DESCRIPTION

Package: D2C
Type: Package
Title: Predicting Causal Direction from Dependency Features
Version: 1.2.1
Date: 2015-01-14
Author: Gianluca Bontempi, Catharina Olsen, Maxime Flauder
Maintainer: Catharina Olsen <colsen@ulb.ac.be>
Description: The relationship between statistical dependency and causality lies
    at the heart of all statistical approaches to causal inference. The D2C
    package implements a supervised machine learning approach to infer the
    existence of a directed causal link between two variables in multivariate
    settings with n>2 variables. The approach relies on the asymmetry of some
    conditional (in)dependence relations between the members of the Markov
    blankets of two variables causally connected. The D2C algorithm predicts
    the existence of a direct causal link between two variables in a
    multivariate setting by (i) creating a set of of features of the
    relationship based on asymmetric descriptors of the multivariate dependency
    and (ii) using a classifier to learn a mapping between the features and the
    presence of a causal link
License: Artistic-2.0
Depends:
    R(>= 2.10.0),
    randomForest
Imports:
    lazy,
    RBGL,
    MASS,
    corpcor,
    methods,
    Rgraphviz,
    foreach,
    igraph,
    graph,
    gRbase,
    kernlab
LazyData: true
Packaged: 2014-12-16 14:21:06 UTC; gbonte
Suggests:
    knitr
VignetteBuilder: knitr
RoxygenNote: 7.2.1