Manage, provision and use Virtual Machines pre-configured for R. Develop, test and build package in a clean environment. Vagrant tool and a provider (such as Virtualbox') have to be installed.

Extending the functionalities of the VGAM package with additional functions and datasets. At present, VGAMextra comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, VGAMextra incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.

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>.

This package provides methods for assessing the applicability domain of models that predict viral load and CD4 (Cluster of Differentiation 4) lymphocyte counts. These methods help determine the extent of extrapolation when making predictions.

Helper and Wrapper functions for making shiny dashboards more easily. Functions are made modular and lower level functions are exported as well, so many use-cases are supported.

Use VirusTotal, a Google service that analyzes files and URLs for viruses, worms, trojans etc., provides category of the content hosted by a domain from a variety of prominent services, provides passive DNS information, among other things. See <https://www.virustotal.com> for more information.

The goal of vetiver is to provide fluent tooling to version, share, deploy, and monitor a trained model. Functions handle both recording and checking the model's input data prototype, and predicting from a remote API endpoint. The vetiver package is extensible, with generics that can support many kinds of models.

An implementation of Vasicek and Song goodness-of-fit tests. Several functions are provided to estimate differential Shannon entropy, i.e., estimate Shannon entropy of real random variables with density, and test the goodness-of-fit of some family of distributions, including uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace and beta distributions; see Lequesne and Regnault (2020) <doi:10.18637/jss.v096.c01>.

Applies affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.

Generate suggestions for validation rules from a reference data set, which can be used as a starting point for domain specific rules to be checked with package validate'.

An interactive document on the topic of variance analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/chisquareVarianceTest/>.

Abstract descriptions of (yet) unobserved variables.

This package implements the Vector Matching algorithm to match multiple treatment groups based on previously estimated generalized propensity scores. The package includes tools for visualizing initial confounder imbalances, estimating treatment assignment probabilities using various methods, defining the common support region, performing matching across multiple groups, and evaluating matching quality. For more details, see Lopez and Gutman (2017) <doi:10.1214/17-STS612>.

Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.

This package provides a continuous version of the receiver operating characteristics (ROC) curve to assess both classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.

This package provides a variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024) <doi:10.48550/arXiv.2406.16227>.

This package implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021).

Video interactivity within shiny applications using video.js'. Enables the status of the video to be sent from the UI to the server, and allows events such as playing and pausing the video to be triggered from the server.

This package provides access to data collected by the Ecuadorian Truth Commission. Allows users to extract and analyze systematized information for human rights research in Ecuador. The package contains datasets documenting human rights violations from 1984-2008, including victim information, violation types, perpetrators, and geographic distribution.

Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.

Generating realizations of a fractal Brownian function on uniform 1D & 2D grid with classic and generic versions of the Voss algorithm (random sequential additions).

An R interface to the Project VoteSmart'<https://justfacts.votesmart.org/> API.

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/>.

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.