Computational Wavelet Analysis

Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.


Version 1.1 (18 March 2018)

Tools for displaying and analyzing periodic phenomena across time have been extended. The main innovations are:

  • All functions of family wt.<> (showing results concerning a single time series) can now also be applied to extract univariate outcomes from cross-wavelet and coherence analysis (objects of class "analyze.coherency").
  • It is possible to control the color gradation of time-period spectrum plots, and accentuate the contrast, by raising the wavelet power values to any (positive) exponent before plotting.
  • Setting a maximum level for the color bar facilitates the visual comparison of time-period spectrum plots. Maximum and minimum plot levels are options for plots of averages too.
  • The time and period axes are now easier to individualize by specifying tick marks and labels. Coordinates on the time axis can be conveniently addressed via an index or a POSIXct object.
  • Graphical parameters of global coverage (cex.axis, font.axis, cex.lab, font.lab, mgp etc., see par) as well as parameters of local coverage (within axis specification options) help fine-tune plots.
  • Two more real-world data sets have been included in WaveletComp, namely: o Data set weather.radiation.Mannheim, containing daily weather and ambient radiation readings from Mannheim (Germany). o Data set USelection2016.Instagram, containing hourly numbers of candidate-related media uploads to Instagram right before the 2016 US presidential election.

Reference manual

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1.1 by Angi Roesch, 4 years ago

Guide booklet at

Browse source code at

Authors: Angi Roesch <[email protected]> and Harald Schmidbauer <[email protected]>

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-2 license

Imported by zeitgebr.

Suggested by sonicscrewdriver.

See at CRAN