This package provides a collection of datasets used in Vega and Vega-Lite examples.

Chaospy is a numerical toolbox for performing uncertainty quantification using polynomial chaos expansions, advanced Monte Carlo methods implemented in Python. It also include a full suite of tools for doing low-discrepancy sampling, quadrature creation, polynomial manipulations, and a lot more.

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.

The RSP markup language provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. Today's date is <%=Sys.Date()%>. Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries. RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things. RSP is ideal for self-contained scientific reports and R package vignettes.

The trimmed k-means clustering method by Cuesta-Albertos, Gordaliza and Matran (1997). This optimizes the k-means criterion under trimming a portion of the points.

XLISP-STAT is a statistical environment based on a Lisp dialect called XLISP. To facilitate statistical computations, standard functions for addition, logarithms, etc., have been modified to operate on lists and arrays of numbers, and a number of basic statistical functions have been added. Many of these functions have been written in Lisp, and additional functions can be added easily by a user. Several basic forms of plots, including histograms, scatterplots, rotatable plots and scatterplot matrices are provided. These plots support various forms of interactive highlighting operations and can be linked so points highlighted in one plot will be highlighted in all linked plots. Interactions with the plots are controlled by the mouse, menus and dialog boxes. An object-oriented programming system is used to allow menus, dialogs, and the response to mouse actions to be customized.

This package provides some basic linear algebra functionality for sparse matrices. It includes Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.

Mixedpower uses pilotdata and a linear mixed model fitted with lme4 to simulate new data sets. Power is computed separate for every effect in the model output as the relation of significant simulations to all simulations. More conservative simulations as a protection against a bias in the pilotdata are available as well as methods for plotting the results.

Command-line tool and C library for reading files from popular stats packages like SAS, Stata and SPSS.

The sourcetools package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code.

This package is a model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.

This package provides the R math library as an independent package.

R is a language and environment for statistical computing and graphics. It provides a variety of statistical techniques, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification and clustering. It also provides robust support for producing publication-quality data plots. A large amount of 3rd-party packages are available, greatly increasing its breadth and scope.

This package provides R functions implementing a standard unit testing framework, with additional code inspection and report generation tools.

This package analyzes data with robust methods such as regression methodology including model selections and multivariate statistics.

This package provides functions to access the RStudio API and provide informative error messages when it's not available.

This package helps accessing files relative to a project root. It provides helpers for robust, reliable and flexible paths to files below a project root. The root of a project is defined as a directory that matches a certain criterion, e.g., it contains a certain regular file.

ArviZ is a Python package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, data storage, model checking, comparison and diagnostics.

This package provides functionalities to build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t family, and provides related statistical methods for data fitting and diagnostics, in the univariate and the multivariate case.

This package provides a number of polymodes for working with mixed R files, including Rmarkdown files.

This package performs KDE operations on multidimensional data to calculate estimated PDFs (probability distribution functions), and resample new data from those PDFs.

rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.

This package provides a small wrapper on regexpr to extract the matches and captured groups from the match of a regular expression to a character vector.

This package provides useful utilities from Seminar fuer Statistik ETH Zurich, including many that are related to graphics.