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: 1.10
Imports: BBmisc (≥ 1.10), checkmate (≥ 1.8.1), methods
Suggests: akima, eaf, emoa, GGally, ggplot2, gridExtra, grid, irace (≥ 2.1), lhs, plyr, reshape2, testthat
Published: 2017-01-05
Author: Bernd Bischl [aut, cre], Michel Lang [aut], Jakob Bossek [aut], Daniel Horn [aut], Karin Schork [ctb], Jakob Richter [aut], Pascal Kerschke [aut]
Maintainer: Bernd Bischl <bernd_bischl at gmx.net>
BugReports: https://github.com/berndbischl/ParamHelpers/issues
License: BSD_2_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_1.10.tar.gz
Windows binaries: r-devel: ParamHelpers_1.10.zip, r-release: ParamHelpers_1.10.zip, r-oldrel: ParamHelpers_1.10.zip
OS X El Capitan binaries: r-release: ParamHelpers_1.10.tgz
OS X Mavericks binaries: r-oldrel: ParamHelpers_1.10.tgz
Old sources: ParamHelpers archive

Reverse dependencies:

Reverse depends: cmaesr, ecr, mlr, mlrMBO, smoof
Reverse imports: aslib, metagen, OpenML
Reverse suggests: llama
Reverse enhances: liquidSVM

Linking:

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