Package: predictionet
Type: Package
Title: Inference for predictive networks designed for (but not limited
        to) genomic data
Version: 1.30.0
Date: 2011-10-05
Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John
        Quackenbush
Maintainer: Benjamin Haibe-Kains <bhaibeka@jimmy.harvard.edu>,
 Catharina Olsen <colsen@ulb.ac.be>
Description: This package contains a set of functions related to
        network inference combining genomic data and prior information
        extracted from biomedical literature and structured biological
        databases. The main function is able to generate networks using
        Bayesian or regression-based inference methods; while the
        former is limited to < 100 of variables, the latter may infer
        networks with hundreds of variables. Several statistics at the
        edge and node levels have been implemented (edge stability,
        predictive ability of each node, ...) in order to help the user
        to focus on high quality subnetworks. Ultimately, this package
        is used in the 'Predictive Networks' web application developed
        by the Dana-Farber Cancer Institute in collaboration with
        Entagen.
Depends: igraph, catnet
Suggests: network, minet, knitr
Imports: penalized, RBGL, MASS
License: Artistic-2.0
URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg
LazyData: yes
Packaged: 2019-05-03 00:18:01 UTC; biocbuild
biocViews: GraphAndNetwork, NetworkInference
git_url: https://git.bioconductor.org/packages/predictionet
git_branch: RELEASE_3_9
git_last_commit: 7e77793
git_last_commit_date: 2019-05-02
Date/Publication: 2019-05-02
NeedsCompilation: yes
Built: R 3.6.1; x86_64-apple-darwin13.4.0; 2019-08-01 18:42:43 UTC; unix
