ParamHelpers (1.12)

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.

Maintainer: Jakob Richter
Author(s): Bernd Bischl [aut], Michel Lang [aut], Jakob Richter [aut, cre], Jakob Bossek [aut], Daniel Horn [aut], Karin Schork [ctb], Pascal Kerschke [aut]

License: BSD_2_clause + file LICENSE

Uses: backports, BBmisc, checkmate, fastmatch, akima, ggplot2, lhs, plyr, testthat, GGally, emoa, gridExtra, reshape2, eaf, irace
Reverse depends: cmaesr, ecr, mlr, mlrCPO, mlrMBO, randomsearch, smoof
Reverse suggests: ChemoSpec2D, dynparam, flacco, llama, OpenML
Reverse enhances: liquidSVM

Released 8 months ago.