boot.strap.bn |
Executes a bootstrap during the learning of a BN structure |
check.algorithms |
Verifies the BN learning algorithms |
check.dichotomic.one.var |
Verify if one specific variable of a data set is dichotomic |
check.levels.one.variable |
Check the levels of a categorical variable |
check.na |
Verify variables with NA |
check.ordered.one.var |
Verify if one specific variable of a data set is an ordered factor |
check.ordered.to.pa |
Verifies if there are ordered factor variables to be declared in the pa model building process |
check.outliers |
Indentifies and gives an option to remove outliers |
check.type.one.var |
Verify the type of one variable |
check.types |
Verify types of variable |
check.variables.to.be.ordered |
Check if the variables need to be ordered |
convert.confusion.matrix |
Converts the position of any element of confusion matrix to VP, FP, FN, VN |
create.cluster |
Create a Parallel Socket Cluster |
create.dummies |
Creates dummy variables in the data set and remove master variables |
dataQualiN |
A qualitative data set to test functions |
dataQuantC |
A quantiative data set to test functions |
gera.bn.structure |
Learn the Bayesian Network structure from data and build a PA model |
gera.pa |
Generates a PA model |
gera.pa.model |
Generates PA input model |
mount.wl.bl.list |
Mounts a white or black list |
outcome.predictor.var |
Builds a black list of predictor and/or outcome variable |
preprocess.outliers |
Extract information of outliers |
transf.into.ordinal |
Transform categorical variables into ordinal |