bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

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

AuthorMarco Scutari
Date of publication2016-05-16 14:47:13
MaintainerMarco Scutari <marco.scutari@gmail.com>
LicenseGPL (>= 2)
Version4.0
http://www.bnlearn.com/

View on CRAN

Man pages

alarm: ALARM Monitoring System (synthetic) data set

alpha.star: Estimate the Optimal Imaginary Sample Size for BDe(u)

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

arc.strength: Measure arc strength

asia: Asia (synthetic) data set by Lauritzen and Spiegelhalter

bnboot: Parametric and nonparametric bootstrap of Bayesian networks

bn.class: The bn class structure

bn.cv: Cross-validation for Bayesian networks

bn.fit: Fit the parameters of a Bayesian network

bn.fit.class: The bn.fit class structure

bn.fit.methods: Utilities to manipulate fitted Bayesian networks

bn.fit.plots: Plot fitted Bayesian networks

bn.kcv.class: The bn.kcv class structure

bnlearn-package: Bayesian network structure learning, parameter learning and...

bn.strength-class: The bn.strength class structure

choose.direction: Try to infer the direction of an undirected arc

ci.test: Independence and Conditional Independence Tests

clgaussian-test: Synthetic (mixed) data set to test learning algorithms

compare: Compare two different Bayesian networks

configs: Construct configurations of discrete variables

constraint: Constraint-based structure learning algorithms

coronary: Coronary Heart Disease data set

cpdag: Equivalence classes, moral graphs and consistent extensions

cpquery: Perform conditional probability queries

deal: bnlearn - deal package integration

dsep: Test d-separation

foreign: Read and write BIF, NET, DSC and DOT files

gaussian-test: Synthetic (continuous) data set to test learning algorithms

gRain: Import and export networks from the gRain package

graph: Utilities to manipulate graphs

graphgen: Generate empty or random graphs

graphpkg: Import and export networks from the graph package

graphviz.plot: Advanced Bayesian network plots

hailfinder: The HailFinder weather forecast system (synthetic) data set

hc: Score-based structure learning algorithms

hybrid: Hybrid structure learning algorithms

insurance: Insurance evaluation network (synthetic) data set

learn: Discover the structure around a single node

learning-test: Synthetic (discrete) data set to test learning algorithms

lizards: Lizards' perching behaviour data set

marks: Examination marks data set

mb: Miscellaneous utilities

mmpc: Local discovery structure learning algorithms

modelstring: Build a model string from a Bayesian network and vice versa

naive.bayes: Naive Bayes classifiers

ordering: Utilities dealing with partial node orderings

parallel: bnlearn - snow/parallel package integration

plot.bn: Plot a Bayesian network

plot.bn.strength: Plot arc strengths derived from bootstrap

preprocessing: Pre-process data to better learn Bayesian networks

rbn: Simulate random data from a given Bayesian network

relevant: Identify Relevant Nodes Without Learning the Bayesian network

score: Score of the Bayesian network

strength.plot: Arc strength plot

test.counter: Manipulating the test counter

Files in this package

bnlearn
bnlearn/inst
bnlearn/inst/CITATION
bnlearn/inst/network.scripts
bnlearn/inst/network.scripts/learning.test.R
bnlearn/inst/network.scripts/clgaussian.test.R
bnlearn/inst/network.scripts/gaussian.test.R
bnlearn/inst/bibtex
bnlearn/inst/bibtex/bnlearn.bib
bnlearn/src
bnlearn/src/per.node.score.c
bnlearn/src/graph.generation.c
bnlearn/src/arcs2amat.c
bnlearn/src/Makevars
bnlearn/src/allocations.c
bnlearn/src/sanitization.c
bnlearn/src/predict.c
bnlearn/src/cg.mutual.information.c
bnlearn/src/shrinkage.c
bnlearn/src/linear.correlation.c
bnlearn/src/common.c
bnlearn/src/rcont2.c
bnlearn/src/acyclic.c
bnlearn/src/rbn.c
bnlearn/src/cache.structure.c
bnlearn/src/tiers.c
bnlearn/src/configurations.c
bnlearn/src/map.lw.c
bnlearn/src/gaussian.loglikelihood.c
bnlearn/src/bn.recovery.c
bnlearn/src/gaussian.monte.carlo.c
bnlearn/src/cextend.c
bnlearn/src/bind.c
bnlearn/src/jonckheere.c
bnlearn/src/cpdist.c
bnlearn/src/htest.c
bnlearn/src/test.counter.c
bnlearn/src/wishart.posterior.c
bnlearn/src/allsubs.test.c
bnlearn/src/hc.cache.lookup.c
bnlearn/src/is.dag.c
bnlearn/src/subsets.c
bnlearn/src/shd.c
bnlearn/src/utest.c
bnlearn/src/filter.arcs.c
bnlearn/src/dedup.c
bnlearn/src/hash.c
bnlearn/src/discrete.tests.c
bnlearn/src/discrete.monte.carlo.c
bnlearn/src/discrete.loglikelihood.c
bnlearn/src/enums.c
bnlearn/src/score.delta.c
bnlearn/src/strings.c
bnlearn/src/graph.priors.c
bnlearn/src/roundrobin.test.c
bnlearn/src/linear.algebra.c
bnlearn/src/data.frame.c
bnlearn/src/df.adjust.c
bnlearn/src/globals.c
bnlearn/src/likelihood.weighting.c
bnlearn/src/bootstrap.c
bnlearn/src/dirichlet.posterior.c
bnlearn/src/which.max.c
bnlearn/src/symmetric.c
bnlearn/src/cg.loglikelihood.c
bnlearn/src/covariance.c
bnlearn/src/mi.matrix.c
bnlearn/src/cg.assumptions.c
bnlearn/src/bayesian.network.c
bnlearn/src/gaussian.tests.c
bnlearn/src/cpdag.c
bnlearn/src/indep.test.c
bnlearn/src/tabu.c
bnlearn/src/arcs2elist.c
bnlearn/src/pdag2dag.c
bnlearn/src/averaging.c
bnlearn/src/path.c
bnlearn/src/loss.c
bnlearn/src/parse.c
bnlearn/src/simulation.c
bnlearn/src/ctest.c
bnlearn/src/nparams.c
bnlearn/src/contincency.tables.c
bnlearn/src/alpha.star.c
bnlearn/src/is.row.equal.c
bnlearn/src/sampling.c
bnlearn/src/include
bnlearn/src/include/loss.h
bnlearn/src/include/rcore.h
bnlearn/src/include/debugging.h
bnlearn/src/include/dataframe.h
bnlearn/src/include/learning.h
bnlearn/src/include/tests.h
bnlearn/src/include/graph.h
bnlearn/src/include/scores.h
bnlearn/src/include/bn.h
bnlearn/src/include/covariance.h
bnlearn/src/include/sets.h
bnlearn/src/include/blas.h
bnlearn/src/include/globals.h
bnlearn/src/include/sampling.h
bnlearn/src/include/matrix.h
bnlearn/src/fitted.c
bnlearn/NAMESPACE
bnlearn/data
bnlearn/data/alarm.rda
bnlearn/data/learning.test.rda
bnlearn/data/gaussian.test.rda
bnlearn/data/lizards.rda
bnlearn/data/insurance.rda
bnlearn/data/asia.rda
bnlearn/data/clgaussian.test.rda
bnlearn/data/marks.rda
bnlearn/data/hailfinder.rda
bnlearn/data/coronary.rda
bnlearn/Changelog
bnlearn/R
bnlearn/R/grow-shrink.R bnlearn/R/fast-iamb.R bnlearn/R/backend-indep.R bnlearn/R/frontend-learning.R bnlearn/R/utils-elist.R bnlearn/R/utils-cluster.R bnlearn/R/bootstrap.R bnlearn/R/inter-iamb.R bnlearn/R/maxmin-pc.R bnlearn/R/utils-tests.R bnlearn/R/frontend-simulation.R bnlearn/R/lattice.R bnlearn/R/tabu.R bnlearn/R/frontend-graph.R bnlearn/R/cv.R bnlearn/R/frontend-score.R bnlearn/R/frontend-predict.R bnlearn/R/foreign-read.R bnlearn/R/graphviz.R bnlearn/R/hiton-pc.R bnlearn/R/ci.test.R bnlearn/R/formula.R bnlearn/R/predict.R bnlearn/R/scores.R bnlearn/R/chow.liu.R bnlearn/R/incremental-association.R bnlearn/R/graph-generation.R bnlearn/R/cpdag.R bnlearn/R/frontend-plot.R bnlearn/R/frontend-print.R bnlearn/R/foreign-write.R bnlearn/R/globals.R bnlearn/R/custom.fit.R bnlearn/R/utils-sanitization.R bnlearn/R/simulation.R bnlearn/R/utils-plot.R bnlearn/R/frontend-bn.R bnlearn/R/choose.direction.R bnlearn/R/frontend-strength.R bnlearn/R/utils-arcs.R bnlearn/R/fit.R bnlearn/R/frontend-packages.R bnlearn/R/learning-algorithms.R bnlearn/R/init.R bnlearn/R/test.R bnlearn/R/fitted.assignment.R bnlearn/R/frontend-amat.R bnlearn/R/utils-amat.R bnlearn/R/frontend-data.R bnlearn/R/classifiers.R bnlearn/R/backend-s4.R bnlearn/R/nparams.R bnlearn/R/utils-misc.R bnlearn/R/frontend-fit.R bnlearn/R/cpq.R bnlearn/R/arc.strength.R bnlearn/R/frontend-lattice.R bnlearn/R/frontend-foreign.R bnlearn/R/utils-graph.R bnlearn/R/data.preprocessing.R bnlearn/R/arc.operations.R bnlearn/R/backend-score.R bnlearn/R/frontend-nodes.R bnlearn/R/loss.R bnlearn/R/hill-climbing.R bnlearn/R/frontend-arcs.R bnlearn/R/frontend-formula.R bnlearn/R/utils-print.R bnlearn/R/frontend-bootstrap.R bnlearn/R/aracne.R bnlearn/R/relevant.R
bnlearn/MD5
bnlearn/DESCRIPTION
bnlearn/man
bnlearn/man/arc.strength.Rd bnlearn/man/bnlearn-package.Rd bnlearn/man/modelstring.Rd bnlearn/man/ci.test.Rd bnlearn/man/bn.kcv.class.Rd bnlearn/man/score.Rd bnlearn/man/rbn.Rd bnlearn/man/foreign.Rd bnlearn/man/bn.fit.methods.Rd bnlearn/man/plot.bn.strength.Rd bnlearn/man/graphviz.plot.Rd bnlearn/man/gaussian-test.Rd bnlearn/man/ordering.Rd bnlearn/man/parallel.Rd bnlearn/man/relevant.Rd bnlearn/man/bn.strength-class.Rd bnlearn/man/mmpc.Rd bnlearn/man/constraint.Rd bnlearn/man/naive.bayes.Rd bnlearn/man/bn.fit.plots.Rd bnlearn/man/alpha.star.Rd bnlearn/man/graphgen.Rd bnlearn/man/dsep.Rd bnlearn/man/graphpkg.Rd bnlearn/man/strength.plot.Rd bnlearn/man/cpquery.Rd bnlearn/man/marks.Rd bnlearn/man/hailfinder.Rd bnlearn/man/lizards.Rd bnlearn/man/preprocessing.Rd bnlearn/man/test.counter.Rd bnlearn/man/choose.direction.Rd bnlearn/man/hc.Rd bnlearn/man/learning-test.Rd bnlearn/man/compare.Rd bnlearn/man/plot.bn.Rd bnlearn/man/bn.cv.Rd bnlearn/man/bn.class.Rd bnlearn/man/hybrid.Rd bnlearn/man/coronary.Rd bnlearn/man/insurance.Rd bnlearn/man/arcops.Rd bnlearn/man/cpdag.Rd bnlearn/man/graph.Rd bnlearn/man/learn.Rd bnlearn/man/clgaussian-test.Rd bnlearn/man/gRain.Rd bnlearn/man/configs.Rd bnlearn/man/asia.Rd bnlearn/man/bnboot.Rd bnlearn/man/bn.fit.Rd bnlearn/man/mb.Rd bnlearn/man/deal.Rd bnlearn/man/bn.fit.class.Rd bnlearn/man/alarm.Rd

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