{ "_id": "6ac63025f511ae58c09a760e", "Package": "MoTBFs", "Title": "Learning Hybrid Bayesian Networks using Mixtures of Truncated\nBasis Functions", "Version": "2.0.1", "Authors@R": "c(\nperson(given = \"Inmaculada\",\nfamily = \"Pérez-Bernabé\",\nrole = c(\"aut\")),\nperson(given = \"Antonio\",\nfamily = \"Salmerón\",\nrole = c(\"aut\")),\nperson(given = \"Thomas D. \",\nfamily = \"Nielsen\",\nrole = c(\"aut\")),\nperson(given = \"Ángel T. \",\nfamily = \"Sáez-Ruiz\",\nrole = c(\"aut\")),\nperson(given = \"Ana D.\",\nfamily = \"Maldonado\",\nrole = c(\"aut\",\"cre\"),\nemail = \"ana.d.maldonado@ual.es\",\ncomment = c(ORCID = \"0000-0001-8253-2526\")))", "Maintainer": "Ana D. Maldonado ", "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'.", "License": "GPL-3", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "RoxygenNote": "7.2.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-07 09:31:28 UTC", "User": "root" }, "Author": "Inmaculada Pérez-Bernabé [aut], Antonio Salmerón [aut], Thomas\nD. Nielsen [aut], Ángel T. Sáez-Ruiz [aut], Ana D. Maldonado\n[aut, cre] (ORCID: )", "Config/pak/sysreqs": "libglpk-dev libxml2-dev", "Repository": "https://admaldonado.r-universe.dev", "Date/Publication": "2026-10-05 22:10:13 UTC", "RemoteUrl": "https://github.com/cran/MoTBFs", "RemoteRef": "HEAD", "RemoteSha": "f9d484aeff479d3ba85da439af10968a50c2c17b", "_user": "admaldonado", "_type": "src", "_file": "MoTBFs_2.0.1.tar.gz", "_fileid": "https://r2.ropensci.org/058d6949ac8a69699d78758c26592eb45bbcede31ef642b7406c3ace360eae35", "_filesize": 2089896, "_sha256": "058d6949ac8a69699d78758c26592eb45bbcede31ef642b7406c3ace360eae35", "_expires": "2027-01-15T11:42:27.000Z", "_created": "2026-10-07T09:31:28.000Z", "_published": "2026-10-07T11:42:29.621Z", "_buildurl": "https://github.com/r-universe/admaldonado/actions/runs/37599121834", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 112725892278, "time": 191, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11473166974" }, { "job": 112725892204, "time": 181, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11472718768" }, { "job": 112725892124, "time": 251, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11479084928" }, { "job": 112725892257, "time": 217, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11478454633" }, { "job": 112721593530, "time": 225, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11473050961" }, { "job": 112725892074, "time": 94, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11473162285" }, { "job": 112725892372, "time": 166, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11472797679" }, { "job": 112725892177, "time": 180, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11472757891" }, { "job": 112725892198, "time": 151, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11472428770" } ], "_upstream": "https://github.com/cran/MoTBFs", "_commit": { "id": "f9d484aeff479d3ba85da439af10968a50c2c17b", "author": "Ana D. 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"title": "Data set Thyroid Disease (thyroid0387)", "object": "thyroid", "file": "thyroid.rda", "class": [ "data.frame" ], "fields": [ "Age", "Sex", "On_thyroxine", "Query_on_thyroxine", "On_antithyroid_medication", "Sick", "Pregnant", "Thyroid_surgery", "I131_treatment", "Query_hypothyroid", "Query_hyperthyroid", "Lithium", "Goitre", "Tumor", "Hypopituitary", "Psych", "TSH", "T3", "TT4", "T4U", "FTI", "Class" ], "rows": 7200, "table": true, "tojson": true } ], "_help": [ { "page": "asMOPString", "title": "Parameters to MOP String", "topics": [ "asMOPString" ] }, { "page": "asMTEString", "title": "Converting MTEs to strings", "topics": [ "asMTEString" ] }, { "page": "BICMoTBF", "title": "Computing the BIC score of an MoTBF function", "topics": [ "BICMoTBF" ] }, { "page": "BICMultiFunctions", "title": "BIC score for multiple functions", "topics": [ "BICMultiFunctions" ] }, { "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": "coercion-motbf", "title": "Coerce MOTBF Objects to Character or Function", "topics": [ "as.character.jointmotbf", "as.character.motbf", "as.function.jointmotbf", "as.function.motbf", "coercion-motbf" ] }, { "page": "conditionalmotbf.learning", "title": "Learning conditional MoTBF densities", "topics": [ "BICscoreMoTBF", "conditional", "conditionalMethod", "conditionalmotbf.learning", "learn.tree.Intervals", "select" ] }, { "page": "confusionMatrix", "title": "Confusion Matrix", "topics": [ "confusionMatrix", "confusionMatrix.motbf_fit_cv" ] }, { "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": "eval.motbf", "title": "Evaluation of MoTBFs", "topics": [ "eval.motbf" ] }, { "page": "evalJointFunction", "title": "Evaluation of joint MoTBFs", "topics": [ "evalJointFunction" ] }, { "page": "expectedValueMOP", "title": "Expected Value of an MoP Density Function", "topics": [ "expectedValueMOP" ] }, { "page": "expectedValueMTE", "title": "Expected Value of an MTE Density Function", "topics": [ "expectedValueMTE" ] }, { "page": "findConditional", "title": "Find Fitted Conditional MoTBFs", "topics": [ "findConditional" ] }, { "page": "generateNormalPriorData", "title": "Prior data generation", "topics": [ "generateNormalPriorData" ] }, { "page": "get_approx_posterior", "title": "Approximate inference", "topics": [ "get_approx_posterior" ] }, { "page": "getChildParentsFromGraph", "title": "Get the list of relations in a graph", "topics": [ "getChildParentsFromGraph" ] }, { "page": "getCoefficients", "title": "Get the coefficients", "topics": [ "getCoefficients" ] }, { "page": "getDAG", "title": "Retrieve DAG from BN", "topics": [ "getDAG" ] }, { "page": "getMotbfDim", "title": "Extract Dimension of MoTBFs", "topics": [ "getMotbfDim" ] }, { "page": "getMotbfVar", "title": "Extract Variables of MoTBFs", "topics": [ "getMotbfVar" ] }, { "page": "getNonNormalisedRandomMoTBF", "title": "Ramdom MoTBF", "topics": [ "getNonNormalisedRandomMoTBF" ] }, { "page": "getStructure", "title": "Hybrid Bayesian Network structure learning", "topics": [ "getStructure" ] }, { "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": "integrate.motbf", "title": "Integrating MoTBFs", "topics": [ "integrate.motbf" ] }, { "page": "is.discrete", "title": "Check discreteness of a node", "topics": [ "is.discrete" ] }, { "page": "is.motbf", "title": "Check MoTBF Classes and Subclasses", "topics": [ "is.jointmotbf", "is.mop", "is.motbf", "is.motbf_fit", "is.motbf_fit_cv", "is.mte", "is.univmotbf", "subclass" ] }, { "page": "is.observed", "title": "Observed Node", "topics": [ "is.observed" ] }, { "page": "is.root", "title": "Root nodes", "topics": [ "is.root" ] }, { "page": "jointmotbf.fit", "title": "Joint MoTBF density learning", "topics": [ "jointmotbf.fit" ] }, { "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": "marginal.jointmotbf", "title": "Marginalization of MoTBFs", "topics": [ "marginal.jointmotbf" ] }, { "page": "marginalJointMoTBF", "title": "Marginalization of MoTBFs", "topics": [ "marginalJointMoTBF" ] }, { "page": "mop.learning", "title": "Fitting mixtures of polynomials", "topics": [ "bestMOP", "mop.learning" ] }, { "page": "MOPTAN", "title": "Fitting MoTBFs TAN models", "topics": [ "fit_tan", "MOPTAN", "mutual_information_tan" ] }, { "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": "motbf.cv", "title": "Cross-validation for MoTBFs", "topics": [ "motbf.cv" ] }, { "page": "motbf.fit", "title": "Learning hybrid BNs with MoTBFs", "topics": [ "motbf.fit", "MoTBFs_Learning" ] }, { "page": "motbf2bnlearn", "title": "Export discrete motbf to bnlearn format", "topics": [ "motbf2bnlearn" ] }, { "page": "motbf2grain", "title": "Export discrete motbf to grain format", "topics": [ "motbf2grain" ] }, { "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": "plot.motbf", "title": "Plots for ''motbf'' objects", "topics": [ "plot.jointmotbf", "plot.motbf", "plot.motbf.fit.node", "plot.piecewisemop", "plot.univmotbf" ] }, { "page": "plotConditional", "title": "Plot Conditional Functions", "topics": [ "plotConditional" ] }, { "page": "predict.motbf_fit", "title": "Predict from an MoTBF Bayesian Network", "topics": [ "predict.motbf_fit" ] }, { "page": "preprocessedData", "title": "Data cleaning", "topics": [ "preprocessedData" ] }, { "page": "print.motbf", "title": "Print object of class motbf 'print' method for class '\"motbf\"'.", "topics": [ "print.jointmotbf", 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MoTBF Bayesian network", "topics": [ "sample_motbfs" ] }, { "page": "subsetData", "title": "Dataset subsetting", "topics": [ "splitdata", "splitFolds", "subsetData", "TrainingandTestData" ] }, { "page": "summary.motbf", "title": "Summarize an '\"motbf\"' object by describing its main features.", "topics": [ "print.summary.jointmotbf", "print.summary.piecewisemop", "print.summary.univmotbf", "summary.jointmotbf", "summary.motbf", "summary.motbf_fit_cv", "summary.piecewisemop", "summary.univmotbf" ] }, { "page": "thyroid", "title": "Data set Thyroid Disease (thyroid0387)", "topics": [ "thyroid" ] }, { "page": "univMoTBF", "title": "Fitting MoTBFs", "topics": [ "univMoTBF" ] }, { "page": "UpperBoundLogLikelihood", "title": "Upper bound of the loglikelihood", "topics": [ "UpperBoundLogLikelihood" ] }, { "page": "variableElimination", "title": "Exact inference", "topics": [ "variableElimination" ] }, { "page": "variableSelection", "title": "Variable selection for MoTBFs", "topics": [ "variableSelection" ] } ], "_readme": "https://github.com/cran/MoTBFs/raw/HEAD/README.md", "_rundeps": [ "BiocGenerics", "BiocManager", "bnlearn", "cli", "codetools", "cpp11", "doParallel", "foreach", "generics", "ggm", "glue", "graph", "igraph", "infotheo", "iterators", "lattice", "lifecycle", "lpSolve", "magrittr", "Matrix", "pkgconfig", "quadprog", "rlang", "vctrs" ], "_vignettes": [ { "source": "packageUsage.Rmd", "filename": "packageUsage.html", "title": "Workflow of the MoTBFs package", "engine": "knitr::rmarkdown", "headings": [ "How to install MoTBFs", "The example dataset", "Split the dataset into train and test", "Learn the directed acyclic graph", "Univariate MoTBFs densities", "Joint MoTBFs densities", "Conditional MoTBFs densities", "MoTBF distributions associated with the network structure", "Learn MoTBF distributions in a full hybrid network using prior knowledge", "Inference", "Structural learning", "References" ], "created": "2026-08-21 21:50:31", "modified": 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