'Giac'
    <https://www-fourier.ujf-grenoble.fr/~parisse/giac/doc/en/cascmd_en/cascmd_en.html>
    is a general purpose symbolic algebra software. It powers the
    graphical interface 'Xcas'. This package allows to execute 'Giac'
    commands in 'R'.

Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>.

A plain 'Rcpp' wrapper of 'MeCab' that can segment Chinese,
    Japanese, and Korean text into tokens. The main goal of this package
    is to provide an alternative to 'tidytext' using morphological
    analysis.

Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages and for types in the 'trip' and 'silicate' packages.

An iterative algorithm that improves the proximity matrix (PM) from a random forest (RF) and the resulting clusters as measured by the silhouette score.

Providing access to the API for Gas Infrastructure Europe's
    natural gas transparency platforms <https://agsi.gie.eu/> and <https://alsi.gie.eu/>. Lets the user
    easily download metadata on companies and gas storage units covered by
    the API as well as the respective data on regional, country, company
    or facility level.

Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <arXiv:1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.