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huge
High-Dimensional Undirected Graph Estimation
View on CRAN: Click
here
Download and install huge package within the R console
Install from CRAN:
install.packages("huge")
Install from Github:
library("remotes")
install_github("cran/huge") Install by package version:
library("remotes")
install_version("huge", "2.0.1") Attach the package and use:
library("huge")
Maintained by
Haoming Jiang
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2010-11-11
Latest Update: 2021-06-30
Description:
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.
How to cite:
Haoming Jiang (2010). huge: High-Dimensional Undirected Graph Estimation. R package version 2.0.1, https://cran.r-project.org/web/packages/huge. Accessed 05 Oct. 2026.
Previous versions and publish date:
(2026-08-04 12:31), 0.7 (2010-11-11 13:40), 0.8.1 (2010-11-17 09:17), 0.8 (2010-11-14 09:42), 0.9.1 (2011-02-13 17:11), 0.9 (2010-11-22 08:50), 1.0.1 (2011-04-11 08:36), 1.0.2 (2011-06-15 20:02), 1.0.3 (2011-06-17 08:45), 1.0 (2011-03-02 18:32), 1.1.0 (2011-07-23 15:55), 1.1.1 (2011-08-10 18:27), 1.1.2 (2011-08-22 21:47), 1.2.1 (2012-01-27 12:03), 1.2.2 (2012-03-21 08:59), 1.2.3 (2012-03-22 09:26), 1.2.4 (2012-08-16 07:52), 1.2.5 (2013-12-07 07:49), 1.2.6 (2014-02-28 07:00), 1.2.7 (2015-09-16 10:05), 1.2 (2012-01-22 21:25), 1.3.0 (2019-02-22 08:00), 1.3.1 (2019-03-12 00:00), 1.3.2 (2019-04-08 14:10), 1.3.3 (2019-09-09 23:00), 1.3.4.1 (2020-04-01 07:40), 1.3.4 (2019-10-28 16:10), 1.3.5 (2021-06-30 22:20), 1.4 (2026-02-19 09:50), 1.5.1 (2026-03-31 07:10), 1.5 (2026-03-11 11:00), 1.6 (2026-04-13 11:00), 2.0.0 (2026-07-26 18:30)
Other packages that cited huge R package
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Other R packages that huge depends,
imports, suggests or enhances
Complete documentation for huge
Functions, R codes and Examples using
the huge R package
Some associated functions: huge-package . huge.ct . huge.generator . huge.glasso . huge.inference . huge.mb . huge.npn . huge . huge.plot . huge.roc . huge.select . huge.tiger . plot.huge . plot.roc . plot.select . plot.sim . print.huge . print.roc . print.select . print.sim . stockdata .
Some associated R codes: RcppExports.R . huge-package.R . huge.R . huge.ct.R . huge.generator.R . huge.glasso.R . huge.inference.R . huge.mb.R . huge.npn.R . huge.plot.R . huge.roc.R . huge.select.R . huge.tiger.R . Full huge package functions and examples
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