Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with nlme and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

Extends classical linear and quadratic discriminant analysis by incorporating permutation group symmetries into covariance matrix estimation. The package leverages methodology from the gips framework to identify and impose permutation structures that act as a form of regularization, improving stability and interpretability in settings with symmetric or exchangeable features. Several discriminant analysis variants are provided, including pooled and class-specific covariance models, as well as multi-class extensions with shared or independent symmetry structures. For more details about gips methodology see and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen, KoÅ odziejek (2025, <doi:10.18637/jss.v112.i07>).

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package GISINTEGRATION offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.

Make efficient Rust implementations of graph adjustment identification distances available in R. These distances (based on ancestor, optimal, and parent adjustment) count how often the respective adjustment identification strategy leads to causal inferences that are incorrect relative to a ground-truth graph when applied to a candidate graph instead. See also Henckel, Würtzen, Weichwald (2024) <doi:10.48550/arXiv.2402.08616>.

The goal of GHCNr is to provide a fast and friendly interface with the Global Historical Climatology Network daily (GHCNd) database, which contains daily summaries of weather station data worldwide (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>). GHCNd is accessed through the web API <https://www.ncei.noaa.gov/access/services/data/v1>. GHCNr main functionalities consist of downloading data from GHCNd, filter it, and to aggregate it at monthly and annual scales.

Create correlation heatmaps with ggplot2 and customise them with flexible annotation and clustering. Symmetric heatmaps can use triangular or mixed layouts, removing redundant information or displaying complementary information in the two halves. There is also support for general heatmaps not displaying correlations.

This package provides multiple palettes based on pride flags with tailored themes.

This package provides a genomic simulation approach for creating biologically informed individual genotypes from empirical data that 1) samples alleles from populations without replacement, 2) segregates alleles based on species-specific recombination rates. gscramble is a flexible simulation approach that allows users to create pedigrees of varying complexity in order to simulate admixed genotypes. Furthermore, it allows users to track haplotype blocks from the source populations through the pedigrees.

Annotation of ggplot2 plots with arbitrary TikZ code, using absolute data or relative plot coordinates.

An interactive document on the topic of goodness of fit analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

Simplifies the creation, management, and updating of local databases using data extracted from Google Earth Engine ('GEE'). It integrates with GEE to store, aggregate, and process spatio-temporal data, leveraging SQLite for efficient, serverless storage. The geeLite package provides utilities for data transformation and supports real-time monitoring and analysis of geospatial features, making it suitable for researchers and practitioners in geospatial science. For details, see Kurbucz and Andrée (2025) "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package" <https://hdl.handle.net/10986/43165>.

This package provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. The package also provides an interface to the SuiteSparse Matrix Collection, <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix benchmarks collected from a wide range of applications.

Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.

Interactively applies the Guidelines for Reporting About Network Data (GRAND) to an igraph object, and generates a uniform narrative or tabular description of the object.

Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.

The American Association Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) BioPharma Collaborative represents a multi-year, multi-institution effort to build a pan-cancer repository of linked clinico-genomic data. The genomic and clinical data are provided in multiple releases (separate releases for each cancer cohort with updates following data corrections), which are stored on the data sharing platform Synapse <https://www.synapse.org/>. The genieBPC package provides a seamless way to obtain the data corresponding to each release from Synapse and to prepare datasets for analysis.

Finds adaptive strategies for sequential symmetric games using a genetic algorithm. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation.

We propose two distribution-free test statistics based on between-sample edge counts and measure the degree of relevance by standardized counts. Users can set edge costs in the graph to compare the parameters of the distributions. Methods for comparing distributions are as described in: Xiaoping Shi (2021) <arXiv:2107.00728>.

Efficiently implements the Graphical Lasso algorithm, utilizing the Armadillo C++ library for rapid computation. This algorithm introduces an L1 penalty to derive sparse inverse covariance matrices from observations of multivariate normal distributions. Features include the generation of random and structured sparse covariance matrices, beneficial for simulations, statistical method testing, and educational purposes in graphical modeling. A unique function for regularization parameter selection based on predefined sparsity levels is also offered, catering to users with specific sparsity requirements in their models. The methodology for sparse inverse covariance estimation implemented in this package is based on the work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.

This package implements iterative conditional expectation (ICE) estimators of the plug-in g-formula (Wen, Young, Robins, and Hernán (2020) <doi: 10.1111/biom.13321>). Both singly robust and doubly robust ICE estimators based on parametric models are available. The package can be used to estimate survival curves under sustained treatment strategies (interventions) using longitudinal data with time-varying treatments, time-varying confounders, censoring, and competing events. The interventions can be static or dynamic, and deterministic or stochastic (including threshold interventions). Both prespecified and user-defined interventions are available.

Saves a ggplot object into multiple files, each with a layer added incrementally. Generally to be used in presentation slides. Flexible enough to allow different file types for the final complete plot, and intermediate builds.

This package provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users.

Group Bayesian Networks: This package implements the inference of group Bayesian networks based on hierarchical feature clustering, and the adaptive refinement of the grouping regarding an outcome of interest, as described in Becker et. al (2021) <doi: 10.1371/journal.pcbi.1008735>.