Infinite Mixtures of Infinite Factor Analysers and Related Models
Control settings for the Bayesian Nonparametric priors for infinite mi...
1st & 2nd Moments of the Pitman-Yor / Dirichlet Processes
Plot Pitman-Yor / Dirichlet Process Priors
Extract results, conduct posterior inference and compute performance m...
Simulate Cluster Labels from Unnormalised Log-Probabilities using the ...
Add a colour key legend to heatmap plots
IMIFA: Infinite Mixtures of Infinite Factor Analysers and Related Mode...
Show the NEWS file
Check for Valid Colours
Check Positive-(Semi)definiteness of a matrix
Ledermann Bound
Left Truncated Gamma Distributions
Convert a numeric matrix to colours
Adaptive Gibbs Sampler for Nonparametric Model-based Clustering using ...
Check the validity of Multiplicative Gamma Process (MGP) hyperparamete...
Control settings for the MGP prior and AGS for infinite factor models
Control settings for the IMIFA family of factor analytic mixtures
Pareto Scaling
Estimate the Number of Free Parameters in Finite Factor Analytic Mixtu...
Plotting output and parameters of inferential interest for IMIFA and r...
Plots a matrix of colours
Posterior Confusion Matrix
Procrustes Transformation
Find sensible inverse gamma hyperparameters for variance/uniqueness pa...
Simulate Mixing Proportions from a Dirichlet Distribution
Decompose factor scores by cluster
Moment Matching Parameters of Shifted Gamma Distributions
Show image of grayscale grid
Plot the posterior mean image
Simulate Data from a Mixture of Factor Analysers Structure
Set storage values for use with the IMIFA family of models
Summarise MCMC samples of clustering labels with a similarity matrix a...
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures 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.