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

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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

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