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.

This package provides a user-friendly R shiny app for performing various statistical tests on datasets. It allows users to upload data in numerous formats and perform statistical analyses. The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons. The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.

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>.

Alternative splicing produces a variety of different protein products from a given gene. VALERIE enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. VALERIE computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) <doi:10.1038/s41467-018-06063-x>. PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) <doi:10.1371/journal.pcbi.1003118>. Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the pheatmap package by Kolde (2010) <https://CRAN.R-project.org/package=pheatmap>. Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the ggplot2 package by Wickham (2007) <https://CRAN.R-project.org/package=ggplot2>.

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>.

Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.

This package provides direct access to linked names for the same entity across the world's major name authority files, including national and regional variations in language, character set, and spelling. For more information go to <https://viaf.org/>.

This package provides an htmlwidgets interface to VChart.js'. VChart', more than just a cross-platform charting library, but also an expressive data storyteller. VChart examples and documentation are available here: <https://www.visactor.io/vchart>.

Functions, Classes & Methods for estimation, prediction, and simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.

The goal of the package is to equip the jmcm package (current version 0.2.1) with estimations of the covariance of estimated parameters. Two methods are provided. The first method is to use the inverse of estimated Fisher's information matrix, see M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) <doi:10.1214/aos/1176351062> for reference.

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>.

Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data and penalized estimating equations for generalized linear models with multiple imputation. Reference: Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized estimating equations for generalized linear models with multiple imputation", <doi:10.1214/22-AOAS1721>. Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data", <doi:10.1093/jrsssc/qlad028>.

This package provides additional data sets, methods and documentation to complement the vcd package for Visualizing Categorical Data and the gnm package for Generalized Nonlinear Models. In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and adds a 3D version in mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of glm and loglm objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.

The algorithm implemented in this package was designed to quickly estimates the distribution of the log-rank especially for heavy unbalanced groups. VALORATE estimates the null distribution and the p-value of the log-rank test based on a recent formulation. For a given number of alterations that define the size of survival groups, the estimation involves a weighted sum of distributions that are conditional on a co-occurrence term where mutations and events are both present. The estimation of conditional distributions is quite fast allowing the analysis of large datasets in few minutes <https://bioinformatics.mx/index.php/bioinfo-tools/>.

Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.

Calculates voter transitions comparing two elections, using the function solve.QP() in package quadprog'.

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.

This package provides probability density, cumulative distribution, quantile, and random number generation functions for the Vasicek distribution. In addition, two functions are available for fitting Generalized Additive Models for Location, Scale and Shape introduced by Rigby and Stasinopoulos (2005, <doi:10.1111/j.1467-9876.2005.00510.x>). Some functions are written in C++ using Rcpp', developed by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>).

This package provides tools for visibility analysis in geospatial data. It offers functionality to perform isovist calculations, using arbitrary geometries as both viewpoints and occluders.

Wrapper around the City of Vancouver Open Data API <https://opendata.vancouver.ca/api/v2/console> to simplify and standardize access to City of Vancouver open data. Functionality to list the data catalogue and access data and geographic records.

R functions are not supposed to print text without giving the user the option to turn the printing off or on using a Boolean verbose in a construct like if(verbose) print(...)'. But this black/white approach is rather rigid, and an approach with shades of gray might be more appropriate in many circumstances.

This package provides functions for validating the structure and properties of data frames. Answers essential questions about a data set after initial import or modification. What are the unique or missing values? What columns form a primary key? What are the properties of the numeric or categorical columns? What kind of overlap or mapping exists between 2 columns?

This package provides a shiny dashboard and plotting utilities to explore and report VALD ForceDecks testing data. Includes interactive modules for metric exploration, radar charts, longitudinal comparisons, quadrant plots, and athlete reports.

Predicate helper functions for testing atomic vectors in R. All functions take a single argument x and check whether it's of the target type of base-R atomic vector (i.e. no class extensions nor attributes other than names'), returning TRUE or FALSE. Some additionally check for value (e.g. absence of missing values, infinities, blank characters, or names attribute; or having length 1).