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 helper functions and wrappers to simplify authentication, data retrieval, and result processing from the VALD APIs'. Designed to streamline integration for analysts and researchers working with VALD's external APIs'. For further documentation on integrating with VALD APIs', see: <https://support.vald.com/hc/en-au/articles/23415335574553-How-to-integrate-with-VALD-APIs>. For a step-by-step guide to using this package, see: <https://support.vald.com/hc/en-au/articles/48730811824281-A-guide-to-using-the-valdr-R-package>.

Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities. Also provided on the graph is the mean and variance of the distribution.

This package provides an R interface for volesti C++ package. volesti computes estimations of volume of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms for sampling, rounding and rotating polytopes. Moreover, volesti provides algorithms for estimating copulas useful in computational finance. Methods implemented in volesti are described in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.

This package implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine. The methods also for variant stratification and allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine.

Create adjacency matrices of vocalisation graphs from dataframes containing sequences of speech and silence intervals, transforming these matrices into Markov diagrams, and generating datasets for classification of these diagrams by flattening them and adding global properties (functionals) etc. Vocalisation diagrams date back to early work in psychiatry (Jaffe and Feldstein, 1970) and social psychology (Dabbs and Ruback, 1987) but have only recently been employed as a data representation method for machine learning tasks including meeting segmentation (Luz, 2012) <doi:10.1145/2328967.2328970> and classification (Luz, 2013) <doi:10.1145/2522848.2533788>.

This package provides functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.

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).

This package provides a comprehensive R interface to the VirusTotal API (v2 and v3), a Google service that analyzes files and URLs for viruses, worms, trojans and other malware. Features include file/URL scanning, domain categorization, passive DNS information, IP reputation analysis, and comment/voting systems. Implements rate limiting, error handling, and response validation for robust security analysis workflows.

This package provides ggplot2'-compatible colour palettes inspired by Vincent van Gogh's paintings. Each palette contains five colours, manually selected by hexadecimal values. Includes tools for assessing colour vision deficiency (CVD) accessibility.

ProPublica <https://projects.propublica.org/represent/> makes United States Congress member votes available and has developed their own unique cartogram to visually represent this data. Tools are provided to retrieve voting data, prepare voting data for plotting with ggplot2', create vote cartograms and theme them.

Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) <doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b) <doi:10.3102/1076998620911934>, Yamaguchi (2020) <doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023) <doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023) <doi:10.1111/bmsp.12308>. To facilitate their applications, variationalDCM is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.

This package provides a way of visualizing collections of time series and, optionally their future values, forecasts for their future values and prediction intervals for the forecasts. A web-based GUI can be used to display the information in a collection of time series.

US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the vaers package from the URL below. The URL and BugReports below correspond to the vaers package, of which vaersvax is a small subset (2018 only). vaers 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)'. "The Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For more information about the data, visit <https://vaers.hhs.gov/>. For information about vaccination/immunization hazards, visit <http://www.questionuniverse.com/rethink.html#vaccine>.

Converts Vietnam's provinces names and ID across different formats. Handles diacritics and different spellings.

This package provides a nonparametric method to estimate Toeplitz covariance matrices from a sample of n independently and identically distributed p-dimensional vectors with mean zero. The data is preprocessed with the discrete cosine matrix and a variance stabilization transformation to obtain an approximate Gaussian regression setting for the log-spectral density function. Estimates of the spectral density function and the inverse of the covariance matrix are provided as well. Functions for simulating data and a protein data example are included. For details see (Klockmann, Krivobokova; 2023), <arXiv:2303.10018>.

This package implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data.

This package provides a set of wrapper functions for Visa Chart Components'. Visa Chart Components <https://github.com/visa/visa-chart-components> is an accessibility focused, framework agnostic set of data experience design systems components for the web.

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.

Abstract descriptions of (yet) unobserved variables.

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.

Collects tweets and metadata for threaded conversations and generates networks.

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>.

This package provides fitting routines for four versions of the Vitality family of mortality models.