R/RcppExports.R

In fastWavelets: Compute Maximal Overlap Discrete Wavelet Transform (MODWT) and À Trous Discrete Wavelet Transform

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# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' Compute the Wavelet Filter
#'
#' Compute the wavelet filter.
#'
#' @param wavelet A character string indicating the wavelet filter desired
#' @return Wavelet filter vector (a numeric vector)
#' @references
#' Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge
#' University Press.
#' @export
wavelet_filter <- function(wavelet) {
    .Call(`_fastWavelets_wavelet_filter`, wavelet)
}

#' Scaling Filter
#'
#' Compute the scaling filter.
#'
#' @param wavelet A character string indicating the scaling filter desired
#' @return Scaling filter vector (a numeric vector)
#' @details The argument `wavelet` can take one of the following values:
#'
#' `c("haar", "d1", "sym1", "bior1.1", "rbio1.1",
#' "d2", "sym2", "d3", "sym3", "d4", "d5", "d6", "d7", "d8", "d9", "d10", "d11",
#' "sym4", "sym5", "sym6", "sym7", "sym8", "sym9", "sym10",
#' "coif1", "coif2", "coif3", "coif4", "coif5",
#' "bior1.3", "bior1.5", "bior2.2", "bior2.4", "bior2.6", "bior2.8", "bior3.1", "bior3.3",
#' "bior3.5", "bior3.7", "bior3.9", "bior4.4", "bior5.5", "bior6.8",
#' "rbio1.3", "rbio1.5", "rbio2.2", "rbio2.4", "rbio2.6", "rbio2.8", "rbio3.1", "rbio3.3",
#' "rbio3.5", "rbio3.7", "rbio3.9", "rbio4.4", "rbio5.5", "rbio6.8",
#' "la8", "la10", "la12", "la14", "la16", "la18", "la20",
#' "bl14", "bl18", "bl20",
#' "fk4", "fk6", "fk8", "fk14", "fk18", "fk22",
#' "b3spline",
#' "mb4.2", "mb8.2", "mb8.3", "mb8.4", "mb10.3", "mb12.3", "mb14.3", "mb16.3", "mb18.3", "mb24.3", "mb32.3",
#' "beyl",
#' "vaid",
#' "han2.3", "han3.3", "han4.5", "han5.5")`
#'
#' @references
#' Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge
#' University Press.
#'
#' Wasilewski, F. (2008). Wavelet browser by pywavelets. Wavelet Properties Browser.
#' Retrieved November 17, 2022, from http://wavelets.pybytes.com/
#'
#' Gregory R. Lee, Ralf Gommers, Filip Wasilewski, Kai Wohlfahrt, Aaron O’Leary (2019).
#' PyWavelets: A Python package for wavelet analysis. Journal of Open Source Software,
#' 4(36), 1237, https://doi.org/10.21105/joss.01237.
#'
#' Olhede, S., &amp; Walden, A. T. (2004). The Hilbert spectrum via wavelet projections.
#' Proceedings of the Royal Society of London. Series A: Mathematical, Physical and
#' Engineering Sciences, 460(2044), 955–975. https://doi.org/10.1098/rspa.2003.1199
#'
#' Maheswaran, R., &amp; Khosa, R. (2012). Comparative study of different wavelets for
#' hydrologic forecasting. Computers &amp; Geosciences, 46, 284–295.
#' https://doi.org/10.1016/j.cageo.2011.12.015
#' @export
scaling_filter <- function(wavelet) {
    .Call(`_fastWavelets_scaling_filter`, wavelet)
}

#' Compute Scaling Coefficients
#'
#' Compute the scaling coefficients.
#'
#' @param X An (N x 1) matrix or a vector
#' @param wavelet A character string indicating the scaling filter
#' @param j The decomposition level \[integer\]
#' @return An (N x 1) matrix scaling coefficients
#' @references
#' Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge
#' University Press.
#' @export
scaling_coefs <- function(X, wavelet, j) {
    .Call(`_fastWavelets_scaling_coefs`, X, wavelet, j)
}

#' Compute Wavelet Coefficients
#'
#' Compute the wavelet coefficients.
#'
#' @param X An (N x 1) matrix or a vector
#' @param wavelet A character string indicating the scaling filter
#' @param j The decomposition level \[integer\]
#' @return (N x 1) matrix of wavelet coefficients
#' @references
#' Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge
#' University Press.
#' @export
wavelet_coefs <- function(X, wavelet, j) {
    .Call(`_fastWavelets_wavelet_coefs`, X, wavelet, j)
}