Functions for calculating the
    fetch (length of open water distance along given directions)
    and estimating wave energy from wind and wave monitoring data.

For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available.

Spatial data are generally auto-correlated, meaning that if two 
  units selected are close to each other, then it is likely that they share the
  same properties. For this reason, when sampling in the population it is often
  needed that the sample is well spread over space. A new method to draw a sample
  from a population with spatial coordinates is proposed. This method is called
  wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
  to a spatial weights matrix to update the inclusion probabilities vector
  into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.

Basic wavelet routines for time series (1D), image (2D) and array 
  (3D) analysis.  The code provided here is based on wavelet methodology 
  developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); 
  the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as
  implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  
  All figures in chapters 4-7 of GSW (2001) are reproducible using this package 
  and R code available at the book website(s) below.

Performs 1, 2 and 3D real and complex-valued wavelet transforms,
	nondecimated transforms, wavelet packet transforms, nondecimated
	wavelet packet transforms, multiple wavelet transforms,
	complex-valued wavelet transforms, wavelet shrinkage for
	various kinds of data, locally stationary wavelet time series,
	nonstationary multiscale transfer function modeling, density
	estimation.

Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).