Computes robust association measures that do not 
    presuppose linearity. The xi correlation (xicor) is based
    on cross correlation between ranked increments.
    The reference for the methods implemented here is 
    Chatterjee, Sourav (2020) <arXiv:1909.10140>
    This package includes the Galton peas example.

Provides a simple XML tree parser/generator. It includes
        functions to read XML files into R objects, get information out of
        and into nodes, and write R objects back to XML code. It's not as
        powerful as the 'XML' package and doesn't aim to be, but for simple
        XML handling it could be useful. It was originally developed for
        the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin
        development easier.

Provides custom geoms and themes to create charts and graphics in 
    the distinctive, hand-drawn 'XKCD' webcomic style using the 'ggplot2' framework. 
    The package utilizes custom layers for jittered lines, segments, circles, and 
    figures, and includes a theme that supports the necessary 'XKCD' font.

The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.

Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.

An R interface to the 'OpenPyXL' 'Python' library to create 
  native 'Excel' charts and work with Microsoft 'Excel' files.

Provides comprehensive functionality to read, write and format Excel data.

Parse entire folders of non-rectangular 'xlsx' files into a single
  rectangular and tidy 'data.frame' based on a custom template file defining the 
  column names of the output.

Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).