MoTBFs: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

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'.

AuthorInmaculada Pérez-Bernabé, Antonio Salmerón
Date of publication2015-09-28 09:26:43
MaintainerInmaculada Pérez-Bernabé <iperezbernabe@gmail.com>
LicenseLGPL-3
Version1.0

View on CRAN

Man pages

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

Files in this package

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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