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gRapHD (version 0.2.5)

Efficient Selection of Undirected Graphical Models for High-Dimensional Datasets

Description

Performs efficient selection of high-dimensional undirected graphical models as described in Abreu, Edwards and Labouriau (2010) . Provides tools for selecting trees, forests and decomposable models minimizing information criteria such as AIC or BIC, and for displaying the independence graphs of the models. It has also some useful tools for analysing graphical structures. It supports the use of discrete, continuous, or both types of variables.

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Version

Install

install.packages('gRapHD')

Monthly Downloads

39

Version

0.2.5

License

GPL (>= 3)

Maintainer

Rodrigo Labouriau

Last Published

January 9th, 2018

Functions in gRapHD (0.2.5)

SubGraph

Generates a subgraph
CI.test

Test of conditional independence
DFS

Depth-first search
minForest

Minimum forest
modelDim

Model's dimension
plot.gRapHD

Plots a gRapHD object
chStat

Internal use
convData

Converts dataset
sortMat

Sort matrix
stepb

Stepwise backward selection
randTree

Random tree
gRapHD-package

The gRapHD package
jTree

Junction tree
neighbours

Finds all direct neighbours
perfSets

Finds a perfect sequence
calcStat

Pairwise weights
Degree

Degree
MCS

Maximum cardinality search
ccoeff

Clustering coefficient
dsCont

Test dataset
dsDiscr

Test dataset
adjMat

Adjacency matrix
fit

Log-likelihood, AIC, BIC
gRapHD-class

Class "gRapHD"
modelFormula

Model's formula
neighbourhood

Neighbourhood of a vertex
dsMixed

Test dataset
findEd

Finds add-eligible edges
rowProds

Row products
shortPath

Shortest path
stepw

Stepwise forward selection