This package provides a common interface to specifying clustering models, in the same style as parsnip. It creates a unified interface across different functions and computational engines.

The TOML configuration format specifies an excellent format suitable for both human editing as well as the common uses of a machine-readable format. This package provides Rcpp bindings to a TOML parser.

This package provides tools for the estimation and simulation of latent variable models.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

This package provides an implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018). It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided.

ACDm is a package for Autoregressive Conditional Duration (ACD, Engle and Russell, 1998) models. It creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them.

This package provides various tools for developers of R packages interfacing with Stan, including functions to set up the required package structure, S3 generics and default methods to unify function naming across Stan-based R packages, and vignettes with recommendations for developers.

This package provides tools for circular statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.

This package provides a dplyr back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a DBI back end; more advanced features require SQL translation to be provided by the package author.

This package Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests. Also now includes some software for quickly recoding survey data and plotting point estimates from interaction terms in regressions (and multiply imputed regressions). NOTE: Weighted partial correlation calculations pulled to address a bug.

The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).

This package provides tools for categorical data analysis with complete or missing responses.

This package implements generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.

This package provides a comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e. mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g. due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g. due to phylogenetic relatedness) can also be conducted.

Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.

This package provides five omnibus tests for testing the composite hypothesis of normality.

To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.

This tool provides an algorithm to identify rare cell types in single-cell data. It also identifies abundant cell types. The method is based on transcript counts obtained with unique molecular identifies.

This package performs approximate bayesian computation (ABC) model choice and parameter inference via random forests. This machine learning tool named random forests (RF) can conduct selection among the highly complex models covered by ABC algorithms.

This package provides tools for the analysis and visualization of bilateral asymmetry in parasitic infections.

This is a package for computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization.

Compare complex R objects and reveal the key differences. This package was designed particularly for use in testing packages where being able to quickly isolate key differences makes understanding test failures much easier.

Changepoint implements various mainstream and specialised changepoint methods. These methods are suitable for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included as well.

This package provides tools to process and print UTF-8 encoded international text (Unicode). Input, validate, normalize, encode, format, and display.