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

This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.

Version: 1.1.2
Depends: R (≥ 3.0.0), igraph
Published: 2017-08-01
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://github.com/QData/JEM
NeedsCompilation: no
CRAN checks: fasjem results

Downloads:

Reference manual: fasjem.pdf
Package source: fasjem_1.1.2.tar.gz
Windows binaries: r-devel: fasjem_1.1.2.zip, r-release: fasjem_1.1.2.zip, r-oldrel: fasjem_1.1.2.zip
OS X El Capitan binaries: r-release: fasjem_1.1.2.tgz
OS X Mavericks binaries: r-oldrel: fasjem_1.1.2.tgz
Old sources: fasjem archive

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