ddgraph-package: ddgraph package overview

Description Details Author(s) References

Description

This package implements the Neighbourhood Consistent PC Algorithm (NCPC) for inferring the causal neighbourhood and Markov Blanket of a target variable, and a Direct Dependence Graphs (DDGraphs) for representing the conditional independence relationships.

The main goal of the NCPC algorithm is to infer direct from indirect dependencies of a set of variable to a target variable. The direct dependencies make up the causal neighbourhood of the target variable. This is achieved by performing conditional independence tests and therefore establishing statistical independence properties. NCPC has been shown to have a larger recall rate in scenarios with highly correlated variables which are weakly associated to a sparse target variable. For more details on the NCPC algorithm see (Stojnic et al, 2012).

Details

Package: ddgraph
Type: Package
License: GPL-3
LazyLoad: yes

This package implements the NCPC/NCPC* algorithms, but also provides a unified front-end for inferring causal neighbourhood and Markov Blanket via Bayesian Network inference as provided by packages bnlearn and pcalg.

The package comes with two example datasets (Zizen et al 2009):

The main front-end function is calcDependence().

Author(s)

Robert Stojnic
Cambridge Systems Biology Centre
University of Cambridge, UK

Maintainer: Robert Stojnic [email protected]

References


ddgraph documentation built on Nov. 17, 2017, 10:50 a.m.