Facilitates use and analysis of data about the armed conflict in Colombia resulting from the joint project between La Jurisdicción Especial para la Paz (JEP), La Comisión para el Esclarecimiento de la Verdad, la Convivencia y la No repetición (CEV), and the Human Rights Data Analysis Group (HRDAG). The data are 100 replicates from a multiple imputation through chained equations as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. With the replicates the user can examine four human rights violations that occurred in the Colombian conflict accounting for the impact of missing fields and fully missing observations.

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>.

The Bank of Canada updated their Valet API <https://www.bankofcanada.ca/valet/docs>, and no R client currently exists. This provides access to all of Valet's endpoints and serves responses in wide format easy for researchers to handle but also provides tools to access API responses as a list.

Simulates and evaluates stochastic scenarios of death and lapse events in life reinsurance contracts with profit commissions. The methodology builds on materials published by the Institute of Actuaries of Japan <https://www.actuaries.jp/examin/textbook/pdf/modeling.pdf>. A paper describing the detailed algorithms will be published by the author within a few months after the initial release of this package.

The vcfpp.h (<https://github.com/Zilong-Li/vcfpp>) provides an easy-to-use C++ API of htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The vcfppR package serves as the R bindings of the vcfpp.h library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation.

This package provides a R interface to the vouch project (<https://github.com/mitchellh/vouch>), which bills itself as "a community trust management system based on explicit vouches to participate". vouch is a Nushell module, so voucher provides a R application programming interface (API) to modify the VOUCHED.td database without Nushell'. voucher does not depend on vouch or Nushell'.

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 dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the tidymodels framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the DALEXtra tool for explainability by Przemyslaw Biecek (2020) <doi:10.48550/arXiv.2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.

This package provides variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.

Vector autoregressive (VAR) model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method. This package is an integrative package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner, such as the multivariate ridge regression in Golub, Heath, and Wahba (1979) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.

Given a partition resulting from any clustering algorithm, the implemented tests allow valid post-clustering inference by testing if a given variable significantly separates two of the estimated clusters. Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). "Post-clustering difference testing: valid inference and practical considerations", <arXiv:2210.13172>.

This package provides a library for creating time based charts, like Gantt or timelines. Possible outputs include ggplot2 diagrams, plotly.js graphs, Highcharts.js widgets and data.frames. Results can be used in the RStudio viewer pane, in RMarkdown documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom.

Make it easy to use vue in R with helper dependency functions and examples.

Data version management on the file system for smaller projects. Manage data pipeline outputs with symbolic folder links, structured logging and reports, using R6 classes for encapsulation and data.table for speed. Directory-specific logs used as source of truth to allow portability of versioned data folders.

Automates set operations (i.e., comparisons of overlap) between multiple vectors. It also contains a function for automating reporting in RMarkdown', by generating markdown output for easy analysis, as well as an RMarkdown template for use with RStudio'.

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.

This package implements variable screening techniques for ultra-high dimensional regression settings. Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>, Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>, Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086> Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.

Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the vaersND package from the URL below. The URL and BugReports below correspond to the vaersND package, of which vaersNDvax is a small subset (2016 only). vaersND is not hosted on CRAN due to the large size of the data set. To install the Suggested vaers and vaersND packages, use the following R code: devtools::install_git("https://gitlab.com/iembry/vaers.git", build_vignettes = TRUE) and devtools::install_git("https://gitlab.com/iembry/vaersND.git", build_vignettes = TRUE)'. "VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a post-marketing safety surveillance program, collecting information about adverse events (possible side effects) that occur after the administration of vaccines licensed for use in the United States." For more information about the data, visit <https://vaers.hhs.gov/index>. For information about vaccination/immunization hazards, visit <http://www.questionuniverse.com/rethink.html/#vaccine>.

Calculate solar potential for LiDAR point clouds using the VOSTOK (Voxel Octree Solar Toolkit) algorithm. This R program provides an interface to the original VOSTOK C++ implementation by Bechtold and Hofle (2020), enabling efficient ray casting and solar position algorithms to compute solar irradiance for each point while accounting for shadowing effects. Integrates seamlessly with the lidR package for LiDAR data processing workflows. The original VOSTOK toolkit is available at <doi:10.11588/data/QNA02B>.

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'.

Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023) <doi:10.48550/arXiv.2208.06465>.

R data pipelines commonly require reading and writing data to versioned directories. Each directory might correspond to one step of a multi-step process, where that version corresponds to particular settings for that step and a chain of previous steps that each have their own versions. This package creates a configuration object that makes it easy to read and write versioned data, based on YAML configuration files loaded and saved to each versioned folder.

This package provides tools for designing virus protein panels through sequence clustering and protein sequence analysis. The package includes functionality for filtering sequences, removing redundancy, identifying outliers, clustering sequences, and calculating entropy to evaluate clustering quality. A publication describing these methods is in preparation and will be added once available.

This package provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the copula API. Corresponding objects from the VineCopula API can easily be converted.