Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, mulset can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.

Compute correlation, association, agreement, and reliability measures for small to high-dimensional datasets through a consistent matrix-oriented interface. Supports classical correlations (Pearson, Spearman, Kendall, Chatterjee's rank correlation), distance correlation, partial correlation with regularised estimators, shrinkage correlation for p >= n settings, robust correlations including biweight mid-correlation, percentage-bend, Winsorized, and skipped correlation, latent-variable methods for binary and ordinal data, pairwise and overall intraclass correlation for wide data, repeated-measures correlation, and agreement/reliability analyses based on Cohen's kappa, weighted kappa, multi-rater kappa, Gwet's AC1/AC2, Krippendorff's alpha, Bland-Altman methods, Lin's concordance correlation coefficient, Poisson GLMM concordance for count data, and repeated-measures intraclass/concordance correlation. Implemented with optimized C++ backends using BLAS/OpenMP and memory-aware symmetric updates, and returns standard R objects with print/summary/plot methods plus optional Shiny viewers for matrix inspection. Methods based on Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>; high-dimensional shrinkage covariance estimation <doi:10.2202/1544-6115.1175>; Lin (1989) <doi:10.2307/2532051>; Wilcox (1994) <doi:10.1007/BF02294395>; Wilcox (2004) <doi:10.1080/0266476032000148821>; Hayes and Krippendorff (2007) <doi:10.1080/19312450709336664>; weighted repeated-measures correlation by Kondo et al. (2025) <doi:10.1002/sim.70046>.

Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.

Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).

This package provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.

Mapping Averaged Pairwise Information (MAPI) is an exploratory method providing graphical representations summarizing the spatial variation of pairwise metrics (eg. distance, similarity coefficient, ...) computed between georeferenced samples.

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.

Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.

Recursively calculates mass properties (mass, center of mass, moments and products of inertia, and optionally, their uncertainties) for arbitrary decomposition trees. R. L. Zimmerman, J. H. Nakai. (2005) <https://www.sawe.org/product/paper-3360/>).

Deploy file changes across multiple GitHub repositories using the GitHub Web API <https://docs.github.com/en/rest>. Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command.

This package provides a low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>, which applies a mass-preserving spline to soil attributes. Splining soil data is a safe way to make continuous down-profile estimates of attributes measured over discrete, often discontinuous depth intervals.

This package performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.

Can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation. Has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.

This package performs multilevel matches for data with cluster- level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis. Details in Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>. The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at <https://github.com/markmfredrickson/optmatch> if not available on CRAN.

This package provides functions to support compatibility between Maelstrom R packages and Opal environment. Opal is the OBiBa core database application for biobanks. It is used to build data repositories that integrates data collected from multiple sources. Opal Maelstrom is a specific implementation of this software. This Opal client is specifically designed to interact with Opal Maelstrom distributions to perform operations on the R server side. The user must have adequate credentials. Please see <https://opaldoc.obiba.org/> for complete documentation.

Two method new of multigroup and simulation of data. The first technique called multigroup PCA (mgPCA) this multivariate exploration approach that has the idea of considering the structure of groups and / or different types of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality Reduction (MDR) it is another multivariate exploration method that is based on projections. In addition, a method called Single Dimension Exploration (SDE) was incorporated for to analyze the exploration of the data. It could help us in a better way to observe the behavior of the multigroup data with certain variables of interest.

This package provides a set of utility functions for analysing and modelling data from continuous report short-term memory experiments using either the 2-component mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users are also able to simulate from these models.

This package implements a model-based clustering method for categorical life-course sequences relying on mixtures of exponential-distance models introduced by Murphy et al. (2021) <doi:10.1111/rssa.12712>. A range of flexible precision parameter settings corresponding to weighted generalisations of the Hamming distance metric are considered, along with the potential inclusion of a noise component. Gating covariates can be supplied in order to relate sequences to baseline characteristics and sampling weights are also accommodated. The models are fitted using the EM algorithm and tools for visualising the results are also provided.

This package provides a toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.

Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model MGHD (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The MGHFA (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The MSGHD is the mixture of multiple scaled generalized hyperbolic distributions, the cMSGHD is a MSGHD with convex contour plots and the MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.

This package provides a single unified interface for computing a wide variety of classical and modern correlation and association measures. Continuous methods include classical correlations (Pearson, Spearman, Kendall), modern dependence measures (distance correlation, maximal information coefficient, Hilbert-Schmidt independence criterion, Chatterjee's xi, Hoeffding's D, mutual information), robust correlations (biweight midcorrelation, percentage bend, Winsorized), ordinal correlations (polychoric, tetrachoric), partial and semi-partial correlations, and nonparametric measures (ball correlation, Bergsma-Dassios tau*). Categorical association measures (Cramer's V, phi coefficient, Goodman-Kruskal gamma, Somers D, contingency coefficient, Tschuprow's T) are available via moderncor_cat().

This package provides functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by à yvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).

Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.

Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).