Algorithms for Automatically Fitting MFA Models

Provides methods for fitting the Mixture of Factor Analyzers (MFA) model automatically. The MFA model is a mixture model where each sub-population is assumed to follow the Factor Analysis model. The Factor Analysis (FA) model is a latent variable model which assumes that observations are normally distributed, but imposes constraints on their covariance matrix. The MFA model contains two hyperparameters; g (the number of components in the mixture) and q (the number of factors in each component Factor Analysis model). Usually, the Expectation-Maximisation algorithm would be used to fit the MFA model, but this requires g and q to be known. This package treats g and q as unknowns and provides several methods which infer these values with as little input from the user as possible.


Reference manual

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1.0.0 by John Davey, 2 months ago

Browse source code at

Authors: John Davey [aut, cre] , Sharon Lee [ctb] , Garique Glonek [ctb] , Suren Rathnayake [ctb] , Geoff McLachlan [ctb] , Albert Ali Salah [ctb] , Heysem Kaya [ctb]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports abind, MASS, Matrix, Rfast, expm, stats, utils, Rdpack, pracma, usethis

See at CRAN