# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "GPopt" in publications use:'
type: software
license: BSD-3-Clause
title: 'GPopt: ''Bayesian'' Optimization using Gaussian Process Regression and Other
  Surrogates (R Interface to Python''s ''GPopt'')'
version: 0.10.2
doi: 10.32614/CRAN.package.GPopt
abstract: '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:
- family-names: Moudiki
  given-names: T.
  email: thierry.moudiki@gmail.com
repository: https://techtonique.r-universe.dev
repository-code: https://github.com/Techtonique/GPopt_r
commit: 7344108ca5e7fb3cfb94e0461265994717c3463e
url: https://github.com/Techtonique/GPopt
date-released: '2026-07-22'
contact:
- family-names: Moudiki
  given-names: T.
  email: thierry.moudiki@gmail.com