Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks. The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'.

Author | Inmaculada Pérez-Bernabé, Antonio Salmerón |

Date of publication | 2015-09-28 09:26:43 |

Maintainer | Inmaculada Pérez-Bernabé <iperezbernabe@gmail.com> |

License | LGPL-3 |

Version | 1.0 |

**as.function.jointmotbf:** Coerce a '"jointmotbf"' Object to a Function

**as.function.motbf:** Coerce an '"motbf"' Object to a Function

**asMOPString:** Parameters to MOP String

**asMTEString:** Parameters to MTE String

**BICMoTBF:** Computing the BIC Score of an MoTBF Function

**BICMultiFunctions:** BIC for Multiple Functions

**Class-JointMoTBF:** Class '"jointmotbf"'

**Class-MoTBF:** Class '"motbf"'

**clean:** Remove Objects from Memory

**coefExpJointCDF:** Degree Function

**coef.jointmotbf:** Extract Coefficients of a '"jointmotbf"' Object

**coef.mop:** Extract MOP Coefficients

**coef.motbf:** Extract MoTBF Coefficients

**coef.mte:** Extract MTE Coefficients

**conditionalmotbf.learning:** Learning Conditional Functions

**dataMining:** Functions to Manipulate a Dataset

**derivMOP:** Derivative MOP

**derivMoTBF:** Derivative MoTBF

**derivMTE:** Derivative MTE

**dimensionFunction:** Dimension of Functions

**ecoli:** Data set Ecoli: Protein Localization Sites

**evalJointFunction:** Evaluate a Joint Function

**generateNormalPriorData:** Prior Data

**getChildParentsFromGraph:** Get Relationships in a Network

**getCoefficients:** Get the Coefficients

**getNonNormalisedRandomMoTBF:** Ramdom MoTBF

**goodnessDiscreteVariables:** Goodness of discrete probabilities

**goodnessMoTBFBN:** BIC of an MoTBF BN

**integralJointMoTBF:** Integral Joint MoTBF

**integralMOP:** Integral MOP

**integralMoTBF:** Integral MoTBF

**integralMTE:** Integral MTE

**jointCDF:** Cumulative Joint Distribution

**jointmotbf.learning:** Learning Joint Functions

**LearningHC:** Learning Hybric Bayesian Networks

**learnMoTBFpriorInformation:** Incorporating Prior Knowledge

**marginalJointMoTBF:** Marginal Joint MoTBF

**mop.learning:** Fitting Polynomial Models

**MoTBF-Distribution:** Random Generation for MoTBFs

**MoTBFs_Learning:** Learning MoTBFs in a Network

**mte.learning:** Fitting Exponential Models

**newRangePriorData:** Redefining the Domain

**nVariables:** Number of Variables in a Joint Function

**plotConditional:** Plots for Conditional Functions

**plot.jointmotbf:** Bidimensional Plots for "jointmotbf" Objects

**plot.motbf:** Plots for "motbf" Objects

**preprocessedData:** Remove Missing Values in a Dataset by Rows

**printBN:** Prints BN Results

**printConditional:** Prints Conditional Functions

**printDiscreteBN:** Prints Discrete Learnings

**probDiscreteVariable:** Probabilities Discrete Variables

**rescaledFunctions:** Rescales an MoTBF Function

**rnormMultiv:** Multivariate Normal Sample

**Subclass-MoTBF:** Subclass '"motbf"' Functions

**subsetData:** Subset a Dataset

**summary.jointmotbf:** Summary of a '"jointmotbf"' Object

**summary.motbf:** Summary of an '"motbf"' Object

**thyroid:** Data set Thyroid Disease (thyroid0387)

**univMoTBF:** Fitting MoTBFs

**UpperBoundLogLikelihood:** Upper Bound Loglikelihood

MoTBFs

MoTBFs/NAMESPACE

MoTBFs/data

MoTBFs/data/ecoli.rda

MoTBFs/data/thyroid.rda

MoTBFs/R

MoTBFs/R/joint.R
MoTBFs/R/conditional.R
MoTBFs/R/rescalatedFunctions.R
MoTBFs/R/mop.R
MoTBFs/R/LearningBN.R
MoTBFs/R/motbf.R
MoTBFs/R/functions.R
MoTBFs/R/structuralLearning.R
MoTBFs/R/priorKnowledge.R
MoTBFs/R/MoTBFClass.R
MoTBFs/R/mte.R
MoTBFs/R/DiscreteLearning.R
MoTBFs/R/datasets.R
MoTBFs/R/rMoTBF.R
MoTBFs/MD5

MoTBFs/DESCRIPTION

MoTBFs/man

MoTBFs/man/coef.motbf.Rd
MoTBFs/man/Subclass-MoTBF.Rd
MoTBFs/man/marginalJointMoTBF.Rd
MoTBFs/man/preprocessedData.Rd
MoTBFs/man/asMOPString.Rd
MoTBFs/man/Class-JointMoTBF.Rd
MoTBFs/man/plot.motbf.Rd
MoTBFs/man/conditionalmotbf.learning.Rd
MoTBFs/man/ecoli.Rd
MoTBFs/man/summary.motbf.Rd
MoTBFs/man/getChildParentsFromGraph.Rd
MoTBFs/man/as.function.jointmotbf.Rd
MoTBFs/man/clean.Rd
MoTBFs/man/summary.jointmotbf.Rd
MoTBFs/man/evalJointFunction.Rd
MoTBFs/man/printConditional.Rd
MoTBFs/man/jointmotbf.learning.Rd
MoTBFs/man/dataMining.Rd
MoTBFs/man/probDiscreteVariable.Rd
MoTBFs/man/MoTBF-Distribution.Rd
MoTBFs/man/integralMoTBF.Rd
MoTBFs/man/rescaledFunctions.Rd
MoTBFs/man/coef.mop.Rd
MoTBFs/man/goodnessMoTBFBN.Rd
MoTBFs/man/getCoefficients.Rd
MoTBFs/man/learnMoTBFpriorInformation.Rd
MoTBFs/man/LearningHC.Rd
MoTBFs/man/asMTEString.Rd
MoTBFs/man/coef.jointmotbf.Rd
MoTBFs/man/coef.mte.Rd
MoTBFs/man/MoTBFs_Learning.Rd
MoTBFs/man/UpperBoundLogLikelihood.Rd
MoTBFs/man/as.function.motbf.Rd
MoTBFs/man/printDiscreteBN.Rd
MoTBFs/man/derivMTE.Rd
MoTBFs/man/Class-MoTBF.Rd
MoTBFs/man/plotConditional.Rd
MoTBFs/man/dimensionFunction.Rd
MoTBFs/man/goodnessDiscreteVariables.Rd
MoTBFs/man/thyroid.Rd
MoTBFs/man/integralMOP.Rd
MoTBFs/man/nVariables.Rd
MoTBFs/man/BICMultiFunctions.Rd
MoTBFs/man/subsetData.Rd
MoTBFs/man/integralMTE.Rd
MoTBFs/man/rnormMultiv.Rd
MoTBFs/man/integralJointMoTBF.Rd
MoTBFs/man/plot.jointmotbf.Rd
MoTBFs/man/jointCDF.Rd
MoTBFs/man/mop.learning.Rd
MoTBFs/man/printBN.Rd
MoTBFs/man/coefExpJointCDF.Rd
MoTBFs/man/derivMoTBF.Rd
MoTBFs/man/BICMoTBF.Rd
MoTBFs/man/univMoTBF.Rd
MoTBFs/man/mte.learning.Rd
MoTBFs/man/getNonNormalisedRandomMoTBF.Rd
MoTBFs/man/derivMOP.Rd
MoTBFs/man/newRangePriorData.Rd
MoTBFs/man/generateNormalPriorData.Rd
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