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mwaved

mwaved is a set of functions that generalise the waved package for wavelet deconvolution in the Fourier domain. These generalisations are the following extensions:

  • Allow a multichannel model where the practitioner has access to multiple channels of data of a common signal of interest.
  • Allow additive long memory errors to be present in the multichannel signals (independent between signals but exhibit long memory within each signal)
  • Allow a data-driven resolution choice in the presence of box car blur (waved does not have this feature)

The user is encouraged to view the embedded Shiny applet that showcases the mwaved pacakge and importantly lists the appropriate R commands to recreate the output given by Shiny applet. The embedded Shiny applet can be viewed as long as the user has the shiny package installed on their machine and then using R command > mwaved::mWaveDDemo().

The code is also written with the use of the Rcpp package to help use the external C FFTW library to achieve speeds around 8-15 times faster than the usual WaveD package (comparing the performance of a single channel waved code to the same code in the mwaved package with various sample sizes). The relative performance improves as the sample size increases.

The package is being developed at https://github.com/jrwishart/mwaved and any bug reports, comments or suggestions are welcomed at https://github.com/jrwishart/mwaved/issues

Optional source compilation instructions (currently only tested in Ubuntu, Slackware Linux and Windows 10)

  • Ensure you have the FFTW3 libraries installed. For ubuntu this requires sudo apt-get install libfftw3-dev. For Windows 10 this requires downloading the windows fftw3 binaries and adding the installed directories to your PATH.
  • Download and install the package from your favourite CRAN repository. That is, run install.packages('mwaved') from the R prompt or download the tarball and run R CMD INSTALL mwaved_1.x.x.tar.gz (where x.x is replaced with the appropriate version name) from the linux terminal.

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Version

Install

install.packages('mwaved')

Monthly Downloads

43

Version

1.1.8

License

GPL

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Maintainer

Justin Rory Wishart

Last Published

October 28th, 2021

Functions in mwaved (1.1.8)

plot.mWaveD

Plot Output for the mWaveD object
gammaBlur

Multichannel Gamma density blur
mWaveDDemo

Interactive Demonstration
multiProj

Meyer wavelet projection given a set of wavelet coefficients
multiSigma

Noise level estimation among multichannel signal
multiEstimate

Wavelet deconvolution signal estimate from the noisy multichannel convoluted signal
blurSignal

Blur an input signal
theoreticalEta

Find optimal theoretical Eta
boxcarBlur

Multichannel box car blur
makeSignals

Generate test signals for simulation
waveletThresh

Apply thresholding regime to a set of wavelet coefficients
multiCoef

Wavelet coefficient estimation from a multichannel signal
plot.waveletCoef

Multi-Resolution Analysis plot of wavelet coefficients
resolutionMethod

Select appropriate resolution method for blur type
detectBlur

Detect type of blur
multiNoise

Generate multichannel noise
multiWaveD

Full mWaveD analysis
multiThresh

Resolution level thresholds for hard thresholded wavelet deconvolution estimator
sigmaSNR

Determine noise scale levels from specified Signal to Noise Ratios
directBlur

Direct kernel matrix
summary.mWaveD

Summary Output for the mWaveD object
mwaved

Multichannel wavelet deconvolution with long memory using mwaved.