Package: remote 1.2.1

remote: Empirical Orthogonal Teleconnections in R

Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time.

Authors:Tim Appelhans, Florian Detsch, Thomas Nauss

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remote.pdf |remote.html
remote/json (API)

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • australiaGPCP - Monthly GPCP precipitation data for Australia
  • pacificSST - Monthly SSTs for the tropical Pacific Ocean
  • vdendool - Mean seasonal (DJF) 700 mb geopotential heights

On CRAN:

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

2.78 score 100 scripts 1.2k downloads 19 exports 28 dependencies

Last updated 8 years agofrom:c61109973b. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 14 2024
R-4.5-win-x86_64NOTEOct 14 2024
R-4.5-linux-x86_64NOTEOct 14 2024
R-4.4-win-x86_64OKOct 14 2024
R-4.4-mac-x86_64OKOct 14 2024
R-4.4-mac-aarch64OKOct 14 2024
R-4.3-win-x86_64OKOct 14 2024
R-4.3-mac-x86_64OKOct 14 2024
R-4.3-mac-aarch64OKOct 14 2024

Exports:anomalizecalcVarcovWeightcutStackdeg2raddenoisedeseasoneotEotCyclegeoWeightgetWeightslagalizelongtermMeansnmodesnXplainplotpredictsubsetwriteEot

Dependencies:clicolorspacedeldirfarvergluegridExtragtableinterpjpeglabelinglatticelatticeExtralifecyclemapdatamapsMASSmunsellpngR6rasterRColorBrewerRcppRcppEigenrlangscalesspterraviridisLite

Readme and manuals

Help Manual

Help pageTopics
R EMpirical Orthogonal TEleconnectionsremote-package remote
Create an anomaly RasterStackanomalize
Monthly GPCP precipitation data for AustraliaaustraliaGPCP
Calculate space-time variance of a RasterStack or RasterBrickcalcVar
Create a weighted covariance matrixcovWeight
Shorten a RasterStackcutStack
Convert degrees to radiansdeg2rad
Noise filtering through principal componentsdenoise
Create seasonal anomaliesdeseason deseason,numeric-method deseason,RasterStackBrick-method
EOT analysis of a predictor and (optionally) a response RasterStackeot eot,RasterStackBrick-method
Calculate a single EOTEotCycle
Class EotModeEotMode-class
Class EotStackEotStack-class
Geographic weightinggeoWeight
Calculate weights from latitudegetWeights
Create lagged RasterStackslagalize
Calculate long-term means from a 'RasterStack'longtermMeans
Names of Eot* objectsnames names,EotMode-method names,EotStack-method names<- names<-,EotMode-method names<-,EotStack-method
Number of modes of an EotStacknmodes nmodes,EotStack-method
Number of EOTs needed for variance explanationnXplain nXplain,EotStack-method
Monthly SSTs for the tropical Pacific OceanpacificSST
Plot an Eot* objectplot plot,EotMode,ANY-method plot,EotStack,ANY-method
EOT based spatial predictionpredict predict,EotMode-method predict,EotStack-method
Subset modes in EotStackssubset subset,EotStack-method [[,EotStack,ANY,ANY-method
Mean seasonal (DJF) 700 mb geopotential heightsvdendool
Write Eot* objects to diskwriteEot writeEot,EotMode-method writeEot,EotStack-method