bn.fit

a data frame, containing the variables in the model.

data

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

debug

Fit the parameters of a Bayesian network conditional
  on its structure.

multivariate

Bayesian network structure learning via constraint-based
        (also known as 'conditional independence'), score-based and
        hybrid algorithms. 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 Hill-Climbing (HC) greedy search algorithm, the
        Max-Min Hill-Climbing (MMHC) algorithm for both discrete and
        Gaussian networks, along with many score functions and
        conditional independence tests. Some utility functions (model
        comparison and manipulation, random data generation, arc
        orientation testing, simple and advanced plots) are included,
        as well as basic parametric and bootstrap inference functions.

bn.fit: Fit the parameters of a Bayesian network

Description

Usage

Arguments

Value