CVS log for pkgsrc/math/R-DEoptimR/distinfo

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Default branch: MAIN

Revision 1.5 / (download) - annotate - [select for diffs], Sun Dec 18 06:53:21 2022 UTC (7 weeks, 3 days ago) by mef
Branch: MAIN
CVS Tags: pkgsrc-2022Q4-base, pkgsrc-2022Q4, HEAD
Changes since 1.4: +4 -4 lines
Diff to previous 1.4 (colored)

(math/R-DEoptimR) Updated 1.0.9 to 1.0.11, NEWS not updated

Revision 1.4 / (download) - annotate - [select for diffs], Tue Oct 26 10:55:21 2021 UTC (15 months, 2 weeks ago) by nia
Branch: MAIN
CVS Tags: pkgsrc-2022Q3-base, pkgsrc-2022Q3, pkgsrc-2022Q2-base, pkgsrc-2022Q2, pkgsrc-2022Q1-base, pkgsrc-2022Q1, pkgsrc-2021Q4-base, pkgsrc-2021Q4
Changes since 1.3: +2 -2 lines
Diff to previous 1.3 (colored)

math: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes

Revision 1.3 / (download) - annotate - [select for diffs], Thu Oct 7 14:27:43 2021 UTC (16 months ago) by nia
Branch: MAIN
Changes since 1.2: +1 -2 lines
Diff to previous 1.2 (colored)

math: Remove SHA1 hashes for distfiles

Revision 1.2 / (download) - annotate - [select for diffs], Sun Sep 19 02:26:35 2021 UTC (16 months, 3 weeks ago) by mef
Branch: MAIN
CVS Tags: pkgsrc-2021Q3-base, pkgsrc-2021Q3
Changes since 1.1: +5 -5 lines
Diff to previous 1.1 (colored)

(math/R-DEoptimR) Updated 1.0.8 to 1.0.9, NEWS not updated

Revision 1.1 / (download) - annotate - [select for diffs], Fri Aug 9 15:43:48 2019 UTC (3 years, 6 months ago) by brook
Branch: MAIN
CVS Tags: pkgsrc-2021Q2-base, pkgsrc-2021Q2, pkgsrc-2021Q1-base, pkgsrc-2021Q1, pkgsrc-2020Q4-base, pkgsrc-2020Q4, pkgsrc-2020Q3-base, pkgsrc-2020Q3, pkgsrc-2020Q2-base, pkgsrc-2020Q2, pkgsrc-2020Q1-base, pkgsrc-2020Q1, pkgsrc-2019Q4-base, pkgsrc-2019Q4, pkgsrc-2019Q3-base, pkgsrc-2019Q3

R-DEoptimR: initial commit

Differential Evolution (DE) stochastic algorithms for global
optimization of problems with and without constraints. The aim is to
curate a collection of its state-of-the-art variants that (1) do not
sacrifice simplicity of design, (2) are essentially tuning-free, and
(3) can be efficiently implemented directly in the R language.
Currently, it only provides an implementation of the 'jDE' algorithm
by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.

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