General functions for performing extreme value analysis on a circular domain as part of the statistical methodology in the paper by Konzen, E., Neves, C., and Jonathan, P. (2021). Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference. Environmetrics, 32(4), e2667.

CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association Study) provides a dynamically adaptive framework for genome-wide association studies (GWAS) that integrates chromosome-scale haplotypes with single nucleotide polymorphism (SNP) analysis. The method identifies and extends haplotype variants based on their phenotypic associations rather than predefined linkage blocks, enabling high-resolution detection of quantitative trait loci (QTL). By leveraging long-range phased haplotype information, CHAP-GWAS improves statistical power and offers a more comprehensive view of the genetic architecture underlying complex traits.

This package provides a wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. Available datasets include the Decennial Census, American Community Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.

This package provides a fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the a la carte embeddings approach developed by Khodak et al. (2018) <doi:10.48550/arXiv.1805.05388> and evaluate hypotheses about covariate effects on embeddings using the regression framework developed by Rodriguez et al. (2021)<doi:10.1017/S0003055422001228>. New version of the package applies a new estimator to measure the distance between word embeddings as described in Green et al. (2025) <doi:10.1017/pan.2024.22>.

This package implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828> (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) <doi:10.2307/3315735> and Lockhart, Spinelli and Stephens (2007) <doi:10.1002/cjs.5550350111> (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) <doi:10.2307/3316040>. Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values.

Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).

This package provides a wrapper around the COVID Tracking Project API <https://covidtracking.com/api/> providing data on cases of COVID-19 in the US.

Utility functions that provides wrapper to descriptive base functions like cor, mean and table. It makes use of the formula interface to pass variables to functions. It also provides operators to concatenate (%+%), to repeat (%n%) and manage character vectors for nice display.

This package implements cluster-polarization coefficient for measuring distributional polarization in single or multiple dimensions, as well as associated functions. Contains support for hierarchical clustering, k-means, partitioning around medoids, density-based spatial clustering with noise, and manually imposed cluster membership. Mehlhaff (2024) <doi:10.1017/S0003055423001041>.

This package provides a reliable and efficient tool for cleaning univariate time series data. It implements reliable and efficient procedures for automating the process of cleaning univariate time series data. The package provides integration with already developed and deployed tools for missing value imputation and outlier detection. It also provides a way of visualizing large time-series data in different resolutions.

This package provides a tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .

CIFTI files contain brain imaging data in "grayordinates," which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). ciftiTools provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing.

Computes conditional multivariate normal densities, probabilities, and random deviates.

This package performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.

Core visualizations and summaries for the CRAN package database. The package provides comprehensive methods for cleaning up and organizing the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, producing package dependence trees, and for computing useful summaries and producing interactive visualizations from the resulting networks and summaries. The resulting networks can be coerced to igraph <https://CRAN.R-project.org/package=igraph> objects for further analyses and modelling.

Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

Compile inline C code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file.

This package provides igraph objects representing engineering plans of study across multiple disciplines and institutions. The data are intended for use with the CurricularComplexity package (Reeping, 2026) <https://CRAN.R-project.org/package=CurricularComplexity> to support analyses of curricular structure. The package leverages network analysis approaches implemented in igraph (Csárdi et al., 2025) <doi:10.5281/zenodo.7682609>.

Creation of interactive tables, listings and figures ('TLFs') and associated report for exploratory analysis of data in a clinical trial, e.g. for clinical oversight activities. Interactive figures include sunburst, treemap, scatterplot, line plot and barplot of counts data. Interactive tables include table of summary statistics (as counts of adverse events, enrollment table) and listings. Possibility to compare data (summary table or listing) across two data batches/sets. A clinical data review report is created via study-specific configuration files and template R Markdown reports contained in the package.

Colorful Data Frames in the terminal. The new class does change the behaviour of any of the objects, but adds a style definition and a print method. Using ANSI escape codes, it colors the terminal output of data frames. Some column types (such as p-values and identifiers) are automatically recognized.

Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size.