mcfa 0.1.6

Mixtures of Common Factor Analyzers with missing data

This python package implements the Mixtures of Common Factor Analyzers model introduced by Baek+ 2010. It uses tensorflow to implement a stochastic gradient descent, which allows for model training without prior imputation of missing data. The interface resembles the sklearn model API.

Documentation

Refer to the docs/documentation.ipynb for the documentation and docs/4d_gaussian.ipynb for an example application.

Install

Install from PyPi using pip:

 $ pip install mcfa

The minimum required python version is 3.8.

Alternatives

• EMMIXmfa in R
• Casey+ 2019 in python

Compared to this implementation, Casey+ 2019 use an EM-algorithm instead of a stochastic gradient descent. This requires the imputation of the missing values before the model training. On the other hand, there are more initialization routines the lower space loadings and factors available in the Casey+ 2019 implementation.