bayesCaret              Provides a caret-compatible wrapper around
                        functionality for classification and
                        regression, as implemented by mmb.
bayesComputeMarginalFactor
                        Compute a marginal factor (continuous or
                        discrete random variable).
bayesConvertData        Convert data for usage within Bayesian models.
bayesFeaturesToSample   Transform a collection of Bayesian features
                        back to a sample.
bayesInferSimple        Perform simple (network) Bayesian inferencing
                        and regression.
bayesProbability        Full Bayesian inferencing for determining the
                        probability or relative likelihood of a given
                        value.
bayesProbabilityAssign
                        Assign probabilities to one or more samples,
                        given some training data.
bayesProbabilityNaive   Naive Bayesian inferencing for determining the
                        probability or relative likelihood of a given
                        value.
bayesProbabilitySimple
                        Assign a probability using a simple (network)
                        Bayesian classifier.
bayesRegress            Perform full-dependency Bayesian regression for
                        a sample.
bayesRegressAssign      Regression for one or more samples, given some
                        training data.
bayesRegressSimple      Perform simple (network) Bayesian regression.
bayesToLatex            Create a string that can be used in Latex in an
                        e.g. equation-environment.
centralities            Given a neighborhood of data, computes the
                        similarity of each sample in the neighborhood
                        to the neighborhood.
conditionalDataMin      Segment data according to one or more random
                        variables.
createFeatureForBayes   Create a Bayesian feature by name and value.
discretizeVariableToRanges
                        Discretize a continuous random variable to
                        ranges/buckets.
distance                Given a neighborhood of data and two samples
                        from that neighborhood, calculates the distance
                        between the samples.
estimatePdf             Safe PDF estimation that works also for sparse
                        random variables.
getDefaultRegressor     Get the system-wide default regressor.
getMessages             Get a boolean indicating whether messages are
                        enabled system-wide.
getProbForDiscrete      Get a probability of a discrete value.
getRangeForDiscretizedValue
                        Get the range-/bucket-ID of a given value.
getValueKeyOfBayesFeatures
                        Obtain the type of the value of a Bayesian
                        feature.
getValueOfBayesFeatures
                        Obtain the value of a Bayesian feature.
getWarnings             Get a boolean indicating whether warnings are
                        enabled system-wide.
neighborhood            Given Bayesian features, returns those samples
                        from a dataset that exhibit a similarity (i.e.,
                        the neighborhood).
sampleToBayesFeatures   Transform an entire sample into a collection of
                        Bayesian features.
setDefaultRegressor     Set a system-wide default regressor.
setMessages             Enable or disable messages system-wide.
setWarnings             Enable or disable warnings system-wide.
vicinities              Segment a dataset by each row once, then
                        compute vicinities of samples in the
                        neighborhood.
vicinitiesForSample     Segment a dataset by a single sample and
                        compute vicinities for it and the remaining
                        samples in the neighborhood.
