Learn R Programming

⚠️There's a newer version (5.0.1) of this package.Take me there.

bnlearn (version 4.6.1)

Bayesian Network Structure Learning, Parameter Learning and Inference

Description

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) 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, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from .

Copy Link

Version

Install

install.packages('bnlearn')

Monthly Downloads

25,548

Version

4.6.1

License

GPL (>= 2)

Maintainer

Last Published

September 21st, 2020

Functions in bnlearn (4.6.1)

ALARM monitoring system (synthetic) data set

Nonparametric bootstrap of Bayesian networks

Cross-validation for Bayesian networks

The bn class structure

bn.fit utilities

Utilities to manipulate fitted Bayesian networks

Equivalence classes, moral graphs and consistent extensions

Plot fitted Bayesian networks

bnlearn-package

Bayesian network structure learning, parameter learning and inference

graph enumeration

Count graphs with specific characteristics

Estimate the optimal imaginary sample size for BDe(u)

Coronary heart disease data set

Test d-separation

Fit the parameters of a Bayesian network

constraint-based algorithms

Constraint-based structure learning algorithms

foreign files utilities

Read and write BIF, NET, DSC and DOT files

utilities for whitelists and blacklists

Get or create whitelists and blacklists

The bn.fit class structure

bn.strength class

The bn.strength class structure

Perform conditional probability queries

Examination marks data set

Equivalence classes in the presence of interventions

The bn.kcv class structure

Advanced Bayesian network plots

Construct configurations of discrete variables

independence-tests

Conditional independence tests

graph integration

Import and export networks from the graph package

Miscellaneous utilities

gRain integration

Import and export networks from the gRain package

Measure arc strength

Drop, add or set the direction of an arc or an edge

plot.bn.strength

Plot arc strengths derived from bootstrap

clgaussian.test

Synthetic (mixed) data set to test learning algorithms

Plotting networks with probability bars

choose.direction

Try to infer the direction of an undirected arc

Compare two or more different Bayesian networks

Insurance evaluation network (synthetic) data set

graph utilities

Utilities to manipulate graphs

score-based algorithms

Score-based structure learning algorithms

Synthetic (continuous) data set to test learning algorithms

Pre-process data to better learn Bayesian networks

Independence and conditional independence tests

single-node local discovery

Discover the structure around a single node

Plot a Bayesian network

The HailFinder weather forecast system (synthetic) data set

pcalg integration

Import and export networks from the pcalg package

graph generation utilities

Generate empty or random graphs

ROCR integration

Generating a prediction object for ROCR

whitelists-blacklists

Whitelists and blacklists in structure learning

Simulate random samples from a given Bayesian network

model string utilities

Build a model string from a Bayesian network and vice versa

local discovery algorithms

Local discovery structure learning algorithms

Arc strength plot

hybrid algorithms

Hybrid structure learning algorithms

Lizards' perching behaviour data set

Synthetic (discrete) data set to test learning algorithms

Predict or impute missing data from a Bayesian network

igraph integration

Import and export networks from the igraph package

node operations

Manipulate nodes in a graph

Structure learning from missing data

Score of the Bayesian network

Naive Bayes classifiers

node ordering utilities

Partial node orderings

structure-learning

Structure learning algorithms

Produce lm objects from Bayesian networks

Manipulating the test counter

Asia (synthetic) data set by Lauritzen and Spiegelhalter

Bayes factor between two network structures

network-classifiers

Bayesian network Classifiers