Facilitate reporting for regression and correlation modeling, hypothesis testing, variance analysis, outlier detection, and detailed descriptive statistics.

This package provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.

Implementation of functions, which combines binomial calculation and data visualisation, to analyse the differences in publishing authorship by gender described in Day et al. (2020) <doi:10.1039/C9SC04090K>. It should only be used when self-reported gender is unavailable.

This package implements Bayesian parameter and Generalized Process Capability Indices (GPCIs) estimation using the Lindley approximation method (Lindley, 1980 <doi:10.2307/2345271>) under progressive Type-II censored data (Balakrishnan & Aggarwala, 2000 <doi:10.1007/978-1-4612-1334-5>). Evaluates point estimates and posterior expectations for classical and non-normal capability indices, including Cpy (Maiti et al., 2010 <doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019 <doi:10.1007/s41872-019-00081-4>), CpTk (Saha et al., 2019 <doi:10.1007/s13198-019-00789-7>), Cpc (Saha et al., 2022 <doi:10.1080/02664763.2021.1971632>), CNpmc (Alotaibi et al., 2022 <doi:10.1155/2022/3135264>), CNpmkc (Saha et al., 2024 <doi:10.1142/S021853932450013X>), CNpk (Saha et al., 2018 <doi:10.1080/21681015.2018.1437793>), and Vannman's Cp(u,v) family (Vannman, 1995 <doi:10.1111/j.1467-9574.1995.tb01472.x>). Calculates point estimates, bias, mean squared error (MSE), Bayes risk under Linex and squared error loss, Highest Posterior Density (HPD) credible intervals at 90%, 95%, and 99% levels, and Heidelberger and Welch's MCMC convergence diagnostics (Heidelberger & Welch, 1983 <doi:10.1287/opre.31.6.1109>) with convergence probabilities. Accommodates user-defined probability density/mass functions, cumulative distribution functions, and survival functions. Supports progressive parametric and non-parametric bootstrap confidence intervals (Efron, 1987 <doi:10.1080/01621459.1987.10478410>) at 90%, 95%, and 99% significance levels.

This package provides a data visualization design that compares two (usually on a par with each other) data sources on one grid of taichi (yin-yang) diagrams, where the two interlocking fish of every symbol are filled by the two sources, while inheriting ggplot2 features.

This package implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the randnet package <https://CRAN.R-project.org/package=randnet>. For more details, see the "proposed method" section of Liu, Bi, and Li (2025) <doi:10.48550/arXiv.2507.00402>.

Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.

The ggplot2 package provides a powerful set of tools for visualising and investigating data. The ggsoccer package provides a set of functions for elegantly displaying and exploring soccer event data with ggplot2'. Providing extensible layers and themes, it is designed to work smoothly with a variety of popular sports data providers.

This package provides functions to read in the geometry format under the Neuroimaging Informatics Technology Initiative ('NIfTI'), called GIFTI <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.

This package provides ggplot2 equivalents of fixest::coefplot() and fixest::iplot(), for producing nice coefficient plots and interaction plots. Enables some additional functionality and convenience features, including grouped multi-'fixest object faceting and programmatic updates to existing plots (e.g., themes and aesthetics).

Obtain standardized data from multiple Git services, including GitHub and GitLab'. Designed to be Git service-agnostic, this package assists teams with activities spread across various Git platforms by providing a unified way to access repository data.

This package provides a Chernoff face geom for ggplot2'. Maps multivariate data to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.

Turns one-off iterative R procedures (such as for loops, lapply() or pmap() from purrr') into production-grade workflows by wrapping them with orthogonal, composable execution layers. Two layers are always active: structured logging with real traceback and per-case timing; and reproducibility capture, which records the R version, loaded package versions, execution environment, the exact iteration mask, and a stat-based fingerprint of every input file referenced in the mask (with a diff_inputs() helper to detect silent drift between runs). Parallel execution (built on the future framework, Bengtsson (2021) <doi:10.32614/RJ-2021-048>), non-blocking background jobs, and opt-in progress reporting (via progressr') are implemented as optional, composable layers. Further layers (error replay, content-hash input fingerprinting, content-based case identifiers) are planned and will remain composable with the default layers.

Access data provided by the United States Government Publishing Office (GPO) GovInfo API (<https://github.com/usgpo/api>).

An implementation of ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014. <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>. Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided. The structure files can be adopted for localization or for insurance companies who use Internal Models (IM). Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts. With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment).

Set of functions to create datasets using a correlation matrix.

Extends the capabilities of ggplot2 by providing grammatical elements and plot helpers designed for visualizing temporal patterns. The package implements a grammar of temporal graphics, which leverages calendar structures to highlight changes over time. The package also provides plot helper functions to quickly produce commonly used time series graphics, including time plots, season plots, and seasonal sub-series plots.

Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).

The goal of gsDesign2 is to enable fixed or group sequential design under non-proportional hazards. To enable highly flexible enrollment, time-to-event and time-to-dropout assumptions, gsDesign2 offers piecewise constant enrollment, failure rates, and dropout rates for a stratified population. This package includes three methods for designs: average hazard ratio, weighted logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and MaxCombo tests. Substantial flexibility on top of what is in the gsDesign package is intended for selecting boundaries.

Modified versions of the lag() and summary() functions: glag() and gsummary(). The prefix g is a reminder of who to blame if things do not work as they should.

Mapping tools that convert place names to coordinates on the fly. These ggplot2 extensions make maps from a data frame where one of the columns contains place names, without having to directly work with the underlying geospatial data and tools. The corresponding map data must be registered with cartographer either by the user or by another package.

Organize a so-called ragged array as generalized arrays, which is simply an array with sub-dimensions denoting the subdivision of dimensions (grouping of members within dimensions). By the margins (names of dimensions and sub-dimensions) in generalized arrays, operators and utility functions provided in this package automatically match the margins, doing map-reduce style parallel computation along margins. Generalized arrays are also cooperative to R's native functions that work on simple arrays.

This package provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive RStudio plot for better guessing dynamically of initial values for ease of included optimization and simulating.

Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.