Create a user-friendly plotting GUI for 'R'.
    In addition, one purpose of creating the 'R' package is to facilitate third-party software to call 'R' for drawing, for example, 'Phoenix WinNonlin' software calls 'R' to draw the drug concentration versus time curve.

Show graphically the results of profiling R functions by tracking their execution time.

This 'R' package provides a single procedure guix.install(),
  which allows users to install 'R' packages via 'Guix' right from within
  their running 'R' session.  If the requested 'R' package does not exist
  in 'Guix' at this time, the package and all its missing dependencies
  will be imported recursively and the generated package definitions
  will be written to ~/.Rguix/packages.scm.  This record of imported
  packages can be used later to reproduce the environment, and to add
  the packages in question to a proper 'Guix' channel (or 'Guix' itself).
  guix.install() not only supports installing packages from CRAN, but
  also from Bioconductor or even arbitrary 'git' or 'mercurial'
  repositories, replacing the need for installation via 'devtools'.

Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).

Uses jackknife and bootstrap methods to quantify the sampling uncertainty in goodness-of-fit statistics. Full details are in Clark et al. (2021), "The abuse of popular performance metrics in hydrologic modeling", Water Resources Research, <doi:10.1029/2020WR029001>.

A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances,  Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and  (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.