Package: gif 0.1.1

Shiyun Lin

gif: Graphical Independence Filtering

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.

Authors:Shiyun Lin [aut, cre], Jin Zhu [aut], Junxian Zhu [aut], Xueqin Wang [aut], SC2S2 [cph]

gif_0.1.1.tar.gz
gif_0.1.1.tar.gz(r-4.5-noble)gif_0.1.1.tar.gz(r-4.4-noble)
gif_0.1.1.tgz(r-4.4-emscripten)gif_0.1.1.tgz(r-4.3-emscripten)
gif.pdf |gif.html
gif/json (API)
NEWS

# Install 'gif' in R:
install.packages('gif', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • ar1 - Synthetic multivariate Gaussian data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.70 score 157 downloads 1 mentions 3 exports 5 dependencies

Last updated 1 years agofrom:d579dcd833. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 06 2025
R-4.5-linux-x86_64OKFeb 06 2025

Exports:ggm.generatorhgtsgt

Dependencies:latticeMASSMatrixRcppRcppEigen

gif: Graphical Independence Filtering for Learning Large-Scale Sparse Graphical Models

Rendered fromgif.Rmdusingknitr::rmarkdownon Feb 06 2025.

Last update: 2024-01-13
Started: 2020-06-03