Package: widals 0.6.2
widals: Weighting by Inverse Distance with Adaptive Least Squares
Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.
Authors:
widals_0.6.2.tar.gz
widals_0.6.2.zip(r-4.7)widals_0.6.2.zip(r-4.6)widals_0.6.2.zip(r-4.5)
widals_0.6.2.tgz(r-4.6-x86_64)widals_0.6.2.tgz(r-4.6-arm64)widals_0.6.2.tgz(r-4.5-x86_64)widals_0.6.2.tgz(r-4.5-arm64)
widals_0.6.2.tar.gz(r-4.7-arm64)widals_0.6.2.tar.gz(r-4.7-x86_64)widals_0.6.2.tar.gz(r-4.6-arm64)widals_0.6.2.tar.gz(r-4.6-x86_64)
widals_0.6.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
widals/json (API)
| # Install 'widals' in R: |
| install.packages('widals', repos = c('https://davezes.r-universe.dev', 'https://cloud.r-project.org')) |
- O3 - California Ozone
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:e8a93c09ba. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 163 | ||
| linux-devel-x86_64 | OK | 146 | ||
| source / vignettes | OK | 240 | ||
| linux-release-arm64 | OK | 148 | ||
| linux-release-x86_64 | OK | 135 | ||
| macos-release-arm64 | OK | 117 | ||
| macos-release-x86_64 | OK | 156 | ||
| macos-oldrel-arm64 | OK | 82 | ||
| macos-oldrel-x86_64 | OK | 269 | ||
| windows-devel | OK | 73 | ||
| windows-release | OK | 70 | ||
| windows-oldrel | OK | 63 | ||
| wasm-release | OK | 91 |
Exports:applystnd.Hsapplystnd.Hst.lscreate.rm.ndx.lscrispifydistancedlog.normfun.load.hals.afun.load.hals.fillfun.load.widals.afun.load.widals.fillfuse.Hst.lsH.als.bH.Earth.solarHals.fastcv.snowHals.sesHals.snowHst.sumupload.Hst.ls.2Zsload.Hst.ls.ZMSS.snowrm.cols.Hst.lsstnd.Hsstnd.Hst.lsstnd.Htsubsetsites.Hst.lsunif.mhunload.Hst.lswidals.predictwidals.snowZ.clean.up
Last update: 2025-03-24
Started: 2014-03-02
Last update: 2025-03-24
Started: 2014-03-02
