# MoEClust R Package

## Finite Gaussian Mixtures of Experts -

## Parsimonious Model-Based Clustering

## with Gating and Expert Network Covariates

### Written by Keefe Murphy

Fits *MoEClust* models introduced by Murphy and Murphy (2017) <arXiv:1711.05632>, i.e. fits finite Gaussian mixture of experts models with gating and expert network covariates using parsimonious covariance parameterisations from **mclust** via the EM algorithm. Also visualises mixture of experts models with parsimonious covariance structures using generalised pairs plots.

The most important function in the **MoEClust** package is: `MoE_clust`

, for fitting the model via EM with gating and/or expert network covariates, supplied via formula interfaces. Other functions also exist, e.g. `MoE_control`

, `MoE_crit`

, `MoE_dens`

, `MoE_estep`

, and `MoE_aitken`

, which are all used within `MoE_clust`

but are nonetheless made available for standalone use. `MoE_compare`

is provided for conducting model selection between different results from `MoE_clust`

using different covariate combinations &/or initialisation strategies, etc.

A dedicated plotting function exists for visualising the results using generalised pairs plots, for examining the gating network &/or log-likelihood, and/or graphing model selection criteria values. The generalised pairs plots (`MoE_gpairs`

) visualise all pairwise relationships between clustered response variables and associated gating &/or expert network continuous &/or categorical variables, coloured according to the MAP classification, and also give the marginal distributions of each variable along the diagonal.

An `as.Mclust`

method is provided to coerce the output of class `"MoEClust"`

from `MoE_clust`

to the `"Mclust"`

class, to facilitate use of plotting and other functions for the `"Mclust"`

class within the **mclust** package. As per **mclust**, **MoEClust** also facilitates modelling with an additional noise component (with or without the mixing proportion for the noise component depending on covariates).

The package also contains two data sets: `ais`

and `CO2data`

.

To install the development version of the package type:

```
# If required install devtools:
# install.packages('devtools')
devtools::install_github('Keefe-Murphy/MoEClust')
```

You can then explore the package with:

```
library(MoEClust)
help(MoE_clust) # Help on the main modelling function
```