The mlr3 package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core mlr3 packages.

This package completes R's functional programming tools with missing features present in other programming languages.

This package contains a list of functional time series, sliced functional time series, and functional data sets. Functional time series is a special type of functional data observed over time. Sliced functional time series is a special type of functional time series with a time variable observed over time.

This package provides a ggplot2 extension for drawing gene arrow maps.

This is an R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

This is a package for the analysis of music and speech. Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read MP3, read MIDI, perform steps of a transcription, ...

This is a package for regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. The rms package is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. The package works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.

This package provides classes and methods to locate, setup, subset, navigate and iterate file sets, i.e. sets of files located in one or more directories on the file system. The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats. Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags. This adds additional flexibility to identify file sets and individual files.

This package provides a variety of simple fish stock assessment methods.

This package provides a functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

This package lets you take formulas including random-effects components (formatted as in lme4, glmmTMB, etc.) and process them. It includes various helper functions.

This package provides a collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future.

This package provides procedures to create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using ggplot2.

This package contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX code, and recoding variables.

The ggplot2 package is an excellent and flexible package for elegant data visualization in R. However the default generated plots require some formatting before we can send them for publication. The ggpubr package provides some easy-to-use functions for creating and customizing ggplot2-based publication-ready plots.

This package provides tools to check the latest release version of R and R packages (on CRAN, Bioconductor or Github).

This R package contains examples from the book Regression for Categorical Data, Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.

This package contains a function to do exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS).

This package provides interactive visualizations for profiling R code.

This package provides an interface (wrapper) to MPI APIs. It also provides an interactive R manager and worker environment.

In this package Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.

This package provides a simple and intuitive pipe-friendly framework, coherent with the tidyverse design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.

The package includes the necessary functions to construct a self-organizing map of data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.

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