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.

Implements various Gifi methods in a user-friendly way: categorical principal component analysis (princals), multiple correspondence analysis (homals), monotone regression analysis (morals).

Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. 
    Converts images to GIF animations using pngquant's efficient cross-frame 
    palettes and temporal dithering with thousands of colors per frame.

Retrieving regional plant checklists, species traits and
  distributions, and environmental data from the Global Inventory of Floras and
  Traits (GIFT). More information about the GIFT database can be found at
  <https://gift.uni-goettingen.de/about> and the map of available floras can be
  visualized at <https://gift.uni-goettingen.de/map>. The API and associated
  queries can be accessed according the following scheme:
  <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.

Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.

A Gibbs sampler corresponding to a Group 
    Inverse-Gamma Gamma (GIGG) regression model with adjustment covariates. 
    Hyperparameters in the GIGG prior specification can either be fixed by the 
    user or can be estimated via Marginal Maximum Likelihood Estimation.
    Jonathan Boss, Jyotishka Datta, Xin Wang, Sung Kyun Park, Jian Kang, Bhramar 
    Mukherjee (2021) <arXiv:2102.10670>.