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IMIFA R Package

Infinite Mixture of Infinite Factor Analysers

Written by Keefe Murphy

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. (2017) \href{https://arxiv.org/abs/1701.07010}{arXiv:1701.07010}. 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 and conducting posterior inference on parameters of interest.

To install the development version of the package type:

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

To install the latest stable official release of the package from CRAN go to R and type:

install.packages('IMIFA')

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

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

To read the vignette guide to using the package, type the following within R:

vignette('IMIFA', package = 'IMIFA')

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Version

Install

install.packages('IMIFA')

Monthly Downloads

417

Version

1.3.1

License

GPL (>= 2)

Maintainer

Keefe Murphy

Last Published

July 7th, 2017

Functions in IMIFA (1.3.1)

Ledermann

Ledermann Bound
MGP_check

Check the validity of Multiplicative Gamma Process (MGP) hyperparameters
PGMM_dfree

Estimate the Number of Free Parameters in Finite Factor Analytic Mixture Models (PGMM)
Procrustes

Procrustes Transformation
G_variance

2nd Moment of Dirichlet / Pitman-Yor processes
IMIFA

IMIFA: Fitting, Diagnostics, and Plotting Functions for Infinite Mixtures of Infinite Factor Analysers and Related Models
G_expected

1st Moment of the Dirichlet / Pitman-Yor processes
G_priorDensity

Plot Dirichlet / Pitman-Yor process Priors
Zsimilarity

Summarises MCMC clustering labels with a similarity matrix and finds the 'average' clustering
coffee

Chemical composition of Arabica and Robusta coffee samples
heat_legend

Add a colour key legend to heatmap plots
is.posi_def

Check Postive-(Semi)definiteness of a matrix
get_IMIFA_results

Extract results, conduct posterior inference and compute performance metrics for MCMC samples of models from the IMIFA family
plot.Results_IMIFA

Plotting output and parameters of inferential interest for IMIFA and related models
plot_cols

Plots a matrix of colours
shift_GA

Moment Matching Parameters of Shifted Gamma Distributions
sim_IMIFA_data

Simulating Data from a Mixture of Factor Analysers Structure
mcmc_IMIFA

Adaptive Gibbs Sampler for Nonparameteric Model-based Clustering using models from the IMIFA family
olive

Fatty acid composition of Italian olive oils
gumbel_max

Simulate Cluster Labels from Unnormalised Log-Probabilities using the Gumbel-Max Trick
is.cols

Check for Valid Colours
psi_hyper

Find sensible inverse gamma hyperparameters for variance/uniqueness parameters
rDirichlet

Simulate Mixing Proportions from a Dirichlet Distribution
mat2cols

Convert a numeric matrix to colours