This package implements the Regional Association Score (RAS) method for genome-wide association studies (GWAS). For each single nucleotide polymorphism (SNP), RAS quantifies the strength of association within its surrounding genomic region, arranges these regional scores along the chromosome into a signal profile, and applies changepoint detection to locate association regions, improving statistical power while controlling the false positive rate. The method is described in Jiang and Zhang (2025) <doi:10.1073/pnas.2419721122>.

This package implements Random Graphical Models for multivariate data analysis across multiple environments, providing tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E. C., and Richter, F. (2026) "Random Graphical Model of Microbiome Interactions in Related Environments" <doi:10.1007/s13253-024-00638-6>.

This package lets you determine the significance of pre-defined sets of genes with respect to an outcome variable, such as a group indicator, a quantitative variable or a survival time.

The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions.

This package computes standardized mean differences and confidence intervals for multiple data types based on Yang, D., & Dalton, J. E. (2012) <https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.

This package provides a cross-platform Zip compression library for R. It is a replacement for the zip function, that does not require any additional external tools on any platform.

Rizin is a reverse engineering framework and a set of small command-line utilities, providing a complete binary analysis experience with features like disassembler, hexadecimal editor, emulation, binary inspection, debugger, and more.

Run basic pattern analyses on character sets, digits, or combined input containing both characters and numeric digits. Useful for data cleaning and for identifying columns containing multiple or nonstandard formats.

Bootstrap bias correction for the performance of the cross-validation procedure. The relevant paper is Tsamardinos I., Greasidou E. and Borboudakis G. (2018), <doi:10.1007/s10994-018-5714-4>.

This package provides an interface to data provided by the Bank for International Settlements <https://www.bis.org>, allowing for programmatic retrieval of a large quantity of (central) banking data.

Simple helpers for GDAL data source names ('DSN'), prefix and suffix and other handling. GDAL is the Geospatial Data Abstraction Library <https://gdal.org/>, not used by this package directly.

Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.

Simple helpers for matrix multiplication on data.frames. These allow for more concise code during low level mathematical operations, and help ensure code is more easily read, understood, and serviced.

This package provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

This package implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.

Following the method of Bailey et al., computes for a collection of candidate models the probability of backtest overfitting, the performance degradation and probability of loss, and the stochastic dominance.

Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter. Allows the application of Personalised Synthetic Controls.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.

Rizin is a reverse engineering framework and a set of small command-line utilities, providing a complete binary analysis experience with features like disassembler, hexadecimal editor, emulation, binary inspection, debugger, and more.

Allows caching of raw data directly in R code. This allows R scripts and R Notebooks to be shared and re-run on a machine without access to the original data. Cached data is encoded into an ASCII string that can be pasted into R code. When the code is run, the data is automatically loaded from the cached version if the original data file is unavailable. Works best for small datasets (a few hundred observations).

This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.