H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Implement HGLM with family = Binomial; rand_family=["gaussisan"]. Ideally with a SUBJECT field (Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated). #7215
Implement HGLM with family = Binomial; rand_family=["gaussisan"]. Ideally with a SUBJECT field (Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated).
Implement HGLM with family = Binomial; rand_family=["gaussisan"]. Ideally with a SUBJECT field (Responses from different subjects are assumed to be statistically independent, and responses within subjects are assumed to be correlated).
R lme4: http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html
SAS GENMOD: https://documentation.sas.com/doc/en/statug/15.2/statug_genmod_syntax26.htm