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MoTBFs (version 1.4.2)

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. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) ; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) ; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (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

249

Version

1.4.2

License

LGPL-3

Maintainer

Ana D. Maldonado

Last Published

July 22nd, 2025

Functions in MoTBFs (1.4.2)

Data pre-processing utilities

Extract coefficients from MOPs

getCoefficients

Get the coefficients

Derivative of a MOP

goodnessDiscreteVariables

BIC scxore and log-likelihood

getNonNormalisedRandomMoTBF

findConditional

Find fitted conditional MoTBFs

forward_sampling

Forward Sampling

goodnessMoTBFBN

BIC of a hybrid BN

generateNormalPriorData

Prior data generation

getChildParentsFromGraph

Get the list of relations in a graph

jointmotbf.learning

Joint MoTBF density learning

learnMoTBFpriorInformation

Incorporating prior knowledge in the estimation process

marginalJointMoTBF

Marginalization of MoTBFs

discreteStatesFromBN

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

Fitting mixtures of polynomials

dimensionFunction

Dimension of MoTBFs

Value of parent nodes

plot.jointmotbf

Bidimensional plots for 'jointmotbf' objects

Integrating MoTBFs

Integrating MTEs

Derivating MoTBFs

Derivating MTEs

Data set Ecoli: Protein Localization Sites

integralJointMoTBF

Integration with MoTBFs

Integration of MOPs

evalJointFunction

Evaluation of joint MoTBFs

Check discreteness of a node

Plots for 'motbf' objects

printConditional

Summary of conditional MoTBF densities

printDiscreteBN

Printing discrete Bayesian networks

preprocessedData

Fitting mixtures of truncated exponentials.

plotConditional

Plot Conditional Functions

Data set Thyroid Disease (thyroid0387)

Sample generation from conditional MoTBFs

Dataset subsetting

Multivariate Normal sampling

rescaledFunctions

Rescaling MoTBF functions

Summary of an "motbf" object

summary.jointmotbf

Summary of a "jointmotbf" object

Joint MoTBFs CDFs

newRangePriorData

Redefining the Domain

Number of Variables in a Joint Function

probDiscreteVariable

Probability distribution of discrete variables

Data frame initialization for forward sampling

Computing the BIC score of an MoTBF function

Subclass "motbf" Functions

Score-based hybrid Bayesian Network structure learning

Class-JointMoTBF

Class "jointmotbf"

Class "motbf"

UpperBoundLogLikelihood

Upper bound of the loglikelihood

BICMultiFunctions

BIC score for multiple functions

MoTBFs_Learning

Learning hybrid BNs with MoTBFs

as.function.jointmotbf

Coerce a "jointmotbf" Object to a Function

coef.jointmotbf

Coefficients of a "jointmotbf" object

as.function.motbf

Coerce an "motbf" object to a Function

conditionalmotbf.learning

Learning conditional MoTBF densities

coefExpJointCDF

Degree Function

MoTBF-Distribution

Random generation for MoTBF distributions

Extracting the coefficients of an MTE

Remove Objects from Memory

Extract the coefficients of an MoTBF

Converting MTEs to strings

Parameters to MOP String