ParamHelpers: Helpers for Parameters in Black-Box Optimization, Tuning and Machine Learning (original) (raw)

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: 1.14.2
Imports: backports, BBmisc (≥ 1.10), checkmate (≥ 1.8.2), fastmatch, methods
Suggests: akima, covr, eaf, emoa, GGally, ggplot2, grid, gridExtra, irace (≥ 2.1), lhs, plyr, reshape2, testthat
Published: 2025-01-09
DOI: 10.32614/CRAN.package.ParamHelpers
Author: Bernd Bischl ORCID iD [aut], Michel Lang ORCID iD [aut], Jakob Richter ORCID iD [aut], Jakob Bossek [aut], Daniel Horn [aut], Karin Schork [ctb], Pascal Kerschke [aut], Martin Binder [ctb, cre]
Maintainer: Martin Binder <mlr.developer at mb706.com>
BugReports: https://github.com/mlr-org/ParamHelpers/issues
License: BSD_2_clause + file
URL: https://paramhelpers.mlr-org.com,https://github.com/mlr-org/ParamHelpers
NeedsCompilation: yes
Materials: NEWS
CRAN checks: ParamHelpers results

Documentation:

Downloads:

Reverse dependencies:

Reverse depends: cmaesr, ecr, mlr, mlrCPO, mlrMBO, smoof
Reverse imports: aslib, FactorHet, glmnetr, Polytect, roseRF, tramnet, tuneRanger, varycoef
Reverse suggests: binaryRL, bnclassify, ChemoSpec2D, dynparam, flacco, llama, mlrintermbo, OmnipathR, OpenML

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