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Gpboost

Gpboost is an open-source software library for tree-boosting, Gaussian processes and mixed-effects models, including methods that combine boosting with latent Gaussian effects. It also supports applying these model families independently. The library is predominantly written in C++ and provides a C interface, Python package and R package.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Gpboost — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Gpboost when a modeling task calls for flexible tree-based effects together with structured latent variation, or when you want to apply one of its supported model families independently. The project also documents use cases involving high-cardinality categories and spatial, spatio-temporal, longitudinal and panel data.

Who it is for

  • Data scientists building prediction models with structured or latent Gaussian effects.
  • Researchers and practitioners modeling spatial, spatio-temporal, longitudinal or panel data.
  • Python and R users exploring mixed-effects or Gaussian-process models alongside tree-boosting.

Documented uses

  • Combine nonlinear tree-based predictor functions with Gaussian processes or random effects.
  • Model grouped effects, including nested, crossed and random-coefficient structures.
  • Apply Gaussian processes, linear or generalized linear mixed-effects models, or tree-boosting independently.
  • Explore models for high-cardinality categorical variables and spatial or spatio-temporal prediction.
Before you choose

Requirements & limits

The project is identified as open source. It provides a C++ library, C interface, Python package and R package; the repository also links to a CLI installation guide.

  • Choose between a combined boosting-plus-latent-Gaussian model and independent model families based on the structure of the task.
  • The documented Gaussian and non-Gaussian likelihood formulations differ; consult the supported-likelihood reference for model selection.
  • Covariance functions and scalable Gaussian-process approximations are documented settings to consider for GP models.
  • Python and R installation instructions and examples are linked separately; the CLI has its own installation guide.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Find GPBoost installation instructions and examples for Python or R

Use the project README to locate installation instructions and examples for a chosen language package.

Before you start

  • Choose the Python package or R package.

Steps

  1. Open the GPBoost README.

  2. Follow the link for the Python package or R package to find its installation instructions.

  3. For usage examples, open the Python examples or R examples linked from the README.

Expected outcome

The README points to language-specific package instructions and examples; it does not provide the installation or modeling steps themselves.

Guide limitations

  • The README does not include package installation commands or a worked model-building workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
fabsig/GPBoost · Official homepage title · retained in its source language
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