{ "_id": "6a169fdbacfb0bcc41d80544", "Package": "MoTBFs", "Type": "Package", "Title": "Learning Hybrid Bayesian Networks using Mixtures of Truncated\nBasis Functions", "Version": "1.4.2", "Authors@R": "c(person(given = \"Inmaculada\",\nfamily = \"Pérez-Bernabé\",\nrole = \"aut\"),\nperson(given = \"Antonio\",\nfamily = \"Salmerón\",\nrole = \"aut\"),\nperson(given = c(\"Thomas D.\"),\nfamily = \"Nielsen\",\nrole = \"aut\"),\nperson(given = c(\"Ana D.\"),\nfamily = \"Maldonado\",\nrole = c(\"aut\", \"cre\"),\nemail = \"ana.d.maldonado@ual.es\"))", "Description": "Learning, manipulation and evaluation of mixtures of\ntruncated basis functions (MoTBFs), which include mixtures of\npolynomials (MOPs) and mixtures of truncated exponentials\n(MTEs). MoTBFs are a flexible framework for modelling hybrid\nBayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth\n(2015) ; H. Langseth, T.D.\nNielsen, I. Pérez-Bernabé, A. Salmerón (2014)\n; I. Pérez-Bernabé, A.\nFernández, R. Rumí, A. Salmerón (2016)\n). The package provides\nfunctionality for learning univariate, multivariate and\nconditional densities, with the possibility of incorporating\nprior knowledge. Structural learning of hybrid Bayesian\nnetworks is also provided. A set of useful tools is provided,\nincluding plotting, printing and likelihood evaluation. This\npackage makes use of S3 objects, with two new classes called\n'motbf' and 'jointmotbf'.", "Encoding": "UTF-8", "License": "LGPL-3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-27 07:35:47 UTC", "User": "root" }, "RoxygenNote": "7.2.3", "Author": "Inmaculada Pérez-Bernabé [aut], Antonio Salmerón [aut], Thomas\nD. Nielsen [aut], Ana D. Maldonado [aut, cre]", "Maintainer": "Ana D. 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"dimensionFunction", "discreteStatesFromBN", "discreteVariables_as.character", "discreteVariablesStates", "discretizeVariablesEWdis", "evalJointFunction", "findConditional", "forward_sampling", "generateNormalPriorData", "getBICDiscreteBN", "getChildParentsFromGraph", "getCoefficients", "getlogLikelihoodDiscreteBN", "getNonNormalisedRandomMoTBF", "integralJointMoTBF", "integralMOP", "integralMoTBF", "integralMTE", "inversionMethod", "is.discrete", "is.jointmotbf", "is.mop", "is.motbf", "is.mte", "is.observed", "is.root", "jointCDF", "jointmotbf", "jointMoTBF", "learn.tree.Intervals", "LearningHC", "learnMoTBFpriorInformation", "logLikelihood.MoTBFBN", "marginalJointMoTBF", "meanMOP", "mop.learning", "motbf", "motbf_type", "MoTBFs_Learning", "mte.learning", "newData", "newRangePriorData", "nstates", "nVariables", "parametersJointMoTBF", "parentValues", "plotConditional", "preprocessedData", "printBN", "printConditional", "printDiscreteBN", "probDiscreteVariable", "quantileIntervals", 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'\"motbf\"'", "topics": [ "as.character.motbf", "as.list.motbf", "Class-MoTBF", "is.motbf", "motbf", "print.motbf" ] }, { "page": "clean", "title": "Remove Objects from Memory", "topics": [ "clean" ] }, { "page": "coef.jointmotbf", "title": "Coefficients of a '\"jointmotbf\"' object", "topics": [ "coef.jointmotbf" ] }, { "page": "coef.mop", "title": "Extract coefficients from MOPs", "topics": [ "coef.mop", "coeffMOP", "coeffPol" ] }, { "page": "coef.motbf", "title": "Extract the coefficients of an MoTBF", "topics": [ "coef.motbf" ] }, { "page": "coef.mte", "title": "Extracting the coefficients of an MTE", "topics": [ "coef.mte", "coeffExp", "coeffMTE" ] }, { "page": "coefExpJointCDF", "title": "Degree Function", "topics": [ "coefExpJointCDF" ] }, { "page": "conditionalmotbf.learning", "title": "Learning conditional MoTBF densities", "topics": [ "BICscoreMoTBF", "conditional", "conditionalMethod", "conditionalmotbf.learning", "learn.tree.Intervals", "select" ] }, { "page": "dataMining", "title": "Data pre-processing utilities", "topics": [ "dataMining", "discreteVariablesStates", "discreteVariables_as.character", "discretizeVariablesEWdis", "nstates", "quantileIntervals", "scaleData", "standardizeDataset", "whichDiscrete" ] }, { "page": "derivMOP", "title": "Derivative of a MOP", "topics": [ "derivMOP" ] }, { "page": "derivMoTBF", "title": "Derivating MoTBFs", "topics": [ "derivMoTBF" ] }, { "page": "derivMTE", "title": "Derivating MTEs", "topics": [ "derivMTE" ] }, { "page": "dimensionFunction", "title": "Dimension of MoTBFs", "topics": [ "dimensionFunction" ] }, { "page": "discreteStatesFromBN", "title": "Get the states of all discrete nodes from a MoTFB-BN", "topics": [ "discreteStatesFromBN" ] }, { "page": "ecoli", "title": "Data set Ecoli: Protein Localization Sites", "topics": [ "ecoli" ] }, { "page": "evalJointFunction", "title": "Evaluation of joint MoTBFs", "topics": [ "evalJointFunction" ] }, { "page": "findConditional", "title": "Find fitted conditional MoTBFs", "topics": [ "findConditional" ] }, { "page": "forward_sampling", "title": "Forward Sampling", "topics": [ "forward_sampling" ] }, { "page": "generateNormalPriorData", "title": "Prior data generation", "topics": [ "generateNormalPriorData" ] }, { "page": "getChildParentsFromGraph", "title": "Get the list of relations in a graph", "topics": [ "getChildParentsFromGraph" ] }, { "page": "getCoefficients", "title": "Get the coefficients", "topics": [ "getCoefficients" ] }, { "page": "getNonNormalisedRandomMoTBF", "title": "Ramdom MoTBF", "topics": [ "getNonNormalisedRandomMoTBF" ] }, { "page": "goodnessDiscreteVariables", "title": "BIC scxore and log-likelihood", "topics": [ "getBICDiscreteBN", "getlogLikelihoodDiscreteBN", "goodnessDiscreteVariables" ] }, { "page": "goodnessMoTBFBN", "title": "BIC of a hybrid BN", "topics": [ "BiC.MoTBFBN", "goodnessMoTBFBN", "logLikelihood.MoTBFBN" ] }, { "page": "integralJointMoTBF", "title": "Integration with MoTBFs", "topics": [ "integralJointMoTBF" ] }, { "page": "integralMOP", "title": "Integration of MOPs", "topics": [ "integralMOP" ] }, { "page": "integralMoTBF", "title": "Integrating MoTBFs", "topics": [ "integralMoTBF" ] }, { "page": "integralMTE", "title": "Integrating MTEs", "topics": [ "integralMTE" ] }, { "page": "is.discrete", "title": "Check discreteness of a node", "topics": [ "is.discrete" ] }, { "page": "is.observed", "title": "Observed Node", "topics": [ "is.observed" ] }, { "page": "is.root", "title": "Root nodes", "topics": [ "is.root" ] }, { "page": "jointCDF", "title": "Joint MoTBFs CDFs", "topics": [ "jointCDF" ] }, { "page": "jointmotbf.learning", "title": "Joint MoTBF density learning", "topics": [ "jointMoTBF", "jointmotbf.learning", "parametersJointMoTBF" ] }, { "page": "LearningHC", "title": "Score-based hybrid Bayesian Network structure learning", "topics": [ "LearningHC" ] }, { "page": "learnMoTBFpriorInformation", "title": "Incorporating prior knowledge in the estimation process", "topics": [ "learnMoTBFpriorInformation" ] }, { "page": "marginalJointMoTBF", "title": "Marginalization of MoTBFs", "topics": [ "marginalJointMoTBF" ] }, { "page": "mop.learning", "title": "Fitting mixtures of polynomials", "topics": [ "bestMOP", "mop.learning" ] }, { "page": "motbf_type", "title": "Type of MoTBF", "topics": [ "motbf_type" ] }, { "page": "MoTBF-Distribution", "title": "Random generation for MoTBF distributions", "topics": [ "inversionMethod", "MoTBF-Distribution", "rMoTBF" ] }, { "page": "MoTBFs_Learning", "title": "Learning hybrid BNs with MoTBFs", "topics": [ "MoTBFs_Learning" ] }, { "page": "mte.learning", "title": "Fitting mixtures of truncated exponentials.", "topics": [ "bestMTE", "mte.learning" ] }, { "page": "newRangePriorData", "title": "Redefining the Domain", "topics": [ "newRangePriorData" ] }, { "page": "nVariables", "title": "Number of Variables in a Joint Function", "topics": [ "nVariables" ] }, { "page": "parentValues", "title": "Value of parent nodes", 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