R: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

# Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

## Documentation for package ‘MoTBFs’ version 1.4.2

- DESCRIPTION file

## Help Pages

A B C D E F G I J L M N P Q R S T U W

## -- A --

as.character.jointmotbf | Class '"jointmotbf"'
as.character.motbf | Class '"motbf"'
as.function.jointmotbf | Coerce a '"jointmotbf"' Object to a Function
as.function.motbf | Coerce an '"motbf"' object to a Function
as.list.jointmotbf | Class '"jointmotbf"'
as.list.motbf | Class '"motbf"'
asMOPString | Parameters to MOP String
asMTEString | Converting MTEs to strings

## -- B --

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

## -- C --

Class-JointMoTBF | Class '"jointmotbf"'
Class-MoTBF | Class '"motbf"'
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
coefExpJointCDF | Degree Function
coeffExp | Extracting the coefficients of an MTE
coeffMOP | Extract coefficients from MOPs
coeffMTE | Extracting the coefficients of an MTE
coeffPol | Extract coefficients from MOPs
conditional | Learning conditional MoTBF densities
conditionalMethod | Learning conditional MoTBF densities
conditionalmotbf.learning | Learning conditional MoTBF densities

## -- D --

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

## -- E --

ecoli | Data set Ecoli: Protein Localization Sites
evalJointFunction | Evaluation of joint MoTBFs

## -- F --

findConditional | Find fitted conditional MoTBFs
forward_sampling | Forward Sampling

## -- G --

generateNormalPriorData | Prior data generation
getBICDiscreteBN | BIC scxore and log-likelihood
getChildParentsFromGraph | Get the list of relations in a graph
getCoefficients | Get the coefficients
getlogLikelihoodDiscreteBN | BIC scxore and log-likelihood
getNonNormalisedRandomMoTBF | Ramdom MoTBF
goodnessDiscreteVariables | BIC scxore and log-likelihood
goodnessMoTBFBN | BIC of a hybrid BN

## -- I --

integralJointMoTBF | Integration with MoTBFs
integralMOP | Integration of MOPs
integralMoTBF | Integrating MoTBFs
integralMTE | Integrating MTEs
inversionMethod | Random generation for MoTBF distributions
is.discrete | Check discreteness of a node
is.jointmotbf | Class '"jointmotbf"'
is.mop | Subclass '"motbf"' Functions
is.motbf | Class '"motbf"'
is.mte | Subclass '"motbf"' Functions
is.observed | Observed Node
is.root | Root nodes

## -- J --

jointCDF | Joint MoTBFs CDFs
jointMoTBF | Joint MoTBF density learning
jointmotbf | Class '"jointmotbf"'
jointmotbf.learning | Joint MoTBF density learning

## -- L --

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

## -- M --

marginalJointMoTBF | Marginalization of MoTBFs
meanMOP | Rescaling MoTBF functions
mop.learning | Fitting mixtures of polynomials
motbf | Class '"motbf"'
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.

## -- N --

newData | Dataset subsetting
newRangePriorData | Redefining the Domain
nstates | Data pre-processing utilities
nVariables | Number of Variables in a Joint Function

## -- P --

parametersJointMoTBF | Joint MoTBF density learning
parentValues | Value of parent nodes
plot.jointmotbf | Bidimensional plots for "jointmotbf" objects
plot.motbf | Plots for "motbf" objects
plotConditional | Plot Conditional Functions
preprocessedData | Data cleaning
print.jointmotbf | Class '"jointmotbf"'
print.motbf | Class '"motbf"'
print.summary.jointmotbf | Summary of a '"jointmotbf"' object
print.summary.motbf | Summary of an '"motbf"' object
printBN | BN printing
printConditional | Summary of conditional MoTBF densities
printDiscreteBN | Printing discrete Bayesian networks
probDiscreteVariable | Probability distribution of discrete variables

## -- Q --

quantileIntervals | Data pre-processing utilities

## -- R --

r.data.frame | Data frame initialization for forward sampling
rescaledFunctions | Rescaling MoTBF functions
rescaledMOP | Rescaling MoTBF functions
rescaledMoTBFs | Rescaling MoTBF functions
rescaledMTE | Rescaling MoTBF functions
rMoTBF | Random generation for MoTBF distributions
rnormMultiv | Multivariate Normal sampling

## -- S --

sample_MoTBFs | Sample generation from conditional MoTBFs
scaleData | Data pre-processing utilities
select | Learning conditional MoTBF densities
splitdata | Dataset subsetting
standardizeDataset | Data pre-processing utilities
subclass | Subclass '"motbf"' Functions
Subclass-MoTBF | Subclass '"motbf"' Functions
subsetData | Dataset subsetting
summary.jointmotbf | Summary of a '"jointmotbf"' object
summary.motbf | Summary of an '"motbf"' object

## -- T --

thyroid | Data set Thyroid Disease (thyroid0387)
ToStringRe_MOP | Rescaling MoTBF functions
ToStringRe_MTE | Rescaling MoTBF functions
TrainingandTestData | Dataset subsetting

## -- U --

univMoTBF | Fitting MoTBFs
UpperBoundLogLikelihood | Upper bound of the loglikelihood

## -- W --

whichDiscrete | Data pre-processing utilities