Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.

This package provides tools to simulate morphological traits along phylogenetic trees with branch lengths representing evolutionary distance or time. Includes functions for visualizing evolutionary processes along trees and within morphological character matrices.

Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.

Simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021) <doi:10.1016/j.agrformet.2020.108233>].

This package provides methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) <doi:10.1214/19-EJS1576>, simplex plots of quartet concordance factors as presented by Allman et al. (2020) <doi:10.1101/2020.02.13.948083>, species tree inference methods based on quartet distances of Rhodes (2019) <doi:10.1109/TCBB.2019.2917204> and Yourdkhani and Rhodes (2019) <doi:10.1007/s11538-020-00773-4>, the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) <doi:10.1186/s13015-019-0159-2>, the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) <doi:10.1007/s00285-022-01838-9>, NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Rhodes et al.(2024) <doi:10.1186/s13015-025-00274-w>, and the ECToBlob algorithm for inference of a network with no anomalous quartets of Allman et al. (2026) (forthcoming). Software announcement by Rhodes et al. (2020) <doi:10.1093/bioinformatics/btaa868>.

Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets.

Model based clustering using the multivariate multiple Scaled t (MST) and multivariate multiple scaled contaminated normal (MSCN) distributions. The MST is an extension of the multivariate Student-t distribution to include flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.

Computes martingale difference correlation (MDC), martingale difference divergence, and their partial extensions to assess conditional mean dependence. The methods are based on Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>. Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted).

This package provides methods and functions to analyze the quantitative or qualitative performance for diagnostic assays, and outliers detection, reader precision and reference range are discussed. Most of the methods and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) recommendations and NMPA (National Medical Products Administration) guidelines. In additional, relevant plots are constructed by ggplot2'.

Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

This package provides a new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.

Calculate multiple statistics with confidence intervals for matched case-control data including risk difference, risk ratio, relative difference, and the odds ratio. Results are equivalent to those from Stata', and you can choose how to format your input data. Methods used are those described on page 56 the Stata documentation for "Epitab - Tables for Epidemologists" <https://www.stata.com/manuals/repitab.pdf>.

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>.

Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.

Several robust estimators for linear regression and variable selection are provided. Included are Maximum tangent likelihood estimator by Qin, et al., (2017), arXiv preprint <doi:10.48550/arXiv.1708.05439>, least absolute deviance estimator and Huber regression. The penalized version of each of these estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to produce consistent estimates for both fixed and high-dimensional settings.

Dimension reduction for multivariate data of extreme events with a PCA like procedure as described in Reinbott, Janà en, (2024), <doi:10.48550/arXiv.2408.10650>. Tools for necessary transformations of the data are provided.

This package contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.

Missing data imputation based on the missForest algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.

Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) <doi:10.1177/1029864919844809>. Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) <doi:10.1098/rsos.171520>.

An R port of the margins command from Stata', which can be used to calculate marginal (or partial) effects from model objects.

This package implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.

Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.

Focus-glue-context (FGC) fisheye transformations to two-dimensional coordinates and spatial vector geometries. Implements a smooth radial distortion that enlarges a focal region, transitions through a glue ring, and preserves outside context. Methods build on generalized fisheye views and focus+context mapping. For more details see Furnas (1986) <doi:10.1145/22339.22342>, Furnas (2006) <doi:10.1145/1124772.1124921> and Yamamoto et al. (2009) <doi:10.1145/1653771.1653788>.

The routine gof_test() in this package runs the goodness-of-fit test using various test statistic for multivariate data. Models under the null hypothesis can either be simple or allow for parameter estimation. p values are found via the parametric bootstrap (simulation). The routine gof_test_adjusted_pvalues() runs several tests and then finds a p value adjusted for simultaneous inference. The routine gof_power() allows the estimation of the power of the tests. hybrid_test() and hybrid_power() do the same by first generating a Monte Carlo data set under the null hypothesis and then running a number of two-sample methods. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package via a large number of case studies. For details of the methods and references see the included vignettes.