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MoTBFs (version 1.4.1)

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

213

Version

1.4.1

License

LGPL-3

Maintainer

Ana D. Maldonado

Last Published

April 18th, 2022

Functions in MoTBFs (1.4.1)

as.function.jointmotbf

Coerce a "jointmotbf" Object to a Function

MoTBF-Distribution

Random generation for MoTBF distributions

UpperBoundLogLikelihood

Upper bound of the loglikelihood

Class "motbf"

MoTBFs_Learning

Learning hybrid BNs with MoTBFs

Computing the BIC score of an MoTBF function

Score-based hybrid Bayesian Network structure learning

Subclass "motbf" Functions

Class-JointMoTBF

Class "jointmotbf"

BICMultiFunctions

BIC score for multiple functions

Extract the coefficients of an MoTBF

coefExpJointCDF

Degree Function

conditionalmotbf.learning

Learning conditional MoTBF densities

Data pre-processing utilities

Derivative of a MOP

Extract coefficients from MOPs

coef.jointmotbf

Coefficients of a "jointmotbf" object

goodnessMoTBFBN

BIC of a hybrid BN

goodnessDiscreteVariables

BIC scxore and log-likelihood

Derivating MTEs

Derivating MoTBFs

Extracting the coefficients of an MTE

forward_sampling

Forward Sampling

Remove Objects from Memory

findConditional

Find fitted conditional MoTBFs

Converting MTEs to strings

getNonNormalisedRandomMoTBF

getCoefficients

Get the coefficients

Data set Ecoli: Protein Localization Sites

Value of parent nodes

plot.jointmotbf

Bidimensional plots for 'jointmotbf' objects

evalJointFunction

Evaluation of joint MoTBFs

Integrating MTEs

Data set Thyroid Disease (thyroid0387)

Joint MoTBFs CDFs

as.function.motbf

Coerce an "motbf" object to a Function

Integrating MoTBFs

generateNormalPriorData

Prior data generation

Fitting mixtures of truncated exponentials.

preprocessedData

Check discreteness of a node

Dataset subsetting

Sample generation from conditional MoTBFs

jointmotbf.learning

Joint MoTBF density learning

getChildParentsFromGraph

Get the list of relations in a graph

newRangePriorData

Redefining the Domain

Number of Variables in a Joint Function

Parameters to MOP String

dimensionFunction

Dimension of MoTBFs

discreteStatesFromBN

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

rescaledFunctions

Rescaling MoTBF functions

Integration of MOPs

integralJointMoTBF

Integration with MoTBFs

marginalJointMoTBF

Marginalization of MoTBFs

Multivariate Normal sampling

Summary of an "motbf" object

summary.jointmotbf

Summary of a "jointmotbf" object

printConditional

Summary of conditional MoTBF densities

probDiscreteVariable

Probability distribution of discrete variables

Data frame initialization for forward sampling

printDiscreteBN

Printing discrete Bayesian networks

Fitting mixtures of polynomials

learnMoTBFpriorInformation

Incorporating prior knowledge in the estimation process

Plots for 'motbf' objects

plotConditional

Plot Conditional Functions