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AdaptGauss (version 1.1.0)

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

1.1.0

License

GPL-3

Maintainer

Michael Thrun

Last Published

October 14th, 2015

Functions in AdaptGauss (1.1.0)

AdaptGauss

Adapt Gaussian Mixture Model (GMM)
randomLogGMM

Random Number Generator for Log or Gaussian Mixture Model
BayesForMixes

Posterioris of Bayes Theorem
BayesDecisionBoundaries

Decision Boundaries calculated through Bayes Theorem
Intersect2Mixes

Intersect of two Gaussians
CDFMixtures

cumulative distribution of mixture model
AdaptGauss-package

AdaptGauss-package
ClassifyByDecisionBoundaries

Classify Data according to decision Boundaries
PlotGaussMixesAndBoundaries

Shows GMM with Boundaries
KStestMixtures

Kolmogorov-Smirnov test
qqplotGMM

Quantile Quantile Plot of Data
paretoDensityEstimationForGMM

Pareto Density Estimation
EMGauss

EM Algorithm for GMM
PlotGaussianMixtures

Shows GMM
paretoRadiusForGMM

ParetoRadius for GMM
Chi2testMixtures

Pearson's chi-squared test
OptimalNoBins

Optimal Number Of Bins