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alkahest: Pre-Processing XY Data from Experimental Methods
A lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) <doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.
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Package details |
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| Author | Nicolas Frerebeau [aut, cre] (<https://orcid.org/0000-0001-5759-4944>), Brice Lebrun [art] (<https://orcid.org/0000-0001-7503-8685>, Logo designer), Université Bordeaux Montaigne [fnd] (03pbgwk21), CNRS [fnd] (02feahw73) |
| Maintainer | Nicolas Frerebeau <nicolas.frerebeau@u-bordeaux-montaigne.fr> |
| License | GPL (>= 3) |
| Version | 1.3.0 |
| URL | https://codeberg.org/tesselle/alkahest https://packages.tesselle.org/alkahest/ |
| Package repository | View on CRAN |
| Installation |
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