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>.

An R client for the vatcheckapi.com VAT number validation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://vatcheckapi.com/docs> .

An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages nlme', lmer', and saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.

This package provides an interface to the VK API <https://vk.com/dev/methods>. VK <https://vk.com/> is the largest European online social networking service, based in Russia.

New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.

This package provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', glm', coxph', rlm', gam', locfit', lmer', randomForest', etc.).

This package provides model-agnostic visual diagnostics for vector autoregressive (VAR) models. Given empirical data, model predictions, residuals, and optionally simulated data, the package assembles a multi-panel diagnostic grid: empirical vs. predicted time series, residual inspection, residuals vs. predictions scatter, and posterior predictive style checks via simulated trajectories. Output is a patchwork object composed of ggplot2 plots, allowing further customisation via standard ggplot2 theme calls. Follows the approach described in Haslbeck et al. (2026) <doi:10.31234/osf.io/k6uz4_v3>.

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.

Fits the extended Vasicek single-factor credit loss model where the probability of default depends on macroeconomic covariates. Maximum likelihood estimates of all parameters, including asset value correlation, are obtained via closed-form probit-transformed OLS regression; see Mayorov (2026) <doi:10.2139/ssrn.6506378> for derivation.

An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.

This package provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization of spectra and results of analysis in the ggplot2 style.

Collection of functions to evaluate presence-absence models. It comprises functions to adjust discrimination statistics for the representativeness effect through case-weighting, along with functions for visualizing the outcomes. Originally outlined in: Jiménez-Valverde (2022) The uniform AUC: dealing with the representativeness effect in presence-absence models. Methods Ecol. Evol, 13, 1224-1236.

An implementation of the Verhoeff algorithm for calculating check digits (Verhoeff, J. (1969) <doi:10.1002/zamm.19710510323>). Functions are provided to calculate a check digit given an input number, calculate and append a check digit to an input number, and validate that a check digit is correct given an input number.

This package implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.

This package provides a set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: - components descriptive analysis - heat plot of the two-by-two component combinations - leaving one component combination out scatter plot - violin plot for specific component combinations effects - density plot for components effects - waterfall plot for the interventions effects that differ by a certain component combination - network graph of components - rank heat plot of components for multiple outcomes. The implemented tools are described by Seitidis et al. (2023) <doi:10.1002/jrsm.1617>.

Practicals, data sets, helper functions and interactive Shiny apps used in the introductory course on Bayesian inference at the Valencia International Bayesian Summer School. Installing vibass installs all the other packages used during the course and downloads all necessary materials for working off line.

Headless companion to the Venn Diagram Lab web tool (<https://www.venndiagramlab.org/>). Build, render, and statistically analyze Venn / UpSet diagrams from CSV / TSV / GMT / GMX inputs. Provides the same 44 SVG models, intersection / Jaccard / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent TSV exports (parity-tested against the published Python package). Integrates with ggplot2', tidygraph', and broom'.

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.

This package provides a general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <doi:10.48550/arXiv.1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).

Utilizes multiple variable selection methods to estimate Average Treatment Effect.

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>.

Facilitate the analysis of inter-limb and intra-limb coordination in human movement. It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks. Needham, R., Naemi, R., & Chockalingam, N. (2014) <doi:10.1016/j.jbiomech.2013.12.032>. Needham, R., Naemi, R., & Chockalingam, N. (2015) <doi:10.1016/j.jbiomech.2015.07.023>. Tepavac, D., & Field-Fote, E. C. (2001) <doi:10.1123/jab.17.3.259>. Park, J.H., Lee, H., Cho, Js. et al. (2021) <doi:10.1038/s41598-020-80237-w>.

An interface between R and the Valhalla API. Valhalla is a routing service based on OpenStreetMap data. See <https://valhalla.github.io/valhalla/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometer distance).

Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).