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MoTBFs (version 1.2)

Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

Description

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 (I. Prez-Bernab, A. Salmern, H. Langseth (2015) ; H. Langseth, T.D. Nielsen, I. Prez-Bernab, A. Salmern (2014) ; I. Prez-Bernab, A. Fernndez, R. Rum, A. Salmern (2016) ). 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'.

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Version

Install

install.packages('MoTBFs')

Monthly Downloads

194

Version

1.2

License

LGPL-3

Maintainer

Ana D. Maldonado

Last Published

January 14th, 2020

Functions in MoTBFs (1.2)

coef.jointmotbf

Extract Coefficients of a "jointmotbf" Object

findConditional

Find Fitted Conditional MoTBFs

Subclass "motbf" Functions

conditionalmotbf.learning

Learning Conditional Functions

Derivative MoTBF

forward_sampling

Forward Sampling

coefExpJointCDF

Degree Function

as.function.motbf

Coerce an "motbf" Object to a Function

Extract MOP Coefficients

Parameters to MTE String

Extract MoTBF Coefficients

evalJointFunction

Evaluate a Joint Function

Remove Objects from Memory

integralJointMoTBF

Integral Joint MoTBF

Parameters to MOP String

Data set Ecoli: Protein Localization Sites

Extract MTE Coefficients

goodnessDiscreteVariables

Goodness of discrete probabilities

Prints BN Results

Cumulative Joint Distribution

Functions to Manipulate a Dataset

preprocessedData

Remove Missing Values in a Dataset by Rows

dimensionFunction

Dimension of Functions

Generate Samples From Conditional MoTBFs

jointmotbf.learning

Learning Joint Functions

Subset a Dataset

Number of Variables in a Joint Function

getCoefficients

Get the Coefficients

newRangePriorData

Redefining the Domain

Check discreteness of a node

discreteStatesFromBN

Get the states of all discrete nodes from a MoTFB-BN

getNonNormalisedRandomMoTBF

rescaledFunctions

Rescales an MoTBF Function

Multivariate Normal Sample

generateNormalPriorData

learnMoTBFpriorInformation

Incorporating Prior Knowledge

plotConditional

Plots for Conditional Functions

probDiscreteVariable

Probabilities Discrete Variables

Plots for 'motbf' Objects

Initialize Data Frame

marginalJointMoTBF

Marginal Joint MoTBF

getChildParentsFromGraph

Get Relationships in a Network

Fitting Polynomial Models

printConditional

Prints Conditional Functions

printDiscreteBN

Prints Discrete Learnings

summary.jointmotbf

Summary of a "jointmotbf" Object

Summary of an "motbf" Object

Fitting Exponential Models

goodnessMoTBFBN

BIC of an MoTBF BN

plot.jointmotbf

Bidimensional Plots for 'jointmotbf' Objects

Data set Thyroid Disease (thyroid0387)

Value of Parent Nodes

MoTBF-Distribution

Random Generation for MoTBFs

Learning Hybric Bayesian Networks

MoTBFs_Learning

Learning MoTBFs in a Network

BICMultiFunctions

BIC for Multiple Functions

Class-JointMoTBF

Class "jointmotbf"

Class "motbf"

Computing the BIC Score of an MoTBF Function

UpperBoundLogLikelihood

Upper Bound Loglikelihood

as.function.jointmotbf

Coerce a "jointmotbf" Object to a Function