Package: D2C
Type: Package
Title: Predicting Causal Direction from Dependency Features
Version: 1.1
Date: 2014-06-10
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: gRbase, lazy, RBGL, MASS, corpcor, methods, Rgraphviz
LazyData: true
Packaged: 2014-12-16 11:26:20 UTC; bontempi
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-12-16 13:19:36

Imports: gRbase, lazy, RBGL, MASS, corpcor, methods, Rgraphviz, foreach

Title: Data Driving Multiple Classifier System
Provides a novel framework to able to automatically develop and deploy
an accurate Multiple Classifier System based on the feature-clustering
distribution achieved from an input dataset. 'D2MCS' was developed focused on
four main aspects: (i) the ability to determine an effective method to
evaluate the independence of features, (ii) the identification of the
optimal number of feature clusters, (iii) the training and tuning of ML
models and (iv) the execution of voting schemes to combine the outputs of
each classifier comprising the Multiple  Classifier System.
Imports: caret, devtools, dplyr, FSelector, ggplot2, ggrepel,
gridExtra, infotheo, mccr, mltools, ModelMetrics, questionr,
Suggests: grDevices, knitr, rmarkdown, testthat (>= 3.0.2)