# IMIFA R Package

## Infinite Mixture
of Infinite Factor Analysers

### Written by Keefe Murphy

## Description

The IMIFA package provides flexible Bayesian estimation of Infinite
Mixtures of Infinite Factor Analysers and related models, for
nonparametric model-based clustering of high-dimensional data,
introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>.
The IMIFA model assumes factor analytic covariance structures within
mixture components and simultaneously achieves dimension reduction and
clustering without recourse to model selection criteria to choose the
number of clusters or cluster-specific latent factors, mostly via
efficient Gibbs updates. Model-specific diagnostic tools are also
provided, as well as many options for plotting results, conducting
posterior inference on parameters of interest, posterior predictive
checking, and quantifying uncertainty.

The package also contains three data sets: `olive`

,
`USPSdigits`

, and `coffee`

.

## Installation

You can install the latest stable official release of the
`IMIFA`

package from CRAN:

`install.packages("IMIFA")`

or the development version from GitHub:

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

In either case, you can then explore the package with:

```
library(IMIFA)
help(mcmc_IMIFA) # Help on the main modelling function
```

Generally, `mcmc_IMIFA()`

is used for running the model
and creating a raw results object, on which
`get_IMIFA_results()`

is then called to prepare these results
for posterior inference. The output of the second call be visualised in
many ways using `plot.Results_IMIFA()`

.

For a more thorough intro, the vignette document is available as
follows:

`vignette("IMIFA", package="IMIFA")`

However, if the package is installed from GitHub the vignette is not
automatically created. It can be accessed when installing from GitHub
with the code:

`devtools::install_github('Keefe-Murphy/IMIFA', build_vignettes = TRUE)`

Alternatively, the vignette is available on the package’s CRAN
page.

### References

Murphy, K., Viroli, C., and Gormley, I. C. (2020). Infinite mixtures
of infinite factor analysers. *Bayesian Analysis*, 15(3):
937–863. <doi:10.1214/19-BA1179>.