This is a GitHub API wrapper for R. <https://docs.github.com/en/rest> It uses the gh package but has things wrapped up for convenient use cases.

Finite candidate-set approximate optimal designs for group testing and related experiments, using convex optimization and equivalence checks. Implements the information matrix and cost structure for the prevalence / sensitivity / specificity model used in Huang and colleagues (2020), as in Chi-Kuang Yeh, Weng Kee Wong, and Julie Zhou (<doi:10.48550/arXiv.2508.08445>).

Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

Server implementation of GraphQL <http://spec.graphql.org/>, a query language originally created by Facebook for describing data requirements on complex application data models. Visit <https://graphql.org> to learn more about GraphQL'.

This package implements regression models for bounded continuous data in the open interval (0,1) using the five-parameter Generalized Kumaraswamy distribution. Supports modeling all distribution parameters (alpha, beta, gamma, delta, lambda) as functions of predictors through various link functions. Provides efficient maximum likelihood estimation via Template Model Builder ('TMB'), offering comprehensive diagnostics, model comparison tools, and simulation methods. Particularly useful for analyzing proportions, rates, indices, and other bounded response data with complex distributional features not adequately captured by simpler models.

Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>.

This package provides tools for assessing and diagnosing convergence of Markov Chain Monte Carlo simulations, as well as for graphically display results from full MCMC analysis. The package also facilitates the graphical interpretation of models by providing flexible functions to plot the results against observed variables, and functions to work with hierarchical/multilevel batches of parameters (Fernández-i-Marà n, 2016 <doi:10.18637/jss.v070.i09>).

This package implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.

Large language models are readily accessible via API. This package lowers the barrier to use the API inside of your development environment. For more on the API, see <https://platform.openai.com/docs/introduction>.

The ggplot2 package is the state-of-the-art toolbox for creating and formatting graphs. However, it is easy to forget how certain formatting commands are named and sometimes users find themselves asking: How do you rotate the x-axis labels again? Or how do you hide the legend...? This package allows users to issue natural language commands related to theme-related styling of plots (colors, font size and such), which then are translated into valid ggplot2 commands.

Providing access to the API for Gas Infrastructure Europe's natural gas transparency platforms <https://agsi.gie.eu/> and <https://alsi.gie.eu/>. Lets the user easily download metadata on companies and gas storage units covered by the API as well as the respective data on regional, country, company or facility level.

This package provides a collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.

This package contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.

This package provides a collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's Struggle for Existence in the journal Ecology and Evolution.

Local structure in genomic data often induces dependence between observations taken at different genomic locations. Ignoring this dependence leads to underestimation of the standard error of parameter estimates. This package uses block bootstrapping to estimate asymptotically correct standard errors of parameters from any standard generalised linear model that may be fit by the glm() function.

This package performs Granger causality tests on pairs of time series to determine causal relationships. Uses Vector Autoregressive (VAR) models to test whether one time series helps predict another beyond what the series own past values provide. Returns structured results including p-values, test statistics, and causality conclusions for both directions.

This package provides functions for survey data including svydesign objects from the survey package that call ggplot2 to make bar charts, histograms, boxplots, and hexplots of survey data.

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.

Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of spatial objects.

This package provides tools to streamline the extraction, processing, and visualization of Computable General Equilibrium (CGE) results from GTAP models. Designed for compatibility with both .har and .sl4 files, the package enables users to automate data preparation, apply mapping metadata, and generate high-quality plots and summary tables with minimal coding. GTAPViz supports flexible export options (e.g., Text, CSV, Stata', or Excel formats). This facilitates efficient post-simulation analysis for economic research and policy reporting. Includes helper functions to filter, format, and customize outputs with reproducible styling.

Generates dense or sparse graphs using graphon mixtures and graphettes. Graphon mixtures uses two graphons U and W to generate graphs. Sparse graphs are generated in this case using the inverse line graph (root) operation. Graphettes have 3 components, the graphon W, a real-valued sequence and a graph edit function. Both techniques can generate dense or sparse graphs. Kandanaarachchi and Ong (2026) <doi:10.48550/arXiv.2505.13864>, Wijesinghe et al (2026) <doi:10.48550/arXiv.2602.23566>.

This package provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.

This package provides tools for creating animated glassmorphism-style tab navigation and select filter widgets in Shiny applications. Provides a tab navigation component with a sliding glass halo animation, a searchable multi-select dropdown, and a single-select dropdown - all with multiple colour themes and server-side update helpers. Tabs support icons, numeric badges, disable/enable toggling, runtime append/remove, reactive rendering via renderGlassTabs()', URL bookmarking, and compact mode for dashboard card layouts. glassTabCondition() generates conditionalPanel() condition strings without needing to recall the internal input key pattern. glasstabs_news() displays the release notes from the R console. Built-in example apps can be launched with runGlassExample()'. All widgets are compatible with standard Shiny layouts and bs4Dash dashboards and bslib themed applications. For full documentation and examples see Arthur (2026) <https://prigasg.github.io/glasstabs/>.

This package provides functions for matching student-answers to teacher answers for a variety of data types.