Package: WASP 1.4.4

Ze Jiang

WASP: Wavelet System Prediction

The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.

Authors:Ze Jiang [aut, cre], Md. Mamunur Rashid [aut], Ashish Sharma [aut], Fiona Johnson [aut]

WASP_1.4.4.tar.gz
WASP_1.4.4.tar.gz(r-4.5-noble)WASP_1.4.4.tar.gz(r-4.4-noble)
WASP_1.4.4.tgz(r-4.4-emscripten)WASP_1.4.4.tgz(r-4.3-emscripten)
WASP.pdf |WASP.html✨
WASP/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/zejiang-unsw/wasp/issues

Datasets:
  • Ind_AWAP.2.5 - Sample data: Index of AWAP grids with no missing data
  • SPI.12 - Sample data: Standardized Precipitation Index with 12 month accumulation period.
  • aus.coast - Sample data: Australia map
  • data.AWAP.2.5 - Sample data: AWAP rainfall data over Australia
  • data.CI - Sample data: Climate indices strongly influencing Australia climate
  • data.HL - Sample data: Hysteresis loop
  • data.SW1 - Sample data: Sinewave model 1
  • data.SW3 - Sample data: Sinewave model 3
  • lat_lon.2.5 - Sample data: Latitude and longitude of AWAP grids
  • obs.mon - Sample data: NCEP reanalysis data averaged over Sydney region
  • rain.mon - Sample data: Rainfall station data over Sydney region

29 exports 6.08 score 52 dependencies 97 mentions 18 scripts 2.7k downloads

Last updated 2 months agofrom:68f078ed73. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-linuxNOTEAug 20 2024

Exports:at.vtat.vt.valat.wddata.gen.ar1data.gen.ar4data.gen.ar9data.gen.HLdata.gen.Rosslerdata.gen.SWdata.gen.tar1data.gen.tar2dwt.vtdwt.vt.valfig.dwt.vtknnknnregl1cvmodwt.vtmodwt.vt.valmra.plotnon.bdypaddingpic.calcr2.bootscal2freqMscal2freqRSPI.calcstepwise.VTstepwise.VT.valwave.var

Dependencies:bitbit64clicliprcolorspacecpp11crayondplyrfansifarverfitdistrplusgenericsggplot2gluegtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmultitapermunsellnlmepillarpkgconfigprettyunitsprogresspurrrR6RColorBrewerreadrrlangscalesspstringistringrsurvivaltibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwaveslimwithrzoo

WASP: An R package for Wavelet System Prediction

Ze Jiang, Md. Mamunur Rashid, Ashish Sharma, and Fiona Johnson

Rendered fromWASP.Rmdusingknitr::rmarkdownon Aug 20 2024.

Last update: 2024-07-21
Started: 2021-08-23

Readme and manuals

Help Manual

Help pageTopics
WASP: WAvelet System PredictionWASP-package
Variance Transformation Operation - AT(a trous)at.vt
Variance Transformation Operation for Validationat.vt.val
a trous (AT) based additive decompostion using Daubechies family waveletat.wd
Sample data: Australia mapaus.coast
Sample data: AWAP rainfall data over Australiadata.AWAP.2.5
Sample data: Climate indices strongly influencing Australia climatedata.CI
Generate predictor and response data from AR1 model.data.gen.ar1
Generate predictor and response data from AR4 model.data.gen.ar4
Generate predictor and response data from AR9 model.data.gen.ar9
Generate predictor and response data: Hysteresis Loopdata.gen.HL
Generate predictor and response data: Rossler systemdata.gen.Rossler
Generate predictor and response data: Sinewave modeldata.gen.SW
Generate predictor and response data from TAR1 model.data.gen.tar1
Generate predictor and response data from TAR2 model.data.gen.tar2
Sample data: Hysteresis loopdata.HL
Sample data: Sinewave model 1 (SW1)data.SW1
Sample data: Sinewave model 3 (SW3)data.SW3
Variance Transformation Operation - MRAdwt.vt
Variance Transformation Operation for Validationdwt.vt.val
Plot function: Variance structure before and after variance transformationfig.dwt.vt
Sample data: Index of AWAP grids with no missing dataInd_AWAP.2.5
Modified k-nearest neighbour conditional bootstrap or regression function estimation with extrapolationknn
Leave one out cross validation.knnregl1cv
Sample data: Latitude and longitude of AWAP gridslat_lon.2.5
Variance Transformation Operation - MODWTmodwt.vt
Variance Transformation Operation for Validationmodwt.vt.val
Plot function: Plot original time series and decomposed frequency componentsmra.plot
Replace Boundary Wavelet Coefficients with Missing Values (NA).non.bdy
Sample data: NCEP reanalysis data averaged over Sydney regionobs.mon
Padding data to dyadic sample sizepadding
Calculate PICpic.calc
R2 threshold by re-sampling approachr2.boot
Sample data: Rainfall station data over Sydney regionrain.mon
Scale to frequency by Matlabscal2freqM
Scale to frequency by Rscal2freqR
Sample data: Standardized Precipitation Index with 12 month accumulation period.SPI.12
Calculate Standardized Precipitation Index, SPISPI.calc
Calculate stepwise high order VT in calibrationstepwise.VT
Calculate stepwise high order VT in validationstepwise.VT.val
Produces an estimate of the multiscale variance along with approximate confidence intervals.wave.var