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Fast application of Continuous Wavelet Transformation ('CWT') on time series with special attention to spectroscopy. It is written using data.table and 'C++' language and in some functions it is possible to use parallel processing to speed-up the computation over samples. Currently, only the second derivative of a Gaussian wavelet function is implemented.
Version: | 0.2.1 |
Depends: | R (≥ 4.0.0) |
Imports: | data.table (≥ 1.14.0), Rcpp |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | testthat (≥ 3.2.0) |
Published: | 2024-06-28 |
DOI: | 10.32614/CRAN.package.CWT |
Author: | J. Antonio Guzmán Q.
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Maintainer: | J. Antonio Guzmán Q. <antguz06 at gmail.com> |
BugReports: | https://github.com/Antguz/CWT/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/Antguz/CWT |
NeedsCompilation: | yes |
SystemRequirements: | GNU make |
Language: | en-US |
Materials: | README NEWS |
CRAN checks: | CWT results |
Reference manual: | CWT.pdf |
Package source: | CWT_0.2.1.tar.gz |
Windows binaries: | r-devel: CWT_0.2.1.zip, r-release: CWT_0.2.1.zip, r-oldrel: CWT_0.2.1.zip |
macOS binaries: | r-release (arm64): CWT_0.2.1.tgz, r-oldrel (arm64): CWT_0.2.1.tgz, r-release (x86_64): CWT_0.2.1.tgz, r-oldrel (x86_64): CWT_0.2.1.tgz |
Old sources: | CWT archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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