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 Blein--Nicolas et al (2024) <DOI:10.1093/jrsssc/qlae012>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).

A high-level interface for creating and exporting summary tables to
  'Excel'. Built on 'dplyr' and 'openxlsx', it provides tools for generating
  one-way to n-way tables, and summarizing multiple response questions 
  and question blocks. Tables are exported with native 'Excel' formatting, 
  including titles, footnotes, and basic styling options.

Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.