Detailed Python and R Examples
==============================

* **GPBoost and LaGaBoost algorithms** for Gaussian data ("regression") and non-Gaussian data ("classification", etc.): `in Python <https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/GPBoost_algorithm.py>`__ and `in R <https://github.com/fabsig/GPBoost/blob/master/R-package/demo/GPBoost_algorithm.R>`__
* **Parameter tuning** using deterministic or random grid search: `in Python <https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/parameter_tuning.py>`__ and `in R <https://github.com/fabsig/GPBoost/blob/master/R-package/demo/parameter_tuning.R>`__
* **Generalized linear Gaussian process and mixed effects models**: `in Python <https://github.com/fabsig/GPBoost/tree/master/examples/python-guide/generalized_linear_Gaussian_process_mixed_effects_models.py>`__ and `in R <https://github.com/fabsig/GPBoost/blob/master/R-package/demo/generalized_linear_Gaussian_process_mixed_effects_models.R>`__