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AdaptGauss (version 1.2.0)

Gaussian Mixture Models (GMM)

Description

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test.

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Version

Install

install.packages('AdaptGauss')

Monthly Downloads

685

Version

1.2.0

License

GPL-3

Maintainer

Michael Thrun

Last Published

March 16th, 2016

Functions in AdaptGauss (1.2.0)

EMGauss

EM Algorithm for GMM
QQplotGMM

Quantile Quantile Plot of Data
ClassifyByDecisionBoundaries

Classify Data according to decision Boundaries
AdaptGauss

Adapt Gaussian Mixture Model (GMM)
ParetoDensityEstimation

Pareto Density Estimation
RandomLogGMM

Random Number Generator for Log or Gaussian Mixture Model
Pdf4Mixtures

Calculates pdf for GMM
PlotMixturesAndBoundaries

Shows GMM with Boundaries
Chi2testMixtures

Pearson's chi-squared test
PlotMixtures

Shows GMM
LikelihoodRatio4Mixtures

Likelihood Ratio for Gaussian Mixtures
Intersect2Mixtures

Intersect of two Gaussians
CDFMixtures

cumulative distribution of mixture model
ParetoRadius

ParetoRadius for distributions
AdaptGauss-package

AdaptGauss-package
LogLikelihood4Mixtures

LogLikelihood for Gaussian Mixture Models
Bayes4Mixtures

Posterioris of Bayes Theorem
KStestMixtures

Kolmogorov-Smirnov test
InformationCriteria4GMM

Information Criteria For GMM
OptimalNoBins

Optimal Number Of Bins
BayesDecisionBoundaries

Decision Boundaries calculated through Bayes Theorem