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 2.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

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

## -- A --

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

## -- 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 --

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

## -- 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
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

## -- F --

findConditional | Find Fitted Conditional MoTBFs
fit_tan | Fitting MoTBFs TAN models

## -- G --

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

## -- I --

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

## -- J --

jointMoTBF | Joint MoTBF density learning
jointmotbf.fit | Joint MoTBF density learning
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 --

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

## -- N --

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

## -- P --

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

## -- Q --

quantileIntervals | Data pre-processing utilities
query | Conditional probability queries

## -- R --

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

## -- S --

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.

## -- T --

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

## -- U --

univMoTBF | Fitting MoTBFs
UpperBoundLogLikelihood | Upper bound of the loglikelihood

## -- V --

variableElimination | Exact inference
variableSelection | Variable selection for MoTBFs

## -- W --

whichDiscrete | Data pre-processing utilities