quant_clust

Returns a quantile-based clustering for univariate data.

clustering

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.

Keefe Murphy

MoEClust

Gaussian Parsimonious Clustering Models with Covariates and a
Noise Component

Thomas Brendan Murphy

quant_clust function

<dl><dt>x</dt>
<dd>A vector of numeric data.</dd>
<dt>G</dt>
<dd>The desired number of clusters.</dd></dl>

Arguments

Quantile-Based Clustering for Univariate Data — quant_clust

<dl>

<dt>x</dt>
<dd>A vector of numeric data.</dd>


<dt>G</dt>
<dd>The desired number of clusters.</dd>

</dl>

quant_clust: Quantile-Based Clustering for Univariate Data

Description

Usage

Value

Arguments

Examples