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mmb (version 0.13.3)

Arbitrary Dependency Mixed Multivariate Bayesian Models

Description

Supports Bayesian models with full and partial (hence arbitrary) dependencies between random variables. Discrete and continuous variables are supported, and conditional joint probabilities and probability densities are estimated using Kernel Density Estimation (KDE). The full general form, which implements an extension to Bayes' theorem, as well as the simple form, which is just a Bayesian network, both support regression through segmentation and KDE and estimation of probability or relative likelihood of discrete or continuous target random variables. This package also provides true statistical distance measures based on Bayesian models. Furthermore, these measures can be facilitated on neighborhood searches, and to estimate the similarity and distance between data points. Related work is by Bayes (1763) and by Scutari (2010) .

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Install

install.packages('mmb')

Monthly Downloads

193

Version

0.13.3

License

GPL-3

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0

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Maintainer

Sebastian Hc3<b6>nel

Last Published

September 23rd, 2020

Functions in mmb (0.13.3)

bayesProbabilityAssign

Assign probabilities to one or more samples, given some training data.
bayesConvertData

Convert data for usage within Bayesian models.
bayesComputeMarginalFactor

Compute a marginal factor (continuous or discrete random variable).
bayesCaret

Provides a caret-compatible wrapper around functionality for classification and regression, as implemented by mmb.
bayesFeaturesToSample

Transform a collection of Bayesian features back to a sample.
bayesInferSimple

Perform simple (network) Bayesian inferencing and regression.
bayesComputeProductFactor

Computes one single factor that is needed for full Bayesian inferencing.
bayesProbability

Full Bayesian inferencing for determining the probability or relative likelihood of a given value.
bayesProbabilitySimple

Assign a probability using a simple (network) Bayesian classifier.
bayesProbabilityNaive

Naive Bayesian inferencing for determining the probability or relative likelihood of a given value.
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.
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.
make.varClosure

Creates a closure over a variable and returns its getter and setter.
bayesRegress

Perform full-dependency Bayesian regression for a sample.
checkBayesFeature

Validate a Bayesian feature using some sanity checks.
bayesRegressSimple

Perform simple (network) Bayesian regression.
getWarnings

Get a boolean indicating whether warnings are enabled system-wide.
getValueOfBayesFeatures

Obtain the value of a Bayesian feature.
conditionalDataMin

Segment data according to one or more random variables.
getDefaultRegressor

Get the system-wide default regressor.
bayesRegressAssign

Regression for one or more samples, given some training data.
setDefaultRegressor

Set a system-wide default regressor.
getMessages

Get a boolean indicating whether messages are enabled system-wide.
estimatePdf

Safe PDF estimation that works also for sparse random variables.
sampleToBayesFeatures

Transform an entire sample into a collection of Bayesian features.
neighborhood

Given Bayesian features, returns those samples from a dataset that exhibit a similarity (i.e., the neighborhood).
setMessages

Enable or disable messages system-wide.
getProbForDiscrete

Get a probability of a discrete value.
setWarnings

Enable or disable warnings system-wide.
getRangeForDiscretizedValue

Get the range-/bucket-ID of a given value.
vicinities

Segment a dataset by each row once, then compute vicinities of samples in the neighborhood.
getValueKeyOfBayesFeatures

Obtain the type of the value of a Bayesian feature.
vicinitiesForSample

Segment a dataset by a single sample and compute vicinities for it and the remaining samples in the neighborhood.