--- title: GPopt for R: Bayesian and conformal optimization of... description: GPopt is now available for R, providing Bayesian and conformal optimization of black-box functions and machine learning hyperparameter tuning. It uses Gaussian... canonical: https://daily.dev/posts/gpopt-for-r-bayesian-and-conformal-optimization-of-black-box-functions-and-hyperparameter-tuning-xatslstom twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning | daily.dev og:description: GPopt is now available for R, providing Bayesian and conformal optimization of black-box functions and machine learning hyperparameter tuning. It uses Gaussian... og:url: https://daily.dev/posts/gpopt-for-r-bayesian-and-conformal-optimization-of-black-box-functions-and-hyperparameter-tuning-xatslstom og:image: https://api.daily.dev/og/posts/xaTSlSTOM.png og:image:alt: GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning **[R-bloggers](https://daily.dev/sources/rbloggers)** · 4 min read · 0 upvotes · 0 comments ## Summary GPopt is now available for R, providing Bayesian and conformal optimization of black-box functions and machine learning hyperparameter tuning. It uses Gaussian Process Regression and conformalized surrogate models. The package is ported from Python using uv for isolated virtual environments and reticulate for R-Python interop. Examples cover minimizing the Branin test function, tuning scikit-learn RandomForestClassifier hyperparameters on the iris dataset, Bayesian optimization with early stopping, and using custom conformalized surrogate models. Installation requires creating a Python virtual environment with uv, then installing the R package from GitHub. ## Full article daily.dev links to this article rather than hosting it. 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