local discovery algorithms {bnlearn}

Local discovery structure learning algorithms

Description

ARACNE and Chow-Liu learn simple graphs structures from data using pairwise mutual information coefficients.

Usage

aracne(x, whitelist = NULL, blacklist = NULL, mi = NULL, debug = FALSE)
chow.liu(x, whitelist = NULL, blacklist = NULL, mi = NULL, debug = FALSE)

Arguments

x

a data frame containing the variables in the model.

whitelist

a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.

blacklist

a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.

mi

a character string, the estimator used for the pairwise (i.e. unconditional) mutual information coefficients in the ARACNE and Chow-Liu algorithms. Possible values are mi (discrete mutual information) and mi-g (Gaussian mutual information).

debug

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

Value

An object of class bn. See bn-class for details.

Author(s)

Marco Scutari

See Also

constraint-based algorithms, score-based algorithms, hybrid algorithms.