Matching algorithm based on network-flow structure. Users are able to modify the emphasis on three different optimization goals: two different distance measures and the number of treated units left unmatched. The method is proposed by Pimentel and Kelz (2019) <doi:10.1080/01621459.2020.1720693>. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

Tokenize text into morphemes. The morphemepiece algorithm uses a lookup table to determine the morpheme breakdown of words, and falls back on a modified wordpiece tokenization algorithm for words not found in the lookup table.

Provide a sample size calculator for micro-randomized trials (MRTs) based on methodology developed in Sample Size Calculations for Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.

Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.

This package provides a flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.

Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.

This package creates sophisticated models of training data and validates the models with an independent test set, cross validation, or Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.

This package provides a collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the data.table package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.

This package provides functions for metabolomics data analysis: data preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis, PLS-DA analysis, classification, feature selection, correlation analysis, data visualisation and re-sampling strategies.

Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.

This package provides a collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. Many of these functions are designed for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D and for rendering matrix expressions and equations in LaTeX.

This package provides tools for importing and cleaning Experience Sampling Method (ESM) data collected via the m-Path platform. The goal is to provide with a few utility functions to be able to read and perform some common operations in ESM data collected through the m-Path platform (<https://m-path.io/landing/>). Functions include raw data handling, format standardization, and basic data checks, as well as to calculate the response rate in data from ESM studies.

Engaging the private sector in contraceptive method supply is critical for equitable, sustainable, and accessible healthcare systems. This package implements Bayesian hierarchical models to estimate public and private contraceptive supply shares over time at national and subnational levels, using Demographic and Health Survey (DHS) data. Penalized splines are used to track supply shares over time, and spatial correlation structures link national and subnational estimates in data- sparse settings. For more details see Comiskey (2025) <doi:10.48550/arXiv.2510.25153>.

Automatically segments a 3D array of voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been accepted for publication in the journal Landscape Ecology. Detailed references will be updated here once those are known.

Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.

This package performs multiple empirical likelihood tests. It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.

This package provides a comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, HUC8', HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) <https://nugis.tfi.org/tabular_data/> and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the ECHO Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) <https://echo.epa.gov/trends/loading-tool/water-pollution-search>. Data are downloaded on demand from the Open Science Framework ('OSF') repository to minimize package size while maintaining full functionality. The integrated manureshed framework methodology is described in Akanbi et al. (2025) <doi:10.1016/j.resconrec.2025.108697>. Designed for nutrient management planning, environmental analysis, and circular economy research at watershed/administrative to national scales. This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. We thank Dr. Robert D. Sabo (U.S. Environmental Protection Agency) for his valuable contributions to the conceptual development and review of this work. We acknowledge Dr. Sheri Spiegal (U.S. Department of Agricultureâ Agricultural Research Service) for foundational contributions to the manureshed classification framework (Spiegal et al. 2020) <doi:10.1016/j.agsy.2020.102813>.

Detection of migration events and segments of continuous residence based on irregular time series of location data as published in Chi et al. (2020) <doi:10.1371/journal.pone.0239408>.

Generalization of Shapiro-Wilk test for multivariate variables.

Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>), adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>), adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and univariate Metropolis with Gaussian proposal.

This package provides access to teaching materials for various statistics courses, including R and Python programs, Shiny apps, data, and PDF/HTML documents. These materials are stored on the Internet as a ZIP file (e.g., in a GitHub repository) and can be downloaded and displayed or run locally. The content of the ZIP file is temporarily or permanently stored. By default, the package uses the GitHub repository sigbertklinke/mmstat4.data. Additionally, the package includes association_measures.R from the archived package ryouready by Mark Heckman and some auxiliary functions.

This package implements the MST-kNN clustering algorithm proposed by Inostroza-Ponta (2008) <https://trove.nla.gov.au/work/28729389>. The algorithm determines the number of clusters automatically by recursively intersecting the Minimum Spanning Tree (MST) and the k-Nearest Neighbor (kNN) proximity graphs constructed from a pairwise distance matrix. The value of k is selected via a connectivity criterion (the smallest k such that the kNN graph is connected, bounded by floor(log(n))). The package requires only a distance matrix as input and returns cluster assignments, an igraph network, and partition metadata.

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival and reproduction Data. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See our companion paper Baudrot and Charles (2021) <doi:10.21105/joss.03200>, as well as complementary details in Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>.