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.
For the next roadmap for H2O-3:
Could the addition of NGBoost be considered?
https://stanfordmlgroup.github.io/projects/ngboost/ https://github.com/stanfordmlgroup/ngboost
The approach for adoption could be similar to that of XGBoost.
Also lightgbm (https://github.com/microsoft/LightGBM) or catboost (https://github.com/catboost/catboost) could have its momentum.