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bnstruct  

Bayesian Network Structure Learning from Data with Missing Values
View on CRAN: Click here


Download and install bnstruct package within the R console
Install from CRAN:
install.packages("bnstruct")

Install from Github:
library("remotes")
install_github("cran/bnstruct")

Install by package version:
library("remotes")
install_version("bnstruct", "1.0.15")



Attach the package and use:
library("bnstruct")
Maintained by
Alberto Franzin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-11-10
Latest Update: 2022-11-30
Description:
Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.
How to cite:
Alberto Franzin (2016). bnstruct: Bayesian Network Structure Learning from Data with Missing Values. R package version 1.0.15, https://cran.r-project.org/web/packages/bnstruct
Previous versions and publish date:
1.0.1 (2016-11-10 13:43), 1.0.2 (2016-12-13 15:33), 1.0.4 (2018-08-31 15:50), 1.0.5 (2019-07-02 11:40), 1.0.6 (2019-07-09 12:23), 1.0.7 (2020-05-09 12:10), 1.0.8 (2020-05-22 19:10), 1.0.9 (2021-03-09 13:50), 1.0.10 (2021-05-06 21:20), 1.0.11 (2021-05-19 10:10), 1.0.12 (2022-01-09 20:52), 1.0.13 (2022-10-20 19:15), 1.0.14 (2022-11-30 15:10), 1.0 (2015-07-30 09:59)
Other packages that cited bnstruct R package
View bnstruct citation profile
Other R packages that bnstruct depends, imports, suggests or enhances
Functions, R codes and Examples using the bnstruct R package
Some associated functions: BN-class . BNDataset-class . InferenceEngine-class . addservations-set . asia . asia_10000 . asia_2_layers . belief.propagation . bn-method . bn-set . boot . boots-set . boots . bootstrap . build.junction.tree . child . child_NA_5000 . complete . cpts-set . cpts . dag-set . dag . dag.to.cpdag . data.file-set . data.file . discreteness-set . discreteness . edge.dir.wpdag . em . get.most.probable.values . has.boots . has.imputed.boots . has.imputed.data . has.raw.data . header.file-set . header.file . imp.boots-set . imp.boots . impute . imputed.data-set . imputed.data . interventions-set . interventions . jpts-set . jpts . jt.cliques-set . jt.cliques . junction.tree-set . junction.tree . knn.impute . layering . learn.dynamic.network . learn.network . learn.params . learn.structure . marginals . name-set . name . node.sizes-set . node.sizes . num.boots-set . num.boots . num.items-set . num.items . num.nodes-set . num.nodes . num.time.steps-set . num.time.steps . num.variables-set . num.variables . observations-set . observations . plot . print . quantiles-set . quantiles . raw.data-set . raw.data . read.bif . read.dataset . read.dsc . read.net . sample.dataset . sample.row . save.to.eps . scoring.func-set . scoring.func . shd . show . struct.algo-set . struct.algo . test.updated.bn . tune.knn.impute . updated.bn-method . updated.bn-set . variables-set . variables . wpdag-set . wpdag.from.dag . wpdag . write.dsc . write_xgmml . 
Some associated R codes: AllClasses.R . AllGenerics.R . BN-methods.R . BNDataset-methods.R . InferenceEngine-methods.R . JunctionTree-methods.R . beliefPropagation-methods.R . bnstruct.R . buildJunctionTree-methods.R . datasets.R . em.R . imputation.R . io-methods.R . io.R . learn-methods.R . mmhc.R . sem.R . sm.R . util.R .  Full bnstruct package functions and examples
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