research. The package (<http://www.kleinomicslab.com/software/>) converts 1D or 2D data into a matrix of values

research. The package (<http://www.kleinomicslab.com/software/>) converts 1D
or 2D data into a matrix of values suitable for further data analysis and
performs basic processing steps in a reproducible way. Negative values, a
common issue in such data, are replaced by positive values. All used
parameters are stored in a readable text file and can be restored from that
file to enable exact reproduction of the data at a later time.

research. The package (<doi:10.1021/acs.jproteome.0c00684>) converts 1D

Description: Nuclear Magnetic Resonance is widely used for metabolite analysis.
This package is a collection for processing and analyzing metabolite data.
(<doi:10.1021/acs.jproteome.0c00684>). The namesake function converts 1D

This package is a collection of functions for processing and analyzing metabolite data.

Title: Metabolomics Data Analysis Functions
Description: A collection of functions for processing and analyzing metabolite data.
The namesake function "mrbin" converts 1D
or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and
common issue in such data, are replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used
file to enable exact reproduction of the data at a later time. The function "fia" ranks features according
to their impact on classifier models, especially artificial neural network models.

The namesake function mrbin() converts 1D
common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used
file to enable exact reproduction of the data at a later time. The function fia() ranks features according

Title: Scale Space Multiresolution Analysis of Random Signals
Authors@R: c(person("Thimo", "Schuster", email = "thimo.schuster@gmail.com", role = c("cre", "aut")),
person("Leena", "Pasanen", email = "leena.pasanen@oulu.fi", role = "ctb"),
person("Reinhard", "Furrer", email = "reinhard.furrer@math.uzh.ch", role = "ctb"))
Description: 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) <DOI:10.1016/j.csda.2011.04.011>.
Packaged: 2017-04-02 19:06:56 UTC; Thimo Schuster