Summarize multiple biomarker responses of aquatic organisms to contaminants using Cliffâ s delta, as described in Pham & Sokolova (2023) <doi:10.1002/ieam.4676>.

Estimates the multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal networks, contemporaneous networks and between-subjects networks.

This package provides clustering of genes with similar dose response (or time course) profiles. It implements the method described by Lin et al. (2012).

Peek into published shinylive applications by retrieving their source code. Supports downloading and extracting shiny application source from quarto documents and standalone shinylive applications.

Function library for processing collective movement data (e.g. fish schools, ungulate herds, baboon troops) collected from GPS trackers or computer vision tracking software.

The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.

Density, distribution function, quantile function and random generation for the sum of independent non-identical binomial distribution with parameters \codesize and \codeprob.

Combine topic modeling and sentiment analysis to identify individual students gaps, and highlight their strengths and weaknesses across predefined competency domains and professional activities.

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

Detects change points in long univariate time series using the SCAN framework. The implementation uses a native Rust backend exposed to R via extendr'.

Reconstructs all possible raw data that could have led to reported summary statistics. Provides a wrapper for the Rust implementation of the CLOSURE algorithm.

This package provides tools for audio data analysis, including feature extraction, pitch detection, and speaker identification. Designed for voice research and signal processing applications.

Scan R package dependencies for known security vulnerabilities using the OSV database, with support for renv.lock, installed libraries, and user-supplied package versions.

Assortativity coefficients, centrality measures, and clustering coefficients for weighted and directed networks. Rewiring unweighted networks with given assortativity coefficients. Generating general preferential attachment networks.

The GNU/Linux distribution, a set of tools for managing development environments, home environments, and operating systems, a set of predefined configurations, practices and workflows.

This package provides a comprehensive framework for fitting regularized finite mixture regression models via an MM algorithm. The sparse group lasso (sgl) penalty is applied to parameter updates within the MM algorithm for variable selection with respect to groups and covariates. The package provides multiple functions for estimation and allows users to fit models over different lambda-alpha sgl penalties and group counts.

Predicts statistics of a reference distribution from a mixture of raw clinical measurements (healthy and pathological). Uses pretrained CNN models to estimate the mean, standard deviation, and reference fraction from 1D or 2D sample data. Methods are described in LeBien, Velev, and Roche-Lima (2026) "RINet: synthetic data training for indirect estimation of clinical reference distributions" <doi:10.1016/j.jbi.2026.104980>.

Quantitative Structure-Activity Relationship (QSAR) modeling is a valuable tool in computational chemistry and drug design, where it aims to predict the activity or property of chemical compounds based on their molecular structure. In this vignette, we present the rQSAR package, which provides functions for variable selection and QSAR modeling using Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Random Forest algorithms.

This package provides a novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) <https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) <https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.

This package provides methods for randomization inference in group-randomized trials. Specifically, it can be used to analyze the treatment effect of stratified data with multiple clusters in each stratum with treatment given on cluster level. User may also input as many covariates as they want to fit the data. Methods are described by Dylan S Small et al., (2012) <doi:10.1198/016214507000000897>.

This package ofers functions for importation, normalization, visualization, and quality control to correct identified sources of variability in array of CGH experiments.

This package is a micro-package for getting your IP address, either the local/internal or the public/external one. Currently only IPv4 addresses are supported.

It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This package does exactly that.