This package creates a common framework for organizing, naming, and gathering population, age, race, and ethnicity data from the Census Bureau. Accesses the API <https://www.census.gov/data/developers/data-sets.html>. Provides tools for adding information to existing data to line up with Census data.

This package provides a GUI with which users can construct and interact with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.

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).

Choose the appropriate map projection for a given application, visualise the resulting distortion, and georeference data from unknown projections. The full functionalities of the package are described in Pozzi et al. (2026) <doi:10.31223/X5K17P> (pre-print).

Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.

Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.

Computing, comparing, and demonstrating top informative centrality measures within a network. "CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis" provides a comprehensive overview of the package functionality Ashtiani et al. (2018) <doi:10.1093/bioinformatics/bty819>.

This package provides functions to calculate the relative crystallinity of starch by X-ray Diffraction (XRD) and Infrared Spectroscopy (FTIR). Starch is biosynthesized by plants in the form of granules semicrystalline. For XRD, the relative crystallinity is obtained by separating the crystalline peaks from the amorphous scattering region. For FTIR, the relative crystallinity is achieved by setting of a Gaussian holocrystalline-peak in the 800-1300 cm-1 region of FTIR spectrum of starch which is divided into amorphous region and crystalline region. The relative crystallinity of native starch granules varies from 14 of 45 percent. This package was supported by FONDECYT 3150630 and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.

This package provides a flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) <doi:10.13140/RG.2.2.28323.53280>.

This package provides a collection of command-line color styles based on the crayon package. Colt styles are defined in themes that can easily be switched, to ensure command line output looks nice on dark as well as light consoles.

Estimation and goodness-of-fit functions for copula-based models of bivariate data with arbitrary distributions (discrete, continuous, mixture of both types). The copula families considered here are the Gaussian, Student, Clayton, Frank, Gumbel, Joe, Plackett, BB1, BB6, BB7,BB8, together with the following non-central squared copula families in Nasri (2020) <doi:10.1016/j.spl.2020.108704>: ncs-gaussian, ncs-clayton, ncs-gumbel, ncs-frank, ncs-joe, and ncs-plackett. For theoretical details, see, e.g., Nasri and Remillard (2023) <arXiv:2301.13408>.

This package creates publication-ready causal diagrams using ggplot2'. Provides simple templates for common causal diagrams (e.g., mediating mechanisms and parallel pathways) with customizable labels, colors, fonts, and export-friendly defaults.

Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.

This package provides a data package with 2 main package variables: signature and etiology'. The signature variable contains the latest mutational signature profiles released on COSMIC <https://cancer.sanger.ac.uk/signatures/> for 3 mutation types: * Single base substitutions in the context of preceding and following bases, * Doublet base substitutions, and * Small insertions and deletions. The etiology variable provides the known or hypothesized causes of signatures. cosmicsig stands for COSMIC signatures. Please run ?'cosmicsig for more information.

Supporting functionality to run caret with spatial or spatial-temporal data. caret is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using caret'. It includes the newly suggested Nearest neighbor distance matching cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>; Schumacher et al. (2025) <doi:10.5194/gmd-18-10185-2025>. The package is described in detail in Meyer et al. (2026) <doi:10.1007/978-3-031-99665-8_11>.

OpenAI's ChatGPT <https://chat.openai.com/> coding assistant for RStudio'. A set of functions and RStudio addins that aim to help the R developer in tedious coding tasks.

Parameter estimation, one-step ahead forecast and new location prediction methods for spatio-temporal data.

The maximum likelihood estimation (MLE) of the count data models along with standard error of the estimates and Akaike information model section criterion are provided. The functions allow to compute the MLE for the following distributions such as the Bell distribution, the Borel distribution, the Poisson distribution, zero inflated Bell distribution, zero inflated Bell Touchard distribution, zero inflated Poisson distribution, zero one inflated Bell distribution and zero one inflated Poisson distribution. Moreover, the probability mass function (PMF), distribution function (CDF), quantile function (QF) and random numbers generation of the Bell Touchard and zero inflated Bell Touchard distribution are also provided.

Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the Certara.RsNLME package.

An implementation of Conic Multivariate Adaptive Regression Splines (CMARS) in R. See Weber et al. (2011) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization, <DOI:10.1080/17415977.2011.624770>. It constructs models by using the terms obtained from the forward step of MARS and then estimates parameters by using Tikhonov regularization and conic quadratic optimization. It is possible to construct models for prediction and binary classification. It provides performance measures for the model developed. The package needs the optimisation software MOSEK <https://www.mosek.com/> to construct the models. Please follow the instructions in Rmosek for the installation.

Implementation of Librino, Levorato, and Zorzi (2014) <doi:10.1002/wcm.2305> algorithm for computation of the intersection areas of an arbitrary number of circles.

Easy and convenient access to the datasets of the "Centre d'Estudis d'Opinio", the Catalan institution for polling and public opinion. The package retrieves microdata directly from the open data platform of the Generalitat de Catalunya and returns it in a tidy format.

Compute Chinese capital stocks in provinces level, based on Zhang (2008) <DOI:10.1080/14765280802028302>.

The Chinese ID number contains a lot of information, this package helps you get the region, date of birth, age, age based on year, gender, zodiac, constellation information from the Chinese ID number.