fasjem: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models

The FASJEM (A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models) is a joint estimator which is fast and scalable for learning multiple related sparse Gaussian graphical models. For more details, please see <https://www.cs.virginia.edu/yanjun/paperA14/2017_JEM_combined.pdf>.

Version: 1.1.0
Depends: R (≥ 3.0.0), igraph
Published: 2017-05-25
Author: Beilun Wang [aut, cre], Yanjun Qi [aut]
Maintainer: Beilun Wang <bw4mw at virginia.edu>
BugReports: https://github.com/QData/JEM
License: GPL-2
URL: https://www.cs.virginia.edu/~bw4mw, https://github.com/QData/JEM
NeedsCompilation: no
CRAN checks: fasjem results

Downloads:

Reference manual: fasjem.pdf
Package source: fasjem_1.1.0.tar.gz
Windows binaries: r-devel: fasjem_1.1.0.zip, r-release: fasjem_1.1.0.zip, r-oldrel: fasjem_1.1.0.zip
OS X El Capitan binaries: r-release: fasjem_1.1.0.tgz
OS X Mavericks binaries: r-oldrel: fasjem_1.1.0.tgz
Old sources: fasjem archive

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