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MoTBFs
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", "1.4.1")
Attach the package and use:
library("MoTBFs")
Maintained by
Ana D. Maldonado
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-09-28
Latest Update: 2022-04-18
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 1.4.1, https://cran.r-project.org/web/packages/MoTBFs. Accessed 22 Dec. 2024.
Previous versions and publish date:
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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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