Function reference • SGS

All functions

Asia: Asia dataset

Asiamat: Asiamat

initialize(<BN>) BN(): BN class definition.

BNDataset() initialize(<BNDataset>): BNDataset class.

initialize(<InferenceEngine>) InferenceEngine(): InferenceEngine class.

`add.observations<-`(): add further evidence to an existing list of observations of an InferenceEngine.

approxInference(): Importance Sampling (IS) in Bayesian Networks

belief.propagation(): perform belief propagation.

benchmarkMultipleNets(): Benchmark Inference Methods

bn(): get the BN object contained in an InferenceEngine.

`bn<-`(): set the original BN object contained in an InferenceEngine.

boot(): get selected element of bootstrap list.

`boots<-`(): set list of bootstrap samples of a BNDataset.

boots(): get list of bootstrap samples of a BNDataset.

bootstrap(): Perform bootstrap.

build.junction.tree(): build a JunctionTree.

complete(): Subset a BNDataset to get only complete cases.

convertBNBestieToSGS(): Bayesian network Conversion Bestie to SGS format

convertCPTsBestieToSGS(): CPTs Conversion Bestie to SGS format

`cpts<-`(): set the list of conditional probability tables of a network.

cpts(): get the list of conditional probability tables of a BN.

`dag<-`(): set adjacency matrix of an object.

dag(): get adjacency matrix of a network.

dag.to.cpdag(): convert a DAG to a CPDAG

`data.file<-`(): set data file of a BNDataset.

data.file(): get data file of a BNDataset.

`discreteness<-`(): set status (discrete or continuous) of the variables of an object.

discreteness(): get status (discrete or continuous) of the variables of an object.

edge.dir.wpdag(): counts the edges in a WPDAG with their directionality

em(): expectation-maximization algorithm.

exactInference(): Exact inference via SupGroupSeparation

get.allAncestors(): Get relevant DAG nodes (ancestors)

get.allSubGroups(): Get All Conditionally Independent Subgroups (CIS)

get.most.probable.values(): compute the most probable values to be observed.

get.subGroup(): Get Conditionally Independent Subgroup (CIS)

has.boots(): check whether a BNDataset has bootstrap samples or not.

has.imputed.boots(): check whether a BNDataset has bootstrap samples from imputed data or not.

has.imputed.data(): check if a BNDataset contains impited data.

has.raw.data(): check if a BNDataset contains raw data.

`header.file<-`(): set header file of a BNDataset.

header.file(): get header file of a BNDataset.

`imp.boots<-`(): set list of bootstrap samples from imputed data of a BNDataset.

imp.boots(): get list of bootstrap samples from imputed data of a BNDataset.

impute(): Impute a BNDataset raw data with missing values.

`imputed.data<-`(): add imputed data.

imputed.data(): get imputed data of a BNDataset.

`interventions<-`(): set the list of interventions for an InferenceEngine.

interventions(): get the list of interventions of an InferenceEngine.

`jpts<-`(): set the list of joint probability tables compiled by an InferenceEngine.

jpts(): get the list of joint probability tables compiled by an InferenceEngine.

`jt.cliques<-`(): set the list of cliques of the junction tree of an InferenceEngine.

jt.cliques(): get the list of cliques of the junction tree of an InferenceEngine.

`junction.tree<-`(): set the junction tree of an InferenceEngine.

junction.tree(): get the junction tree of an InferenceEngine.

knn.impute(): Perform imputation of a data frame using k-NN.

layering(): return the layering of the nodes.

learn.dynamic.network(): learn a dynamic network (structure and parameters) of a BN from a BNDataset.

learn.network(): learn a network (structure and parameters) of a BN from a BNDataset.

learn.params(): learn the parameters of a BN.

learn.structure(): learn the structure of a network.

learn_bn(): learn_bn

loopy_belief.propagation(): perform LOOPY belief propagation.

makeAllPlots(): Visualize benchmark results

marginals(): compute the list of inferred marginals of a BN.

`name<-`(): set name of an object.

name(): get name of an object.

`node.sizes<-`(): set the size of variables of an object.

node.sizes(): get size of the variables of an object.

`num.boots<-`(): set number of bootstrap samples of a BNDataset.

num.boots(): get number of bootstrap samples of a BNDataset.

`num.items<-`(): set number of items of a BNDataset.

num.items(): get number of items of a BNDataset.

`num.nodes<-`(): set number of nodes of an object.

num.nodes(): get number of nodes of an object.

`num.time.steps<-`(): set number of time steps of a BN or a BNDataset.

num.time.steps(): get number of time steps observed in a BN or a BNDataset.

`num.variables<-`(): set number of variables of a BNDataset.

num.variables(): get number of variables of a BNDataset.

`observations<-`(): set the list of observations of an InferenceEngine.

observations(): get the list of observations of an InferenceEngine.

plot(<BN>): plot a BN as a picture.

plot_bn(): plot_bn

plot_dag(): plot_dag

print(<BN>) print(<BNDataset>) print(<InferenceEngine>): print a BN, BNDataset or InferenceEngine to stdout.

`quantiles<-`(): set the list of quantiles of an object.

quantiles(): get the list of quantiles of an object.

randomBN(): Create random Bayesian network

`raw.data<-`(): add raw data.

raw.data(): get raw data of a BNDataset.

read.bif(): Read a network from a .bif file.

read.dataset(): Read a dataset from file.

read.dsc(): Read a network from a .dsc file.

read.net(): Read a network from a .net file.

sample.dataset(): sample a BNDataset from a network of an inference engine.

sample.row(): sample a row vector of values for a network.

sample.subGroupSampling(): Sub Group Sampling (SGS) in Bayesian Networks

`scoring.func<-`(): Set the scoring function used to learn the structure of a network.

scoring.func(): Read the scoring function used to learn the structure of a network.

shd(): compute the Structural Hamming Distance between two adjacency matrices.

show(): Show method for objects.

`struct.algo<-`(): Set the algorithm used to learn the structure of a network.

struct.algo(): Read the algorithm used to learn the structure of a network.

sub_belief.propagation(): perform SUB belief propagation.

test.updated.bn(): check if an updated BN is present in an InferenceEngine.

tune.knn.impute(): tune the parameter k of the knn algorithm used in imputation.

updated.bn(): get the updated BN object contained in an InferenceEngine.

`updated.bn<-`(): set the updated BN object contained in an InferenceEngine.

`variables<-`(): set variables of an object.

variables(): get variables of an object.

`wpdag<-`(): set WPDAG of the object.

wpdag(): get the WPDAG of an object.

wpdag.from.dag(): Initialize a WPDAG from a DAG.

write.dsc(): Write a network saving it in a .dsc file.

write_xgmml(): Write a network saving it in an XGMML file.