ParamHelpers: Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning

Functions for parameter descriptions and operations in black-box optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.

Version: 1.5
Imports: BBmisc (≥ 1.9), checkmate (≥ 1.5.1)
Suggests: emoa, lhs, irace, testthat, soobench
Published: 2015-02-03
Author: Bernd Bischl [aut, cre], Michel Lang [aut], Jakob Bossek [aut], Daniel Horn [aut]
Maintainer: Bernd Bischl <bernd_bischl at gmx.net>
BugReports: https://github.com/berndbischl/ParamHelpers/issues
License: BSD_3_clause + file LICENSE
URL: https://github.com/berndbischl/ParamHelpers
NeedsCompilation: yes
Materials: NEWS
CRAN checks: ParamHelpers results

Downloads:

Reference manual: ParamHelpers.pdf
Package source: ParamHelpers_1.5.tar.gz
Windows binaries: r-devel: ParamHelpers_1.5.zip, r-release: ParamHelpers_1.5.zip, r-oldrel: ParamHelpers_1.5.zip
OS X Snow Leopard binaries: r-oldrel: ParamHelpers_1.5.tgz
OS X Mavericks binaries: r-release: ParamHelpers_1.5.tgz
Old sources: ParamHelpers archive

Reverse dependencies:

Reverse depends: mlr, smoof
Reverse imports: metagen
Reverse suggests: llama