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 |
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