A suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.
| Version: | 0.2.0 |
| Suggests: | R.rsp, laGP |
| Published: | 2020-11-03 |
| Author: | Tony Pourmohamad [aut, cre] |
| Maintainer: | Tony Pourmohamad <tpourmohamad at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| Materials: | ChangeLog |
| CRAN checks: | CompModels results |
| Reference manual: | CompModels.pdf |
| Vignettes: |
An introduction to the CompModels package CompModels: Diagrams of physics based computer models |
| Package source: | CompModels_0.2.0.tar.gz |
| Windows binaries: | r-devel: CompModels_0.2.0.zip, r-release: CompModels_0.2.0.zip, r-oldrel: CompModels_0.2.0.zip |
| macOS binaries: | r-release (arm64): CompModels_0.2.0.tgz, r-oldrel (arm64): CompModels_0.2.0.tgz, r-release (x86_64): CompModels_0.2.0.tgz, r-oldrel (x86_64): CompModels_0.2.0.tgz |
| Old sources: | CompModels archive |
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