configs

data

a boolean value. If <code>TRUE</code> all configuration are included as
   levels in the return value;  otherwise only configurations which are actually
   observed are considered.

Create configurations of discrete variables, which can be used in
  modelling conditional probability tables.

manip

Bayesian network structure learning, parameter learning and
  inference.
  This package implements constraint-based (GS, IAMB, Inter-IAMB, Fast-IAMB,
  MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing
  and Tabu Search) and hybrid (MMHC and RSMAX2) structure learning algorithms
  for discrete, Gaussian and conditional Gaussian networks, along with many
  score functions and conditional independence tests.
  The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are
  also implemented.
  Some utility functions (model comparison and manipulation, random data
  generation, arc orientation testing, simple and advanced plots) are
  included, as well as support for parameter estimation (maximum likelihood
  and Bayesian) and inference, conditional probability queries and
  cross-validation. Development snapshots with the latest bugfixes are
  available from www.bnlearn.com.

configs: Construct configurations of discrete variables

Description

Usage

Arguments

Value

Details

Examples