Package: widals 0.6.1

widals: Weighting by Inverse Distance with Adaptive Least Squares

Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.

Authors:Dave Zes

widals_0.6.1.tar.gz
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widals.pdf |widals.html✨
widals/json (API)

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

Peer review:

Datasets:
  • O3 - California Ozone

On CRAN:

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

2.89 score 39 scripts 146 downloads 30 exports 2 dependencies

Last updated 5 years agofrom:c431b52c04. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-win-x86_64NOTENov 07 2024
R-4.5-linux-x86_64NOTENov 07 2024
R-4.4-win-x86_64OKNov 07 2024
R-4.4-mac-x86_64OKNov 07 2024
R-4.4-mac-aarch64OKNov 07 2024
R-4.3-win-x86_64OKNov 07 2024
R-4.3-mac-x86_64OKNov 07 2024
R-4.3-mac-aarch64OKNov 07 2024

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

Dependencies:snowsnowfall

Package \texttt{widals}: Fun with \texttt{fun.load()

Rendered fromfunwithfunload.Snwusingutils::Sweaveon Nov 07 2024.

Last update: 2019-12-07
Started: 2014-03-02

Package widals

Rendered fromwidals.Snwusingutils::Sweaveon Nov 07 2024.

Last update: 2019-12-07
Started: 2014-03-02