Package: bnlearn 5.0.1
Marco Scutari
bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference
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 <https://www.bnlearn.com/>.
Authors:
bnlearn_5.0.1.tar.gz
bnlearn_5.0.1.tar.gz(r-4.5-noble)bnlearn_5.0.1.tar.gz(r-4.4-noble)
bnlearn.pdf |bnlearn.html✨
bnlearn/json (API)
| # Install 'bnlearn' in R: |
| install.packages('bnlearn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- alarm - ALARM monitoring system (synthetic) data set
- asia - Asia (synthetic) data set by Lauritzen and Spiegelhalter
- clgaussian.test - Synthetic (mixed) data set to test learning algorithms
- coronary - Coronary heart disease data set
- gaussian.test - Synthetic (continuous) data set to test learning algorithms
- hailfinder - The HailFinder weather forecast system (synthetic) data set
- insurance - Insurance evaluation network (synthetic) data set
- learning.test - Synthetic (discrete) data set to test learning algorithms
- lizards - Lizards' perching behaviour data set
- marks - Examination marks data set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 29 days agofrom:443571479a. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 11 2024 |
| R-4.5-linux-x86_64 | OK | Sep 11 2024 |
Exports:acyclicadd.nodealpha.staramatamat<-ancestorsaracnearc.strengtharcsarcs<-as.bnas.bn.fitas.grainas.graphAMas.graphNELas.igraphas.lmaveraged.networkBFbf.strengthblacklistbn.bootbn.cvbn.fitbn.fit.barchartbn.fit.dotplotbn.fit.histogrambn.fit.qqplotbn.fit.xyplotbn.netboot.strengthcextendchildrenchildren<-chow.liuci.testcolliderscomparecompelled.arcscomplete.graphconfigscount.graphscpdagcpdistcpquerycustom.fitcustom.strengthdedupdegreedescendantsdirecteddirected.arcsdiscretizedrop.arcdrop.edgedsepempty.graphfast.iambgbn2mvnormgraphviz.chartgraphviz.comparegraphviz.plotgsHh2pchamminghchpciambiamb.fdridentifiableimputein.degreeincident.arcsinclusion.thresholdincoming.arcsincrement.test.counterinter.iambisolated.nodesKLleaf.nodeslearn.mblearn.nbrlossmbmmhcmmpcmodel2networkmodelstringmodelstring<-moralmutilatedmvnorm2gbnnaive.bayesnarcsnbrnnodesnode.orderingnodesnodes<-nparamsntestsordering2blacklistout.degreeoutgoing.arcsparentsparents<-path.existspc.stablepdag2dagrandom.graphrbnread.bifread.dscread.netremove.noderename.nodesreset.test.counterreverse.arcreversible.arcsroot.nodesrsmax2scoreset.arcset.edgeset2blacklistshdshielded.colliderssi.hiton.pcsingularskeletonspousesstrength.plotstructural.emsubgraphtabutest.countertiers2blacklisttree.bayesundirected.arcsunshielded.collidersvstructswhitelistwrite.bifwrite.dotwrite.dscwrite.net
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian network structure learning, parameter learning and inference | bnlearn-package bnlearn |
| ALARM monitoring system (synthetic) data set | alarm |
| Estimate the optimal imaginary sample size for BDe(u) | alpha.star |
| Drop, add or set the direction of an arc or an edge | arc operations drop.arc drop.edge reverse.arc set.arc set.edge |
| Measure arc strength | arc.strength averaged.network bf.strength boot.strength custom.strength inclusion.threshold mean.bn.strength |
| Asia (synthetic) data set by Lauritzen and Spiegelhalter | asia |
| Bayes factor between two network structures | BF |
| The bn class structure | bn class bn-class |
| Nonparametric bootstrap of Bayesian networks | bn.boot |
| Cross-validation for Bayesian networks | bn.cv loss plot.bn.kcv plot.bn.kcv.list |
| Fit the parameters of a Bayesian network | $<-.bn.fit bn.fit bn.net custom.fit |
| The bn.fit class structure | bn.fit class bn.fit-class bn.fit.dnode bn.fit.gnode |
| Plot fitted Bayesian networks | bn.fit plots bn.fit.barchart bn.fit.dotplot bn.fit.histogram bn.fit.qqplot bn.fit.xyplot |
| Utilities to manipulate fitted Bayesian networks | AIC.bn.fit BIC.bn.fit bn.fit utilities coef.bn.fit coef.bn.fit.cgnode coef.bn.fit.dnode coef.bn.fit.gnode coef.bn.fit.onode fitted.bn.fit fitted.bn.fit.cgnode fitted.bn.fit.dnode fitted.bn.fit.gnode identifiable logLik.bn.fit residuals.bn.fit residuals.bn.fit.cgnode residuals.bn.fit.dnode residuals.bn.fit.gnode sigma sigma.bn.fit sigma.bn.fit.cgnode sigma.bn.fit.gnode singular |
| The bn.kcv class structure | bn.kcv class bn.kcv-class bn.kcv.list class bn.kcv.list-class |
| The bn.strength class structure | bn.strength bn.strength class bn.strength-class |
| Independence and conditional independence tests | ci.test |
| Synthetic (mixed) data set to test learning algorithms | clgaussian.test |
| Compare two or more different Bayesian networks | all.equal.bn compare graphviz.compare hamming shd |
| Construct configurations of discrete variables | configs |
| Constraint-based structure learning algorithms | constraint-based algorithms fast.iamb gs hpc iamb iamb.fdr inter.iamb mmpc pc.stable si.hiton.pc |
| Coronary heart disease data set | coronary |
| Equivalence classes, moral graphs and consistent extensions | cextend colliders cpdag moral shielded.colliders unshielded.colliders vstructs |
| Perform conditional probability queries | cpdist cpquery mutilated |
| Pre-process data to better learn Bayesian networks | dedup discretize |
| Test d-separation | dsep |
| Read and write BIF, NET, DSC and DOT files | read.bif read.dsc read.net write.bif write.dot write.dsc write.net |
| Synthetic (continuous) data set to test learning algorithms | gaussian.test |
| Import and export networks from the gRain package | as.bn.fit as.bn.fit.grain as.bn.grain as.grain as.grain.bn as.grain.bn.fit gRain integration |
| Count graphs with specific characteristics | count.graphs graph enumeration |
| Generate empty, complete or random graphs | complete.graph empty.graph graph generation utilities random.graph |
| Import and export networks from the graph package | as.bn.graphAM as.bn.graphNEL as.graphAM as.graphAM.bn as.graphAM.bn.fit as.graphNEL as.graphNEL.bn as.graphNEL.bn.fit graph integration |
| Utilities to manipulate graphs | acyclic directed graph utilities path path,bn-method path,bn.fit-method path,bn.naive-method path,bn.tan-method path.exists pdag2dag skeleton subgraph |
| Plotting networks with probability bars | graphviz.chart |
| Advanced Bayesian network plots | graphviz.plot |
| The HailFinder weather forecast system (synthetic) data set | hailfinder |
| Hybrid structure learning algorithms | h2pc hybrid algorithms mmhc rsmax2 |
| Import and export networks from the igraph package | as.bn.igraph as.igraph as.igraph.bn as.igraph.bn.fit igraph integration |
| Conditional independence tests | independence tests independence-tests |
| Compute the distance between two fitted Bayesian networks | H KL |
| Insurance evaluation network (synthetic) data set | insurance |
| Synthetic (discrete) data set to test learning algorithms | learning.test |
| Lizards' perching behaviour data set | lizards |
| Produce lm objects from Bayesian networks | as.lm as.lm.bn as.lm.bn.fit as.lm.bn.fit.gnode lm integration |
| Local discovery structure learning algorithms | aracne chow.liu local discovery algorithms |
| Examination marks data set | marks |
| Miscellaneous utilities | amat amat<- ancestors arcs arcs<- children children<- compelled.arcs degree degree,bn-method degree,bn.fit-method degree,bn.naive-method degree,bn.tan-method descendants directed.arcs in.degree incident.arcs incoming.arcs isolated.nodes leaf.nodes mb misc utilities narcs nbr nnodes nodes nodes,bn-method nodes,bn.fit-method nodes,bn.naive-method nodes,bn.tan-method nparams ntests out.degree outgoing.arcs parents parents<- reversible.arcs root.nodes spouses undirected.arcs |
| Build a model string from a Bayesian network and vice versa | as.bn as.bn.character as.character.bn model string utilities model2network modelstring modelstring<- |
| Gaussian Bayesian networks and multivariate normals | gbn2mvnorm mvnorm2gbn |
| Naive Bayes classifiers | naive.bayes predict.bn.naive predict.bn.tan tree.bayes |
| Bayesian network Classifiers | network classifiers network-classifiers |
| Network scores | network scores network-scores |
| Manipulate nodes in a graph | add.node node operations nodes<- nodes<-,bn-method nodes<-,bn.fit-method nodes<-,bn.naive-method nodes<-,bn.tan-method remove.node rename.nodes |
| Partial node orderings | node ordering utilities node.ordering |
| Import and export networks from the pcalg package | as.bn.pcAlgo pcalg integration |
| Plot a Bayesian network | plot.bn |
| Plot arc strengths derived from bootstrap | plot.bn.strength |
| Predict or impute missing data from a Bayesian network | impute predict.bn.fit |
| Simulate random samples from a given Bayesian network | rbn |
| Score of the Bayesian network | AIC.bn BIC.bn logLik.bn score score,bn-method score,bn.naive-method score,bn.tan-method |
| Score-based structure learning algorithms | hc score-based algorithms tabu |
| Discover the structure around a single node | learn.mb learn.nbr single-node local discovery |
| Arc strength plot | strength.plot |
| Structure learning from missing data | em-based algorithms structural.em |
| Structure learning algorithms | structure learning structure-learning |
| Manipulating the test counter | increment.test.counter reset.test.counter test.counter |
| Get or create whitelists and blacklists | blacklist ordering2blacklist set2blacklist tiers2blacklist whitelist |
| Whitelists and blacklists in structure learning | whitelists and blacklists whitelists-blacklists |