diff options
| -rw-r--r-- | var/spack/repos/builtin/packages/r-mlrmbo/package.py | 28 |
1 files changed, 15 insertions, 13 deletions
diff --git a/var/spack/repos/builtin/packages/r-mlrmbo/package.py b/var/spack/repos/builtin/packages/r-mlrmbo/package.py index 23ae339e12..05e2f0447b 100644 --- a/var/spack/repos/builtin/packages/r-mlrmbo/package.py +++ b/var/spack/repos/builtin/packages/r-mlrmbo/package.py @@ -7,24 +7,26 @@ from spack import * class RMlrmbo(RPackage): - """Flexible and comprehensive R toolbox for model-based optimization - ('MBO'), also known as Bayesian optimization. It is designed for both - single- and multi-objective optimization with mixed continuous, - categorical and conditional parameters. The machine learning toolbox - 'mlr' provide dozens of regression learners to model the performance of - the target algorithm with respect to the parameter settings. It provides - many different infill criteria to guide the search process. Additional - features include multi-point batch proposal, parallel execution as well - as visualization and sophisticated logging mechanisms, which is - especially useful for teaching and understanding of algorithm behavior. - 'mlrMBO' is implemented in a modular fashion, such that single - components can be easily replaced or adapted by the user for specific - use cases.""" + """Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions + + Flexible and comprehensive R toolbox for model-based optimization ('MBO'), + also known as Bayesian optimization. It is designed for both single- and + multi-objective optimization with mixed continuous, categorical and + conditional parameters. The machine learning toolbox 'mlr' provide dozens + of regression learners to model the performance of the target algorithm + with respect to the parameter settings. It provides many different infill + criteria to guide the search process. Additional features include + multi-point batch proposal, parallel execution as well as visualization and + sophisticated logging mechanisms, which is especially useful for teaching + and understanding of algorithm behavior. 'mlrMBO' is implemented in a + modular fashion, such that single components can be easily replaced or + adapted by the user for specific use cases.""" homepage = "https://github.com/mlr-org/mlrMBO/" url = "https://cloud.r-project.org/src/contrib/mlrMBO_1.1.1.tar.gz" list_url = "https://cloud.r-project.org/src/contrib/Archive/mlrMBO" + version('1.1.5', sha256='7ab9d108ad06f6c5c480fa4beca69e09ac89bb162ae6c85fe7d6d25c41f359b8') version('1.1.2', sha256='8e84caaa5d5d443d7019128f88ebb212fb095870b3a128697c9b64fe988f3efe') version('1.1.1', sha256='e87d9912a9b4a968364584205b8ef6f7fea0b5aa043c8d31331a7b7be02dd7e4') version('1.1.0', sha256='6ae82731a566333f06085ea2ce23ff2a1007029db46eea57d06194850350a8a0') |