Man pages for MoTBFs
Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions
| as.function.jointmotbf | Coerce a '"jointmotbf"' Object to a Function |
| as.function.motbf | Coerce an '"motbf"' object to a Function |
| asMOPString | Parameters to MOP String |
| asMTEString | Converting MTEs to strings |
| BICMoTBF | Computing the BIC score of an MoTBF function |
| BICMultiFunctions | BIC score for multiple functions |
| Class-JointMoTBF | Class '"jointmotbf"' |
| Class-MoTBF | Class '"motbf"' |
| clean | Remove Objects from Memory |
| coefExpJointCDF | Degree Function |
| 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 |
| conditionalmotbf.learning | Learning conditional MoTBF densities |
| 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 |
| ecoli | Data set Ecoli: Protein Localization Sites |
| evalJointFunction | Evaluation of joint MoTBFs |
| findConditional | Find fitted conditional MoTBFs |
| forward_sampling | Forward Sampling |
| generateNormalPriorData | Prior data generation |
| getChildParentsFromGraph | Get the list of relations in a graph |
| getCoefficients | Get the coefficients |
| getNonNormalisedRandomMoTBF | Ramdom MoTBF |
| goodnessDiscreteVariables | BIC scxore and log-likelihood |
| goodnessMoTBFBN | BIC of a hybrid BN |
| integralJointMoTBF | Integration with MoTBFs |
| integralMOP | Integration of MOPs |
| integralMoTBF | Integrating MoTBFs |
| integralMTE | Integrating MTEs |
| is.discrete | Check discreteness of a node |
| is.observed | Observed Node |
| is.root | Root nodes |
| jointCDF | Joint MoTBFs CDFs |
| jointmotbf.learning | Joint MoTBF density learning |
| LearningHC | Score-based hybrid Bayesian Network structure learning |
| learnMoTBFpriorInformation | Incorporating prior knowledge in the estimation process |
| marginalJointMoTBF | Marginalization of MoTBFs |
| mop.learning | Fitting mixtures of polynomials |
| MoTBF-Distribution | Random generation for MoTBF distributions |
| MoTBFs_Learning | Learning hybrid BNs with MoTBFs |
| motbf_type | Type of MoTBF |
| mte.learning | Fitting mixtures of truncated exponentials. |
| newRangePriorData | Redefining the Domain |
| nVariables | Number of Variables in a Joint Function |
| parentValues | Value of parent nodes |
| plotConditional | Plot Conditional Functions |
| plot.jointmotbf | Bidimensional plots for "jointmotbf" objects |
| plot.motbf | Plots for "motbf" objects |
| preprocessedData | Data cleaning |
| printBN | BN printing |
| printConditional | Summary of conditional MoTBF densities |
| printDiscreteBN | Printing discrete Bayesian networks |
| probDiscreteVariable | Probability distribution of discrete variables |
| r.data.frame | Data frame initialization for forward sampling |
| rescaledFunctions | Rescaling MoTBF functions |
| rnormMultiv | Multivariate Normal sampling |
| sample_MoTBFs | Sample generation from conditional MoTBFs |
| Subclass-MoTBF | Subclass '"motbf"' Functions |
| subsetData | Dataset subsetting |
| summary.jointmotbf | Summary of a '"jointmotbf"' object |
| summary.motbf | Summary of an '"motbf"' object |
| thyroid | Data set Thyroid Disease (thyroid0387) |
| univMoTBF | Fitting MoTBFs |
| UpperBoundLogLikelihood | Upper bound of the loglikelihood |
