Package: mmb 0.13.3

Sebastian Hönel

mmb: Arbitrary Dependency Mixed Multivariate Bayesian Models

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) <doi:10.1098/rstl.1763.0053> and by Scutari (2010) <doi:10.18637/jss.v035.i03>.

Authors:Sebastian Hönel

mmb_0.13.3.tar.gz
mmb_0.13.3.zip(r-4.5)mmb_0.13.3.zip(r-4.4)mmb_0.13.3.zip(r-4.3)
mmb_0.13.3.tgz(r-4.4-any)mmb_0.13.3.tgz(r-4.3-any)
mmb_0.13.3.tar.gz(r-4.5-noble)mmb_0.13.3.tar.gz(r-4.4-noble)
mmb_0.13.3.tgz(r-4.4-emscripten)mmb_0.13.3.tgz(r-4.3-emscripten)
mmb.pdf |mmb.html✨
mmb/json (API)

# Install 'mmb' in R:
install.packages('mmb', repos = c('https://mrshoenel.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mrshoenel/r-mmb/issues

On CRAN:

bayes-classifierkernel-density-estimationneighborhood-searchregression-models

3.70 score 5 scripts 103 downloads 33 exports 6 dependencies

Last updated 4 years agofrom:b4803f6e22. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTEOct 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winNOTEOct 27 2024
R-4.4-macNOTEOct 27 2024
R-4.3-winNOTEOct 27 2024
R-4.3-macNOTEOct 27 2024

Exports:bayesCaretbayesComputeMarginalFactorbayesConvertDatabayesFeaturesToSamplebayesInferSimplebayesProbabilitybayesProbabilityAssignbayesProbabilityNaivebayesProbabilitySimplebayesRegressbayesRegressAssignbayesRegressSimplebayesToLatexcentralitiesconditionalDataMincreateFeatureForBayesdiscretizeVariableToRangesdistanceestimatePdfgetDefaultRegressorgetMessagesgetProbForDiscretegetRangeForDiscretizedValuegetValueKeyOfBayesFeaturesgetValueOfBayesFeaturesgetWarningsneighborhoodsampleToBayesFeaturessetDefaultRegressorsetMessagessetWarningsvicinitiesvicinitiesForSample

Dependencies:codetoolsdoParallelforeachiteratorsrbibutilsRdpack

Cluster Analysis

Sebastian Hönel

Rendered fromCluster-Analysis.rmdusingknitr::rmarkdownon Oct 27 2024.

Last update: 2020-08-01
Started: 2020-08-01

Readme and manuals

Help Manual

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