Package: huge 1.3.5

Haoming Jiang

huge: High-Dimensional Undirected Graph Estimation

Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.

Authors:Haoming Jiang, Xinyu Fei, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman, Xingguo Li, and Tuo Zhao

huge_1.3.5.tar.gz
huge_1.3.5.tar.gz(r-4.5-noble)huge_1.3.5.tar.gz(r-4.4-noble)
huge_1.3.5.tgz(r-4.4-emscripten)huge_1.3.5.tgz(r-4.3-emscripten)
huge.pdf |huge.html
huge/json (API)

# Install 'huge' in R:
install.packages('huge', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • stockdata - Stock price of S&P 500 companies from 2003 to 2008

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

cppopenmp

7.39 score 4 stars 19 packages 484 scripts 2.8k downloads 8 mentions 11 exports 14 dependencies

Last updated 3 years agofrom:a8e1b05cb2. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linux-x86_64NOTEDec 02 2024

Exports:hugehuge.cthuge.generatorhuge.glassohuge.inferencehuge.mbhuge.npnhuge.plothuge.rochuge.selecthuge.tiger

Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMASSMatrixpkgconfigRcppRcppEigenrlangvctrs

vignette

Rendered fromvignette.Rnwusingutils::Sweaveon Dec 02 2024.

Last update: 2019-09-09
Started: 2013-12-04