An advanced, interactive data table and data explorer for R, delivered as a modern, self-contained htmlwidget with a high-performance virtualized grid. ViewR renders Kaggle'-style micro-dashboard column headers complete with data-type badges, mini distribution spark-histograms, and data-completeness (missingness) bars. It provides hover metadata cards, a sliding Data Insights drawer with interactive histograms and Pareto category charts, a multi-condition visual query builder (AND/OR), a column visibility picker, and a reproducible code generator that emits dplyr', base R, and SQL that matches the active filter and column state. The interface is implemented entirely in dependency-free vanilla JavaScript (no React or build toolchain) and works in the RStudio'/'Positron Viewer, inside Shiny apps, in R Markdown'/'Quarto', or as a portable standalone HTML file. A single call to viewr() opens the explorer; the legacy Shiny'-gadget ViewR() editor remains available.

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.

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.

Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for civilian women, women military service members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the existing American College of Cardiology/American Heart Association CVD risk assessment tool in predicting longâ term CVD risk for VA women, particularly in young and racial/ethnic minority women. See the reference: Jeonâ Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. (2021) <doi:10.1161/JAHA.120.019217>.

The right test, visualised. visStatistics automatically selects and visualises statistical hypothesis tests comparing two vectors, based on their class, distribution, and sample size. Visual outputs, including box plots, bar charts, regression lines with confidence bands, mosaic plots, residual plots, and Q-Q plots, are annotated with relevant test statistics, assumption checks, and post-hoc analyses where applicable. The algorithmic workflow shifts attention from ad-hoc test selection to visual diagnostic assessment and statistical interpretation. It is particularly suited for server-side R applications, where end users interact solely through a web interface to select data groups and receive a complete visual statistical analysis automatically. The same automation makes it useful in time-constrained contexts such as statistical consulting, where it reduces effort spent on test selection and leaves more room for interpretation. The implemented tests cover the most frequently applied inferential methods in biomedical research (Hayat et al. (2017) <doi:10.1371/journal.pone.0179032>). The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical; those of class ordered are considered ordinal. Assumptions of residual normality and homogeneity of variances are considered met if the corresponding test yields a p-value greater than the significance level alpha = 1 - conf.level. (1) When the response is numerical and the predictor is categorical, a test comparing central tendencies is selected. If every group contains more than 50 observations, the sampling distribution of the group means is assumed approximately normal by the central limit theorem (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>); otherwise, residual normality is assessed using shapiro.test() applied to the standardised residuals of lm(). If normality is not met, wilcox.test() is used when the predictor has two levels and kruskal.test() followed by pairwise.wilcox.test() otherwise. If normality is met, levene.test() assesses variance homogeneity. For two-level predictors, Student's t.test(var.equal = TRUE) is applied if variances are homogeneous and Welch's t.test() otherwise. For predictors with more than two levels, aov() followed by TukeyHSD() is applied if variances are homogeneous, and oneway.test() followed by games.howell() otherwise. (2) When both vectors are numerical, lm() is fitted by default (correlation = FALSE). If correlation = TRUE, Spearman rank correlation is performed. (3) When the response is ordinal, it is converted to numeric ranks and the non-parametric path from (1) is followed (Wilcoxon or Kruskal-Wallis). When both variables are ordinal and correlation = TRUE, Kendall's tau_b is used instead. (4) When both vectors are categorical, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is applied to test independence either by chisq.test() or fisher.test().

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 port of Inspect', a widely adopted Python framework for large language model evaluation. Specifically aimed at ellmer users who want to measure the effectiveness of their large language model-based products, the package supports prompt engineering, tool usage, multi-turn dialog, and model graded evaluations.

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

This package provides a set of basic tools to transform functions into functions with input validation checks, in a manner suitable for both programmatic and interactive use.

This package provides a new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown.

Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.

Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.

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>.

Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.

Via Foundry API provides streamlined tools for interacting with and extracting data from structured responses, particularly for use cases involving hierarchical data from Foundry's API. It includes functions to fetch and parse process-level and file-level metadata, allowing users to efficiently query and manipulate nested data structures. Key features include the ability to list all unique process names, retrieve file metadata for specific or all processes, and dynamically load or download files based on their type. With built-in support for handling various file formats (e.g., tabular and non-tabular files) and seamless integration with API through authentication, this package is designed to enhance workflows involving large-scale data management and analysis. Robust error handling and flexible configuration ensure reliable performance across diverse data environments. Please consult the documentation for the API endpoint for your installation.

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

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.

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.

Visualize Variance is an intuitive shiny applications tailored for agricultural research data analysis, including one-way and two-way analysis of variance, correlation, and other essential statistical tools. Users can easily upload their datasets, perform analyses, and download the results as a well-formatted document, streamlining the process of data analysis and reporting in agricultural research.The experimental design methods are based on classical work by Fisher (1925) and Scheffe (1959). The correlation visualization approaches follow methods developed by Wei & Simko (2021) and Friendly (2002) <doi:10.1198/000313002533>.

Compared with the similar graph embedding method such as Laplacian Eigenmaps, Vicus can exploit more local structures of graph data. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/Vicus>.

This package produces violin plots with optional nonparametric (Mann-Whitney test) and parametric (Tukey's honest significant difference) mean comparison and linear regression. This package aims to be a simple and quick visualization tool for comparing means and assessing trends of categorical factors.

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 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>.