The Cauchy distribution is a special case of the t distribution when the degrees of freedom are equal to 1. The functions are related to the multivariate Cauchy distribution and include simulation, computation of the density, maximum likelihood estimation, contour plot of the bivariate Cauchy distribution, and discriminant analysis. References include: Nadarajah S. and Kotz S. (2008). "Estimation methods for the multivariate t distribution". Acta Applicandae Mathematicae, 102(1): 99--118. <doi:10.1007/s10440-008-9212-8>, and Kanti V. Mardia, John T. Kent and John M. Bibby (1979). "Multivariate analysis", ISBN:978-0124712522. Academic Press, London.

Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.

Perform library searches against electron ionization mass spectral databases using either the API provided by MS Search software (<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or custom implementations of the Identity and Similarity algorithms.

This package provides a framework for multiple hypothesis testing based on distribution of p values. It is well known that the p values come from different distribution for null and alternatives, in this package we provide functions to detect that change. We provide a method for using the change in distribution of p values as a way to detect the true signals in the data.

Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.

This package provides tools that extend the functionality of the RODBC package to work with Microsoft SQL Server databases. Makes it easier to browse the database and examine individual tables and views.

It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>. Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>. Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>. Li et al. (2018) <doi:10.1177/0962280217692830>. PDI with variance estimation using Dover et al. (2021) <doi:10.1002/sim.9187>. We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.

This package provides functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depend on the suggested ctmcd package.

An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res, and covariates in gaad_cov. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.

Provides an interactive toolkit for educational and psychological measurement implemented using the shiny framework. The package supports content validity analysis, dimensionality assessment, and Classical Test Theory using the CTT package (Willse, 2018) <doi:10.32614/CRAN.package.CTT>.Item Response Theory (IRT) analyses are conducted via mirt (Chalmers, 2012) <doi:10.18637/jss.v048.i06>. Exploratory Factor Analysis is performed using psych (Revelle, 2025), while Confirmatory Factor Analysis and Structural Equation Modeling are based on the lavaan framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. The application allows users to upload data, evaluate statistical models, visualize results, and export outputs through an intuitive graphical interface without requiring programming experience.

This package provides a framework that boosts the imputation of missForest by Stekhoven, D.J. and Bühlmann, P. (2012) <doi:10.1093/bioinformatics/btr597> by harnessing parallel processing and through the fast Gradient Boosted Decision Trees (GBDT) implementation LightGBM by Ke, Guolin et al.(2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. misspi has the following main advantages: 1. Allows embrassingly parallel imputation on large scale data. 2. Accepts a variety of machine learning models as methods with friendly user portal. 3. Supports multiple initializations methods. 4. Supports early stopping that prohibits unnecessary iterations.

This is the core functions needed by the tsmp package. The low level and carefully checked mathematical functions are here. These are implementations of the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.

Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, <doi:10.18637/jss.v110.i03>).

This package implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence. Also includes a powerful set of object oriented survival data simulation functions.

Estimates multivariate subgaussian stable densities and probabilities as well as generates random variates using product distribution theory. A function for estimating the parameters from data to fit a distribution to data is also provided, using the method from Nolan (2013) <doi:10.1007/s00180-013-0396-7>.

This package provides a nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).

Fitting and testing multinomial processing tree (MPT) models, a class of nonlinear models for categorical data. The parameters are the link probabilities of a tree-like graph and represent the latent cognitive processing steps executed to arrive at observable response categories (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009 <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988 <doi:10.1037/0033-295x.95.3.318>).

This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series.

This package provides a tidy workflow for landscape-scale analysis. multilandr offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the landscapemetrics package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about multilandr is provided in an introductory vignette included in this package and in the paper by Huais (2024) <doi:10.1007/s10980-024-01930-z>; see citation("multilandr") for details.

Algorithms to build set partitions and commutator matrices and their use in the construction of multivariate d-Hermite polynomials; estimation and derivation of theoretical vector moments and vector cumulants of multivariate distributions; conversion formulae for multivariate moments and cumulants. Applications to estimation and derivation of multivariate measures of skewness and kurtosis; estimation and derivation of asymptotic covariances for d-variate Hermite polynomials, multivariate moments and cumulants and measures of skewness and kurtosis. The formulae implemented are discussed in Terdik (2021, ISBN:9783030813925), "Multivariate Statistical Methods".

Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: glmnet <https://CRAN.R-project.org/package=glmnet>, ranger <https://CRAN.R-project.org/package=ranger>, xgboost <https://CRAN.R-project.org/package=xgboost>, and lightgbm <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the mlexperiments R package.

Multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>, with support for multi-step adaptive MCP-net (MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.

This package performs the execution of the main procedures of multiple comparisons in the literature, Scott-Knott (1974) <http://www.jstor.org/stable/2529204>, Batista (2016) <http://repositorio.ufla.br/jspui/handle/1/11466>, including graphic representations and export to different extensions of its results. An additional part of the package is the presence of the performance evaluation of the tests (Type I error per experiment and the power). This will assist the user in making the decision for the chosen test.

This package performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors. These model-based non-parametric clustering techniques can extract clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.