Mesh Generation and Surface Tessellation

Makes the 'Qhull' library < http://www.qhull.org> available in R, in a similar manner as in Octave and MATLAB. Qhull computes convex hulls, Delaunay triangulations, halfspace intersections about a point, Voronoi diagrams, furthest-site Delaunay triangulations, and furthest-site Voronoi diagrams. It runs in 2D, 3D, 4D, and higher dimensions. It implements the Quickhull algorithm for computing the convex hull. Qhull does not support constrained Delaunay triangulations, or mesh generation of non-convex objects, but the package does include some R functions that allow for this.

Functions to Inline C, C++, Fortran Function Calls from R

Functionality to dynamically define R functions and S4 methods with 'inlined' C, C++ or Fortran code supporting the .C and .Call calling conventions.

Miscellaneous Functions for 'SciViews::R'

Functions required for the 'SciViews::R' dialect or for general use: manage a temporary environment attached to the search path, define synonyms for R functions using aka(), test if 'Aqua', 'Mac', 'Win' ... Show progress bar, etc.

Base Package for Phylogenetic Structures and Comparative Data

Provides a base S4 class for comparative methods, incorporating one or more trees and trait data.

Additional Binary Operators

A set of binary operators for common tasks such as regex manipulation.

Take Screenshots of Web Pages

Takes screenshots of web pages, including Shiny applications and R Markdown documents.

Extension to 'ggplot2'

The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Syntax Highlighter

Syntax highlighter for R code based on the results of the R parser. Rendering in HTML and latex markup. Custom Sweave driver performing syntax highlighting of R code chunks.

Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Handling Missing Values with Multivariate Data Analysis

Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA or MCA.