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: 1.12
Imports: backports, BBmisc (≥ 1.10), checkmate (≥ 1.8.2), fastmatch, methods
Suggests: akima, eaf, emoa, GGally, ggplot2, gridExtra, grid, irace (≥ 2.1), lhs, plyr, reshape2, testthat
Published: 2019-01-18
Author: Bernd Bischl [aut], Michel Lang [aut], Jakob Richter [aut, cre], Jakob Bossek [aut], Daniel Horn [aut], Karin Schork [ctb], Pascal Kerschke [aut]
Maintainer: Jakob Richter <code at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
Materials: NEWS
CRAN checks: ParamHelpers results


Reference manual: ParamHelpers.pdf
Package source: ParamHelpers_1.12.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: ParamHelpers_1.12.tgz, r-oldrel: ParamHelpers_1.12.tgz
Old sources: ParamHelpers archive

Reverse dependencies:

Reverse depends: cmaesr, ecr, mlr, mlrCPO, mlrMBO, randomsearch, smoof
Reverse imports: aslib, CEGO, metagen, OpenML, tuneRanger
Reverse suggests: bnclassify, flacco, llama
Reverse enhances: liquidSVM


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