discretize

a data frame containing either numeric or factor columns.

a character string, either <code>interval</code> for interval
 discretization, <code>quantile</code> for quantile discretization 
 (the default) or <code>hartemink</code> for Hartemink's pairwise mutual
 information met

method

if <code>method</code> is set to <code>hartemink</code>, an integer number,
 the number of levels the variables are to be discretized into. Otherwise,
 a vector of integer numbers, one for each column of the data set, specifying
 the number of le

breaks

a boolean value. If <code>TRUE</code> a lot of debugging output
 is printed; otherwise the function is completely silent.

debug

Discretize data to learn discrete Bayesian networks.

manip

Bayesian network structure learning (via constraint-based, score-based and
  hybrid algorithms), parameter learning (via ML and Bayesian estimators) and 
  inference. 
  This package implements the Grow-Shrink (GS) algorithm, the Incremental Association
  (IAMB) algorithm, the Interleaved-IAMB (Inter-IAMB) algorithm, the Fast-IAMB
  (Fast-IAMB) algorithm, the Max-Min Parents and Children (MMPC) algorithm, the Hiton-PC
  algorithm, the ARACNE and Chow-Liu algorithms, the Hill-Climbing (HC) greedy search
  algorithm, the Tabu Search (TABU) algorithm, the Max-Min Hill-Climbing (MMHC) 
  algorithm and the two-stage Restricted Maximization (RSMAX2) algorithm for both discrete
  and 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 and inference, conditional probability queries and cross-validation.

discretize: Discretize data to learn discrete Bayesian networks

Description

Usage

Arguments

Value

References

Examples