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.
| Version: |
1.3.5 |
| Depends: |
R (≥ 3.0.0) |
| Imports: |
Matrix, igraph, MASS, grDevices, graphics, methods, stats, utils, Rcpp |
| LinkingTo: |
Rcpp, RcppEigen |
| Published: |
2021-06-30 |
| DOI: |
10.32614/CRAN.package.huge |
| Author: |
Haoming Jiang, Xinyu Fei, Han Liu, Kathryn Roeder, John Lafferty, Larry
Wasserman, Xingguo Li, and Tuo Zhao |
| Maintainer: |
Haoming Jiang <jianghm.ustc at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| In views: |
GraphicalModels |
| CRAN checks: |
huge results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse imports: |
fake, HeteroGGM, netassoc, netgwas, nethet, noisysbmGGM, nutriNetwork, rgm, sdafilter, sparsenetgls, SparseTSCGM, stminsights, Tlasso, TransGraph, UNPaC |
| Reverse suggests: |
BDgraph, bootnet, CompareCausalNetworks, CTD, edgebundleR, pcalg, pulsar, qgraph, sand, stm |
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