IDCeMPy
IDCeMPy: Python Package for Inflated Discrete Choice Models - Published in JOSS (2021)
mixmod
Supervised, unsupervised and semi-supervised classification with mixture modelling
aldvmm
The goal of ‘aldvmm’ is to fit adjusted limited dependent variable mixture models of health state utilities in R. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package ‘aldvmm’ uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership.
https://github.com/cgre-aachen/bayseg
An unsupervised machine learning algorithm for the segmentation of spatial data sets.