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bnlearn (version 0.8)

Bayesian network structure learning

Description

Bayesian network structure learning via constraint-based (also known as 'conditional independence') and score-based 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 and the Hill-Climbing (HC) greedy search 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) are also included.

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Version

Install

install.packages('bnlearn')

Monthly Downloads

24,198

Version

0.8

License

GPL (>= 2)

Maintainer

Marco Scutari

Last Published

January 7th, 2025

Functions in bnlearn (0.8)

arc operations

Drop, add or set the direction of an arc
iamb

Incremental Association (IAMB) learning algorithm
inter.iamb

Interleaved Incremental Association (Inter-IAMB) learning algorithm
fast.iamb

Fast Incremental Association (Fast-IAMB) learning algorithm
hc

Hill-Climbing (HC) learning algorithm
model string tools

Build a model string from a Bayesian network and vice versa
compare

Compare two different Bayesian networks
score

Score of the Bayesian network
deal integration

bnlearn - deal package integration
snow integration

bnlearn - snow package integration
plot.bn

Plot a Bayesian network
asia

Asia (synthetic) data set by Lauritzen and Spiegelhalter
node ordering tools

Partial node ordering utilities
graph generation tools

Generate an empty or random graph
rbn

Generate random data from a given Bayesian network
bnlearn-package

Bayesian network constraint-based structure learning.
network tools

Utilities to manipulate graphs
bn class

The bn class structure
gaussian.test

Synthetic (continuous) dataset to test learning algorithms
choose.direction

Try to infer the direction of an undirected arc
gs

Grow-Shrink (GS) learning algorithm
learning.test

Synthetic (discrete) data set to test learning algorithms
arc.strength

Measure the strength of the arcs