| Name : R-huge
| |
| Version : 1.3.5
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp155.7.3
| Date : 2024-09-10 06:32:52
|
| Group : Development/Libraries/Other
| Source RPM : R-huge-1.3.5-lp155.7.3.src.rpm
|
| Size : 1.57 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : High-dimensional Undirected Graph Estimation
|
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
