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bnstruct

R package for Bayesian Network Structure Learning

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Install

install.packages('bnstruct')

Monthly Downloads

428

Version

1.0

License

GPL (>= 2) | file LICENSE

Maintainer

Francesco Sambo

Last Published

July 30th, 2015

Functions in bnstruct (1.0)

asia

load Asia dataset.
boots

get list of bootstrap samples of a BNDataset.
child

load Child dataset.
cpts<-

set the list of conditional probability tables of a network.
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.
jpts<-

set the list of joint probability tables compiled by an InferenceEngine.
learn.network

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

get size of the variables of an object.
num.items

get number of items of a BNDataset.
show

Show method for objects.
save.to.eps

save a BN picture as .eps file.
variables

get variables of an object.
read.net

Read a network from a .net file.
variables<-

set variables of an object.
BNDataset-class

BNDataset class.
build.junction.tree

build a JunctionTree.
data.file

get data file of a BNDataset.
em

expectation-maximization algorithm.
junction.tree

get the junction tree of an InferenceEngine.
junction.tree<-

set the junction tree of an InferenceEngine.
node.sizes<-

set the size of variables of an object.
raw.data<-

add raw data.
read.dsc

Read a network from a .dsc file.
num.items<-

set number of items of a BNDataset.
wpdag<-

set WPDAG of the object.
sample.row

sample a row vector of values for a network.
num.nodes<-

set number of nodes of an object.
InferenceEngine-class

InferenceEngine class.
has.imputed.data

check if a BNDataset contains impited data.
imp.boots

get list of bootstrap samples from imputed data of a BNDataset.
knn.impute

Perform imputation of a data frame using k-NN.
layering

return the layering of the nodes.
num.variables

get number of variables of a BNDataset.
num.variables<-

set number of variables of a BNDataset.
read.bif

Read a network from a .bif file.
updated.bn<-

set the updated BN object contained in an InferenceEngine.
name

get name of an object.
write.dsc

Write a network saving it in a .dsc file.
test.updated.bn

check if an updated BN is present in an InferenceEngine.
BN-class

BN class definition.
child_NA_5000

Child dataset.
has.boots

check whether a BNDataset has bootstrap samples or not.
impute

Impute a BNDataset raw data with missing values.
plot

plot a BN as a picture.
scoring.func

Read the scoring function used to learn the structure of a network.
name<-

set name of an object.
struct.algo<-

Set the algorithm used to learn the structure of a network.
tune.knn.impute

tune the parameter k of the knn algorithm used in imputation.
asia_10000

Asia dataset.
bn

get the BN object contained in an InferenceEngine.
add.observations<-

add further evidence to an existing list of observations of an InferenceEngine.
jt.cliques

get the list of cliques of the junction tree of an InferenceEngine.
boots<-

set list of bootstrap samples of a BNDataset.
learn.params

learn the parameters of a BN.
observations<-

set the list of observations of an InferenceEngine.
print

print an object to stdout.
discreteness<-

set status (discrete or continuous) of the variables of an object.
cpts

get the list of conditional probability tables of a BN.
has.raw.data

check if a BNDataset contains raw data.
discreteness

get status (discrete or continuous) of the variables of an object.
jt.cliques<-

set the list of cliques of the junction tree of an InferenceEngine.
imputed.data<-

add imputed data.
raw.data

get raw data of a BNDataset.
observations

get the list of observations of an InferenceEngine.
boot

get selected element of bootstrap list.
jpts

get the list of joint probability tables compiled by an InferenceEngine.
num.boots<-

set number of bootstrap samples of a BNDataset.
marginals

compute the list of inferred marginals of a BN.
wpdag

get the WPDAG of an object.
num.nodes

get number of nodes of an object.
shd

compute the Structural Hamming Distance between two adjacency matrices.
num.boots

get number of bootstrap samples of a BNDataset.
belief.propagation

perform belief propagation.
dag.to.cpdag

convert a DAG to a CPDAG
get.most.probable.values

compute the most probable values to be observed.
learn.structure

learn the structure of a network.
scoring.func<-

Set the scoring function used to learn the structure of a network.
sample.dataset

sample a BNDataset from a network of an inference engine.
updated.bn

get the updated BN object contained in an InferenceEngine.
wpdag.from.dag

Initialize a WPDAG from a DAG.
dag<-

set adjacency matrix of an object.
bootstrap

Perform bootstrap.
has.imputed.boots

check whether a BNDataset has bootstrap samples from imputed data or not.
dag

get adjacency matrix of a network.
complete

Subset a BNDataset to get only complete cases.
read.dataset

Read a dataset from file.
bn<-

set the original BN object contained in an InferenceEngine.
imputed.data

get imputed data of a BNDataset.
struct.algo

Read the algorithm used to learn the structure of a network.
data.file<-

set data file of a BNDataset.