Documentation of GPBoost

GPBoost is a software library for combining tree-boosting with Gaussian process and grouped random effects models (aka mixed effects models or latent Gaussian models). It also allows for independently applying tree-boosting as well as Gaussian process and (generalized) linear mixed effects models (LMMs and GLMMs). For more information on the GPBoost library including examples, methodology and modeling background, references, and issues, see the GPBoost GitHub page.

Contents:

• Python package
• R package
• Main parameters
• All tree-boosting related parameters
• Python and R Examples
• C API
• Computational efficiency

Indices and Tables

• Index