Robust Model-Based Clustering
Generates random data from OTRIMLE output model
Robust Initialization for Model-based Clustering Methods
Aggregated distance to elliptical unimodal density over clusters
Statistic measuring closeness to symmetric unimodal distribution
Closeness of multivariate distribution to elliptical unimodal distribu...
Mean and standard deviation of unimodality statistic
Optimally Tuned Robust Improper Maximum Likelihood Clustering
OTRIMLE for a range of numbers of clusters with density-based cluster ...
Adequacy approach for number of clusters for OTRIMLE
Plot Methods for OTRIMLE Objects
Plot Methods for RIMLE Objects
Robust Improper Maximum Likelihood Clustering
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) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.