bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

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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.

Author
Marco Scutari
Date of publication
2016-05-16 14:47:13
Maintainer
Marco Scutari <marco.scutari@gmail.com>
License
GPL (>= 2)
Version
4.0
URLs

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