Package: rgw 0.3.0

rgw: Goodman-Weare Affine-Invariant Sampling

Implementation of the affine-invariant method of Goodman & Weare (2010) <doi:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.

Authors:Adam Mantz

rgw_0.3.0.tar.gz
rgw_0.3.0.zip(r-4.5)rgw_0.3.0.zip(r-4.4)rgw_0.3.0.zip(r-4.3)
rgw_0.3.0.tgz(r-4.5-any)rgw_0.3.0.tgz(r-4.4-any)rgw_0.3.0.tgz(r-4.3-any)
rgw_0.3.0.tar.gz(r-4.5-noble)rgw_0.3.0.tar.gz(r-4.4-noble)
rgw_0.3.0.tgz(r-4.4-emscripten)rgw_0.3.0.tgz(r-4.3-emscripten)
rgw.pdf |rgw.html✨
rgw/json (API)

# Install 'rgw' in R:
install.packages('rgw', repos = c('https://abmantz.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/abmantz/rgw/issues

On CRAN:

Conda:

markov-chain-monte-carlostatistics

3.00 score 2 stars 2 scripts 164 downloads 2 exports 0 dependencies

Last updated 2 years agofrom:7cb69aa12e. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-winOKMar 17 2025
R-4.5-macOKMar 17 2025
R-4.5-linuxOKMar 17 2025
R-4.4-winOKMar 17 2025
R-4.4-macOKMar 17 2025
R-4.4-linuxOKMar 17 2025
R-4.3-winOKMar 17 2025
R-4.3-macOKMar 17 2025

Exports:GoodmanWeareGoodmanWeare.rem

Dependencies: