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 |