This package provides tools for defensive programming. It is inspired by purrr mappers and based on rlang. Attempt extends and facilitates defensive programming by providing a consistent grammar, and a set of functions for common tests and conditions. Attempt only depends on rlang, and focuses on speed, so it can be integrated with other functions and used in the data analysis.

This package provides unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using the ggplot2 package.

Alabama stands for Augmented Lagrangian Adaptive Barrier Minimization Algorithm; it is used for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

The Munsell package contains functions for exploring and using the Munsell colour system.

This package provides interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio.

This is a package for operations on triangular meshes based on VCGLIB. This package integrates nicely with the R-package rgl to render the meshes processed by Rvcg. The Visualization and Computer Graphics Library (VCG for short) is a library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes.

This package enhances the ROI with the lp_solve solver.

This is a package for interactive Reingold-Tilford tree diagrams created using D3.js, where every node can be expanded and collapsed by clicking on it. Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame.

With this package it is possible to define parameter spaces, constraints and dependencies for arbitrary algorithms, and to program on such spaces. It also includes statistical designs and random samplers. Objects are implemented as R6 classes.

This package provides a Cairo graphics device that can be use to create high-quality vector (PDF, PostScript and SVG) and bitmap output (PNG, JPEG, TIFF), and high-quality rendering in displays (X11 and Win32). Since it uses the same back-end for all output, copying across formats is WYSIWYG. Files are created without the dependence on X11 or other external programs. This device supports alpha channel (semi-transparent drawing) and resulting images can contain transparent and semi-transparent regions. It is ideal for use in server environments (file output) and as a replacement for other devices that don't have Cairo's capabilities such as alpha support or anti-aliasing. Backends are modular such that any subset of backends is supported.

This package provides an R Shiny application to create visual abstracts for original research. A variety of user defined options and formatting are included.

This package provides a set of predicates and assertions for checking the properties of numbers. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

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.

Fit Bayesian generalized (non-)linear multivariate multilevel models using Stan for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation.

This package provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class family, link-glm, lm, glm and betareg. The resulting objects preserve their class, so all methods associated with them still apply. The package also provides the enriched_glm function that has the same interface as glm but results in objects of class enriched_glm. In addition to the usual components in a glm object, enriched_glm objects carry an object-specific simulate method and functions to compute the scores, the observed and expected information matrix, the first-order bias, as well as model densities, probabilities, and quantiles at arbitrary parameter values. The package can also be used to produce customizable source code templates for the structured implementation of methods to compute new components and enrich arbitrary objects.

This package implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

This package provides a system for reporting messages, which offers certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".

This package provides a dependency management toolkit for R. Using renv, you can create and manage project-local R libraries, save the state of these libraries to a lockfile, and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.

This package provides helper functions to install and maintain the LaTeX distribution named TinyTeX, a lightweight, cross-platform, portable, and easy-to-maintain version of TeX Live. This package also contains helper functions to compile LaTeX documents, and install missing LaTeX packages automatically.

This package calls the Jupyter script nbconvert to create vignettes from notebooks. Those notebooks (.ipynb files) are files containing rich text, code, and its output. Code cells can be edited and evaluated interactively.

This package provides several analysis-related functions for the book entitled "R statistics and graph for medical articles" (written in Korean), version 1, by Keon-Woong Moon with Korean demographic data with several plot functions.

This package provides functions to impute using random forest. It operates under full conditional specifications (multivariate imputation by chained equations).

This package provides an interface from R to Python modules, classes, and functions. When calling into Python, R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types.

This package provides tools to compute and represent gene set enrichment or depletion from your data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN) or user imported maps. The gene set enrichment can be run with hypergeometric test or Fisher exact test, and can use multiple corrections. Visualization of data can be done either by barplots or heatmaps.