A B C D E F G I J L M N P Q R S T U V W
| as.character.jointmotbf | Coerce MOTBF Objects to Character or Function |
| as.character.motbf | Coerce MOTBF Objects to Character or Function |
| as.function.jointmotbf | Coerce MOTBF Objects to Character or Function |
| as.function.motbf | Coerce MOTBF Objects to Character or Function |
| asMOPString | Parameters to MOP String |
| asMTEString | Converting MTEs to strings |
| bestMOP | Fitting mixtures of polynomials |
| bestMTE | Fitting mixtures of truncated exponentials. |
| BiC.MoTBFBN | BIC of a hybrid BN |
| BICMoTBF | Computing the BIC score of an MoTBF function |
| BICMultiFunctions | BIC score for multiple functions |
| BICscoreMoTBF | Learning conditional MoTBF densities |
| clean | Remove Objects from Memory |
| coef.jointmotbf | Coefficients of a '"jointmotbf"' object |
| coef.mop | Extract coefficients from MOPs |
| coef.motbf | Extract the coefficients of an MoTBF |
| coef.mte | Extracting the coefficients of an MTE |
| coeffExp | Extracting the coefficients of an MTE |
| coeffMOP | Extract coefficients from MOPs |
| coeffMTE | Extracting the coefficients of an MTE |
| coeffPol | Extract coefficients from MOPs |
| coercion-motbf | Coerce MOTBF Objects to Character or Function |
| conditional | Learning conditional MoTBF densities |
| conditionalMethod | Learning conditional MoTBF densities |
| conditionalmotbf.learning | Learning conditional MoTBF densities |
| confusionMatrix | Confusion Matrix |
| confusionMatrix.motbf_fit_cv | Confusion Matrix |
| dataMining | Data pre-processing utilities |
| derivMOP | Derivative of a MOP |
| derivMoTBF | Derivating MoTBFs |
| derivMTE | Derivating MTEs |
| dimensionFunction | Dimension of MoTBFs |
| discreteStatesFromBN | Get the states of all discrete nodes from a MoTFB-BN |
| discreteVariablesStates | Data pre-processing utilities |
| discreteVariables_as.character | Data pre-processing utilities |
| discretizeVariablesEWdis | Data pre-processing utilities |
| ecoli | Data set Ecoli: Protein Localization Sites |
| eval.motbf | Evaluation of MoTBFs |
| evalJointFunction | Evaluation of joint MoTBFs |
| expectedValueMOP | Expected Value of an MoP Density Function |
| expectedValueMTE | Expected Value of an MTE Density Function |
| findConditional | Find Fitted Conditional MoTBFs |
| fit_tan | Fitting MoTBFs TAN models |
| generateNormalPriorData | Prior data generation |
| getChildParentsFromGraph | Get the list of relations in a graph |
| getCoefficients | Get the coefficients |
| getDAG | Retrieve DAG from BN |
| getMotbfDim | Extract Dimension of MoTBFs |
| getMotbfVar | Extract Variables of MoTBFs |
| getNonNormalisedRandomMoTBF | Ramdom MoTBF |
| getStructure | Hybrid Bayesian Network structure learning |
| get_approx_posterior | Approximate inference |
| goodnessMoTBFBN | BIC of a hybrid BN |
| integralJointMoTBF | Integration with MoTBFs |
| integralMOP | Integration of MOPs |
| integralMoTBF | Integrating MoTBFs |
| integralMTE | Integrating MTEs |
| integrate.motbf | Integrating MoTBFs |
| inversionMethod | Random generation for MoTBF distributions |
| is.discrete | Check discreteness of a node |
| is.jointmotbf | Check MoTBF Classes and Subclasses |
| is.mop | Check MoTBF Classes and Subclasses |
| is.motbf | Check MoTBF Classes and Subclasses |
| is.motbf_fit | Check MoTBF Classes and Subclasses |
| is.motbf_fit_cv | Check MoTBF Classes and Subclasses |
| is.mte | Check MoTBF Classes and Subclasses |
| is.observed | Observed Node |
| is.root | Root nodes |
| is.univmotbf | Check MoTBF Classes and Subclasses |
| jointMoTBF | Joint MoTBF density learning |
| jointmotbf.fit | Joint MoTBF density learning |
| jointmotbf.learning | Joint MoTBF density learning |
| learn.tree.Intervals | Learning conditional MoTBF densities |
| LearningHC | Score-based hybrid Bayesian Network structure learning |
| learnMoTBFpriorInformation | Incorporating prior knowledge in the estimation process |
| logLikelihood.MoTBFBN | BIC of a hybrid BN |
| marginal.jointmotbf | Marginalization of MoTBFs |
| marginalJointMoTBF | Marginalization of MoTBFs |
| meanMOP | Rescaling MoTBF functions |
| mop.learning | Fitting mixtures of polynomials |
| MOPTAN | Fitting MoTBFs TAN models |
| MoTBF-Distribution | Random generation for MoTBF distributions |
| motbf.cv | Cross-validation for MoTBFs |
| motbf.fit | Learning hybrid BNs with MoTBFs |
| motbf2bnlearn | Export discrete motbf to bnlearn format |
| motbf2grain | Export discrete motbf to grain format |
| MoTBFs_Learning | Learning hybrid BNs with MoTBFs |
| motbf_type | Type of MoTBF |
| mte.learning | Fitting mixtures of truncated exponentials. |
| mutual_information_tan | Fitting MoTBFs TAN models |
| newRangePriorData | Redefining the Domain |
| nstates | Data pre-processing utilities |
| nVariables | Number of Variables in a Joint Function |
| parametersJointMoTBF | Joint MoTBF density learning |
| plot.jointmotbf | Plots for "motbf" objects |
| plot.motbf | Plots for "motbf" objects |
| plot.motbf.fit.node | Plots for "motbf" objects |
| plot.piecewisemop | Plots for "motbf" objects |
| plot.univmotbf | Plots for "motbf" objects |
| plotConditional | Plot Conditional Functions |
| predict.motbf_fit | Predict from an MoTBF Bayesian Network |
| preprocessedData | Data cleaning |
| print.jointmotbf | Print object of class motbf 'print' method for class '"motbf"'. |
| print.motbf | Print object of class motbf 'print' method for class '"motbf"'. |
| print.motbf.fit.node | Print object of class motbf 'print' method for class '"motbf"'. |
| print.motbf_fit | Print object of class motbf 'print' method for class '"motbf"'. |
| print.motbf_fit_cv | Print object of class motbf 'print' method for class '"motbf"'. |
| print.piecewisemop | Print object of class motbf 'print' method for class '"motbf"'. |
| print.summary.jointmotbf | Summarize an '"motbf"' object by describing its main features. |
| print.summary.piecewisemop | Summarize an '"motbf"' object by describing its main features. |
| print.summary.univmotbf | Summarize an '"motbf"' object by describing its main features. |
| print.univmotbf | Print object of class motbf 'print' method for class '"motbf"'. |
| printConditional | Summary of conditional MoTBF densities |
| probDiscreteVariable | Probability distribution of discrete variables |
| quantileIntervals | Data pre-processing utilities |
| query | Conditional probability queries |
| r.data.frame | Initialize Data Frame |
| rescaledFunctions | Rescaling MoTBF functions |
| rescaledMOP | Rescaling MoTBF functions |
| rescaledMoTBFs | Rescaling MoTBF functions |
| rescaledMTE | Rescaling MoTBF functions |
| rescale_data | Scale data |
| rescale_motbf_fit | Rescaling MoTBF functions |
| rMoTBF | Random generation for MoTBF distributions |
| rnormMultiv | Multivariate Normal sampling |
| sample_motbfs | Generate Samples From an MoTBF Bayesian network |
| scaleData | Data pre-processing utilities |
| select | Learning conditional MoTBF densities |
| splitdata | Dataset subsetting |
| splitFolds | Dataset subsetting |
| standardizeDataset | Data pre-processing utilities |
| subclass | Check MoTBF Classes and Subclasses |
| subsetData | Dataset subsetting |
| summary.jointmotbf | Summarize an '"motbf"' object by describing its main features. |
| summary.motbf | Summarize an '"motbf"' object by describing its main features. |
| summary.motbf_fit_cv | Summarize an '"motbf"' object by describing its main features. |
| summary.piecewisemop | Summarize an '"motbf"' object by describing its main features. |
| summary.univmotbf | Summarize an '"motbf"' object by describing its main features. |
| thyroid | Data set Thyroid Disease (thyroid0387) |
| ToStringRe_MTE | Rescaling MoTBF functions |
| TrainingandTestData | Dataset subsetting |
| univMoTBF | Fitting MoTBFs |
| UpperBoundLogLikelihood | Upper bound of the loglikelihood |
| variableElimination | Exact inference |
| variableSelection | Variable selection for MoTBFs |
| whichDiscrete | Data pre-processing utilities |