Package: RGN 1.0.0

David McInerney

RGN: Robust-Gauss Newton (RGN) Optimization of Sum-of-Squares Objective Function

Implementation of the Robust Gauss-Newton (RGN) algorithm, designed for solving optimization problems with a sum of least squares objective function. For algorithm details please refer to Qin et. al. (2018) <doi:10.1029/2017WR022488>.

Authors:David McInerney [cre, aut], Michael Leonard [aut], Dmitri Kavetski [aut], Youwei Qin [aut], George Kuczera [aut]

RGN_1.0.0.tar.gz
RGN_1.0.0.zip(r-4.5)RGN_1.0.0.zip(r-4.4)RGN_1.0.0.zip(r-4.3)
RGN_1.0.0.tgz(r-4.4-x86_64)RGN_1.0.0.tgz(r-4.4-arm64)RGN_1.0.0.tgz(r-4.3-x86_64)RGN_1.0.0.tgz(r-4.3-arm64)
RGN_1.0.0.tar.gz(r-4.5-noble)RGN_1.0.0.tar.gz(r-4.4-noble)
RGN_1.0.0.tgz(r-4.4-emscripten)RGN_1.0.0.tgz(r-4.3-emscripten)
RGN.pdf |RGN.html✨
RGN/json (API)
NEWS

# Install 'RGN' in R:

install.packages('RGN', repos = c('https://climateanalytics.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/climateanalytics/rgn/issues

Uses libs:

fortran– Runtime library for GNU Fortran applications

Datasets:

BassRiverData - Hydrological data for Bass River catchment in Victoria, Australia

On CRAN:

2 exports 1.10 score 0 dependencies 1 dependents 2 scripts 176 downloads

Last updated 12 months agofrom:4a82b7f4ed. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 13 2024
R-4.5-win-x86_64	OK	Sep 13 2024
R-4.5-linux-x86_64	OK	Sep 13 2024
R-4.4-win-x86_64	OK	Sep 13 2024
R-4.4-mac-x86_64	OK	Sep 13 2024
R-4.4-mac-aarch64	OK	Sep 13 2024
R-4.3-win-x86_64	OK	Sep 13 2024
R-4.3-mac-x86_64	OK	Sep 13 2024
R-4.3-mac-aarch64	OK	Sep 13 2024