Package: WASP 1.4.5

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_1.4.5.tar.gz
WASP_1.4.5.zip(r-4.7)WASP_1.4.5.zip(r-4.6)WASP_1.4.5.zip(r-4.5)
WASP_1.4.5.tgz(r-4.6-any)WASP_1.4.5.tgz(r-4.5-any)
WASP_1.4.5.tar.gz(r-4.7-any)WASP_1.4.5.tar.gz(r-4.6-any)
WASP_1.4.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
WASP/json (API)

# Install 'WASP' in R:

install.packages('WASP', repos = c('https://zejiang-unsw.r-universe.dev', 'https://cloud.r-project.org'))

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

Datasets:

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
Ind_AWAP.2.5 - Sample data: Index of AWAP grids with no missing data
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
SPI.12 - Sample data: Standardized Precipitation Index with 12 month accumulation period.

On CRAN:

prediction transformation wavelet

6.36 score 10 stars 23 scripts 729 downloads 97 mentions 29 exports 48 dependencies

Last updated from:5096903c45. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	255
source / vignettes	OK	225
linux-release-x86_64	OK	218
macos-release-arm64	OK	156
macos-oldrel-arm64	OK	121
windows-devel	OK	150
windows-release	OK	160
windows-oldrel	OK	140
wasm-release	OK	174

Exports:at.vt at.vt.val at.wd data.gen.ar1 data.gen.ar4 data.gen.ar9 data.gen.HL data.gen.Rossler data.gen.SW data.gen.tar1 data.gen.tar2 dwt.vt dwt.vt.val fig.dwt.vt knn knnregl1cv modwt.vt modwt.vt.val mra.plot non.bdy padding pic.calc r2.boot scal2freqM scal2freqR SPI.calc stepwise.VT stepwise.VT.val wave.var

Dependencies:bit bit64 cli clipr cpp11 crayon dplyr farver fitdistrplus generics ggplot2 glue gtable hms isoband labeling lattice lifecycle magrittr MASS Matrix multitaper pillar pkgconfig prettyunits progress purrr R6 RColorBrewer readr rlang S7 scales sp stringi stringr survival tibble tidyr tidyselect tzdb utf8 vctrs viridisLite vroom waveslim withr zoo

WASP: An R package for Wavelet System Prediction

Rendered fromWASP.Rmdusingknitr::rmarkdown

Last update: 2024-08-04
Started: 2020-05-03

Help page	Topics
WASP: WAvelet System Prediction	WASP-package
Variance Transformation Operation - AT(a trous)	at.vt
Variance Transformation Operation for Validation	at.vt.val
a trous (AT) based additive decompostion using Daubechies family wavelet	at.wd
Sample data: Australia map	aus.coast
Sample data: AWAP rainfall data over Australia	data.AWAP.2.5
Sample data: Climate indices strongly influencing Australia climate	data.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 Loop	data.gen.HL
Generate predictor and response data: Rossler system	data.gen.Rossler
Generate predictor and response data: Sinewave model	data.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 loop	data.HL
Sample data: Sinewave model 1 (SW1)	data.SW1
Sample data: Sinewave model 3 (SW3)	data.SW3
Variance Transformation Operation - MRA	dwt.vt
Variance Transformation Operation for Validation	dwt.vt.val
Plot function: Variance structure before and after variance transformation	fig.dwt.vt
Sample data: Index of AWAP grids with no missing data	Ind_AWAP.2.5
Modified k-nearest neighbour conditional bootstrap or regression function estimation with extrapolation	knn
Leave one out cross validation.	knnregl1cv
Sample data: Latitude and longitude of AWAP grids	lat_lon.2.5
Variance Transformation Operation - MODWT	modwt.vt
Variance Transformation Operation for Validation	modwt.vt.val
Plot function: Plot original time series and decomposed frequency components	mra.plot
Replace Boundary Wavelet Coefficients with Missing Values (NA).	non.bdy
Sample data: NCEP reanalysis data averaged over Sydney region	obs.mon
Padding data to dyadic sample size	padding
Calculate PIC	pic.calc
R2 threshold by re-sampling approach	r2.boot
Sample data: Rainfall station data over Sydney region	rain.mon
Scale to frequency by Matlab	scal2freqM
Scale to frequency by R	scal2freqR
Sample data: Standardized Precipitation Index with 12 month accumulation period.	SPI.12
Calculate Standardized Precipitation Index, SPI	SPI.calc
Calculate stepwise high order VT in calibration	stepwise.VT
Calculate stepwise high order VT in validation	stepwise.VT.val
Produces an estimate of the multiscale variance along with approximate confidence intervals.	wave.var

Package: WASP 1.4.5

WASP: Wavelet System Prediction

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Help Manual

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