>. This regression model is useful for the case where you want to model data of a nature limited to the intervals [0,1], (0,1], [0,1) or (0,1) and you want to use a quantile approach.
Implements the Quantitative Classification-based on
    Association Rules (QCBA)  algorithm (<doi:10.1007/s10489-022-04370-x>). 
    QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. 
    Supported are 'CBA' implementations from 'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR', 'FOIL2' and 'PRM' implementations 
    from 'arulesCBA' package and 'SBRL' implementation from the 'sbrl' package. The result of the post-processing is an ordered CBA-like rule list.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Functions to calculate Average Sample Numbers (ASN), Average Run Length (ARL1) and value of k, k1 and k2 for quality control charts under repetitive sampling as given in Aslam et al. (2014) (<DOI:10.7232/iems.2014.13.1.101>).
Tools for (automated and manual) quality control of 
  the results of Epigenome-Wide Association Studies.
Tools for (automated and manual) quality control of the results of Genome Wide Association Studies.
Provides functions for the joint analysis of Q sets of
    p-values obtained for the same list of items. This joint analysis is
    performed by querying a composite hypothesis, i.e. an arbitrary
    complex combination of simple hypotheses, as described in Mary-Huard
    et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et
    al.(2025) <doi: 10.1093/nargab/lqaf118>. In this approach, the Q-uplet
    of p-values associated with each item is distributed as a multivariate
    mixture, where each of the 2^Q components corresponds to a specific
    combination of simple hypotheses. The dependence between the p-value
    series is considered using a Gaussian copula function. A p-value for
    the composite hypothesis test is derived from the posterior
    probabilities.
Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.
Implements the Quantile Composite-based Path Modeling approach 
	(Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; 
	 Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to 'Vii7' software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).
Univariate and multivariate SQC tools that completes and increases
    the SQC techniques available in R. Apart from integrating different R packages 
    devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly 
    useful when Gaussian assumption is not met. 
    This package computes standard univariate control charts for individual measurements, 
    'X-bar', 'S', 'R', 'p', 'np', 'c', 'u', 'EWMA' and 'CUSUM'. In addition, it 
    includes functions to perform multivariate control charts such as 'Hotelling T2', 
    'MEWMA' and 'MCUSUM'. As representative feature, multivariate nonparametric 
    alternatives based on data depth are implemented in this package: 'r', 'Q' and 
    'S' control charts. In addition, Phase I and II control charts for functional 
    data are included. This package also allows the estimation of the most complete 
    set of capability indices from first to fourth generation, covering the nonparametric 
    alternatives, and performing the corresponding capability analysis graphical outputs,
    including the process capability plots. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.
An R implementation of quality control–based robust LOESS(local
    polynomial regression fitting) signal correction for metabolomics data
    analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011)
    <doi:10.1038/nprot.2011.335>. The optimisation of LOESS's span parameter
    using generalized cross-validation (GCV) is provided as an option. In
    addition to signal correction, 'qcrlscR' includes some utility functions
    like batch shifting and data filtering.
Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2
             qubit quantum gates.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Primarily, the 'qcv' package computes key indices related to the Quantifying Construct
    Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; 
    see also Furr & Heuckeroth, in press). The qcv() function is the heart of the 'qcv' package, 
    but additional functions in the package provide useful ancillary information related to the QCV procedure.
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse
              including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted
              analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user
              to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R
              packages that undertake higher level analysis and visualization of text. This affords the user a more efficient
              and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other
              areas of Text Mining/ Natural Language Processing.
A collection of text analysis dictionaries and word lists for use with
        the 'qdap' package.
A collection of regular expression tools associated with
        the 'qdap' package that may be useful outside of the context of
        discourse analysis. Tools include
        removal/extraction/replacement of abbreviations, dates, dollar
        amounts, email addresses, hash tags, numbers, percentages,
        citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package that may be useful outside of the
            context of text analysis.
Allows practitioners to determine (i) if two univariate distributions (which can be continuous, discrete, or even mixed) are equal, (ii) how two distributions differ (shape differences, e.g., location, scale, etc.), and (iii) where two distributions differ (at which quantiles), all using nonparametric LP statistics. The primary reference is Jungreis, D. (2019, Technical Report).
R implementation of Quantile Data Envelopment Analysis. 
 The package 'qDEA' allows a user specified proportion of observations to lie external to a given
 Decision Making Units's (DMU's)reference hyperplane. 'qDEA' can be used to detect and
 address influential outliers or to implement quantile benchmarking, as discussed in Atwood and
 Shaik (2020). 
 Quantile benchmarking is accomplished by using heuristic procedures to find a DMU's closest
 input-output projection point in a specified direction while allowing a specified 
 proportion of observations to lie external to the projected point's hyperplane. 
 The 'qDEA' package accommodates standard (DEA) and quantile DEA estimation, 
 returns to scale CRS(constant),VRS(variable),DRS(decreasing) or IRS(increasing),
 the use of directional vectors, bias correction through subsample bootstrapping and subsample size 
 selection procedures. The user can also recover each DMU's reference DMUs and external DMUs if desired.
 The implemented procedures are based on discussions in: 
 Atwood and Shaik (2020) <doi:10.1016/j.ejor.2020.03.054>
 Atwood and Shaik (2018) <doi:10.1007/978-3-319-68678-3_4>
 Walden and Atwood (2023) <doi:10.1086/724932>
 Walden and Atwood (2025) <doi:10.1086/736554>.
This package provides different specifications of a Quadrilateral
    Dissimilarity Model which can be used to fit same-different judgments
    in order to get a predicted matrix that satisfies regular minimality
    [Colonius & Dzhafarov, 2006, Measurement and representations of
    sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
    computed.
The letters 'qe' in the package title stand for "quick and
   easy," alluding to the convenience goal of the package. We bring
   together a variety of machine learning (ML) tools from standard R
   packages, providing wrappers with a simple, convenient, 
   and uniform interface.
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.
Implementation of quantile frequency analysis (QFA) for time series based on trigonometric quantile regression and of spline quantile regression (SQR) for estimating the coefficients in linear quantile regression models as smooth functions of the quantile level.
 References:
    [1] Li, T.-H. (2012). ''Quantile periodograms,'' J. of the American Statistical Association,
         107, 765–776. <doi:10.1080/01621459.2012.682815>
    [2] Li, T.-H. (2014). Time Series with Mixed Spectra, CRC Press. <doi:10.1201/b15154>
    [3] Li, T.-H. (2025). ''Quantile Fourier transform, quantile series, and nonparametric
         estimation of quantile spectra,'' Communications in Statistics: Simulation and
         Computation, 1–22. <doi:10.1080/03610918.2025.2509820>
    [4] Li, T.-H. (2025). ''Quantile-crossing spectrum and spline autoregression estimation,''
         Statistical Inference for Stochastic Processes, 28, 20. <doi:10.1007/s11203-025-09336-7>
    [5] Li, T.-H. (2025). ''Spline autoregression method for estimation of quantile spectrum,'' J. of
         Computational and Graphical Statistics, 1-15. <doi:10.1080/10618600.2025.2549452>
    [6] Li, T.-H., and Megiddo, N. (2026). ''Spline quantile regression,'' J. of Statistical Theory
         and Practice, 20, 30. <doi:10.1007/s42519-026-00545-8>
    [7] Li, T.-H. (2026). ''Spline quantile regression with cubic and linear smoothing splines,''
         <doi:10.48550/arXiv.2603.22408>.
Accurate estimates of the diets of predators are required
    in many areas of ecology, but for many species current methods are
    imprecise, limited to the last meal, and often biased. The diversity
    of fatty acids and their patterns in organisms, coupled with the
    narrow limitations on their biosynthesis, properties of digestion in
    monogastric animals, and the prevalence of large storage reservoirs of
    lipid in many predators, led to the development of quantitative
    fatty acid signature analysis (QFASA) to study predator diets.
Evaluates moments of ratios (and products) of quadratic forms
    in normal variables, specifically using recursive algorithms developed by
    Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al.
    (2014) <doi:10.1017/S0266466613000364>. Also provides distribution,
    quantile, and probability density functions of simple ratios of quadratic
    forms in normal variables with several algorithms. Originally developed as
    a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for
    evaluating average evolvability measures in evolutionary quantitative
    genetics, but can be used for a broader class of statistics. Generating 
    functions for these moments are also closely related to the top-order zonal
    and invariant polynomials of matrix arguments.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. 
  This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra  (<https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.
Smooth additive quantile regression models, fitted using
    the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>.
    See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from
    'quantreg', the smoothing parameters are estimated automatically by marginal
    loss minimization, while the regression coefficients are estimated using either
    PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian
    credible intervals of the estimated effects have approximately the correct
    coverage. The main function is qgam() which is similar to gam() in 'mgcv', but
    fits non-parametric quantile regression models.
General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.
Fits quadratic generalized autoregressive conditional
    heteroskedasticity-in-mean (QGARCH-M) models motivated by Campbell and
    Hentschel (1992). The package supports models with lambda fixed at zero,
    lambda restricted to a function of the remaining parameters, lambda
    estimated freely, and a threshold extension with state-dependent lambda.
    It also provides tools for starting values, estimation, forecasting,
    likelihood-ratio testing, moment diagnostics, and replication with the
    included monthly U.S. stock market dataset.
Density, distribution function, quantile function and 
  random generation for the q-gaussian distribution with parameters mu and sig.
G-computation for a set of time-fixed exposures with
    quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. This approach estimates a regression line
    corresponding to the expected change in the outcome (on the link
    basis) given a simultaneous increase in the quantile-based category
    for all exposures. Works with continuous, binary, and right-censored
    time-to-event outcomes.  Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
G-computation for a set of time-fixed exposures
    with quantile-based basis functions, possibly under linearity and
    homogeneity assumptions. Effect measure modification in this method is a way
    to assess how the effect of the mixture varies by a binary, categorical or continuous variable.  
    Reference: Alexander P. Keil, Jessie P.
    Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and
    Alexandra J. White (2019) A quantile-based g-computation approach to
    addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses.
    Rohde et al. (2019) <doi:10.1101/503631>.
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Provides seamless access to the 'QGIS'
    (<https://qgis.org>) processing toolbox using the standalone
    'qgis_process' command-line utility.  Both native and third-party
    (plugin) processing providers are supported.  Beside referring data
    sources from file, also common objects from 'sf', 'terra' and 'stars'
    are supported. The native processing algorithms are documented by QGIS.org 
    (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.
Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
A 'shiny' application for teaching introductory quantitative genetics
    and plant breeding through interactive simulations. The application relies on 
    established plant breeding and quantitative genetic theory found in 
    Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
Nomograms are constructed to predict the cumulative incidence
    rate which is calculated after adjusting for competing causes to the event of interest. 
    K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index.
    Methods are as described in: Kattan MW, Heller G, 
    Brennan MF (2003).
The quantity-intensity (Q/I) relationships, first introduced by Beckett
  (1964), can be employed to assess the K supplying capacity of different soils 
  based on solid-solution exchange equilibria. Such relationships describe the 
  changes in K+ concentration in the soil solution (or the intensity factor) in 
  relation to the corresponding changes in K+ at exchange sites of the soil 
  (or the capacity or quantity factor). Activity ratio of K to Ca or Ca+Mg 
  is generally used as the variable denoting the intensity, whereas, change 
  in exchangeable K is used to denote the quantity factor.
Functions for making run charts 
    [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>] and basic
    Shewhart control charts [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>] for measure and count data.
    The main function, qic(), creates run and control charts and has a
    simple interface with a rich set of options to control data analysis
    and plotting, including options for automatic data aggregation by
    subgroups, easy analysis of before-and-after data, exclusion of one
    or more data points from analysis, and splitting charts into
    sequential time periods.
    Missing values and empty subgroups are handled gracefully.
Functions for making run charts, Shewhart control charts and
    Pareto charts for continuous quality improvement. Included control charts
    are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation
    in the form of minor to moderate persistent shifts in data over time is
    identified by the Anhoej rules for unusually long runs and unusually few
    crossing  [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>].
    Non-random variation in the form of larger, possibly transient, shifts is
    identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008)
    <doi:10.1136/qshc.2004.012047>].
Developed to perform the estimation and inference for regression 
    coefficient parameters in longitudinal marginal models using the method of 
    quadratic inference functions. Like generalized estimating equations, this 
    method is also a quasi-likelihood inference method. It has been showed that 
    the method gives consistent estimators of the regression coefficients even if 
    the correlation structure is misspecified, and it is more efficient than GEE 
    when the correlation structure is misspecified. Based on Qu, A., Lindsay, 
    B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.
Qiita is a technical knowledge sharing and collaboration platform for programmers.
  See <https://qiita.com/api/v2/docs> for more information.
Select optimal functional regression or dichotomized
       quantile predictors for survival/logistic/numeric outcome
       and perform optimistic bias correction for any optimally
       dichotomized numeric predictor(s), as in Yi, et. al.
       (2023) <doi:10.1016/j.labinv.2023.100158>.
Provide access to 'QUANTkiosk' platform of regulatory and corporate data relevant to financial markets including point-in-time fundamentals, ownership and symbology.
'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version
 of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now
 maintained separately just for the calendaring subset). See the included file 'AUTHORS'
 for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').
Functionality to read, recode, and transcode data as used in 
  quantitative language comparison, specifically to deal with multilingual 
  orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) 
  and with the recoding of nominal data.
Extension of the functionality of the 'Matrix' package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison.
Manages, builds and computes statistics and datasets for the 
   construction of quarterly (sub-annual) life tables by exploiting micro-data from
   either a general or an insured population.
   References: 
   Pavía and Lledó (2022) <doi:10.1111/rssa.12769>.
   Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>.
   Pavía and Lledó (2025) <doi:10.1371/journal.pone.0315937>.
   Acknowledgements:
   The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
A multivariate copula-based dependence measure. For more information, see Griessenberger, Junker, Trutschnig (2022), On a multivariate copula-based dependence measure and its estimation, Electronic Journal of Statistics, 16, 2206-2251.
Analysis of Q methodology, used to identify distinct perspectives existing within a group.
  This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see <https://qmethod.org/>).
  A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
  The package allows to choose either principal components or centroid factor extraction, manual or automatic flagging, a number of mathematical methods for rotation (or none), and a number of correlation coefficients for the initial correlation matrix, among many other options.
  Additional functions are available to import and export data (from raw *.CSV, 'HTMLQ' and 'FlashQ' *.CSV, 'PQMethod' *.DAT and 'easy-htmlq' *.JSON files), to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
  The package also offers functions to print Q cards and to generate Q distributions for study administration.
  See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a graphical user interface (GUI) for online and offline use.
Produces quality scores for each of the US companies from 
    the Russell 3000, following the approach described in 
    "Quality Minus Junk" (Asness, Frazzini, & Pedersen, 2013) 
    <http://www.aqr.com/library/working-papers/quality-minus-junk>. 
    The package includes datasets for users who wish to view the most recently 
    uploaded quality scores. It also provides tools to automatically gather 
    relevant financials and stock price information, allowing users to update 
    their data and customize their universe for further analysis.
Implementation of methods for estimation of quantitative maps
    from Multi-Parameter Mapping (MPM) acquisitions (Weiskopf et al. (2013)
    <doi:10.3389/fnins.2013.00095>) and analysis of Inversion Recovery MRI data.
    Usage of the package is described in
    Polzehl and Tabelow (2023),
    "Magnetic Resonance Brain Imaging", 2nd Edition, Chapter 6 and 7, Springer, Use R! Series.
    <doi:10.1007/978-3-031-38949-8>.
    J. Polzehl and K. Tabelow (2023), "Magnetic Resonance Brain Imaging - Modeling and Data Analysis Using R: Code and Data."
    <doi:10.20347/WIAS.DATA.6> provides extensive example code and data.
Basic functions for building parsers, with an application to PC-AXIS format files.
The queueing model of visual search models the accuracy and response time data in a visual search experiment using queueing models with finite customer population and stopping criteria of completing the service for finite number of customers. It implements the conceptualization of a hybrid model proposed by Moore and Wolfe (2001), in which visual stimuli enter the processing one after the other and then are identified in parallel. This package provides functions that simulate the specified queueing process and calculate the Wasserstein distance between the empirical response times and the model prediction.
The new QOI file format offers a very simple but efficient image compression algorithm. This package provides an easy and simple way to read, write and display bitmap images stored in the QOI (Quite Ok Image) format. It can read and write both files and in-memory raw vectors.
The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p06ciqes4eaqo6n0zyqtz9p21nfb.htm>, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p0jvbbqkt0gs2cn1lo4zndbqs1pe.htm>, tabulation functions which can create any table in different styles <https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/n1ql5xnu0k3kdtn11gwa5hc7u435.htm> and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.
There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.
Methods for statistical analysis of count data and quantal data.
    For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>).
    For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated.
    All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.
Sigmoidal model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR), according to Ritz & Spiess (2008) <doi:10.1093/bioinformatics/btn227> and Spiess et al. (2008) <doi:10.1186/1471-2105-9-221>.
Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase 
    chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression 
    values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation
    of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: 
    Livak & Schmittgen, (2001) <doi:10.1006/meth.2001.1262>.
Content-preserving transformations transformations of PDF files such 
    as split, combine, and compress. This package interfaces directly to the 'qpdf' 
    C++ library <https://qpdf.sourceforge.io/> and does not require any command 
    line utilities. Note that 'qpdf' does not read actual content from PDF files: 
    to extract text and data you need the 'pdftools' package.
Provides functions for estimating ploidy levels and detecting aneuploidy in individuals using 
             allele intensities or allele count data from high-throughput genotyping platforms, including 
             single nucleotide polymorphism (SNP) arrays and sequencing-based technologies. Implements an extended version of the 'PennCNV' 
             signal standardization method by Wang et al. (2007) <doi:10.1101/gr.6861907> for higher ploidy 
             levels. Computes B-allele frequencies (BAF), z-scores, and identifies copy number variation patterns.
Computes noncompartmental pharmacokinetic parameters
 for drug concentration profiles.  For each profile, data
 imputations and adjustments are made as necessary and
 basic parameters are estimated. Supports single dose, multi-dose,
 and multi-subject data.  Supports steady-state calculations
 and various routes of drug administration. See ?qpNCA and vignettes.
 Methodology follows Rowland and Tozer (2011, ISBN:978-0-683-07404-8), 
 Gabrielsson and Weiner (1997, ISBN:978-91-9765-100-4), and
 Gibaldi and Perrier (1982, ISBN:978-0824710422).
Supports risk assessors in performing the entry step of the
    quantitative Pest Risk Assessment. It allows the estimation of the
    amount of a plant pest entering a risk assessment area (in terms of
    founder populations) through the calculation of the imported
    commodities that could be potential pathways of pest entry, and the
    development of a pathway model. Two 'Shiny' apps based on the
    functionalities of the package are included, that simplify the process
    of assessing the risk of entry of plant pests. The approach is based
    on the work of the European Food Safety Authority (EFSA PLH Panel et
    al., 2018) <doi:10.2903/j.efsa.2018.5350>.
Provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous 
    testing bands to asses significance of sample deviation from a reference distribution <doi:10.18637/jss.v106.i10>.
Implements Quantile-on-Quantile Kernel-Based Regularized
    Least Squares (QQKRLS) as in Adebayo, Ozkan and Eweade (2024)
    <doi:10.1016/j.jclepro.2024.140832>. Combines Kernel-Based
    Regularized Least Squares (KRLS) of Hainmueller and Hazlett (2014)
    <doi:10.1093/pan/mpt019> with the Quantile-on-Quantile regression
    of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>: for
    each quantile theta of the independent variable the response is fit
    by KRLS on the corresponding sub-sample and the tau-quantile of the
    resulting pointwise marginal effects yields beta(theta, tau).
    Standard errors come from a paired bootstrap. Visualisations use
    the 'MATLAB' 'Parula' colour map by default.
Create Q-Q and manhattan plots for GWAS data from PLINK results.
Extensions of 'ggplot2' Q-Q plot functionalities.
A collection of routines for finding reference limits using, where 
    appropriate, QQ methodology.  All use a data vector X of cases from the 
    reference population. The default is to get the central 95% reference range 
    of the population, namely the 2.5 and 97.5 percentile, with optional 
    adjustment of the range.  Along with the reference limits, we want 
    confidence intervals which, for historical reasons, are typically at 90% 
    confidence.  A full analysis provides six numbers:
      – the upper and the lower reference limits, and 
      - each of their confidence intervals.
    For application details, see Hawkins and Esquivel (2024) 
    <doi:10.1093/jalm/jfad109>.
Provides the function qqtest which incorporates uncertainty in its
    qqplot display(s) so that the user might have a better sense of the
    evidence against the specified distributional hypothesis.  qqtest draws a
    quantile quantile plot for visually assessing whether the data come from a
    test distribution that has been defined in one of many ways.  The vertical
    axis plots the data quantiles, the horizontal those of a test distribution.
    The default behaviour generates 1000 samples from the test distribution and
    overlays the plot with shaded pointwise interval estimates for the ordered
    quantiles from the test distribution.  A small number of independently
    generated exemplar quantile plots can also be overlaid.  Both the interval
    estimates and the exemplars provide different comparative information to
    assess the evidence provided by the qqplot for or against the hypothesis
    that the data come from the test distribution (default is normal or
    gaussian).  Finally, a visual test of significance (a lineup plot) can also
    be displayed to test the null hypothesis that the data come from the test
    distribution.
Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.
This function performs QR factorization without pivoting to a real or complex matrix. It is based on Anderson. E. and ten others (1999) "LAPACK Users' Guide". Third Edition. SIAM.
Methods for detecting structural breaks, determining the
    number of breaks, and estimating break locations in linear quantile
    regression, using one or multiple quantiles, based on Qu (2008) and
    Oka and Qu (2011).  Applicable to both time series and repeated
    cross-sectional data. The main function is rq.break().

        References for detailed theoretical and empirical explanations:

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

        (2) Oka, T., and Qu, Z. (2011).  "Estimating Structural Changes in
    Regression Quantiles."  Journal of Econometrics, 162(2), 248-267
    <doi:10.1016/j.jeconom.2011.01.005>.
Functions are provided that implement the 
    use of the Fieller's formula methodology, for 
    calculating a confidence interval for a ratio of
    (commonly, correlated) means.  See Fieller (1954)
    <doi:10.1111/j.2517-6161.1954.tb00159.x>.  Here,
    the application of primary interest is to studies
    of insect mortality response to increasing doses
    of a fumigant, or, e.g., to time in coolstorage.
    The formula is used to calculate a confidence
    interval for the dose or time required to achieve
    a specified mortality proportion, commonly 0.5 
    or 0.99.  Vignettes demonstrate link functions
    that may be considered, checks on fitted models,
    and alternative choices of error family.  Note
    in particular the betabinomial error family.
    See also Maindonald, Waddell, and Petry (2001) 
    <doi:10.1016/S0925-5214(01)00082-5>.
Upload raster data and easily create interactive quantitative risk analysis 'QRA' visualizations. Select
    from numerous color palettes, base-maps, and different configurations.
A Quantile Rank-score based test for the identification of expression quantitative trait loci.
Parametric modeling of quantile regression coefficient functions.
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.
Create static QR codes in R. The content of the QR code is
    exactly what the user defines. We don't add a redirect URL, making it
    impossible for us to track the usage of the QR code. This allows to
    generate fast, free to use and privacy friendly QR codes.
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
Quick Response codes (QR codes) are a type of matrix bar code and can be
    used to authenticate transactions, provide access to multi-factor authentication
    services and enable general data transfer in an image. QR codes use four standardized 
    encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store 
    data. Matrix barcode generation is performed efficiently in C via the included
    'libqrencoder' library created by Kentaro Fukuchi.
Provides statistical components, tables, and graphs
    that are useful in 'Quarto' and 'RMarkdown' reports and that produce 'Quarto'
    elements for special formatting such as tabs and marginal notes and graphs.
    Some of the functions produce entire report sections with tabs, e.g.,
    the missing data report created by missChk().  Functions for inserting
		variables and tables inside 'graphviz' and 'mermaid' diagrams are included,
		and so are special clinical trial graphics for adverse event reporting.
The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individual’s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.
This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
A no-frills open-source solution for designing plot labels affixed with QR 
    codes. It features 'EasyQrlabelr', a 'BrAPI-compliant' 'shiny' app that 
    simplifies the process of plot label design for non-R users. It builds on the 
    methods described by Wu 'et al.' (2020) <doi:10.1111/2041-210X.13405>.
Quantile regression (QR) for Linear 
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference results 
             for the fixed-effects and variance components. 
             It also provides graphical summaries for assessing the algorithm 
             convergence and fitting results.
Provides functions/methods to accompany the book
 Quantitative Risk Management: Concepts, Techniques and Tools by
 Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
 zero-coupon bond yield curves, volatility, commodities) for Quantitative
 Risk Management practice.
Functions and data sets for reproducing selected results from
  the book "Quantitative Risk Management: Concepts, Techniques and Tools".
  Furthermore, new developments and auxiliary functions for Quantitative
  Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
  high dimensions.
Quantile regression (QR) for Nonlinear
             Mixed-Effects Models via the asymmetric Laplace distribution (ALD). 
             It uses the Stochastic Approximation of the EM (SAEM) algorithm for 
             deriving exact maximum likelihood estimates and full inference result
             is 
             for the fixed-effects and variance components.
             It also provides prediction and graphical summaries for assessing the algorithm
             convergence and fitting results.
Fit quantile regression neural network models with optional
    left censoring, partial monotonicity constraints, generalized additive
    model constraints, and the ability to fit multiple non-crossing quantile
    functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005>
    and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.
Execute multi-step 'SQL' workflows by
    leveraging specially formatted comments to define and control
    execution. This enables users to mix queries, commands, and metadata
    within a single script. Results are returned as named objects for use
    in downstream workflows.
Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.
Molecular descriptors and outcomes for several public domain data sets
Computes feature-specific R-squared (R2) contributions for
    boosting tree models using a Shapley-value-based decomposition of the
    total R-squared in polynomial time. Supports models fitted with
    'XGBoost', 'LightGBM', and 'CatBoost', with optimized backend-specific
    implementations and cached tree summaries suitable for large-scale
    problems. Multiple
    visualization tools are included for interpreting and communicating
    feature contributions. The methodology is described in Jiang, Zhang,
    and Zhang (2025) <doi:10.48550/arXiv.2407.03515>. Optional 'CatBoost'
    support uses the R package 'catboost', which is not distributed on CRAN;
    installation instructions and released binaries are provided by the
    CatBoost project at <https://catboost.ai/docs/en/concepts/r-installation>.
A quantum computer simulator framework with up to 24 qubits. It allows to
    define general single qubit gates and general controlled single
    qubit gates. For convenience, it currently provides the
    most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or
    CCNOT, Fredkin or CSWAP). 'qsimulatR' also implements noise models.
    'qsimulatR' supports plotting of circuits and is able to
    export circuits to 'Qiskit' <https://qiskit.org/>, a python package
    which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.
Implementations of the quantile slice sampler of Heiner et al. (2024+, in preparation) as well as other popular slice samplers are provided. 
    Helper functions for specifying pseudo-target distributions are included, both for diagnostics and for tuning the quantile slice sampler. 
    Other implemented methods include the generalized elliptical slice sampler of Nishihara et al. (2014)<https://jmlr.org/papers/v15/nishihara14a.html},
    latent slice sampler of Li and Walker (2023)<doi:10.1016/j.csda.2022.107652>, and stepping-out slice sampler of Neal (2003)<doi:10.1214/aos/1056562461>,
    and independence Metropolis-Hastings sampler.
Provides routines to create some quaternions splines:
    Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels
    algorithm. The implementations are based on the Python library
    'splines'. Quaternions splines allow to construct spherical curves. 
    References: Barry and Goldman <doi:10.1145/54852.378511>, 
    Kochanek and Bartels <doi:10.1145/800031.808575>.
A collection of utility functions for community ecology analyses,
    with emphasis on workflows using the 'phyloseq' and 'vegan' packages.
    Includes functions for normalizing OTU tables, computing alpha diversity
    via rarefaction (using a fast C++ implementation), differential abundance
    comparisons with compact letter displays, primer checking for amplicon
    sequencing, plotting QIIME 2/DADA2 generated transition stats and miscellaneous helpers for ordination plots and taxonomic
    name formatting.
Provides functions for quickly writing (and 
  reading back) a data.frame to file in 'SQLite' format. The name 
  stands for *Store Tables using 'SQLite'*, or alternatively for *Quick 
  Store Tables* (either way, it could be pronounced as *Quest*). 
  For data.frames containing the supported data 
  types it is intended to work as a drop-in replacement for the 
  'write_*()' and 'read_*()' functions provided by similar packages.
Run lapply() calls in parallel by submitting them to 
    'gridengine' clusters using the 'qsub' command.
Compute the Quantum Toxic Burden Index from multi-exposure
    panels using a fixed quantum-inspired entanglement encoder; method
    reference (2026) <doi:10.5281/zenodo.20476574>. Provides percentile
    encoding, optional potency-weighted readout, and synergy diagnostics
    for environmental mixture burden scores.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).
Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.
Support package for the textbook "An Introduction to
    Quantitative Text Analysis for Linguists: Reproducible Research Using
    R" (Francom, 2024) <doi:10.4324/9781003393764>. Includes functions to
    acquire, clean, and analyze text data as well as functions to document
    and share the results of text analysis. The package is designed to be
    used in conjunction with the book, but can also be used as a standalone
    package for text analysis.
Analysis of experimental crosses to identify genes
  (called quantitative trait loci, QTLs) contributing to variation in
  quantitative traits.
  Broman et al. (2003) <doi:10.1093/bioinformatics/btg112>.
Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.
Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.
Provides a set of tools to perform quantitative
    trait locus (QTL) analysis in experimental crosses. It is a
    reimplementation of the 'R/qtl' package to better handle
    high-dimensional data and complex cross designs.
    Broman et al. (2019) <doi:10.1534/genetics.118.301595>.
Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).
Uses the 'fst' package to store genotype probabilities on disk for the 'qtl2' package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time.
Functions to plot QTL (quantitative trait loci) analysis
    results and related diagnostics.
    Part of 'qtl2', an upgrade of the 'qtl'
    package to better handle high-dimensional data and complex cross
    designs.
Routines in 'qtl2' to study allele patterns in quantitative trait loci (QTL)
    mapping over a chromosome.
    Useful in crosses with more than two alleles to identify how sets of alleles,
    genetically different strands at the same locus, have different response levels.
    Plots show profiles over a chromosome.
    Can handle multiple traits together.
    See <https://github.com/byandell/qtl2pattern>.
We implement an
    adaptation of Jiang & Zeng's (1995) <doi:10.1093/genetics/140.3.1111> likelihood ratio test for testing
    the null hypothesis of pleiotropy against the alternative hypothesis,
    two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995)
    and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in
    that our test accommodates multiparental populations.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
    Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
Web-based interactive charts (using D3.js) for the analysis of
    experimental crosses to identify genetic loci (quantitative trait
    loci, QTL) contributing to variation in quantitative traits.
    Broman (2015) <doi:10.1534/genetics.114.172742>.
Design of QTL (quantitative trait locus) experiments
  involves choosing which strains to cross, the type of cross,
  genotyping strategies, phenotyping strategies, and the number of
  progeny to raise and phenotype. This package provides tools to help
  make such choices.
  Sen and others (2007) <doi:10.1007/s00335-006-0090-y>.
Functions to infer co-mapping trait hotspots and causal models.
  Chaibub Neto E, Keller MP, Broman AF, Attie AD, Jansen RC, Broman KW, Yandell BS (2012) 
  Quantile-based permutation thresholds for QTL hotspots. Genetics 191 : 1355-1365. 
  <doi:10.1534/genetics.112.139451>.
  Chaibub Neto E, Broman AT, Keller MP, Attie AD, Zhang B, Zhu J, Yandell BS (2013)
  Modeling causality for pairs of phenotypes in system genetics. Genetics 193 : 1003-1013.
  <doi:10.1534/genetics.112.147124>.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture.
  Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by
  a few genetic loci (QTL).
  Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010)
  Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.
  Annals of Applied Statistics 4: 320-339.
  <doi:10.1214/09-AOAS288>.
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Functions for unconditional and conditional quantiles. These
    include methods for transformation-based quantile regression,
    quantile-based measures of location, scale and shape, methods for quantiles
    of discrete variables, quantile-based multiple imputation, restricted
    quantile regression, directional quantile classification, and quantile
	ratio regression.
	A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Provides three 'Quarto' website templates as an R project, which are commonly used by academics.
    Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization.
A tool that can be customized to aid in the clean up of ecological data 
    collected using quadrats and can crop quadrats to ensure comparability between
    quadrats collected under different methodologies.
A range of quadratic forms are evaluated, using efficient methods.
 Unnecessary transposes are not performed.  Complex values are handled
 consistently.
A set of functions of increasing complexity allows users to
  (1) convert QuadKey-identified datasets, 
  based on 'Microsoft's Bing Maps Tile System', into Simple Features data frames,
  (2) transform Simple Features data frames into rasters, and 
  (3) process multiple 'Meta' ('Facebook') QuadKey-identified human mobility files
  directly into raster files.
  For more details, see D’Andrea et al. (2024) <doi:10.21105/joss.06500>.
Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.
Create surface forms from matrix or 'raster' data for flexible plotting and
 conversion to other mesh types. The functions 'quadmesh' or 'triangmesh'
 produce a continuous surface as a 'mesh3d' object as used by the 'rgl'
 package. This is used for plotting raster data in 3D (optionally with
 texture), and allows the application of a map projection without data loss and 
 many processing applications that are restricted by inflexible regular grid rasters.
 There are discrete forms of these continuous surfaces available with
 'dquadmesh' and 'dtriangmesh' functions.
This package contains routines and documentation for
        solving quadratic programming problems.
Extends the quadprog package to solve quadratic programs with
  absolute value constraints and absolute values in the objective function.
Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) 
              to guess interactively the mean and the mean deviation. Thereafter,
              using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) 
              and visualize the standard deviation (SD). 
              For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, 
              Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.
It includes test for multivariate normality, test for uniformity on the d-dimensional 
    Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson 
    kernel-based density and clustering algorithm for spherical data. For more information see
    Saraceno G., Markatou M., Mukhopadhyay R. and Golzy M. (2024)
    <doi:10.48550/arXiv.2402.02290>
    Markatou, M. and Saraceno, G. (2024) <doi:10.48550/arXiv.2407.16374>, 
    Ding, Y., Markatou, M. and Saraceno, G. (2023) <doi:10.5705/ss.202022.0347>, 
    and Golzy, M. and Markatou, M. (2020) <doi:10.1080/10618600.2020.1740713>.
It will assist the user to find simple quadratic roots from any quadratic equation.
Fits the solution paths of classical sparse regression models with efficient active set algorithms by solving small sub-problems. Include LASSO, SCAD, MCP, (Sparse) Group-LASSO, Cooperative-LASSO, (Group) LAVA, (Generalized) Fused-Lasso and (Generalized) Elastic-Net. Also provides methods for model selection purpose (information criteria, cross-validation, stability selection).
Estimate quadratic vector autoregression models with the
    strong hierarchy using the Regularization Algorithm under Marginality
    Principle (RAMP) by Hao et al. (2018)
    <doi:10.1080/01621459.2016.1264956>, compare the performance with
    linear models, and construct networks with partial derivatives.
Provides convenience utilities for using 'DuckDB' directly over
    datasets stored in 'Azure Data Lake Storage Gen2' (ADLS Gen2,
    'abfss://'). Opens connections configured for Azure-backed 'Delta Lake'
    and 'Parquet' data, registers Azure credentials as 'DuckDB' secrets, and
    supports optional repository mirrors for restricted networks. Integrates
    well with 'DBI' for SQL workflows and with 'dplyr' and 'dbplyr' for lazy
    table queries.
An easy framework to set a quality control workflow on a dataset.
  Includes a various range of functions that allow to establish an adaptable 
  data quality control.
Quality of care is compared across accountable entities, including hospitals, provider groups, and insurance plans, using standardized quality measures. However, observed variations in quality measure performance might be the result of chance sampling or measurement errors. Contains functions for estimating the reliability of unadjusted and risk-standardized quality measures.
Tools for AI-assisted qualitative data coding using large language
    models ('LLMs') via the 'ellmer' package, supporting providers including
    'OpenAI', 'Anthropic', 'Google', 'Azure', and local models via 'Ollama'.
    Provides a 'codebook'-based workflow for defining coding instructions and
    applying them to texts, images, and other data. Includes built-in 'codebooks'
    for common applications such as sentiment analysis and policy coding, and
    functions for creating custom 'codebooks' for specific research questions.
    Supports systematic replication across models and settings, computing
    inter-coder reliability statistics including Krippendorff's alpha
    (Krippendorff 2019, <doi:10.4135/9781071878781>) and Fleiss' kappa
    (Fleiss 1971, <doi:10.1037/h0031619>), as well as gold-standard validation
    metrics including accuracy, precision, recall, and F1 scores following
    Sokolova and Lapalme (2009, <doi:10.1016/j.ipm.2009.03.002>). Provides audit
    trail functionality for documenting coding workflows following Lincoln and
    Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness
    in qualitative research.
Companion package to 'quallmer' providing an interactive 'shiny'
    application for manual coding, reviewing large language model (LLM)
    generated annotations, and computing inter-rater reliability metrics.
    Supports three modes: blind manual coding, LLM output validation, and
    agreement calculation. Computes standard reliability metrics including
    Krippendorff's alpha (Krippendorff 2019 <doi:10.4135/9781071878781>),
    Cohen's kappa, Fleiss' kappa (Fleiss 1971 <doi:10.1037/h0031619>),
    intraclass correlation coefficient (ICC), and percent agreement for
    nominal, ordinal, interval, and ratio data. Also computes gold-standard
    validation metrics including accuracy, precision, recall, and F1 scores
    following Sokolova and Lapalme (2009 <doi:10.1016/j.ipm.2009.03.002>).
Provides a set of functions for taking qualitative GIS data, hand drawn on a map, and 
   converting it to a simple features object. These tools are focused on data that are drawn on a map
   that contains some type of polygon features. For each area identified on the map, the id numbers
   of these polygons can be entered as vectors and transformed using qualmap.
Automatic generation of maximally distinct qualitative color palettes,
    optionally tailored to color deficiency. A set of colors or a subspace of a
    color space is used as input and a final palette of specified size is
    generated by picking colors that maximize the minimum pairwise difference
    among the chosen colors. Adaptations to color vision deficiency,
    background colors, and white points are supported.
Provides functions to access survey results directly into R
    using the 'Qualtrics' API. 'Qualtrics'
    <https://www.qualtrics.com/about/> is an online survey and data
    collection software platform. See <https://api.qualtrics.com/> for
    more information about the 'Qualtrics' API.  This package is
    community-maintained and is not officially supported by 'Qualtrics'.
Qualitative methods for the validation of dynamic models.
    It contains (i) an orthogonal set of deviance measures for absolute,
    relative and ordinal scale and (ii) approaches accounting for time
    shifts. The first approach transforms time to take time delays and speed
    differences into account. The second divides the time series into
    interval units according to their main features and finds the longest
    common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox
    (1973).
These functions apply an analysis of variance to incomplete ensembles of climate projections.
    It provides estimates of climate change responses of all simulation chains and of all uncertainty
    variables. It has been applied to different ensembles of projections simulated to study the impact of climate change: 
    for climate indicators in Evin et al. (2019) <doi:10.1175/JCLI-D-18-0606.1>; seasonal precipitation
    and temperature in Evin, Somot and Hingray (2021) <doi:10.5194/esd-12-1543-2021>; hydrological variables
    in Evin et al. (2026) <doi:10.5194/hess-30-1023-2026>; photovoltaic energy in Bichet et al. (2019)
    <doi:10.1088/1748-9326/ab500a>.
Implements quantile-based discriminant analysis (QuanDA) for
    imbalanced classification in high-dimensional, low-sample-size settings.
    The method fits penalized quantile regression directly on discrete class labels and
    tunes the quantile level to reflect class imbalance.
Implements the Quantification Evidence Standard algorithm for computing
    Bayesian evidence sufficiency from binary evidence matrices. It provides
    posterior estimates, credible intervals, percentiles, and optional visual
    summaries. The method is universal, reproducible, and independent of
    any specific clinical or rule based framework. For details see The Quantitative Omics Epidemiology Group et al. (2025) <doi:10.64898/2025.12.02.25341503>.
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.
Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.
An implementation of dimension reduction techniques
    for conditional quantiles. Nonparametric estimation of 
    conditional quantiles is also available.
A fast, flexible, and comprehensive framework for 
    quantitative text analysis in R.  Provides functionality for corpus management,
    creating and manipulating tokens and n-grams, exploring keywords in context, 
    forming and manipulating sparse matrices
    of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
    distances, applying content dictionaries, applying supervised and unsupervised machine learning, 
    visually representing text and text analyses, and more.
Scaling models and classifiers for sparse matrix objects representing 
    textual data in the form of a document-feature matrix.  Includes original 
    implementations of 'Laver', 'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
    'Wordscores' model, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, 
    and the 'Slapin' and 'Proksch' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish'
    model, as well as methods for correspondence analysis, latent semantic analysis,
    and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.
Plotting functions for visualising textual data.  Extends 'quanteda' and 
   related packages with plot methods designed specifically for text data, textual statistics, 
   and models fit to textual data. Plot types include word clouds, lexical dispersion plots, 
   scaling plots, network visualisations, and word 'keyness' plots.
Textual statistics functions formerly in the 'quanteda' package.
    Textual statistics for characterizing and comparing textual data. Includes 
    functions for measuring term and document frequency, the co-occurrence of 
    words, similarity and distance between features and documents, feature entropy, 
    keyword occurrence, readability, and lexical diversity.  These functions 
    extend the 'quanteda' package and are specially designed for sparse textual data.
Enables 'tidyverse' operations on 'quanteda' corpus objects by extending 'dplyr' verbs to work directly with corpus objects and their document-level variables ('docvars'). Implements row operations for 'subsetting' and reordering documents; column operations for managing document variables; grouped operations; and two-table verbs for merging external data. For more on 'quanteda' see 'Benoit et al.' (2018) <doi:10.21105/joss.00774>. For 'dplyr' see 'Wickham et al.' (2023) <doi:10.32614/CRAN.package.dplyr>.
Provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach, the balance approach and the conditional expectations method.
Code for centroid, median and quantile classifiers.
Functions for simulation, estimation, and model selection of
        finite mixtures of Tukey g-and-h distributions.
Estimation of different quantile models, at the moment only Conditional autoregressive value at risk (CAViaR) proposed by Engle & Manganelli (2004) <doi:10.1198/073500104000000370> with also the specification proposed in Huang et al. (2009) <doi:10.1016/j.eneco.2008.12.006> and it's multivariate extension, Multi-variate multi-quantile CAViaR (MVMQ-CAViaR) proposed by White et al. (2015) <doi:10.1016/j.jeconom.2015.02.004> are available, however, in further updates, other models and extensions will be included.
Based on Alan D. Hutson (1999) <doi:10.1080/02664769922458>, "Calculating nonparametric confidence intervals for quantiles using fractional order statistics", Journal of Applied Statistics, 26:3, 343-353.
Implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>. QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with 'MATLAB'-style color schemes ('Jet', 'Viridis', 'Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses 'quantreg' for quantile regression and 'plotly' for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions.
Simulating and estimating peer effect models including the quantile-based specification (Houndetoungan, 2025 <doi:10.48550/arXiv.2506.12920>), and the models with Constant Elasticity of Substitution (CES)-based social norm (Boucher et al., 2024 <doi:10.3982/ECTA21048>).
Estimation and inference methods for the cross-quantilogram.
    The cross-quantilogram is a measure of nonlinear dependence between
    two variables, based on either unconditional or conditional quantile
    functions.  It can be considered an extension of the correlogram,
    which is a correlation function over multiple lag periods that mainly
    focuses on linear dependency.  One can use the cross-quantilogram to
    detect the presence of directional predictability from one time series
    to another.  This package provides a statistical inference method
    based on the stationary bootstrap.  For detailed theoretical and
    empirical explanations, see Linton and Whang (2007) for univariate
    time series analysis and Han, Linton, Oka and Whang (2016) for
    multivariate time series analysis.  The full references for these key
    publications are as follows: (1) Linton, O., and Whang, Y. J. (2007).
    The quantilogram: with an application to evaluating directional
    predictability.  Journal of Econometrics, 141(1), 250-282
    <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T.,
    and Whang, Y. J. (2016).  The cross-quantilogram: measuring quantile
    dependence and testing directional predictability between time series.
    Journal of Econometrics, 193(1), 251-270
    <doi:10.1016/j.jeconom.2016.03.001>.
Integration of the 'units' and 'errors' packages for a complete
    quantity calculus system for R vectors, matrices and arrays, with automatic
    propagation, conversion, derivation and simplification of magnitudes and
    uncertainties. Documentation about 'units' and 'errors' is provided in the
    papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
    by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
    those packages as vignettes; see 'citation("quantities")' for details.
A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>.  With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems.  In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.
Specify, build, trade, and analyse quantitative financial trading strategies.
Contains tools useful for data screening, testing
        moderation (Cohen et. al. 2003)<doi:10.4324/9780203774441>, 
        mediation (MacKinnon et. al. 2002)<doi:10.1037/1082-989x.7.1.83> 
        and estimating power (Murphy & Myors 2014)<ISBN:9781315773155>.
Estimation and inference methods for models for conditional quantile functions: 
  Linear and nonlinear parametric and non-parametric (total variation penalized) models 
  for conditional quantiles of a univariate response and several methods for handling
  censored survival data.  Portfolio selection methods based on expected shortfall
  risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press,
  <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, 
  CRC Press, <doi:10.1201/9781315120256>.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
Quantile Regression Forests is a tree-based ensemble
        method for estimation of conditional quantiles. It is
        particularly well suited for high-dimensional data. Predictor
        variables of mixed classes can be handled. The package is
        dependent on the package 'randomForest', written by Andy Liaw.
Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying
  coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded
  in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter 
  selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by
  HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies.
  (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).
Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties.  
    Random intercepts and variable selection are allowed via the lasso penalties.
    The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity 
    constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, 
    and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.
This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
A sigmoidal quantile function estimator based on a newly defined 
    generalized expectile function. The generalized sigmoidal quantile function 
    can estimate quantiles beyond the range of the data, which is important for 
    certain applications given smaller sample sizes. The package is based on the 
    method introduced in Hutson (2024) <doi:10.1080/03610918.2022.2032161>.
Methods to determine, smooth and plot quantile periodograms for
    univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03>
    for a description and tutorial.
A wrapper around the 'quaqc' program described in Tremblay and
    Questa (2024) <doi:10.1093/bioinformatics/btae649>. 'quaqc' allows for
    assay for transposase-accessible chromatin using sequencing (ATAC-seq)
    specific quality control and read filtering of next-generation sequencing
    (NGS) data with minimal processing time and extremely low memory overhead.
    Any number of samples can be processed, using multiple threads if desired.
    'quaqc' outputs a comprehensive set of aligned read metrics, including
    alignment size, fragment size, percent duplicates, mapq scores, read depth,
    GC content, and others. Although designed for ATAC-seq data, 'quaqc' can
    also be used for other unspliced DNA sequencing experiments (such as
    chromatin immunoprecipitation sequencing, or ChIP-seq) as many of the
    metrics are related to general sequencing quality. This R package also
    provides additional utilities for custom analyses and plotting of 'quaqc'
    results.
Enables the user to calculate Value at Risk (VaR)
    and Expected Shortfall (ES) by means of various types of historical
    simulation. Currently plain-, age-, volatility-weighted- and filtered
    historical simulation are implemented in this package. Volatility weighting
    can be carried out via an exponentially weighted moving average model
    (EWMA) or other GARCH-type models. The performance can be assessed via
    Traffic Light Test, Coverage Tests and Loss Functions. The methods of the
    package are described in Gurrola-Perez, P. and Murphy, D. (2015)
    <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J.,
    Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.
An implementation of two interaction indices between extractive
    activity and groundwater resources based on hazard and vulnerability
    parameters used in the assessment of natural hazards. One index is based
    on a discrete choice model and the other is relying on an artificial
    neural network.
Dynamically generate tabset panels
    <https://quarto.org/docs/output-formats/html-basics.html#tabsets> in
    'Quarto' HTML documents using a data frame as input.
Calculates the number of four-taxon subtrees consistent with a pair
  of cladograms, calculating the symmetric quartet distance of Bandelt & Dress
  (1986), Reconstructing the shape of a tree from observed dissimilarity data,
  Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, 
  and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for
  computing the quartet and triplet distances between binary or general trees,
  Bioinformatics, 30, 2079–2080 <doi:10.1093/bioinformatics/btu157>
  for pairs of binary trees.
In the spirit of Anscombe's quartet, this package includes datasets
   that demonstrate the importance of visualizing your data, the importance of 
   not relying on statistical summary measures alone, and why additional 
   assumptions about the data generating mechanism are needed when estimating 
   causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>,
   D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, 
   "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and
   Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
Converts R scripts (.R) into 'Quarto' markdown documents (.qmd) with
    automatic formatting. Recognizes 'RStudio' code sections, preserves comments as
    narrative text, extracts metadata from special comments, and provides both
    programmatic functions and an interactive 'RStudio' add-in for easy conversion.
Convert R Markdown documents and 'Jupyter' notebooks to a
    variety of output formats using 'Quarto'.
Provides helper functions to work programmatically within a quarto document. It allows 
    the user to create section headers, tabsets, divs, and spans, and formats these objects
    into quarto syntax when printed into a document.
The approach is based on the closed testing procedure to control familywise error rate in a strong sense.
  The local tests implemented are Wald-type and rank-score. 
  The method is described in De Santis, et al., (2026), <doi:10.48550/arXiv.2511.07999>.
Functions for estimating the potential dispersal of tree species 
  using regeneration densities and dispersal distances to nearest seed trees. 
  A quantile regression is implemented to determine the dispersal potential.
  Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.
Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883  <doi:10.1214/19-EJS1640>.
Syntax for defining complex filtering expressions in a programmatic way.
  A filtering query, built as a nested list configuration, can be easily stored in other formats like 'YAML' or 'JSON'.
  What's more, it's possible to convert such configuration to a valid expression that can be applied to 
  popular 'dplyr' package operations.
Adds an LLM-powered chatbot to your 'shiny' app, that can
    turn your users' natural language questions into 'SQL' queries that
    run against your data, and return the result as a reactive data frame.
    Use it to drive reactive calculations, visualizations, downloads, and
    more.
Translate 'SQL' 'SELECT' statements into lists of 'R' expressions.
Retrieve protein information from
    the 'UniProtKB' REST API (see <https://www.uniprot.org/help/api_queries>).
Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.
Set of functions to make the processing and analysis of
    surveys easier : interactive shiny apps and addins for data recoding,
    contingency tables, dataset metadata handling, and several convenience
    functions.
A method for prediction of environmental conditions based on 
    transcriptome data linked with the environmental gradients. This package 
    provides functions to overview gene-environment relationships, to construct 
    the prediction model, and to predict environmental conditions where the 
    transcriptomes were generated. This package can quest for candidate genes 
    for the model construction even in non-model organisms' transcriptomes 
    without any genetic information.
Implementation of a computationally efficient method for
    simulating queues with arbitrary arrival and service times. 
    Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) 
    for further details.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
    and Single and Multiclass Product-Form Queueing Networks.
    It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
    Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
    Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
    and Single Channel Multiple Class Mixed Networks.
    Also it provides a B-Erlang, C-Erlang and Engset calculators.
    This work is dedicated to the memory of D. Sixto Rios Insua.
Provides functions for assigning treatments in randomized experiments using
    near-optimal threshold blocking. The package is made with large data sets in
    mind and derives blocks more than an order of magnitude quicker than other
    methods.
Property based testing, inspired by 
    the original 'QuickCheck'. This package builds on 
    the property based testing framework provided by 
    'hedgehog' and is designed to seamlessly integrate with 
    'testthat'.
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.
A 'Shiny' application that provides nice interface for
    browsing, exploring, summarising, and converting datasets stored in 'SAS'
    (.sas7bdat, .xpt), CSV (.csv), and 'R' (.rds) formats.  Users can register
    multiple directory-based libraries, interactively filter data using 'dplyr'
    expressions, inspect per-variable statistics, and export datasets to Excel,
    JSON, CSV, 'R' data, or 'SAS' transport formats.
An 'R' interface to the 'QuickJS' portable 'JavaScript'
    engine.  The engine and all 'R' to 'JavaScript' interoperability is bundled
    within the package, requiring no dependencies beyond a 'C' compiler.
Provides functions for constructing near-optimal generalized full matching.
    Generalized full matching is an extension of the original full matching method
    to situations with more intricate study designs. The package is made with
    large data sets in mind and derives matches more than an order of magnitude
    quicker than other methods.
For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".
Implements a suite of tools for outlier detection and treatment 
    in data mining. It includes univariate methods (Z-score, Interquartile Range), 
    multivariate detection using Mahalanobis distance, and density-based 
    detection (Local Outlier Factor) via the 'dbscan' package. It also 
    provides functions for visualization using 'ggplot2' and data cleaning 
    via Winsorization.
A high-level plotting system, compatible with `ggplot2` objects, 
    maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the 
    'grid' package. The objective of the package is to provide a plotting system 
    that is built for speed and modularity. This is useful for quick visualizations 
    when testing code and for plotting multiple figures to the same device from 
    independent sources that may be independent of one another (i.e., different 
    function or modules the create the visualizations).
Quickly fits and plots psychometric functions (normal, logistic,
    Weibull or any or any function defined by the user) for multiple groups.
A collection of functions for constructing large pairwised comparisons 
             and rating them using Elo rating system with supporting parallel processing.
             The method of random sample pairs is based on Reservoir Sampling proposed by 
             JVitter (1985) <doi:10.1145/3147.3165>.
Compile R functions annotated with type and shape
    declarations for extremely fast performance and robust runtime
    type checking. Supports both just-in-time (JIT) and ahead-of-time
    (AOT) compilation. Compilation is performed by lowering R code to
    Fortran.
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.
A high-level pipeline that simplifies text classification into three streamlined steps: 
    preprocessing, model training, and standardized prediction. 
    It unifies the interface for multiple algorithms (including 'glmnet', 'ranger', 
    'xgboost', and 'naivebayes') and memory-efficient sparse matrix vectorization 
    methods (Bag-of-Words, Term Frequency, TF-IDF, and Binary). Users can go from 
    raw text to a fully evaluated sentiment model, complete with ROC-optimized 
    thresholds, in just a few function calls. The resulting model artifact 
    automatically aligns the vocabulary of new datasets during the prediction phase, 
    safely appending predicted classes and probability matrices directly to the 
    user's original dataframe to preserve metadata.
A toolkit for analysis and visualization of data from fluorophore-assisted seed amplification assays, such as Real-Time Quaking-Induced Conversion (RT-QuIC) and Fluorophore-Assisted Protein Misfolding Cyclic Amplification (PMCA). 'QuICSeedR' addresses limitations in existing software by automating data processing, supporting large-scale analysis, and enabling comparative studies of analysis methods. It incorporates methods described in Henderson et al. (2015) <doi:10.1099/vir.0.069906-0>, Li et al. (2020) <doi:10.1038/s41598-021-96127-8>, Rowden et al. (2023) <doi:10.3390/pathogens12020309>, Haley et al. (2013) <doi:10.1371/journal.pone.0081488>, and Mair and Wilcox (2020) <doi:10.3758/s13428-019-01246-w>. Please refer to the original publications for details.
Test whether equality and order constraints hold for all 
    individuals simultaneously by comparing Bayesian mixed models through Bayes 
    factors. A tutorial style vignette and a quickstart guide are available, via
    vignette("manual", "quid"), and vignette("quickstart", "quid") respectively.
    See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder
    (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.
Provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters."
Simplifies output suppression logic in R packages, as it's common
    to develop some form of it in R. 'quietR' intends to simplify that problem 
    and allow a set of simple toggle functions to be used to suppress console 
    output.
Functions to convert text data for labelling into format appropriate for importing into Qualtrics. Supports multiple language, including right-to-left scripts as well as different response types. Outputs an Advance Format .txt file that read into Qualtrics.
Programmatic access to the 'PGS' Catalog.
    This package provides easy access to 'PGS' Catalog data by
    accessing the REST API <https://www.pgscatalog.org/rest/>.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial. More information in Dusseldorp, E., Doove, L., & Van Mechelen, I. (2016) <doi:10.3758/s13428-015-0594-z>.
Functions to compute Euclidean minimum spanning trees using single-,
    sesqui-, and dual-tree Boruvka algorithms.  Thanks to K-d trees, they are
    fast in spaces of low intrinsic dimensionality.  Mutual reachability
    distances (used in the definition of the 'HDBSCAN*' algorithm)
    are supported too.  The package also includes relatively fast fallback
    minimum spanning tree and nearest-neighbours algorithms for spaces of
    higher dimensionality.  The 'Python' version of 'quitefastmst' is available
    via 'PyPI'.
To construct a model in 2-D space from 2-D nonlinear
    dimension reduction data and then lift it to the high-dimensional
    space. Additionally, provides tools to visualise the model overlay the
    data in 2-D and high-dimensional space. Furthermore, provides
    summaries and diagnostics to evaluate the nonlinear dimension
    reduction layout.
Collect  your data on digital marketing campaigns from 'Quora' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Full text, in data frames containing one row per verse, of the 
    Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali 
    and Saheeh International translations), formatted to be convenient for 
    text analysis.
High-throughput analysis of growth curves and fluorescence
    data using three methods: linear regression, growth model fitting, and
    smooth spline fit. Analysis of dose-response relationships via
    smoothing splines or dose-response models. Complete data analysis
    workflows can be executed in a single step via user-friendly wrapper
    functions. The results of these workflows are summarized in detailed
    reports as well as intuitively navigable 'R' data containers. A 'shiny'
    application provides access to all features without
    requiring any programming knowledge. The package is described in further
    detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.
Provide a variety of Q-matrix validation methods for the generalized
    cognitive diagnosis models, including the method based on the generalized
    deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011)
    <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by
    de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method
    by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method
    (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the
    multiple logistic regression‑based Q‑matrix validation method (the MLR-B method)
    by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on
    signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and
    Q-matrix validation based on relative fit index by Chen et al. (2013)
    <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative
    procedures during Q-matrix validating are available
    <DOI:10.3758/s13428-024-02547-5>.
Finding hidden clusters in structured data can be hindered
  by the presence of masking variables. If not detected,
  masking variables are used to calculate the overall similarities between units, 
  and therefore the cluster attribution is more imprecise.
  The algorithm q-vars implements an optimization method to find the variables
  that most separate units between clusters. In this way, masking variables can be 
  discarded from the data frame and the clustering is more accurate.
  Tests can be found in Benati et al.(2017) <doi:10.1080/01605682.2017.1398206>.
Functions to compute quasi variances and associated measures of approximation error.
Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.
Functions and tools for creating, visualizing, and investigating properties of continuous-time quantum walks, including efficient calculation of matrices such as the mixing matrix, average mixing matrix, and spectral decomposition of the Hamiltonian. E. Farhi (1997): <arXiv:quant-ph/9706062v2>; C. Godsil (2011) <arXiv:1103.2578v3>.
The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation. A paper is attached to the package for reference.
A collection of (wrapper) functions the creator found useful
    for quickly placing data summaries and formatted regression results into
    '.Rnw' or '.Rmd' files. Functions for generating commonly used graphics,
    such as receiver operating curves or Bland-Altman plots, are also provided
    by 'qwraps2'.  'qwraps2' is an updated version of a package 'qwraps'. The
    original version 'qwraps' was never submitted to CRAN but can be found at
    <https://github.com/dewittpe/qwraps/>. The implementation and limited scope
    of the functions within 'qwraps2' <https://github.com/dewittpe/qwraps2/> is
    fundamentally different from 'qwraps'.
Generalized eigenvalues and eigenvectors
        use QZ decomposition (generalized Schur decomposition).
        The decomposition needs an N-by-N non-symmetric
        matrix A or paired matrices (A,B) with eigenvalues reordering
        mechanism. The decomposition functions are mainly based Fortran
        subroutines in complex*16 and double precision of LAPACK
        library (version 3.10.0 or later).
A collection of personal functions designed to simplify and
    streamline common R programming tasks. This package provides reusable
    tools and shortcuts for frequently used calculations and workflows.
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Common Dimensions (ComDim) is a multi-block method that simultaneously
    considers multiple data tables to find latent components that are common
    to all the tables as well as those specific to each data table, along
    with the contribution of each table to each component.
    See Jouan-Rimbaud Bouveresse and Rutledge (2024)
    <doi:10.1002/cem.3454>, Boccard and Rutledge (2013)
    <doi:10.1016/j.aca.2013.01.022>, and Puig-Castellví et al. (2021)
    <doi:10.1016/j.chemolab.2021.104422>.
Functions for creating plots and image files in a unified way
    regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.). Default
    device options as well as scales and aspect ratios are controlled in a uniform
    way across all device types. Switching output format requires minimal changes
    in code. This package is ideal for large-scale batch processing, because it
    will never leave open graphics devices or incomplete image files behind, even on
    errors or user interrupts.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
Performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
The use of proxies is required in certain network
    environments.  Despite the power of system level software, it is still
    inconvenient to switch proxy networks at random in R's console. This
    package is designed to provide one-click switching between proxy and
    non-proxy states.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
An integrated solution to perform
    a series of text mining tasks such as importing and cleaning a corpus, and
    analyses like terms and documents counts, lexical summary, terms
    co-occurrences and documents similarity measures, graphs of terms,
    correspondence analysis and hierarchical clustering. Corpora can be imported
    from spreadsheet-like files, directories of raw text files,
    as well as from 'Dow Jones Factiva', 'LexisNexis', 'Europresse' and 'Alceste' files.
Utility functions useful when programming and developing R packages.
Estimation of reproduction numbers for disease outbreak, based on
    incidence data. The R0 package implements several documented methods. It is
    therefore possible to compare estimations according to the methods used.
    Depending on the methods requested by user, basic reproduction number
    (commonly denoted as R0) or real-time reproduction number (referred to as
    R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
    will give different graphs depending on the methods requested : basic
    reproductive number estimations will only show the epidemic curve
    (collected data) and an adjusted model, whereas real-time methods will also
    show the R(t) variations throughout the outbreak time period. Sensitivity
    analysis tools are also provided, and allow for investigating effects of
    varying Generation Time distribution or time window on estimates.
This is a companion package of the book "R Programming: Zero to Pro"  <https://r02pro.github.io/>. It contains the datasets used in the book and provides interactive exercises corresponding to the book. It covers a wide range of topics including visualization, data transformation, tidying data, data input and output.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
R^2 measure of explained variation under the semiparametric additive hazards model is estimated. The measure can be used as a measure of predictive capability and therefore it can be adopted in model selection process. Rava, D. and Xu, R. (2020) <arXiv:2003.09460>.
A series of functions to call 'AD Model Builder' (i.e.,
    compile and run models) from within R, read the results back
    into R as 'admb' objects, and provide standard accessors (i.e.
    coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
Multivariate optimal allocation for different domains in one and two stages stratified sample design. 'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from <https://github.com/DiegoZardetto/ReGenesees>.
Builds Camera Trap Data Packages ('Camtrap DP') from arbitrary spreadsheets in a schema-driven way: table structure, types, constraints and relations are read from the 'Frictionless' table schemas of the requested 'Camtrap DP' version, so any version and custom columns are handled automatically. Provides validation against the schemas and an optional bridge to the 'frictionless' 'Python' framework. The 'Camtrap DP' standard is described in Bubnicki et al. (2023) <doi:10.1002/rse2.374>.
Obtain information about countries around the globe. Information for names, states, languages, time, capitals, currency and many more. Data source are 'Wikipedia' <https://www.wikipedia.org>, 'TimeAndDate' <https://www.timeanddate.com> and 'CountryCode' <https://countrycode.org>.
Generic functions to analyze the distribution of two continuous
  variables: 'conf2d' to calculate a smooth empirical confidence region, and
  'freq2d' to calculate a frequency distribution.
Implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) <doi:10.1007/s11222-025-10667-x>. In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in 'rstan'. Please cite the paper if you use these codes.
Suite of tools for using 'D3', a library for producing dynamic, interactive data
  visualizations. Supports translating objects into 'D3' friendly data structures, rendering
  'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating
  interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R
  packages.
Despite the predominant use of R for data manipulation and various robust statistical calculations, in recent years, more people from various disciplines are beginning to use R for other purposes. In doing this seemlessly, further tools are needed users to easily and freely write in R for all kinds of purposes. The r2dictionary introduces a means for users to directly search for definitions of terms within the R environment.
These tools help you to assess if a corporate lending
    portfolio aligns with climate goals. They summarize key climate
    indicators attributed to the portfolio (e.g. production, emission
    factors), and calculate alignment targets based on climate scenarios.
    They implement in R the last step of the free software 'PACTA' (Paris
    Agreement Capital Transition Assessment;
    <https://www.transitionmonitor.com/>). Financial institutions use 'PACTA'
    to study how their capital allocation decisions align with climate
    change mitigation goals.
These datasets support the implementation in R of the
    software 'PACTA' (Paris Agreement Capital Transition Assessment),
    which is a free tool that calculates the alignment between corporate
    lending portfolios and climate scenarios
    (<https://www.transitionmonitor.com/>). Financial institutions use
    'PACTA' to study how their capital allocation decisions align with
    climate change mitigation goals. Because both financial institutions
    and market data providers keep their data private, this package
    provides fake, public data to enable the development and use of
    'PACTA' in R.
These tools implement in R a fundamental part of the software
    'PACTA' (Paris Agreement Capital Transition Assessment), which is a
    free tool that calculates the alignment between financial portfolios
    and climate scenarios (<https://www.transitionmonitor.com/>). Financial
    institutions use 'PACTA' to study how their capital allocation
    decisions align with climate change mitigation goals. This package
    matches data from corporate lending portfolios to asset level data
    from market-intelligence databases (e.g. power plant capacities,
    emission factors, etc.). This is the first step to assess if a
    financial portfolio aligns with climate goals.
Create plots to visualize the alignment of a corporate
    lending financial portfolio to climate change scenarios based on
    climate indicators (production and emission intensities) across key
    climate relevant sectors of the 'PACTA' methodology (Paris Agreement Capital 
    Transition Assessment; <https://www.transitionmonitor.com/>).
    Financial institutions use 'PACTA' to study how their capital
    allocation decisions align with climate change mitigation goals.
Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.
Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow the display of fireworks on a document. Toolkit to easily incorporate celebratory splashes in 'Rmarkdown' and 'shiny' apps.
The model R squared and semi-partial R squared for the linear and
    generalized linear mixed model (LMM and GLMM) are computed with confidence
    limits. The R squared measure from Edwards et.al (2008) <DOI:10.1002/sim.3429>
    is extended to the GLMM using penalized quasi-likelihood (PQL) estimation
    (see Jaeger et al. 2016 <DOI:10.1080/02664763.2016.1193725>). Three methods
    of computation are provided and described as follows. First, The
    Kenward-Roger approach. Due to some inconsistency between the 'pbkrtest'
    package and the 'glmmPQL' function, the Kenward-Roger approach in the
    'r2glmm' package is limited to the LMM. Second, The method introduced
    by Nakagawa and Schielzeth (2013) <DOI:10.1111/j.2041-210x.2012.00261.x>
    and later extended by Johnson (2014) <DOI:10.1111/2041-210X.12225>.
    The 'r2glmm' package only computes marginal R squared for the LMM and does
    not generalize the statistic to the GLMM; however, confidence limits and
    semi-partial R squared for fixed effects are useful additions. Lastly, an
    approach using standardized generalized variance (SGV) can be used for
    covariance model selection. Package installation instructions can be found
    in the readme file.
Includes HTML function and methods to write in an HTML
        file. Thus, making HTML reports is easy. Includes a function
        that allows redirection on the fly, which appears to be very
        useful for teaching purpose, as the student can keep a copy of
        the produced output to keep all that he did during the course.
        Package comes with a vignette describing how to write HTML
        reports for statistical analysis. Finally, a driver for 'Sweave'
        allows to parse HTML flat files containing R code and to
        automatically write the corresponding outputs (tables and
        graphs).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
Generates both total- and level-specific R-squared measures from
    Rights and Sterba’s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel
    models with random intercepts and/or slopes, which is based on a complete
    decomposition of variance. Additionally generates graphical 
    representations of these R-squared measures to allow visualizing and 
    interpreting all measures in the framework together as an integrated set.
    This framework subsumes 10 previously-developed R-squared measures for 
    multilevel models as special cases of 5 measures from the framework, and it
    also includes several newly-developed measures. Measures in the framework 
    can be used to compute R-squared differences when comparing multilevel 
    models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>).
    Bootstrapped confidence intervals can also be calculated. To use the 
    confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.
An R command interface to the 'MLwiN' multilevel
    modelling software package.
Using this package,
 it is possible to call a BUGS model, summarize inferences and
 convergence in a table and graph, and save the simulations in arrays for easy access
 in R.
R wrapper for the 'JPMML-R' library <https://github.com/jpmml/jpmml-r>,
    which converts R models to Predictive Model Markup Language ('PMML').
Provides a simple set of wrappers to easily use 
         RDCOMClient for generating Microsoft PowerPoint presentations. Warning:this package is soon to be archived from CRAN.
Provides a friendly, object oriented API for creating PowerPoint
    slide decks in R.
Implementation of hash tables (hash sets and hash maps) in R, 
	featuring arbitrary R objects as keys, 
	arbitrary hash and key-comparison functions, 
	and customizable behaviour upon queries of missing keys.
R2 statistic for significance test. Variance and covariance of R2 values used to assess the 95% CI and p-value of the R2 difference.
Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly 'shiny' applications, 'markdown' reports, and 'quarto' documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience.
Area under the receiver operating characteristic curves (AUC) statistic for significance test. Variance and covariance of AUC values used to assess the 95% Confidence interval (CI) and p-value of the AUC difference for both nested and non-nested model.
Create production-ready Rich Text Format (RTF) tables and figures
    with flexible format.
The routine twosample_test() in this package runs the
    two sample test using various test statistic. The p values are
    found via permutation or large sample theory. The routine twosample_power() allows the
    calculation of the power in various cases, and plot_power() draws the corresponding power graphs.
    The routine run.studies allows a user to quickly study the power of a new method and 
    how it compares to some of the standard ones.
When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.
Implementation of 'JQuery' <https://jquery.com> and 'CSS' styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to 'Shiny' applications or dashboards and 'Rmarkdown' documents.Sharing capability on social media platforms including 'Facebook' <https://www.facebook.com>, 'Linkedin' <https://www.linkedin.com>, 'X/Twitter' <https://x.com>, 'Tumblr' <https://www.tumblr.com>, 'Pinterest' <https://www.pinterest.com>, 'Whatsapp' <https://www.whatsapp.com>, 'Reddit' <https://www.reddit.com>, 'Baidu' <https://www.baidu.com>, 'Blogger' <https://www.blogger.com>, 'Weibo' <https://www.weibo.com>, 'Instagram' <https://www.instagram.com>, 'Telegram' <https://www.telegram.me>, 'Youtube' <https://www.youtube.com>.
Create plots and LaTeX tables that look like SPSS output for use in teaching materials.  Rather than copying-and-pasting SPSS output into documents, R code that mocks up SPSS output can be integrated directly into dynamic LaTeX documents with tools such as knitr.  Functionality includes statistical techniques that are typically covered in introductory statistics classes: descriptive statistics, common hypothesis tests, ANOVA, and linear regression, as well as box plots, histograms, scatter plots, and line plots (including profile plots).
Converts data to STL (stereolithography) files
    that can be used to feed a 3-dimensional printer.  The
    3-dimensional output from a function can be materialized
    into a solid surface in a plastic material, therefore allowing
    more detailed examination. There are many possible uses for
    this new tool, such as to examine mathematical expressions
    with very irregular shapes, to aid teaching people with
    impaired vision, to create raised relief maps from digital
    elevation maps (DEMs), to bridge the gap between mathematical
    tools and rapid prototyping, and many more.  Ian Walker created
    the function r2stl() and Jose' Gama assembled the package.
Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) <doi:10.1145/1089014.1089020>.
Direct insertion of over 1000 symbols (e.g. currencies, letters, emojis, arrows, mathematical symbols and so on) into 'Rmarkdown' documents and 'Shiny' applications by incorporating 'HTML' hex codes.
Performs goodness-of-fit tests for capture-recapture models
    as described by Gimenez et al. (2018) <doi:10.1111/2041-210X.13014>.
    Also contains several functions to process capture-recapture data.
This package uses either the statconnDCOM server (via the
        rcom package) or the RDCOMClient to communicate with MS-Word
        via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS' 
  results and functions to work with that class.
  Function write.model() allows a 'BUGS' model file to be written.  
  The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
  The suggested package 'BRugs' (only needed for function openbugs()) is only available from the CRAN archives, 
  see <https://cran.r-project.org/package=BRugs>.
Access and handle APIs that use the international 'open311'
    'GeoReport v2' standard for civic issue tracking
    <https://wiki.open311.org/GeoReport_v2/>. Retrieve civic service types
    and request data. Select and add available 'open311' endpoints and
    jurisdictions. Implicitly supports custom queries and 'open311'
    extensions.  Requires a minimal number of hard dependencies while
    still allowing the integration in common R formats ('xml2', 'tibble',
    'sf').
Create rich and fully interactive 3D visualizations of molecular data.
    Visualizations can be included in Shiny apps and R markdown documents, or viewed
    from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API
    to manipulate the visualization after creation, and supports getting data out of
    the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.
Provides R and 'JavaScript' functions to allow 'WebGL'-based 3D plotting
    using the 'three.js' 'JavaScript' library. Interactivity through roll-over 
    highlighting and toggle buttons is also supported.
Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) <doi:10.1016/S0378-1127(97)00026-1> and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) <doi:10.1016/j.ecolmodel.2015.07.010>.
Create and combine HTML and PDF reports from within R.
    Possibility to design tables and listings for reporting and also include R plots.
When assigned "R for Data Science" (Wickham, Çetinkaya-Rundel, 
  and Grolemund (2023, ISBN: 1492097402)), students should read the book and
  type in all the associated R commands themselves. Sadly, that never happens.
  These tutorials allow students to demonstrate (and their instructors
  to be sure) that all work has been completed. See Kane (2023)
  <https://ppbds.github.io/tutorial.helpers/articles/instructions.html> from
  the 'tutorial.helpers' package for a background discussion.
Make optimal decisions for your personal or
    household finances. Use tools and methods that are selected carefully
    to align with academic consensus, bridging the gap between theoretical
    knowledge and practical application. They help you find
    your own personalized optimal discretionary spending or
    optimal asset allocation, and prepare you for retirement 
    or financial independence.
    The optimal solution to this problems is extremely complex,
    and we only have a single lifetime to get it right.
    Fortunately, we now have the user-friendly tools implemented,
    that integrate life-cycle models 
    with single-period net-worth mean-variance optimization models. 
    Those tools can be used by anyone who wants to see 
    what highly-personalized optimal decisions can look like.
    For more details see: 
    Idzorek T., Kaplan P. (2024, ISBN:9781952927379),
    Haghani V., White J. (2023, ISBN:9781119747918).
Interface for the 'Google Ads API'. 'Google Ads' is an online
    advertising service that enables advertisers to display advertising to web
    users (see <https://developers.google.com/google-ads/> for more information).
A collection of datasets that accompany the forthcoming book "R for Health Care Research".
Since the early 1970s eyewitness testimony researchers have recognised the 
    importance of estimating properties such as lineup bias (is the lineup biased against 
    the suspect, leading to a rate of choosing higher than one would expect by chance?), 
    and lineup size (how many reasonable choices are in fact available to the witness? 
    A lineup is supposed to consist of a suspect and a number of additional members, 
    or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup 
    measures are descriptive, in the first instance, but since the earliest articles in 
    the literature researchers have recognised the importance of reasoning inferentially 
    about them. This package contains functions to compute various properties of 
    laboratory or police lineups, and is intended for use by researchers in forensic 
    psychology and/or eyewitness testimony research. Among others, the r4lineups package 
    includes functions for calculating lineup proportion, functional size, various 
    estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity 
    ratio, ROC curves for confidence x accuracy data and the degree of similarity of 
    faces in a lineup.
Datasets and utility functions to support the book
    "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease,
    assessing spatial patterns, and modeling plant disease epidemics based on weather predictors.
    These tools are intended for teaching and research in plant disease epidemiology. Several functions
    are based on classical and contemporary methods, including those discussed in Laurence V. Madden,
    Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>.
A collection of R functions for use with Stock Synthesis, a
    fisheries stock assessment modeling platform written in ADMB by Dr. Richard
    D. Methot at the NOAA Northwest Fisheries Science Center. The functions
    include tools for summarizing and plotting results, manipulating files,
    visualizing model parameterizations, and various other common stock
    assessment tasks.
    This version of '{r4ss}' is compatible with Stock Synthesis versions
    3.24 through 3.30 (specifically version 3.30.19.01, from April
    2022).
The 'r4sub' package is a meta-package that installs and loads
    core packages of the R4SUB (R for Regulatory Submission) clinical
    submission readiness ecosystem. Loading 'r4sub' attaches 'r4subcore',
    'r4subtrace', 'r4subscore', 'r4subrisk', 'r4subdata', and
    'r4subprofile'.
Foundational package in the R4SUB (R for Regulatory Submission)
    ecosystem. Defines the core evidence table schema, parsers, indicator
    abstractions, and scoring primitives needed to quantify clinical submission
    readiness. Provides a standardized contract for ingesting heterogeneous
    sources (validation outputs, metadata, traceability) into a single
    evidence framework.
Provides realistic synthetic example datasets for the R4SUB
    (R for Regulatory Submission) ecosystem. Includes a pharma study evidence
    table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model)
    metadata following CDISC (Clinical Data Interchange Standards Consortium)
    conventions (<https://www.cdisc.org>), traceability mappings, a risk
    register based on ICH (International Council for Harmonisation) Q9
    quality risk management principles
    (<https://www.ich.org/page/quality-guidelines>), and regulatory indicator
    definitions. Designed for demos, vignettes, and package testing.
Defines submission profiles per regulatory authority with
    authority-specific pillar weights, decision thresholds, indicator
    requirements, and risk configuration. Supports the U.S. Food and Drug
    Administration (FDA), European Medicines Agency (EMA), Pharmaceuticals and
    Medical Devices Agency (PMDA), Health Canada, Therapeutic Goods
    Administration (TGA), and Medicines and Healthcare products Regulatory
    Agency (MHRA). Integrates with 'r4subscore' and 'r4subrisk' configuration
    systems.
Quantifies submission risk using a Failure Modes and Effects
    Analysis (FMEA)-inspired framework (probability, impact, detectability).
    Builds risk registers from evidence, computes Risk Priority Numbers (RPN),
    classifies risk levels, and emits standardized R4SUB (R for Regulatory
    Submission) evidence table rows via 'r4subcore'. Supports risk mitigation
    tracking and trend analysis across submission milestones.
Converts standardized R4SUB (R for Regulatory Submission) evidence
    into indicator scores, pillar scores, and a Submission Confidence Index
    (SCI). Provides sensitivity analysis, explainability tables, and decision
    band classification to answer the question: are we ready for regulatory
    submission.
Quantifies and explains end-to-end traceability between clinical
    submission artifacts (ADaM (Analysis Data Model) outputs, derivations,
    SDTM (Study Data Tabulation Model) sources, specs, code). Builds trace
    models from metadata and mapping sheets, computes trace levels, and emits
    standardized R4SUB (R for Regulatory Submission) evidence table rows via
    'r4subcore'.
Rapid realistic routing on multimodal transport networks
    (walk, bike, public transport and car) using 'R5', the Rapid Realistic
    Routing on Real-world and Reimagined networks engine
    <https://github.com/conveyal/r5>. The package allows users to generate
    detailed routing analysis or calculate travel time and monetary cost matrices 
    using seamless parallel computing on top of the R5 Java machine.  While R5
    is developed by Conveyal, the package r5r is independently developed
    by a team at the Institute for Applied Economic Research (Ipea) with
    contributions from collaborators. Apart from the documentation in this
    package, users will find additional information on R5 documentation at
    <https://docs.conveyal.com/>. Although we try to keep new releases of
    r5r in synchrony with R5, the development of R5 follows Conveyal's
    independent update process. Hence, users should confirm the R5 version
    implied by the Conveyal user manual (see
    <https://docs.conveyal.com/changelog>) corresponds with the R5 version
    that r5r depends on. This version of r5r depends on R5 v7.1.
Interactively build and explore public transit routes with
    'r5r' package via a graphical user interface in a 'shiny' app. The
    underlying routing methods are described in Pereira et al. (2021)
    <doi:10.32866/001c.21262>.
Creates classes with reference semantics, similar to R's
    built-in reference classes. Compared to reference classes, R6 classes
    are simpler and lighter-weight, and they are not built on S4 classes
    so they do not require the methods package. These classes allow public
    and private members, and they support inheritance, even when the
    classes are defined in different packages.
After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.
The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
Provides reference classes implementing some useful data structures.
    The package implements these data structures by using the reference class R6.
    Therefore, the classes of the data structures are also reference classes which means that their instances are passed by reference.
    The implemented data structures include stack, queue, double-ended queue, doubly linked list, set, dictionary and binary search tree.
    See for example <https://en.wikipedia.org/wiki/Data_structure> for more information about the data structures.
Generate boilerplate code for R6 classes. Given R6 class create getters 
    and/or setters for selected class fields or use RStudio addins to insert methods 
    straight into class definition.
Build robust and maintainable software with object-oriented design 
    patterns in R. Design patterns abstract and present in neat, well-defined 
    components and interfaces the experience of many software designers and 
    architects over many years of solving similar problems. These are solutions 
    that have withstood the test of time with respect to re-usability,
    flexibility, and maintainability. 'R6P' provides abstract base classes with 
    examples for a few known design patterns. The patterns were selected by 
    their applicability to analytic projects in R. Using these patterns in R 
    projects have proven effective in dealing with the complexity that 
    data-driven applications possess.
A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.
Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.
Conduct simulations of the Response Adaptive Block Randomization (RABR) design to evaluate its type I error rate, power and operating characteristics for binary and continuous endpoints. For more details of the proposed method, please refer to Zhan et al. (2021) <doi:10.1002/sim.9104>.
Solves the individual bioenergetic balance for different aquaculture sea fish (Sea Bream and Sea Bass; Brigolin et al., 2014 <doi:10.3354/aei00093>) and shellfish (Mussel and Clam; Brigolin et al., 2009 <doi:10.1016/j.ecss.2009.01.029>; Solidoro et al., 2000 <doi:10.3354/meps199137>). Allows for spatialized model runs and population simulations.
Provide function for work with 'AcademyOcean API'
    <https://academyocean.com/api>.
Provides a flexible framework for implementing hierarchical access 
    control in 'shiny' applications. Features include user permission management 
    through a two-tier system of access panels and units, pluggable 'shiny' 
    module for administrative interfaces, and support for multiple storage 
    backends (local, 'AWS S3', 'Posit Connect'). The system enables fine-grained
    control over application features, with built-in audit trails and user 
    management capabilities. Integrates seamlessly with 'Posit Connect's 
    authentication system.
An implementation of the RaCE-NMA (Rank-Clustered
    Estimation for Network Meta-Analysis) model for post-hoc clustering
    of treatments or interventions by rank in network  meta-analysis
    data. Functions for model estimation, assessment, and displaying 
    results are provided. For more details, see Pearce and Zhou (2025) 
    <doi:10.1017/rsm.2025.10049>.
Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).
Implements a computational framework for a pattern-based, 
    zoneless analysis, and visualization of (ethno)racial topography 
    (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>).
    It is a reimagined
    approach for analyzing residential segregation and racial diversity based on 
    the concept of 'landscape’ used in the domain of landscape ecology.
Contains functions useful for reading in Licor 6800 files,
    correcting and analyzing rapid A/Ci response (RACiR) data. Requires some
    user interaction to adjust the calibration (empty chamber) data file to 
    a useable range. Calibration uses a 1st to 5th order polynomial as 
    suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>.
    Data can be processed individually or batch processed for all files paired
    with a given calibration file. RACiR is a trademark of LI-COR Biosciences,
    and used with permission.
A toolkit for making antigenic maps from immunological assay data,
    in order to quantify and visualize antigenic differences between different
    pathogen strains as described in
    Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World
    Health Organization influenza vaccine strain selection process. Additional
    functions allow for the diagnostic evaluation of antigenic maps and an
    interactive viewer is provided to explore antigenic relationships amongst
    several strains and incorporate the visualization of associated genetic
    information.
A methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.
Interface for loading data from 'ActiveCampaign API v3' 
    <https://developers.activecampaign.com/reference>. Provide functions
    for getting data by deals, contacts, accounts, campaigns and messages.
Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.
Implementation of the MaxRank normalization method, which enables 
     standardization of Rank Abundance Distributions (RADs) to a specified 
     number of ranks. Rank abundance distributions are widely used in biology 
     and ecology to describe species abundances, and are mathematically 
     equivalent to complementary cumulative distribution functions (CCDFs) used 
     in physics, linguistics, sociology, and other fields. The method is 
     described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.
Fundamental formulas for Radar, for attenuation, range, velocity,
    effectiveness, power, scatter, doppler, geometry, radar equations, etc.
    Based on Nick Guy's Python package PyRadarMet
Creates the radar-boxplot, a plot that was created by the 
    author during his Ph.D. in forest resources. 
    The radar-boxplot is a visualization feature suited for  
    multivariate classification/clustering. It provides an intuitive 
    deep understanding of the data.
Nuclear Decay Data for Dosimetric Calculations from the 
    International Commission on Radiological Protection from ICRP 
    Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 
    <doi:10.1016/j.icrp.2008.10.004> 
    <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. 
    This is a database of the physical data needed in calculations of 
    radionuclide-specific protection and operational quantities. The 
    data is prescribed by the ICRP, the international authority on 
    radiation dose standards, for estimating dose from the intake of or 
    exposure to radionuclides in the workplace and the environment. 
    The database contains information on the half-lives, decay chains, 
    and yields and energies of radiations emitted in nuclear transformations 
    of 1252 radionuclides of 97 elements.
A differential abundance method for the analysis of microbiome data. 'radEmu' estimates fold-differences in the abundance of taxa across samples relative to "typical" fold-differences. Notably, it does not require pseudocounts, nor choosing a denominator taxon. For more details, see Clausen et al. (2026) <doi:10.1093/biomet/asag009>.
A straightforward model to estimate soil migration rates across various soil contexts. 
	Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) 
	<doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>.
	'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived 
	from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. 
	The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated 
	from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. 
	The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://github.com/eead-csic-eesa/RadEro>.
Shiny-based interactive gadgets of radial visualization methods and extensions thereof.
A platform-independent browser-based interface for business
    analytics in R, based on the shiny package. The application combines the
    functionality of 'radiant.data', 'radiant.design', 'radiant.basics',
    'radiant.model', and 'radiant.multivariate'.
The Radiant Basics menu includes interfaces for probability 
    calculation, central limit theorem simulation, comparing means and proportions, 
    goodness-of-fit testing, cross-tabs, and correlation. The application extends 
    the functionality in 'radiant.data'.
The Radiant Data menu includes interfaces for loading, saving,
    viewing, visualizing, summarizing, transforming, and combining data. It also
    contains functionality to generate reproducible reports of the analyses
    conducted in the application.
The Radiant Design menu includes interfaces for design of
    experiments, sampling, and sample size calculation. The application extends
    the functionality in 'radiant.data'.
The Radiant Model menu includes interfaces for linear and logistic
    regression, naive Bayes, neural networks, classification and regression trees,
    model evaluation, collaborative filtering, decision analysis, and simulation.
    The application extends the functionality in 'radiant.data'.
The Radiant Multivariate menu includes interfaces for perceptual
    mapping, factor analysis, cluster analysis, and conjoint analysis. The
    application extends the functionality in 'radiant.data'.
A
        collection of programs for 
        plotting SKEW-T,log p diagrams and wind profiles for data
        collected by radiosondes (the typical weather balloon-borne
        instrument). The format of this plot with companion lines to
	assess atmospheric stability are both standard  in meteorology
	and difficult to create from basic graphics functions. Hence
	this package. One novel feature is being able add several
	profiles to the same plot for comparison. 
	Use "help(ExampleSonde)" for an explanation of the variables
	needed and how they should be named in a data frame. See
	<https://github.com/dnychka/Radiosonde> for the package home page.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.
Make your phrase or sentence into something funny! Pass a string with the keywords in, 
    and get out a bit of humor.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from multiple treatment planning system platforms and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Generate random user data from the Random User Generator API.
    For more information, see <https://randomuser.me/>.
Provides functions for radiation safety, also known as
    "radiation protection" and "radiological control". The science of 
    radiation protection is called "health physics" and its engineering 
    functions are called "radiological engineering". Functions in this 
    package cover many of the computations needed by radiation safety 
    professionals. Examples include: obtaining updated calibration and
    source check values for radiation monitors to account for radioactive 
    decay in a reference source, simulating instrument readings to better
    understand measurement uncertainty, correcting instrument readings 
    for geometry and ambient atmospheric conditions. Many of these 
    functions are described in Johnson and Kirby (2011, ISBN-13:  
    978-1609134198). Utilities are also included for developing inputs 
    and processing outputs with radiation transport codes, such as MCNP, 
    a general-purpose Monte Carlo N-Particle code that can be used for 
    neutron, photon, electron, or coupled neutron/photon/electron transport
    (Werner et. al. (2018) <doi:10.2172/1419730>).
Offers a handful of useful wrapper functions which streamline
    the reading, analyzing, and visualizing of variant call format (vcf) files in R.
    This package was designed to facilitate an explicit pipeline for optimizing
    Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>)
    parameters during de novo (without a reference genome) assembly and variant
    calling of restriction-enzyme associated DNA sequence (RADseq) data. The
    pipeline implemented here is based on the 2017 paper "Lost in Parameter Space"
    (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which
    establishes clear recommendations for optimizing the parameters
    'm', 'M', and 'n', during the process of assembling loci.
An implementation of the radviz projection in R. It enables the visualization of
    multidimensional data while maintaining the relation to the original dimensions.
    This package provides functions to create and plot radviz projections, and a number of summary
    plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) 
    (<doi:10.1145/331770.331775>) for original implementation, 
    see Di Caro *et al* (2012) (<doi:10.1007/978-3-642-13672-6_13>), 
    for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) 
    (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.
Creating 3D radial visualizations of multivariate data.
    The package extends traditional radial coordinate visualization (RadViz) techniques
    to three-dimensional space, enabling enhanced exploration and analysis of
    high-dimensional datasets through interactive 3D plots. Zhu, Dai & Maitra (2022)
    <doi:10.1080/10618600.2021.2020129>.
Aims at loading Google Adwords data into R. Adwords is an online
    advertising service that enables advertisers to display advertising copy to web
    users (see <https://developers.google.com/adwords/> for more information). 
    Therefore the package implements three main features. First, the package
    provides an authentication process for R with the Google Adwords API (see 
    <https://developers.google.com/adwords/api/> for more information) via OAUTH2.
    Second, the package offers an interface to apply the Adwords query language in
    R and query the Adwords API with ad-hoc reports. Third, the received data are
    transformed into suitable data formats for further data processing and data
    analysis.
A model of single-layer groundwater flow in steady-state
    under the Dupuit-Forchheimer assumption can be created by placing
    elements such as wells, area-sinks and line-sinks at arbitrary
    locations in the flow field. Output variables include hydraulic head
    and the discharge vector. Particle traces can be computed numerically
    in three dimensions. The underlying theory is described in Haitjema
    (1995) <doi:10.1016/B978-0-12-316550-3.X5000-4> and references
    therein.
A series of shortcuts for routine tasks originally developed by
    Rafael A. Irizarry to facilitate data exploration.
A cross-validated minimal-optimal feature selection algorithm.
 It utilises popularity counting, hierarchical clustering with feature dissimilarity measures,
 and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.
Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval Method is designed to perform multi-criteria decision-making (MCDM), developed by Mališa Žižovic in 2020 (<doi:10.3390/math8061015>). 
              It calculates the final sorted rankings based on a decision matrix where rows represent alternatives 
              and columns represent criteria. The method uses:
              - A numeric vector of weights for each criterion (the sum of weights must be 1).
              - A numeric vector of ideal values for each criterion.
              - A numeric vector of anti-ideal values for each criterion.
              - Numeric values representing the extent to which the ideal value is preferred over the anti-ideal value, 
                and the extent to which the anti-ideal value is considered worse.
              The function standardizes the decision matrix, normalizes the data, applies weights, and returns the 
              final sorted rankings.
Functions for calculating life history metrics using matrix
    population models ('MPMs'). Described in Jones et al. (2021)
    <doi:10.1101/2021.04.26.441330>.
Enables Retrieval-Augmented Generation (RAG) workflows in R by combining
    local vector search using 'DuckDB' with optional web search via the 'Tavily' API.
    Supports 'OpenAI'- and 'Ollama'-compatible embedding models, full-text and 'HNSW'
    (Hierarchical Navigable Small World) indexing, and modular large language model
    (LLM) invocation. Designed for advanced question-answering, chat-based
    applications, and production-ready AI pipelines. This package is the R
    equivalent of the 'python' package 'RAGFlowChain' available at
    <https://pypi.org/project/RAGFlowChain/>.
Anti-Grain Geometry (AGG) is a high-quality and
    high-performance 2D drawing library. The 'ragg' package provides a set
    of graphic devices based on AGG to use as alternative to the raster
    devices provided through the 'grDevices' package.
Data objects in 'R' can be rendered as 'HTML' tables using the
    'JavaScript' library 'ag-grid' (typically via 'R Markdown' or 'Shiny'). The
    'ag-grid' library has been included in this 'R' package. The package name
    'RagGrid' is an abbreviation of 'R agGrid'.
Provides tools for implementing Retrieval-Augmented
    Generation (RAG) workflows with Large Language Models (LLM). Includes
    functions for document processing, text chunking, embedding
    generation, storage management, and content retrieval. Supports
    various document types and embedding providers ('Ollama', 'OpenAI'),
    with 'DuckDB' as the default storage backend. Integrates with the
    'ellmer' package to equip chat objects with retrieval capabilities.
    Designed to offer both sensible defaults and customization options
    with transparent access to intermediate outputs.  For a review of
    retrieval-augmented generation methods, see Gao et al. (2023)
    "Retrieval-Augmented Generation for Large Language Models: A Survey"
    <doi:10.48550/arXiv.2312.10997>.
Proper L2-penalized maximum likelihood estimators for precision 
  matrices and supporting functions to employ these estimators in a graphical 
  modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) 
  <doi:10.18637/jss.v102.i04> and associated publications.
Algorithms to price American and European equity options,
    convertible bonds and a variety of other financial derivatives. It
    uses an extension of the usual Black-Scholes model in which jump to
    default may occur at a probability specified by a power-law link
    between stock price and hazard rate as found in the paper by
    Takahashi, Kobayashi, and Nakagawa (2001)
    <doi:10.3905/jfi.2001.319302>.  We use ideas and techniques from
    Andersen and Buffum (2002) <doi:10.2139/ssrn.355308> and Linetsky
    (2006) <doi:10.1111/j.1467-9965.2006.00271.x>.
A modified implementation of stepwise regression that greedily searches 
    the space of interactions among features in order to build polynomial regression models.
    Furthermore, the hypothesis tests conducted are valid-post model selection
    due to the use of a revisiting procedure that implements an alpha-investing
    rule. As a result, the set of rejected sequential hypotheses is proven to 
    control the marginal false discover rate. When not searching for polynomials,
    the package provides a statistically valid algorithm
    to run and terminate stepwise regression. For more information, see 
    Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.
Visualizing functional data and identifying functional outliers.
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.
Calculates I30 (maximum 30-minute rainfall intensity) and EI30
    (erosivity index) from rainfall breakpoint data. Supports multiple
    storm events, rainfall validation, and visualization for soil erosion
    modeling and hydrological analysis. Methods are based on
    Brown and Foster (1987) <doi:10.13031/2013.30422>,
    Wischmeier and Smith (1978) "Predicting Rainfall Erosion Losses:
    A Guide to Conservation Planning" <doi:10.22004/ag.econ.171903>,
    and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to
    Conservation Planning with the Revised Universal Soil Loss Equation
    (RUSLE)" (USDA Agriculture Handbook No. 703).
An R implementation of the Reinert text clustering method. For more
    details about the algorithm see the included vignettes or Reinert (1990)
    <doi:10.1177/075910639002600103>.
Determination of rainfall-runoff erosivity factor.
An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.
Efficient CRUD interface for the 'Airtable' API <https://airtable.com/developers/web/api>, supporting batch requests and parallel encoding of large data sets.
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.
Functions for performing spatial microsimulation ('raking')
    in R.
The goal of 'ralger' is to facilitate web scraping in R.
Download, prepare and analyze data from large-scale assessments and
    surveys with complex sampling and assessment design (see 'Rutkowski',
    2010 <doi:10.3102/0013189X10363170>). Such studies are, for example,
    international assessments like 'TIMSS', 'PIRLS' and 'PISA'. A graphical
    interface is available for the non-technical user.The package includes
    functions to covert the original data from 'SPSS' into 'R' data sets
    keeping the user-defined missing values, merge data from different
    respondents and/or countries, generate variable dictionaries, modify
    data, produce descriptive statistics (percentages, means, percentiles,
    benchmarks) and multivariate statistics (correlations, linear
    regression, binary logistic regression). The number of supported
    studies and analysis types will increase in future. For a general
    presentation of the package, see 'Mirazchiyski', 2021a
    (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of
    the package, see 'Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).
Parser generator for R using combinatory parsers. It
    is inspired by combinatory parsers developed in Haskell.
Provides an R interface for using 'AmCharts' Library. Based on
    'htmlwidgets', it provides a global architecture to generate 'JavaScript' source
    code for charts. Most of classes in the library have their equivalent in R
    with S4 classes; for those classes, not all properties have been referenced but
    can easily be added in the constructors. Complex properties (e.g. 'JavaScript'
    object) can be passed as named list. See examples at 
    <https://datastorm-open.github.io/introduction_ramcharts/> 
    and <https://www.amcharts.com/> for
    more information about the library. The package includes the free version
    of 'AmCharts' Library. Its only limitation is a small link to the web site
    displayed on your charts. If you enjoy this library, do not hesitate to refer
    to this page <https://www.amcharts.com/online-store/> to purchase a licence,
    and thus support its creators and get a period of Priority Support. See also
    <https://www.amcharts.com/about/> for more information about 'AmCharts' company.
Creates JavaScript charts. The charts can be included in 'Shiny' apps and R markdown documents, or viewed from the R console and 'RStudio' viewer. Based on the JavaScript library 'amCharts 4' and the R packages 'htmlwidgets' and 'reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart.
It is widely documented in psychology, economics and other disciplines that socio-economic agent may not pay full attention to all available alternatives, rendering standard revealed preference theory invalid. This package implements the estimation and inference procedures of Cattaneo, Ma, Masatlioglu and Suleymanov (2020) <arXiv:1712.03448> and Cattaneo, Cheung, Ma, and Masatlioglu (2022) <arXiv:2110.10650>, which utilizes standard choice data to partially identify and estimate a decision maker's preference and attention. For inference, several simulation-based critical values are provided.
Function for adapting the shape of the random walk Metropolis proposal
    as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. 
    The package also includes fast functions for rank-one Cholesky update and downdate.
    These functions can be used directly from R or the corresponding C++ header files 
    can be easily linked to other R packages.
Use R to interface with the 'TD Ameritrade' API <https://developer.tdameritrade.com/>.
    Functions include authentication, trading, price requests, account information, and option 
    chains. A user will need a TD brokerage account and TD Ameritrade developer app. See README 
    for authentication process and examples.
Additional matrix functionality for R including: (1) wrappers for 
    the base matrix function that allow matrices to be created from character
    strings and lists (the former is especially useful for creating block
    matrices), (2) better printing of large matrices via the generic "pretty" 
    print function, and (3) a number of convenience functions for users more
    familiar with other scientific languages like 'Julia', 'Matlab'/'Octave', or
    'Python'+'NumPy'.
Provides an efficient procedure for fitting the entire solution
    path for high-dimensional regularized quadratic generalized linear models with
    interactions effects under the strong or weak heredity constraint.
Value-calibrated color ramps can be useful to emphasize patterns in data from complex distributions. Colors can be tied to specific values, and the association can be expanded into full color ramps that also include the relationship between colors and values. Such ramps can be used in a variety of cases when heatmap-type plots are necessary, including the visualization of vector and raster spatial data, such as topographies.
We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.
Bayesian geostatistical modeling of Gaussian processes using a
    reparameterized and marginalized posterior sampling (RAMPS) algorithm
    designed to lower autocorrelation in MCMC samples.  Package performance is
    tuned for large spatial datasets.
R-based access to mass-spectrometry (MS) data. While many packages 
  exist to process MS data, many of these make it difficult to 
  access the underlying mass-to-charge ratio (m/z), intensity, and 
  retention time of the files 
  themselves. This package is designed to format MS data in a tidy fashion and 
  allows the user perform the plotting and analysis.
Provides a solution path for Reinforced Angle-based Multicategory 
    Support Vector Machines, with linear learning, polynomial learning, and 
    Gaussian kernel learning. C. Zhang, Y. Liu, J. Wang and H. Zhu. (2016) 
    <doi:10.1080/10618600.2015.1043010>.
Provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.
Implements the algorithm by Pourahmadi and Wang (2015) <doi:10.1016/j.spl.2015.06.015> for generating a random p x p correlation matrix. Briefly, the idea is to represent the correlation matrix using Cholesky factorization and p(p-1)/2 hyperspherical coordinates (i.e., angles), sample the angles from a particular distribution and then convert to the standard correlation matrix form. The angles are sampled from a distribution with pdf proportional to sin^k(theta) (0 < theta < pi, k >= 1) using the efficient sampling algorithm described in Enes Makalic and Daniel F. Schmidt (2018) <arXiv:1809.05212>.
Generate random positions (latitude/longitude), 
    Well-known text ('WKT') points or polygons, or 'GeoJSON' points or 
    polygons.
A novel numerical algorithm that provides functionality for estimating the exact 95% confidence interval of the location parameter in the random effects model, and is much faster than the naive method. Works best when the number of studies is between 6-20.
Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.
Provides random number generating functions that
    are much more context aware than the built-in functions. The
    functions are also much safer, as they check for incompatible 
    values, and more reproducible.
The true random number service provided by the RANDOM.ORG
 website created by Mads Haahr samples atmospheric noise via radio tuned to
 an unused broadcasting frequency together with a skew correction algorithm
 due to John von Neumann.  More background is available in the included
 vignette based on an essay by Mads Haahr.  In its current form, the package
 offers functions to retrieve random integers, randomized sequences and
 random strings.
A set of functions to create random Analysis Data Model
    (ADaM) datasets and cached dataset.  ADaM dataset specifications are
    described by the Clinical Data Interchange Standards Consortium
    (CDISC) Analysis Data Model Team.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis. Also weighted correlation matrices may be considered for PA.
Simple methods to generate attractive random colors. The random
  colors are from a wrapper of 'randomColor.js'
  <https://github.com/davidmerfield/randomColor>. In addition, it also generates
  optimally distinct colors based on k-means (inspired by 'IWantHue'
  <https://github.com/medialab/iwanthue>).
Classification and regression based on a forest of trees using random inputs, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.
A set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).
Random Hazard Forests (RHF) extend Random Survival
  Forests (RSF) by directly estimating the hazard function and by
  accommodating time-dependent covariates through counting-process
  style inputs. The package fits tree ensembles for dynamic survival
  prediction, returning hazard, cumulative hazard, integrated hazard,
  and related performance summaries for training and test data. The
  methods build on Random Survival Forests described by Ishwaran et
  al. (2008) <doi:10.1214/08-AOAS169> and on nonparametric hazard
  modeling with time-dependent covariates described by Lee et
  al. (2021) <doi:10.1214/20-AOS2028>.
Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2021) <doi:10.1080/01621459.2021.1950003>.
Implements random forest Super Greedy Trees (SGTs) for
  regression. SGTs extend classification and regression tree splitting
  by fitting lasso-penalized local parametric models at tree nodes,
  producing sparse univariate and multivariate geometric cuts such as
  axis-aligned splits, hyperplanes, ellipsoids, hyperboloids, and
  interaction-based cuts.  Trees are grown best-split-first by
  selecting cuts that reduce empirical risk, and ensembles provide
  out-of-bag error estimation, prediction on new data, variable
  filtering, tuning of the hcut complexity parameter,
  coordinate-descent lasso fitting, variable importance, and local
  coefficient summaries. For the underlying method, 
  see Ishwaran (2026) <doi:10.1007/s10462-026-11541-6>.
Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes.  Extreme random forests and randomized splitting.  Suite of imputation methods for missing data.  Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance.  Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.
Functions for assessing variable relations and associations 
    prior to modeling with a Random Forest algorithm (although these are 
    relevant for any predictive model).
    Metrics such as partial correlations and variance inflation factors
    are tabulated as well as plotted for the user. A function is available
    for tuning the main Random Forest hyper-parameter based on model performance 
    and variable importance metrics. This grid-search technique provides
    tables and plots showing the effect of the main hyper-parameter on each 
    of the assessment metrics. It also returns each of the evaluated models 
    to the user. The package also provides superior variable importance plots 
    for individual models. All of the plots are developed so that the 
    user has the ability to edit and improve further upon the 
    plots. Derivations and methodology are described in Bladen (2022) 
    <https://digitalcommons.usu.edu/etd/8587/>.
Provides an accessible and efficient implementation of a randomized
    feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method
    combines stratified bootstrap resampling with random feature subsampling and
    aggregates predictions via posterior averaging. Support is provided for
    mixed-type predictors and parallel computation. Methods are described in
    Srisuradetchai (2025) <doi:10.3389/fdata.2025.1706417> "Posterior averaging
    with Gaussian naive Bayes and the R package RandomGaussianNB for big-data
    classification".
A bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in 
   Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.
The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes
  into hierarchical categories based on biological process (BP), molecular function (MF) and
  cellular component (CC, i.e., subcellular localization). Tools such as 'GoMiner' (see Zeeberg, B.R.,
  Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform
  ontological analysis of microarray and proteomics studies, typically generating a list of
  significant functional categories. The significance is traditionally determined by randomizing
  the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for
  each category. We explore here the novel alternative of randomizing the GO database rather than
  the gene list.
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of 
   sequences (treatment sequences).
   If a blocksize is given, the randomization will be done within blocks.
   The randomization may be controlled by a Wald-Wolfowitz runs test.
   Functions to obtain the p-value of that test are included.
   The package is mainly intended for randomization of bioequivalence studies
   but may be used also for other clinical crossover studies.
   Contains two helper functions sequences() and williams() to get the sequences 
   of commonly used designs in BE studies.
This tool enables the user to choose a randomization procedure
    based on sound scientific criteria. It comprises the generation of
    randomization sequences as well the assessment of randomization procedures
    based on carefully selected criteria. Furthermore, 'randomizeR' provides a
    function for the comparison of randomization procedures.
Generates random assignments for common experimental designs and 
	    random samples for common sampling designs.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.
A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
Random number generation library implemented in C with multiple 
    engines and distribution functions, providing an R interface focused on 
    correctness, speed, and reproducibility. Supports various PRNGs including 
    xoshiro256++/**, PCG64, Philox, and ChaCha20, with methods for continuous, 
    discrete, and multivariate distributions.
Performs the random projection test (Lopes et al., (2011) <doi:10.48550/arXiv.1108.2401>) for the one-sample and two-sample hypothesis testing problem for equality of means in the high dimensional setting. We are interested in detecting the mean vector in the one-sample problem or the difference between mean vectors in the two-sample problem.
Ensemble model, for classification, regression
	and unsupervised learning, based on a forest of unpruned 
	and randomized binary decision trees. Each tree is grown 
	by sampling, with replacement, a set of variables at each node. 
	Each cut-point is generated randomly, according to the continuous 
	Uniform distribution. For each tree, data are either bootstrapped 
	or subsampled. The unsupervised mode introduces clustering, dimension reduction
	and variable importance, using a three-layer engine. Random Uniform Forests are mainly 
	aimed to lower correlation between trees (or trees residuals), to provide a deep analysis 
	of variable importance and to allow native distributed and incremental learning.
Based on data of real user-agent strings, we can set filtering conditions
    and randomly sample user-agent strings from the user-agent string pool.
Generates random walks of various types by providing a set of functions
    that are compatible with the 'tidyverse'. The functions provided in the package
    make it simple to create random walks with a variety of properties, such as
    how many simulations to run, how many steps to take, and the distribution of
    random walk itself.
Randomization lists are an integral component of randomized clinical trials. 'randotools' provides tools to easily create such lists.
Carry out principal component analysis (PCA) of very large pedigrees such as found
    in breeding populations! This package exploits sparse matrices and randomised
    linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD)
    (and eigen decomposition).
Performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.
Provides several non parametric randomness tests for numeric sequences.
Provides (1) pseudo random generators - general linear congruential generators, 
    multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister
    algorithm (<doi:10.1007/978-3-540-74496-2_36>) and WELL (<doi:10.1145/1132973.1132974>) 
    generators); (2) quasi random generators - the Torus algorithm, the
    Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some
    generator tests - the gap test, the serial test, the poker test, see, e.g., 
    Gentle (2003) <doi:10.1007/b97336>. 
    Take a look at the Distribution task view of types and tests of random number generators.
    The package can be provided without the 'rngWELL' dependency on demand.
    Package in Memoriam of Diethelm and Barbara Wuertz.
Implements random variables by means of S4 classes and methods.
Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.
Streamline the calculation of conditional probabilities for various numeric ranges in an R DataFrame. It automates the need to convert numerical data into categorical data for conditional probability calculation, making it ideal for quick and preliminary data analysis.
Tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.
Range Modeling Metadata Standards (RMMS) address three challenges: 
  they (i) are designed for convenience to encourage use, (ii) accommodate a wide 
  variety of applications, and (iii) are extensible to allow the community of range 
  modelers to steer it as needed. RMMS are based on a data dictionary that specifies 
  a hierarchical structure to catalog different aspects of the range modeling process. 
  The dictionary balances a constrained, minimalist vocabulary to improve 
  standardization with flexibility for users to provide their own values. 
  Merow et al. (2019) <DOI:10.1111/geb.12993> describe the standards in 
  more detail. Note that users who prefer to use the R package 'ecospat' can obtain it from
  <https://github.com/ecospat/ecospat>.
Used for generating randomized community matrices under
             strict range cohesion. The package can handle data where species occurrence
             are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.
Provides a collection of random number generators for common and custom distributions, along with utility functions for sampling and simulation.
A fast implementation of Random Forests, particularly suited for high
          dimensional data. Ensembles of classification, regression, survival and
          probability prediction trees are supported. Data from genome-wide association
          studies can be analyzed efficiently. In addition to data frames, datasets of
          class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') 
          can be directly analyzed.
Displays palette of 5 colors based on photos depicting the unique 
             and vibrant culture of Punjab in Northern India. Since Punjab translates 
             to ``Land of 5 Rivers'' there are 5 colors per palette. If users need 
             more than 5 colors, they can merge 2 to 3 palettes to create their own 
             color-combination, or they can cherry-pick their own custom colors. 
             Users can view up to 3 palettes together. Users can also list all the 
             palette choices. And last but not least, users can see the photo that 
             inspired a particular palette.
Integrates population dynamics and dispersal into a
    mechanistic virtual species simulator. The package can be used to
    study the effects of environmental change on population growth and
    range shifts. It allows for simple and straightforward definition of
    population dynamics (including positive density dependence), extensive
    possibilities for defining dispersal kernels, and the ability to
    generate virtual ecologist data. Learn more about the 'rangr' at
    <https://docs.ropensci.org/rangr/>. This work was supported by 
    the National Science Centre, Poland, grant no. 2018/29/B/NZ8/00066 and 
    the Poznań Supercomputing and Networking Centre (grant no. pl0090-01).
Provides a flexible alternative to the built-in rank() function called smartrank(). 
    Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. 
    smartrank() is suitable for both numerical and categorical data.
Performs aggregation of ordered lists based
        on the ranks using several different algorithms:
        Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
        brute force algorithm (for small problems).
Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. 'RankAggSIgFUR' (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) <doi:10.1016/j.cor.2018.06.007>.
Implementation of a model-based clustering algorithm for
    ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). 
    Multivariate rankings as well as partial rankings are taken
    into account. This algorithm is based on an extension of the Insertion
    Sorting Rank (ISR) model for ranking data, which is a meaningful and
    effective model parametrized by a position parameter (the modal ranking,
    quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
    of the rank population is modelled by a mixture of ISR, whereas conditional
    independence assumption is considered for multivariate rankings.
Estimates the total, between-, and within-cluster Spearman rank correlations for continuous and ordinal clustered data. See Tu et al. (2024) <DOI:10.1002/sim.10326> for details.
Implements Kornbrot's rank difference test as described in 
    <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified 
    Wilcoxon signed-rank test which produces consistent and meaningful 
    results for ordinal or monotonically-transformed data.
Implements distance based probability models for ranking data. 
    The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
    Weighted-tau distance and Weighted Kendall distance.
    Phi-component model and mixture models are also supported.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
	     statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
	     in a crossed design with an arbitrary number of factors.
	     Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.
Provides methods for genome-wide association studies (GWAS)
    using ranked set sampling (RSS) designs. The package includes tools
    for ranked set sample selection, standard and RSS-based association
    analyses, simulation of genotype and phenotype data, statistical
    comparison of RSS and simple random sampling (SRS) approaches,
    visualization of GWAS results, and power analysis under alternative
    sampling schemes.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.
Functions to generate plots and tables for comparing independently-
    sampled populations. Companion package to "A Primer on Visualizations
    for Comparing Populations, Including the Issue of Overlapping Confidence
    Intervals" by Wright, Klein, and Wieczorek (2019)
    <DOI:10.1080/00031305.2017.1392359> and "A Joint Confidence Region for an
    Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020)
    <DOI:10.1111/rssc.12402>.
A supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.
Provides convenient tools for visualising ordinal outcome data following the "Grotta Bar" approach pioneered by The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group (1995) <doi:10.1056/NEJM199512143332401>.
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The 'RankPCA' package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.
For a given suit, if you add up the number of cards that you hold, plus the number that has been played so far, you can easily determine the number that remains in the combined hands of your three opponents. You can also determine the ("special") card of highest rank of the remaining cards for that suit. At some point, you notice that a certain opponent discards that special card. What can you infer about his holding in that suit? A series of simulation studies are reported here that allows a quantitative inference based on the conditional probability, given that the opponent has the special card. The same procedure is also used for the conditional probability, given that the opponent does not have the special card.
Statistical tools for the Mallows-Binomial model, the first joint statistical model for preference learning for rankings and ratings. This project was supported by the National Science Foundation under Grant No. 2019901.
Methods for ranking responses of a single response question or a 
  multiple response question are described in the two papers: 
  1. Wang, H. (2008). Ranking Responses in Multiple-Choice Questions. Journal of 
  Applied Statistics, 35, 465-474. <DOI:10.1080/02664760801924533> 
  2. Wang, H. and Huang, W. H. (2014). Bayesian Ranking Responses in Multiple 
  Response Questions. Journal of the Royal Statistical Society: Series A 
  (Statistics in Society), 177, 191-208. <DOI:10.1111/rssa.12009>.
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze  biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.
Finds the k nearest neighbours for every point in a
    given dataset in O(N log N) time using Arya and Mount's ANN library
    (v1.1.3). There is support for approximate as well as exact searches,
    fixed radius searches and 'bd' as well as 'kd' trees. The distance is
    computed using the L2 (Euclidean) metric. Please see package 'RANN.L1'
    for the same functionality using the L1 (Manhattan, taxicab) metric.
Provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.
Implements reversal association pattern analysis for categorical data. Detects sub-tables exhibiting reversal associations in contingency tables, provides visualization tools, and supports simulation-based validation for complex I × J tables.
Interface for multiple data sources, such as the 
    `EDDS` API <https://evds2.tcmb.gov.tr/index.php?/evds/userDocs> of the 
    Central Bank of the Republic of Türkiye and the 
    `FRED` API <https://fred.stlouisfed.org/docs/api/fred/> of the Federal Reserve Bank. 
    Both data providers require API keys for access, which users can easily obtain 
    by creating accounts on their respective websites. 
    The package provides caching ability with the selection of periods to increase the 
    speed and efficiency of requests. 
    It combines datasets requested from different sources, 
    helping users when the data has common frequencies. 
    While combining data frames whenever possible, it also keeps all requested data
    available as separate data frames to increase efficiency.
Access services specified in OpenAPI (formerly Swagger) format.
  It is not a code generator. Client is generated dynamically as a list of R 
  functions.
Access to the C-level R date and 'datetime' code is provided for
 C-level API use by other packages via registration of native functions.
 Client packages simply include a single header 'RApiDatetime.h' provided
 by this package, and also 'import' it.  The R Core group is the original
 author of the code made available with slight modifications by this package.
Extracts machine-readable variables from natural language text using
    AI APIs. Optimized for speed and cost efficiency through parallel processing
    and direct CSV-formatted responses from language models. Supports multiple AI
    providers with robust error handling and automatic retry mechanisms for
    failed extractions.
Provides a high-performance interface for calculating string similarities and distances, leveraging the efficient library 'RapidFuzz' <https://github.com/rapidfuzz/rapidfuzz-cpp>. This package integrates the 'C++' implementation, allowing 'R' users to access cutting-edge algorithms for fuzzy matching and text analysis. Supported metrics include Levenshtein, Damerau-Levenshtein, Hamming, Jaro, Jaro-Winkler, Longest Common Subsequence (LCS), Optimal String Alignment (OSA), Indel, Prefix, and Postfix distances and similarities, as well as multiple fuzzy matching ratios.
Provides JSON parsing capability through the 'Rapidjson' 'C++' header-only library.
A collection of 'HTML', 'JavaScript', 'CSS' and fonts
  assets that generate 'RapiDoc' documentation from an 'OpenAPI' Specification:
   <https://mrin9.github.io/RapiDoc/>.
Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits without parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".
Rapidly estimates tree-topology from large allele frequency
    data using Root Distances Method, under a Brownian Motion Model. See
    Peng et al. (2021) <doi:10.1016/j.ympev.2021.107142>.
Facilitates efficient polygon search using kd trees.
    Coordinate level spatial data can be aggregated to higher geographical
    identities like census blocks, ZIP codes or police district boundaries.
    This process requires mapping each point in the given data set to a
    particular identity of the desired geographical hierarchy. Unless efficient
    data structures are used, this can be a daunting task. The operation
    point.in.polygon() from the package sp is computationally expensive.
    Here, we exploit kd-trees as efficient nearest neighbor search algorithm
    to dramatically reduce the effective number of polygons being searched.
A 'Java' implementation of the RAKE algorithm ('Rose', S., 'Engel', D., 
  'Cramer', N. and 'Cowley', W. (2010) <doi:10.1002/9780470689646.ch1>), which can 
  be used to extract keywords from documents without any training data.
Accurately estimates the reliability of cognitive tasks using a fast and flexible permutation-based split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2025) <doi:10.3758/s13423-024-02597-y> for details.
Provides XML parsing capability through the 'Rapidxml' 'C++' header-only library.
Access to the internal R serialization code is provided for
 use by other packages at the C function level by using the registration of
 native function mechanism. Client packages simply include a single header
 file RApiSerializeAPI.h provided by this package. This packages builds on
 the Rhpc package by Ei-ji Nakama and Junji Nakano which also includes a
 (partial) copy of the file src/main/serialize.c from R itself. The R Core
 group is the original author of the serialization code made available by
 this package.
Run simple 'R' scripts as command line applications, with
  automatic robust and convenient support for command line arguments.
  This package provides 'Rapp', an alternative 'R' front-end similar to 
  'Rscript', that enables this.
Bindings to kernel methods for enforcing security restrictions. 
    AppArmor can apply mandatory access control (MAC) policies on a given task 
    (process) via security profiles with detailed ACL definitions. In addition
    this package implements bindings for setting process resource limits (rlimit),
    uid, gid, affinity and priority. The high level R function 'eval.secure' 
    builds on these methods to perform dynamic sandboxing: it evaluates a single
    R expression within a temporary fork which acts as a sandbox by enforcing 
    fine grained restrictions without affecting the main R process. A portable 
    version of this function is now available in the 'unix' package.
An easy way to determine which directories on the users
    computer you should use to save data, caches and logs. A port of
    Python's 'Appdirs' (<https://github.com/ActiveState/appdirs>) to R.
Provides functions to load and manage data from Apple Ads 
    accounts using the 'Apple Ads Campaign Management API' 
    <https://developer.apple.com/documentation/apple_ads>.
Facilitating the creation of reproducible statistical
    report templates. Once created, rapport templates can be exported to
    various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
    converter backend.
Helper functions that act as wrappers to more advanced statistical
    methods with the advantage of having sane defaults for quick reporting.
Loading data from AppsFlyer Pull API
    <https://support.appsflyer.com/hc/en-us/articles/207034346-Pull-API-aggregate-data>.
Provides access to 'Rangeland Analysis Platform' (RAP) products <https://rangelands.app/products> for arbitrary extents via 'GDAL' virtual file system.
The Agricultural Production Systems sIMulator ('APSIM') is a widely
    used to simulate the agricultural systems for multiple crops. This package 
    is designed to create, modify and run 'apsimx' files in the 'APSIM' Next 
    Generation <https://www.apsim.info/>.
Assessing the comparative performance of two logistic regression models or results of such models or classification models. Discrimination metrics include Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), and difference in Area Under the Curves (AUCs), Brier scores and Brier skill. Plots include Risk Assessment Plots, Decision curves and Calibration plots. Methods are described in Pickering and Endre (2012) <doi:10.1373/clinchem.2011.167965> and Pencina et al. (2008) <doi:10.1002/sim.2929>.
Performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets 
                  derived from thin sections of woody tissue. The package includes functions for visualisation, 
                  detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position 
                  within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, 
                  e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, 
                  Larix Siberica, Pinus cembra and Pinus sylvestris.
Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software (obtained from
    <https://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.
Offers functions for fetching JSON data from the US EPA Air Quality
             System (AQS) API with options to comply with the API rate limits.
             See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             details of the AQS API.
Retrieve air monitoring data and associated metadata from the US
  Environmental Protection Agency's Air Quality System service using functions.
  See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details about
  the US EPA Data Mart API.
Perform risk-adjusted regression and sensitivity analysis as
    developed in "Mitigating Omitted- and Included-Variable Bias in Estimates of
    Disparate Impact" Jung et al. (2024) <arXiv:1809.05651>.
Implementation of an alternating direction method of multipliers
    algorithm for fitting a linear model with tree-based lasso regularization, 
    which is proposed in Algorithm 1 of Yan and Bien (2020) <doi:10.1080/01621459.2020.1796677>. 
    The package allows efficient model fitting on the entire 2-dimensional 
    regularization path for large datasets. The complete set of functions 
    also makes the entire process of tuning regularization parameters and 
    visualizing results hassle-free.