Stratified linear regression mixture models

Main classes

• Joint2Regressor: Stratified multiple regression with continuity constraints for two (or K) groups.
• JointKRegressor: Stratified regression across K groups, with joint constraints, lasso and quantile.
• Joint2GMMRegressor: EM algorithm for stratified Gaussian mixture regression with constraints.
• JointUtils: Utilities for group splitting, closest point to median finding, etc.

Key Features

• Joint multi-group regression with continuity or custom constraints at the join point.
• Supports quantile regression, penalized regression (lasso, ridge, elasticnet), and stratified GMM.
• Stratified multivariate piecewise regression, not directly available in scikit-learn or statsmodels.

Note : This package is provided “as is” for reproducing results, even if not all features are fully
implemented or tested. For the moment the lasso and ridge includes the $beta_0$, hence the eventual
intercept may be removed (centering $y_\ell$). The code development had involved the use of modern
tools, for code refactoring into classes, writing of the docstring, and help for code cleaning and
debugging, robustfying the data entry types, etc.

Installation

pip install stratifreg

Usage

import stratifreg

from stratifreg import two_groups

References

• Priam, R. (2025). Family of linear regression mixture models stratified along the outcome.
  Open Archive: hal-04179813v3