BN-class                BN class definition.
BNDataset-class         BNDataset class.
InferenceEngine-class   InferenceEngine class.
add.observations<-      add further evidence to an existing list of
                        observations of an 'InferenceEngine'.
asia                    load 'Asia' dataset.
asia_10000              'Asia' dataset.
asia_2_layers           load a two-layers dataset derived from the
                        'Asia' dataset.
belief.propagation      perform belief propagation.
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                   get list of bootstrap samples of a 'BNDataset'.
boots<-                 set list of bootstrap samples of a 'BNDataset'.
bootstrap               Perform bootstrap.
build.junction.tree     build a JunctionTree.
child                   load 'Child' dataset.
child_NA_5000           'Child' dataset.
complete                Subset a 'BNDataset' to get only complete
                        cases.
cpts                    get the list of conditional probability tables
                        of a 'BN'.
cpts<-                  set the list of conditional probability tables
                        of a network.
dag                     get adjacency matrix of a network.
dag.to.cpdag            convert a DAG to a CPDAG
dag<-                   set adjacency matrix of an object.
data.file               get data file of a 'BNDataset'.
data.file<-             set data file of a 'BNDataset'.
discreteness            get status (discrete or continuous) of the
                        variables of an object.
discreteness<-          set 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.
get.most.probable.values
                        compute the most probable values to be
                        observed.
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             get header file of a 'BNDataset'.
header.file<-           set header file of a 'BNDataset'.
imp.boots               get list of bootstrap samples from imputed data
                        of a 'BNDataset'.
imp.boots<-             set list of bootstrap samples from imputed data
                        of a 'BNDataset'.
impute                  Impute a 'BNDataset' raw data with missing
                        values.
imputed.data            get imputed data of a BNDataset.
imputed.data<-          add imputed data.
interventions           get the list of interventions of an
                        'InferenceEngine'.
interventions<-         set the list of interventions for an
                        'InferenceEngine'.
jpts                    get the list of joint probability tables
                        compiled by an 'InferenceEngine'.
jpts<-                  set the list of joint probability tables
                        compiled by an 'InferenceEngine'.
jt.cliques              get the list of cliques of the junction tree of
                        an 'InferenceEngine'.
jt.cliques<-            set the list of cliques of the junction tree of
                        an 'InferenceEngine'.
junction.tree           get the junction tree of an 'InferenceEngine'.
junction.tree<-         set 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.
marginals               compute the list of inferred marginals of a BN.
name                    get name of an object.
name<-                  set name of an object.
node.sizes              get size of the variables of an object.
node.sizes<-            set the size of variables of an object.
num.boots               get number of bootstrap samples of a
                        'BNDataset'.
num.boots<-             set number of bootstrap samples of a
                        'BNDataset'.
num.items               get number of items of a 'BNDataset'.
num.items<-             set number of items of a 'BNDataset'.
num.nodes               get number of nodes of an object.
num.nodes<-             set number of nodes of an object.
num.time.steps          get number of time steps observed in a 'BN' or
                        a 'BNDataset'.
num.time.steps<-        set number of time steps of a 'BN' or a
                        'BNDataset'.
num.variables           get number of variables of a 'BNDataset'.
num.variables<-         set number of variables of a 'BNDataset'.
observations            get the list of observations of an
                        'InferenceEngine'.
observations<-          set the list of observations of an
                        'InferenceEngine'.
plot                    plot a 'BN' as a picture.
print                   print a 'BN', 'BNDataset' or 'InferenceEngine'
                        to 'stdout'.
quantiles               get the list of quantiles of an object.
quantiles<-             set the list of quantiles of an object.
raw.data                get raw data of a BNDataset.
raw.data<-              add raw data.
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.
save.to.eps             save a 'BN' picture as '.eps' file.
scoring.func            Read the scoring function used to learn the
                        structure of a network.
scoring.func<-          Set 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             Read the algorithm used to learn the structure
                        of a network.
struct.algo<-           Set the algorithm used to learn the structure
                        of a network.
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               get variables of an object.
variables<-             set variables of an object.
wpdag                   get the WPDAG of an object.
wpdag.from.dag          Initialize a WPDAG from a DAG.
wpdag<-                 set WPDAG of the object.
write.dsc               Write a network saving it in a '.dsc' file.
write_xgmml             Write a network saving it in an 'XGMML' file.
