Package: evgam 1.0.1

Ben Youngman

evgam: Generalised Additive Extreme Value Models

Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>. Details of how evgam works and various examples are given in Youngman, B.D. (2022) <doi:10.18637/jss.v103.i03>.

Authors:Ben Youngman [aut, cre]

evgam_1.0.1.tar.gz
evgam_1.0.1.tar.gz(r-4.7-arm64)evgam_1.0.1.tar.gz(r-4.7-x86_64)evgam_1.0.1.tar.gz(r-4.6-arm64)evgam_1.0.1.tar.gz(r-4.6-x86_64)
evgam_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
evgam/json (API)
NEWS

# Install 'evgam' in R:
install.packages('evgam', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • COelev - Colorado daily precipitation accumulations
  • COprcp - Colorado daily precipitation accumulations
  • COprcp_meta - Colorado daily precipitation accumulations
  • FCtmax - Fort Collins, Colorado, US daily max. temperatures
  • fremantle - Annual Maximum Sea Levels at Fremantle, Western Australia

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascpp

4.59 score 14 packages 178 scripts 5.1k downloads 11 exports 6 dependencies

Last updated from:9cd48e5b11. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK196
linux-devel-x86_64OK180
source / vignettesOK200
linux-release-arm64OK205
linux-release-x86_64OK182
wasm-releaseOK153

Exports:colplotdfbinddfrunmaxevgamextremalfevgamginv.evgampinvqevrunmaxseq_between

Dependencies:latticeMatrixmgcvnlmeRcppRcppArmadillo