:alarm_clock: This R package provides the tools to perform standard and robust wavelet variance analysis for time series (signal processing). Among others, aside from computing the wavelet variance and cross-covariance (classic and robust), the package provides inference tools (e.g. confidence intervals) and plotting tools allowing to perform some visual analysis and assess the properties of the underlying time series.
I think the documentations for all rcpp functions, in particular the "examples" section, need some modifications to adapt to wv package. Some of the examples don't work because of the old function names in gmwm. For example, the example listed in documentation of "theoretical_wv" is:
model = AR1(.3,2) + RW(.21) + DR(.001) ntau = 8 tau = 2^(1:ntau) wv.theo = theoretical_wv(model$theta, model$desc, model$objdesc, tau)
where AR1(), RW() and DR() don't work anymore inside the wv package.
I think the documentations for all rcpp functions, in particular the "examples" section, need some modifications to adapt to wv package. Some of the examples don't work because of the old function names in gmwm. For example, the example listed in documentation of "theoretical_wv" is:
model = AR1(.3,2) + RW(.21) + DR(.001) ntau = 8 tau = 2^(1:ntau) wv.theo = theoretical_wv(model$theta, model$desc, model$objdesc, tau)whereAR1(),RW()andDR()don't work anymore inside the wv package.