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MoTBFs  

Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions
View on CRAN: Click here


Download and install MoTBFs package within the R console
Install from CRAN:
install.packages("MoTBFs")

Install from Github:
library("remotes")
install_github("cran/MoTBFs")

Install by package version:
library("remotes")
install_version("MoTBFs", "2.0")



Attach the package and use:
library("MoTBFs")
Maintained by
Ana D. Maldonado
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-09-28
Latest Update: 2025-07-22
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. P
How to cite:
Ana D. Maldonado (2015). MoTBFs: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions. R package version 2.0, https://cran.r-project.org/web/packages/MoTBFs. Accessed 29 Oct. 2026.
Previous versions and publish date:
1.0 (2015-09-28 09:26), 1.1 (2019-10-21 20:20), 1.2 (2020-01-14 20:00), 1.3 (2020-04-06 12:12), 1.4.1 (2022-04-18 18:34), 1.4.2 (2025-07-22 13:01), 1.4 (2020-06-19 17:20), 2.0 (2026-08-21 23:50), (2026-10-06 00:10)
Other packages that cited MoTBFs R package
View MoTBFs citation profile
Other R packages that MoTBFs depends, imports, suggests or enhances
Complete documentation for MoTBFs
Functions, R codes and Examples using the MoTBFs R package
Some associated functions: BICMoTBF . BICMultiFunctions . Class-JointMoTBF . Class-MoTBF . LearningHC . MoTBF-Distribution . MoTBFs_Learning . Subclass-MoTBF . UpperBoundLogLikelihood . as.function.jointmotbf . as.function.motbf . asMOPString . asMTEString . clean . coef.jointmotbf . coef.mop . coef.motbf . coef.mte . coefExpJointCDF . conditionalmotbf.learning . dataMining . derivMOP . derivMTE . derivMoTBF . dimensionFunction . discreteStatesFromBN . ecoli . evalJointFunction . findConditional . forward_sampling . generateNormalPriorData . getChildParentsFromGraph . getCoefficients . getNonNormalisedRandomMoTBF . goodnessDiscreteVariables . goodnessMoTBFBN . integralJointMoTBF . integralMOP . integralMTE . integralMoTBF . is.discrete . isserved . is.root . jointCDF . jointmotbf.learning . learnMoTBFpriorInformation . marginalJointMoTBF . mop.learning . motbf_type . mte.learning . nVariables . newRangePriorData . parentValues . plot.jointmotbf . plot.motbf . plotConditional . preprocessedData . printBN . printConditional . printDiscreteBN . probDiscreteVariable . r.data.frame . rescaledFunctions . rnormMultiv . sample_MoTBFs . subsetData . summary.jointmotbf . summary.motbf . thyroid . univMoTBF . 
Some associated R codes: DiscreteLearning.R . Inference.R . LearningBN.R . MoTBFClass.R . conditional.R . datasets.R . functions.R . joint.R . mop.R . motbf.R . mte.R . priorKnowledge.R . rMoTBF.R . rescalatedFunctions.R . structuralLearning.R .  Full MoTBFs package functions and examples
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