Computational tools to represent phylogenetic signals using adapted eigenvector maps.

This package provides functions for carrying out nonparametric hypothesis tests of the MCAR hypothesis based on the theory of Frechet classes and compatibility. Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects.

This package provides tools for constructing, computing, and using distance measures for numerical, categorical, and mixed-type data. The package implements a flexible framework in which continuous and categorical components can be combined under additive, commensurable, and association-aware specifications. Supported methods include classical distances such as Gower, Euclidean, Manhattan, and Mahalanobis-type distances; categorical dissimilarities such as simple matching, occurrence-frequency, and association-based measures; and mixed-type presets designed to reduce biases due to variable type, scale, distribution, redundancy, and number of categories. The package also provides scaling options, supervised and unsupervised distance constructions, leave-one-variable-out tools for distance-based variable importance, and integration with distance-based learning workflows such as nearest-neighbour prediction, partitioning around medoids, and spectral clustering. Methods are motivated by van de Velden, Iodice D'Enza, Markos, and Cavicchia (2026) <doi:10.1080/10618600.2026.2680181> and related work on categorical and mixed-type dissimilarities.

This package provides methods for estimating minimal important change (MIC) and interpretation thresholds for multi-item questionnaires and single-item continuous or ordinal measures. Methods include predictive modelling, adjusted predictive modelling, improved adjusted predictive modelling using anchor reliability, confirmatory factor analysis for anchor reliability, longitudinal confirmatory factor analysis for MIC estimation, longitudinal confirmatory factor analysis-based MIC estimation for single-item measures, and confirmatory factor analysis-based threshold estimation for single-item and multi-item measures. Implemented methods include those developed by Terluin et al. (2015) <doi:10.1016/j.jclinepi.2015.03.015>, Terluin et al. (2017) <doi:10.1016/j.jclinepi.2016.12.015>, Terluin et al. (2022) <doi:10.1016/j.jclinepi.2022.04.018>, Terluin et al. (2023) <doi:10.1007/s11136-023-03355-8>, Terluin et al. (2024) <doi:10.1007/s11136-023-03577-w>, Terluin et al. (2024) <doi:10.1007/s11136-024-03763-4>, and Terluin et al. (2026) <doi:10.1007/s11136-025-04134-3>.

An interactive document on the topic of multidimensional scaling and principal component analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyabolar.shinyapps.io/MDS_PCAShiny/>.

This package performs mean shift classification using linear and k-d tree based nearest neighbor implementations for the Gaussian, Epanechnikov, and biweight product kernels.

This package provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.

This package provides tools to help visualize Major League Baseball analysis in ggplot2 and gt'. You provide team/player information and mlbplotR will transform that information into team colors, logos, or player headshots for graphics.

Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. Genz (2004) <doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009) <doi:10.1007/978-3-642-01689-9>, H. I. Gassmann (2003) <doi:10.1198/1061860032283> and E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>.

MatLab'-Style Modeling of Optimization Problems with R'. This package provides a set of convenience functions to transform a MatLab'-style optimization modeling structure to its ROI equivalent.

Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.

This package provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

This package implements methods for comparing sensitivities and specificities in balanced (or fully crossed) multi-reader multi-case (MRMC) studies with binary diagnostic test results. It implements conditional logistic regression and provides score tests equivalent to Cochran's Q test (which corresponds to McNemar's test when comparing two modalities only). The methodology is based on Lee et al. (2026) <doi:10.1002/sim.70471>.

This package provides a method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.

You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.

Providing the kubernetes-like class ManagedCloudProvider as a child class of the CloudProvider class in the DockerParallel package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from ManagedCloudProvider and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.

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>.

This package provides a collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.

Multivariate functional principal component analysis via fast covariance estimation for multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.

Meta-analysis traditionally assigns more weight to studies with lower standard errors, assuming higher precision. However, in observational research, precision must be estimated and is vulnerable to manipulation, such as p-hacking, to achieve statistical significance. This can lead to spurious precision, invalidating inverse-variance weighting and bias-correction methods like funnel plots. Common methods for addressing publication bias, including selection models, often fail or exacerbate the problem. This package introduces an instrumental variable approach to limit bias caused by spurious precision in meta-analysis. Methods are described in Irsova et al. (2025) <doi:10.1038/s41467-025-63261-0>.

The MIDASim package is a microbiome data simulator for generating realistic microbiome datasets by adapting a user-provided template. It supports the controlled introduction of experimental signals-such as shifts in taxon relative abundances, prevalence, and sample library sizes-to create distinct synthetic populations under diverse simulation scenarios. For more details, see He et al. (2024) <doi:10.1186/s40168-024-01822-z>.

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>).

Econometric analysis of multiple-input-multiple-output production technologies with ray-based input distance functions as suggested by Price and Henningsen (2023): "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application", Journal of Productivity Analysis 60, p. 179-188, <doi:10.1007/s11123-023-00684-1>.