This package provides functions for estimation (parametric, semi-parametric and non-parametric) of copula-based dependence coefficients between a finite collection of random vectors, including phi-dependence measures and Bures-Wasserstein dependence measures. An algorithm for agglomerative hierarchical variable clustering is also implemented. Following the articles De Keyser & Gijbels (2024) <doi:10.1016/j.jmva.2024.105336>, De Keyser & Gijbels (2024) <doi:10.1016/j.ijar.2023.109090>, and De Keyser & Gijbels (2024) <doi:10.48550/arXiv.2404.07141>.

This package implements functions for varying coefficient meta-analysis methods. These methods do not assume effect size homogeneity. Subgroup effect size comparisons, general linear effect size contrasts, and linear models of effect sizes based on varying coefficient methods can be used to describe effect size heterogeneity. Varying coefficient meta-analysis methods do not require the unrealistic assumptions of the traditional fixed-effect and random-effects meta-analysis methods. For details see: Statistical Methods for Psychologists, Volume 5, <https://dgbonett.sites.ucsc.edu/>.

Constructs a virtual population from fertility and mortality rates for any country, calendar year and birth cohort in the Human Mortality Database <https://www.mortality.org> and the Human Fertility Database <https://www.humanfertility.org>. Fertility histories are simulated for every individual and their offspring, producing a multi-generation virtual population.

Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.

Variational Autoencoded Multivariate Spatial Fay-Herriot models are designed to efficiently estimate population parameters in small area estimation. This package implements the variational generalized multivariate spatial Fay-Herriot model (VGMSFH) using NumPyro and PyTorch backends, as demonstrated by Wang, Parker, and Holan (2025) <doi:10.48550/arXiv.2503.14710>. The vmsae package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.

This package provides tools for analysis blinding in confirmatory research contexts by masking and scrambling test-relevant aspects of data. Vector-, data frame-, and row-wise operations support blinding for hierarchical and repeated-measures designs. For more details see MacCoun and Perlmutter (2015) <doi:10.1038/526187a> and Dutilh, Sarafoglou, and Wagenmakers (2019) <doi:10.1007/s11229-019-02456-7>.

Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.

Describe in words the genealogical relationship between two members of a given pedigree, using the algorithm in Vigeland (2022) <doi:10.1186/s12859-022-04759-y>. verbalisr is part of the pedsuite collection of packages for pedigree analysis. For a demonstration of verbalisr', see the online app QuickPed at <https://magnusdv.shinyapps.io/quickped>.

The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.

Comprehensive set of tools for analyzing and manipulating functional data with non-uniform lengths. This package addresses two common scenarios in functional data analysis: Variable Domain Data, where the observation domain differs across samples, and Partially Observed Data, where observations are incomplete over the domain of interest. VDPO enhances the flexibility and applicability of functional data analysis in R'. See Amaro et al. (2024) <doi:10.48550/arXiv.2401.05839>.

Vector binary tree provides a new data structure, to make your data visiting and management more efficient. If the data has structured column names, it can read these names and factorize them through specific split pattern, then build the mappings within double list, vector binary tree, array and tensor mutually, through which the batched data processing is achievable easily. The methods of array and tensor are also applicable. Detailed methods are described in Chen Zhang et al. (2020) <doi:10.35566/isdsa2019c8>.

Import and handling data from vegetation-plot databases, especially data stored in Turboveg 2 (<https://www.synbiosys.alterra.nl/turboveg/>). Also import/export routines for exchange of data with Juice (<https://www.sci.muni.cz/botany/juice/>) are implemented.

This is a sparklyr extension integrating VariantSpark and R. VariantSpark is a framework based on scala and spark to analyze genome datasets, see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, VariantSpark is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a spark data frame.

To computed the variability independent of mean (VIM) or variation independent of mean (VIM). The methodology can be found at Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.

Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs. In particular, vistla can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits. Due to its higher-order nature, vistla can handle multi-modality and assign multiple roles to a single feature.

This package provides pedagogical tools for visualization and numerical computation in vector calculus. Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) <ISBN:9781429215084>, Stewart (2015) <ISBN:9781285741550>, Thomas, Weir and Hass (2018) <ISBN:9780134438986>, Larson and Edwards (2016) <ISBN:9781285255869>, Apostol (1969) <ISBN:9780471000051>, Spivak (1971) <ISBN:9780805390216>, Schey (2005) <ISBN:9780071369080>, Colley (2019) <ISBN:9780321982384>, Lizarazo Osorio (2020) <ISBN:9789585450103>, Sievert (2020) <ISBN:9780367180165>, and Borowko (2013) <ISBN:9781439870791>.

This package performs 20 omnibus tests for testing the composite hypothesis of variance homogeneity.

This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.

Visualizes vowel variation in f0, F1, F2, F3 and duration.

This package implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.

Analysis of minor alleles in Illumina sequencing data of viral genomes. Functions in vivaldi primarily operate on vcf files.

Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions. Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.

This package provides methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the stats package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.

Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the party package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the measures package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).