Package: GPopt 0.10.2

GPopt: 'Bayesian' Optimization using Gaussian Process Regression and Other Surrogates (R Interface to Python's 'GPopt')

An R interface to the Python package 'GPopt' for 'Bayesian' Optimization using Gaussian Process Regression and Other Surrogates <https://github.com/Techtonique/GPopt>, for 'Bayesian' optimization of black-box (and machine learning hyperparameter tuning) objective functions using Gaussian Process Regression and other surrogate models. Ported to R using 'reticulate' and 'uv', following the same technique described in <https://thierrymoudiki.github.io/blog/2025/12/17/r/python/new-nnetsauce-R-uv>. Every function in this package thinly wraps the corresponding Python object: attribute and method access with '$' in R mirrors attribute and method access with '.' in Python.

Authors:T. Moudiki