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.

Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts.

Functionality for creating phase portraits of functions in the complex number plane. Works with R base graphics, whose full functionality is available. Parallel processing is used for optimum performance.

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.

Variance function estimation for models proposed by W. Sadler in his variance function program ('VFP', www.aacb.asn.au/AACB/Resources/Variance-Function-Program). Here, the idea is to fit multiple variance functions to a data set and consequently assess which function reflects the relationship Var ~ Mean best. For in-vitro diagnostic ('IVD') assays modeling this relationship is of great importance when individual test-results are used for defining follow-up treatment of patients.

This package contains selected data from two publications, Campbell et al'. (2016) <DOI:10.1080/14486563.2015.1028486> and Pacioni et al'. (2017) <DOI:10.1071/PC17002>. The data is provided both as raw outputs from the population viability analysis software Vortex and packaged as R objects. The R package vortexR uses the raw data provided here to illustrate its functionality of parsing raw Vortex output into R objects.

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>.

Visualizes vowel variation in f0, F1, F2, F3 and duration.

If f <- function(x)x^2 and g <- function(x)x+1 it is a constant source of annoyance that "f+g" is not defined. Package vfunc allows you to do this, and we have (f+g)(2) returning 5. The other arithmetic operators are similarly implemented. A wide class of coding bugs is eliminated.

This package provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Whereâ s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>. These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.

Analysing vital statistics based on tools consistent with the tidyverse. Tools are provided for data visualization, life table calculations, computing net migration numbers, Lee-Carter modelling; functional data modelling and forecasting.

This package provides a set of functions for manipulating data frames in accordance with specific business rules. In addition, it includes wrapper functions for commonly used functions from the popular tidyverse package, making it easy to integrate these functions into data analysis workflows. The package is designed to streamline data preprocessing and help users quickly and efficiently perform data transformations that are specific to their business needs.

Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" <https://homepages.uc.edu/~qinyn/VDSM/VDSM.html>.

This package provides tools for 3D point cloud voxelisation, projection, geometrical and morphological description of trees (DBH, height, volume, crown diameter), analyses of temporal changes between different measurement times, distance based clustering and visualisation of 3D voxel clouds and 2D projection. Most analyses and algorithms provided in the package are based on the concept of space exploration and are described in Lecigne et al. (2018, <doi:10.1093/aob/mcx095>).

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.

R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

Mainly data sets to accompany the VGAM package and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7>. These are used to illustrate vector generalized linear and additive models (VGLMs/VGAMs), and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column Interaction Models, and constrained and unconstrained ordination models in ecology). This package now contains some old VGAM family functions which have been replaced by newer ones (often because they are now special cases).

Multi-precision library that allows to store and operate with arbitrarily big integers without loss of precision. It includes a large list of tools to work with them, like: - Arithmetic and logic operators - Modular-arithmetic operators - Computer Number Theory utilities - Probabilistic primality tests - Factorization algorithms - Random generators of diferent types of integers.

This package provides methods for calculating the variance scale exponent to identify memory patterns in time series data. Includes tests for white noise, short memory, and long memory. See Fu, H. et al. (2018) <doi:10.1016/j.physa.2018.06.092>.

This package provides tools for estimating vaccine effectiveness and related metrics. The vaccineff_data class manages key features for preparing, visualizing, and organizing cohort data, as well as estimating vaccine effectiveness. The results and model performance are assessed using the vaccineff class.

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>.

The biomarker data set by Vermeulen et al. (2009) <doi:10.1016/S1470-2045(09)70154-8> is provided. The data source, however, is by Ruijter et al. (2013) <doi:10.1016/j.ymeth.2012.08.011>. The original data set may be downloaded from <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>. This data set is for a real-time quantitative polymerase chain reaction (PCR) experiment that comprises the raw fluorescence data of 24,576 amplification curves. This data set comprises 59 genes of interest and 5 reference genes. Each gene was assessed on 366 neuroblastoma complementary DNA (cDNA) samples and on 18 standard dilution series samples (10-fold 5-point dilution series x 3 replicates + no template controls (NTC) x 3 replicates).

This package provides functions to run statistical analyses on surface-based neuroimaging data, computing measures including cortical thickness and surface area of the whole-brain and of the hippocampi. It can make use of FreeSurfer', fMRIprep', XCP-D', HCP and CAT12 preprocessed datasets and HippUnfold hippocampal segmentation outputs for a given sample by restructuring the data values into a single file. The single file can then be used by the package for analyses independently from its base dataset and without need for its access.

Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.