Bayesian Network Structure Learning from Data with Missing Values
learn a network (structure and parameters) of a BN from a BNDataset .
learn the parameters of a BN .
learn the structure of a network.
compute the list of inferred marginals of a BN.
set name of an object.
get name of an object.
set the size of variables of an object.
get size of the variables of an object.
set number of bootstrap samples of a BNDataset.
get number of bootstrap samples of a BNDataset.
set number of items of a BNDataset.
get number of items of a BNDataset.
set number of nodes of an object.
get number of nodes of an object.
set number of time steps of a BN or a BNDataset.
get number of time steps observed in a BN or a BNDataset.
set number of variables of a BNDataset.
get number of variables of a BNDataset.
Write a network saving it in a .dsc file.
set the list of observations of an InferenceEngine.
get the list of observations of an InferenceEngine.
plot a BN as a picture.
print a BN, BNDataset or InferenceEngine to stdout.
set the list of quantiles of an object.
get the list of quantiles of an object.
add raw data.
get raw data of a BNDataset.
Read a network from a .bif file.
Read a dataset from file.
Read a network from a .dsc file.
Read a network from a .net file.
sample a BNDataset from a network of an inference engine.
sample a row vector of values for a network.
save a BN picture as .eps file.
Set the scoring function used to learn the structure of a network.
Read the scoring function used to learn the structure of a network.
compute the Structural Hamming Distance between two adjacency matrices...
Show method for objects.
Set the algorithm used to learn the structure of a network.
Read the algorithm used to learn the structure of a network.
check if an updated BN is present in an InferenceEngine.
tune the parameter k of the knn algorithm used in imputation.
get the updated BN object contained in an InferenceEngine.
set the updated BN object contained in an InferenceEngine.
set variables of an object.
get variables of an object.
set WPDAG of the object.
Initialize a WPDAG from a DAG.
get the WPDAG of an object.
Write a network saving it in an XGMML file.
add further evidence to an existing list of observations of an `Infere...
load Asia dataset.
perform belief propagation.
BN class definition.
get the BN object contained in an InferenceEngine.
set the original BN object contained in an InferenceEngine.
BNDataset class.
get selected element of bootstrap list.
set list of bootstrap samples of a BNDataset.
get list of bootstrap samples of a BNDataset.
Perform bootstrap.
build a JunctionTree.
load Child dataset.
Subset a BNDataset to get only complete cases.
set the list of conditional probability tables of a network.
get the list of conditional probability tables of a BN.
set adjacency matrix of an object.
get adjacency matrix of a network.
convert a DAG to a CPDAG
set data file of a BNDataset.
get data file of a BNDataset.
set status (discrete or continuous) of the variables of an object.
get status (discrete or continuous) of the variables of an object.
counts the edges in a WPDAG with their directionality
expectation-maximization algorithm.
compute the most probable values to be observed.
check whether a BNDataset has bootstrap samples or not.
check whether a BNDataset has bootstrap samples from imputed data or...
check if a BNDataset contains impited data.
check if a BNDataset contains raw data.
set header file of a BNDataset.
get header file of a BNDataset.
set list of bootstrap samples from imputed data of a BNDataset.
get list of bootstrap samples from imputed data of a BNDataset.
Impute a BNDataset raw data with missing values.
add imputed data.
get imputed data of a BNDataset.
InferenceEngine class.
set the list of interventions for an InferenceEngine.
get the list of interventions of an InferenceEngine.
set the list of joint probability tables compiled by an `InferenceEngi...
get the list of joint probability tables compiled by an `InferenceEngi...
set the list of cliques of the junction tree of an InferenceEngine.
get the list of cliques of the junction tree of an InferenceEngine.
set the junction tree of an InferenceEngine.
get the junction tree of an InferenceEngine.
Perform imputation of a data frame using k-NN.
return the layering of the nodes.
learn a dynamic network (structure and parameters) of a BN from a BNDa...
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.