Fits the extended Vasicek single-factor credit loss model where the probability of default depends on macroeconomic covariates. Maximum likelihood estimates of all parameters, including asset value correlation, are obtained via closed-form probit-transformed OLS regression; see Mayorov (2026) <doi:10.2139/ssrn.6506378> for derivation.

This package provides a reference implementation of the Vertical Weighted Strips method explored by Raim, Livsey, and Irimata (2025) <doi:10.48550/arXiv.2401.09696> for rejection sampling.

Mixed type vectors are useful for combining semantically similar classes. Some examples of semantically related classes include time across different granularities (e.g. daily, monthly, annual) and probability distributions (e.g. Normal, Uniform, Poisson). These groups of vector types typically share common statistical operations which vary in results with the attributes of each vector. The vecvec data structure facilitates efficient storage and computation across multiple vectors within the same object.

Debugging pipe chains often consists of viewing the output after each step. This package adds RStudio addins and two functions that allow outputing each or select steps in a convenient way.

An implementation of three procedures developed by John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. Combined, they provide a way to identify, treat, and analyze outliers in two-way (i.e., contingency) tables, as described in his landmark paper "The Future of Data Analysis", Tukey, John W. (1962) <https://www.jstor.org/stable/2237638>.

An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages nlme', lmer', and saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.

This package provides a minimal columnar query engine with lazy execution on datasets larger than RAM. Provides dplyr'-like verbs (filter(), select(), mutate(), group_by(), summarise(), joins, window functions) and common aggregations (n(), sum(), mean(), min(), max(), sd(), first(), last()) backed by a pure C11 pull-based execution engine and a custom on-disk format ('.vtr'). Reads and writes GeoTIFF (including tiled and BigTIFF layouts) and a tiled raster format ('.vec') with overview pyramids and time cubes for larger-than-RAM raster data. Streams vector operations (spatial transforms, point-in-polygon and nearest-feature joins including a two-sided grid-partitioned join, select-by-location, clip, erase, dissolve, rasterization', polygonization', and contouring) through sf', and runs raster operations (zonal statistics, focal windows, terrain derivatives, resample or reproject warp, polygon masking, map algebra, and mosaicking') in native C or over the tiled .vec format, one batch or tile at a time for data larger than RAM.

This package provides a toolset for interactively exploring the differences between two data frames.

Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions. Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.

To computed the variability independent of mean (VIM) or variation independent of mean (VIM). The methodology can be found at Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.

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 an R interface for interacting with the Semestry TermTime services. It allows users to retrieve scheduling data from the API. see <https://github.com/vusaverse/vvtermtime/blob/main/openapi_7.7.0.pdf> for details.

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 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.

This package provides density, cumulative distribution, quantile, and random generation functions for Vasicek-type distributions with standard normal, standard logistic, and standard hyperbolic-secant kernels. The normal-kernel distribution is parameterized by either its mean or a fixed quantile, whereas the logistic- and hyperbolic-secant-kernel distributions use fixed-quantile parameterizations. Zero-augmented, one-augmented, and zero-and-one-augmented extensions of the normal-kernel mean parameterization are also provided for responses that include boundary values. The corresponding NVASIM', NVASIQ', LVASIQ', HVASIQ', ZANVASIM', OANVASIM', and ZOANVASIM families are available for fitting Generalized Additive Models for Location, Scale and Shape (GAMLSS), as introduced by Rigby and Stasinopoulos (2005, <doi:10.1111/j.1467-9876.2005.00510.x>). Two-part random-intercept regression models for zero-augmented longitudinal responses are included with either a beta or a normal-kernel Vasicek positive component, extending the framework of Chen and Li (2016, <doi:10.1093/bioinformatics/btw308>). Some functions are written in C++ using Rcpp', developed by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>).

You can easily visualize your sf polygons or data.frame with h3 address. While leaflet package is too raw for data analysis, this package can save data analysts efforts & time with pre-set visualize options.

Headless companion to the Venn Diagram Lab web tool (<https://www.venndiagramlab.org/>). Build, render, and statistically analyze Venn / UpSet diagrams from CSV / TSV / GMT / GMX inputs. Provides the same 44 SVG models, intersection / Jaccard / hypergeometric statistics, and PDF report layout as the web tool, with byte-equivalent TSV exports (parity-tested against the published Python package). Integrates with ggplot2', tidygraph', and broom'.

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 methods for variance-based sensitivity analysis and weighting estimators in observational studies based on methodology by Huang & Pimentel (2025) <doi:10.1093/biomet/asae040>. Includes bootstrap inference, bias bounds estimation, and visualization tools for sensitivity parameters.

Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with validate'.

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

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.

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.