Graphical Interaction Models
General functions related to iModels
Get information about mixed interaction model objects
Mixed interaction model.
Impose zeros in matrix entries which do not correspond to an edge.
Internal functions for the gRim package
Edge matrix operations
Mean, covariance and counts for grouped data (statistics for condition...
Test for conditional independence in a contingency table
Test for conditional independence in a dataframe
Coerce models to different representations
Generic function for conditional independence test
Test for conditional independence in the multivariate normal distribut...
A function to compute Monte Carlo and asymptotic tests of conditional ...
Graphical Gaussian model
Fast computation of covariance / correlation matrix
Fit Gaussian graphical models
Generate various grapical models
Genrate matrix of N(0, 1) variables
Find edges in a graph or edges not in an undirected graph.
Iterative proportional fitting of graphical Gaussian model
Discrete interaction model (log-linear model)
Return the dimension of a log-linear model
Fitting Log-Linear Models by Message Passing
Modify generating class for a graphical/hierarchical model
Conversion between different parametrizations of mixed models
Parse graphical model formula
Stepwise model selection in (graphical) interaction models
Test edges in graphical models with p-value/AIC value
Test addition of edge to graphical model
Test deletion of edge from an interaction model
Utilities for gRips
Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about 'gRim' is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.