Package: MoTBFs 1.4.1

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 (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumí, A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). 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'.

Authors:Inmaculada Pérez-Bernabé, Antonio Salmerón, Thomas D. Nielsen, Ana D. Maldonado

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MoTBFs.pdf |MoTBFs.html✨
MoTBFs/json (API)

# Install 'MoTBFs' in R:
install.packages('MoTBFs', repos = c('https://admaldonado.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ecoli - Data set Ecoli: Protein Localization Sites
  • thyroid - Data set Thyroid Disease

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

90 exports 1 stars 0.09 score 18 dependencies 1 scripts 260 downloads

Last updated 2 years agofrom:231d1598d7. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-winNOTESep 17 2024
R-4.5-linuxNOTESep 17 2024
R-4.4-winOKSep 17 2024
R-4.4-macOKSep 17 2024
R-4.3-winOKJul 19 2024
R-4.3-macOKJul 19 2024

Exports:asMOPStringasMTEStringbestMOPbestMTEBiC.MoTBFBNBICMoTBFBICMultiFunctionsBICscoreMoTBFcleancoefExpJointCDFcoeffExpcoeffMOPcoeffMTEcoeffPolconditionalconditionalMethodderivMOPderivMoTBFderivMTEdimensionFunctiondiscreteStatesFromBNdiscreteVariables_as.characterdiscreteVariablesStatesdiscretizeVariablesEWdisevalJointFunctionfindConditionalforward_samplinggenerateNormalPriorDatagetBICDiscreteBNgetChildParentsFromGraphgetCoefficientsgetlogLikelihoodDiscreteBNgetNonNormalisedRandomMoTBFintegralJointMoTBFintegralMOPintegralMoTBFintegralMTEinversionMethodis.discreteis.jointmotbfis.mopis.motbfis.mteis.observedis.rootjointCDFjointmotbfjointMoTBFlearn.tree.IntervalsLearningHClearnMoTBFpriorInformationlogLikelihood.MoTBFBNmarginalJointMoTBFmeanMOPmop.learningmotbfmotbf_typeMoTBFs_Learningmte.learningnewDatanewRangePriorDatanstatesnVariablesparametersJointMoTBFparentValuesplotConditionalpreprocessedDataprintBNprintConditionalprintDiscreteBNprobDiscreteVariablequantileIntervalsr.data.framerescaledMOPrescaledMoTBFsrescaledMTErMoTBFrnormMultivsample_MoTBFsscaleDataselectsplitdatastandardizeDatasetsubclassToStringRe_MOPToStringRe_MTETrainingandTestDataunivMoTBFUpperBoundLogLikelihoodwhichDiscrete

Dependencies:BiocGenericsBiocManagerbnlearnclicpp11ggmgluegraphigraphlatticelifecyclelpSolvemagrittrMatrixpkgconfigquadprogrlangvctrs

Readme and manuals

Help Manual

Help pageTopics
Coerce a '"jointmotbf"' Object to a Functionas.function.jointmotbf
Coerce an '"motbf"' object to a Functionas.function.motbf
Parameters to MOP StringasMOPString
Converting MTEs to stringsasMTEString
Computing the BIC score of an MoTBF functionBICMoTBF
BIC score for multiple functionsBICMultiFunctions
Class '"jointmotbf"'as.character.jointmotbf as.list.jointmotbf Class-JointMoTBF is.jointmotbf jointmotbf print.jointmotbf
Class '"motbf"'as.character.motbf as.list.motbf Class-MoTBF is.motbf motbf print.motbf
Remove Objects from Memoryclean
Coefficients of a '"jointmotbf"' objectcoef.jointmotbf
Extract coefficients from MOPscoef.mop coeffMOP coeffPol
Extract the coefficients of an MoTBFcoef.motbf
Extracting the coefficients of an MTEcoef.mte coeffExp coeffMTE
Degree FunctioncoefExpJointCDF
Learning conditional MoTBF densitiesBICscoreMoTBF conditional conditionalMethod conditionalmotbf.learning learn.tree.Intervals select
Data pre-processing utilitiesdataMining discreteVariablesStates discreteVariables_as.character discretizeVariablesEWdis nstates quantileIntervals scaleData standardizeDataset whichDiscrete
Derivative of a MOPderivMOP
Derivating MoTBFsderivMoTBF
Derivating MTEsderivMTE
Dimension of MoTBFsdimensionFunction
Get the states of all discrete nodes from a MoTFB-BNdiscreteStatesFromBN
Data set Ecoli: Protein Localization Sitesecoli
Evaluation of joint MoTBFsevalJointFunction
Find fitted conditional MoTBFsfindConditional
Forward Samplingforward_sampling
Prior data generationgenerateNormalPriorData
Get the list of relations in a graphgetChildParentsFromGraph
Get the coefficientsgetCoefficients
Ramdom MoTBFgetNonNormalisedRandomMoTBF
BIC scxore and log-likelihoodgetBICDiscreteBN getlogLikelihoodDiscreteBN goodnessDiscreteVariables
BIC of a hybrid BNBiC.MoTBFBN goodnessMoTBFBN logLikelihood.MoTBFBN
Integration with MoTBFsintegralJointMoTBF
Integration of MOPsintegralMOP
Integrating MoTBFsintegralMoTBF
Integrating MTEsintegralMTE
Check discreteness of a nodeis.discrete
Observed Nodeis.observed
Root nodesis.root
Joint MoTBFs CDFsjointCDF
Joint MoTBF density learningjointMoTBF jointmotbf.learning parametersJointMoTBF
Score-based hybrid Bayesian Network structure learningLearningHC
Incorporating prior knowledge in the estimation processlearnMoTBFpriorInformation
Marginalization of MoTBFsmarginalJointMoTBF
Fitting mixtures of polynomialsbestMOP mop.learning
Type of MoTBFmotbf_type
Random generation for MoTBF distributionsinversionMethod MoTBF-Distribution rMoTBF
Learning hybrid BNs with MoTBFsMoTBFs_Learning
Fitting mixtures of truncated exponentials.bestMTE mte.learning
Redefining the DomainnewRangePriorData
Number of Variables in a Joint FunctionnVariables
Value of parent nodesparentValues
Bidimensional plots for ''jointmotbf'' objectsplot.jointmotbf
Plots for ''motbf'' objectsplot.motbf
Plot Conditional FunctionsplotConditional
Data cleaningpreprocessedData
BN printingprintBN
Summary of conditional MoTBF densitiesprintConditional
Printing discrete Bayesian networksprintDiscreteBN
Probability distribution of discrete variablesprobDiscreteVariable
Data frame initialization for forward samplingr.data.frame
Rescaling MoTBF functionsmeanMOP rescaledFunctions rescaledMOP rescaledMoTBFs rescaledMTE ToStringRe_MOP ToStringRe_MTE
Multivariate Normal samplingrnormMultiv
Sample generation from conditional MoTBFssample_MoTBFs
Subclass '"motbf"' Functionsis.mop is.mte subclass Subclass-MoTBF
Dataset subsettingnewData splitdata subsetData TrainingandTestData
Summary of a '"jointmotbf"' objectprint.summary.jointmotbf summary.jointmotbf
Summary of an '"motbf"' objectprint.summary.motbf summary.motbf
Data set Thyroid Disease (thyroid0387)thyroid
Fitting MoTBFsunivMoTBF
Upper bound of the loglikelihoodUpperBoundLogLikelihood