Scale Space Multiresolution Analysis of Random Signals

A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features. mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales. To infer which of the captured features are credible, Bayesian analysis is used. The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction. (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found. (3) Posterior credibility analysis of the differences of smooths created. The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) .


News

mrbsizeR 1.1.1 (Release date: 2018-05-02)

Changes:

  • adressing Solaris compiler error: rcpp_dctmatrix.cpp: In function ‘Rcpp::NumericMatrix dctMatrix(int)’: rcpp_dctmatrix.cpp:35:35: error: call of overloaded ‘sqrt(int&)’ is ambiguous dctMatrix(i, j) = 1/sqrt(n);

mrbsizeR 1.1.0 (Release date: 2018-04-29)

Changes:

  • new maintainer Roman Flury
  • dctmmatrix and eigenLaplace via rcpp

Reference manual

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install.packages("mrbsizeR")

1.1.1 by Roman Flury, 9 months ago


http://cc.oulu.fi/~lpasanen/MRBSiZer/


Browse source code at https://github.com/cran/mrbsizeR


Authors: Thimo Schuster [aut] , Roman Flury [cre, ctb] , Leena Pasanen [ctb] , Reinhard Furrer [ctb]


Documentation:   PDF Manual  


GPL-2 license


Imports fields, stats, grDevices, graphics, methods, Rcpp

Depends on maps

Suggests knitr, rmarkdown, testthat

Linking to Rcpp


Suggested by specklestar.


See at CRAN