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otrimle (version 2.0)

Robust Model-Based Clustering

Description

Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) , and Coretto and Hennig (2017) .

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Version

Install

install.packages('otrimle')

Monthly Downloads

350

Version

2.0

License

GPL (>= 2)

Maintainer

Pietro Coretto

Last Published

May 29th, 2021

Functions in otrimle (2.0)

InitClust

Robust Initialization for Model-based Clustering Methods
generator.otrimle

Generates random data from OTRIMLE output model
otrimle

Optimally Tuned Robust Improper Maximum Likelihood Clustering
rimle

Robust Improper Maximum Likelihood Clustering
kmeanfun

Mean and standard deviation of unimodality statistic
banknote

Swiss Banknotes Data
kerndensp

Closeness of multivariate distribution to elliptical unimodal distribution
kerndensmeasure

Statistic measuring closeness to symmetric unimodal distribution
kerndenscluster

Aggregated distance to elliptical unimodal density over clusters
otrimleg

OTRIMLE for a range of numbers of clusters with density-based cluster quality statistic
otrimlesimg

Adequacy approach for number of clusters for OTRIMLE
plot.otrimle

Plot Methods for OTRIMLE Objects
plot.rimle

Plot Methods for RIMLE Objects