rebar3 is an Erlang build tool that makes it easy to compile and test Erlang applications, port drivers and releases.

rebar3 is a self-contained Erlang script, so it's easy to distribute or even embed directly in a project. Where possible, rebar uses standard Erlang/OTP conventions for project structures, thus minimizing the amount of build configuration work. rebar3 also provides dependency management, enabling application writers to easily re-use common libraries from a variety of locations (git, hg, etc).

The SEQC/MAQC-III Consortium has produced benchmark RNA-seq data for the assessment of RNA sequencing technologies and data analysis methods (Nat Biotechnol, 2014). Billions of sequence reads have been generated from ten different sequencing sites. This package contains the summarized read count data for ~2000 sequencing libraries. It also includes all the exon-exon junctions discovered from the study. TaqMan RT-PCR data for ~1000 genes and ERCC spike-in sequence data are included in this package as well.

An interactive shiny application for performing non-compartmental analysis (NCA) on pre-clinical and clinical pharmacokinetic data. The package builds on PKNCA for core estimators and provides interactive visualizations, CDISC outputs ('ADNCA', PP', ADPP') and configurable TLGs (tables, listings, and graphs). Typical use cases include exploratory analysis, validation, reporting or teaching/demonstration of NCA methods. Methods and core estimators are described in Denney, Duvvuri, and Buckeridge (2015) "Simple, Automatic Noncompartmental Analysis: The PKNCA R Package" <doi:10.1007/s10928-015-9432-2>.

This package provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020).

This package provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.

This package provides functions for the flexible integration of heterogeneous scRNA-seq datasets across multiple tissue types, platforms, and experimental batches. Implements the method described in Ming (2022) <doi:10.1093/bib/bbac167>. The package incorporates modified C++ source code from the flashpca library (Abraham, 2014-2016 <https://github.com/gabraham/flashpca>) for efficient principal component analysis, and the Spectra library (Qiu, 2016-2025) for large-scale eigenvalue and singular value decomposition; see inst/COPYRIGHTS for details on third-party code.

The Fill-Mask Association Test ('FMAT') <doi:10.1037/pspa0000396> is an integrative, probability-based social computing method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositional semantic representations in natural language. Supported language models include BERT <doi:10.48550/arXiv.1810.04805> and its variants available at Hugging Face <https://huggingface.co/models?pipeline_tag=fill-mask>. Methodological references and installation guidance are provided at <https://psychbruce.github.io/FMAT/>.

This package provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.

We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) <doi:10.1007/s11634-021-00457-4>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Rapid satellite data streams in operational applications have clear benefits for monitoring land cover, especially when information can be delivered as fast as changing surface conditions. Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables by using satellite data. This package presents the main applications in remote sensing for land surface monitoring and land cover mapping (soil, vegetation, water...). Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.

Modified functions of the package pcalg and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.

Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).

Two stage curvature identification with machine learning for causal inference in settings when instrumental variable regression is not suitable because of potentially invalid instrumental variables. Based on Guo and Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables" <doi:10.48550/arXiv.2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" <doi:10.18637/jss.v114.i07>.

Implementation of the weighted iterative proportional fitting (WIPF) procedure for updating/adjusting a N-dimensional array given a weight structure and some target marginals. Acknowledgements: The author wish to thank Conselleria de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031), Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Fundación Mapfre (grant Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.

Automated methods to assemble population PK (pharmacokinetic) and PKPD (pharmacodynamic) datasets for analysis in NONMEM (non-linear mixed effects modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions to build datasets from SDTM (study data tabulation module) <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset formats. The package will combine population datasets, add covariates, and create documentation to support regulatory submission and internal communication.

This package provides a method for quantifying resilience after a stress event. A set of functions calculate the area of resilience that is created by the departure of baseline y (i.e., robustness) and the time taken x to return to baseline (i.e., rapidity) after a stress event using the Cartesian coordinates of the data. This package has the capability to calculate areas of resilience, growth, and cases in which resilience is not achieved (e.g., diminished performance without return to baseline).

This package implements the Bayesian Clustering Factor Models (BCFM) for simultaneous clustering and latent factor analysis of multivariate longitudinal data. The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via Rcpp'. Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) <doi:10.1002/sim.70350>.

Access chemical, hazard, bioactivity, and exposure data from the Computational Toxicology and Exposure ('CTX') APIs <https://api-ccte.epa.gov/docs/>. ccdR was developed to streamline the process of accessing the information available through the CTX APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') <https://comptox.epa.gov/dashboard/> and other resources found at the EPA Computational Toxicology and Exposure Online Resources <https://www.epa.gov/comptox-tools>.

Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.

The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).

This package provides a set of functions to scrape and analyze rugby data. Supports competitions including the National Rugby League, New South Wales Cup, Queensland Cup, Super League, and various representative and women's competitions. Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. See Woods et al. (2017) <doi:10.1123/ijspp.2016-0187> for an example of rugby league performance analysis methodology.

This package provides tools for building decision and cost-effectiveness analysis models. It enables users to write these models concisely, simulate outcomesâ including probabilistic analysesâ efficiently using optimized vectorized processes and parallel computing, and produce results. The package employs a Grammar of Modeling approach, inspired by the Grammar of Graphics, to streamline model construction. For an interactive graphical user interface, see DecisionTwig at <https://www.dashlab.ca/projects/decision_twig/>. Comprehensive tutorials and vignettes are available at <https://hjalal.github.io/twig/>.

This software ADAM is a Gene set enrichment analysis (GSEA) package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions. The corresponding roles are extracted from the default collections like Gene ontology and Kyoto encyclopedia of genes and genomes (KEGG). ADAM show their significance by calculating the p-values referring to gene diversity and activity. Each group of genes is called Group of functionally associated genes (GFAG).