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.

Version: 1.1.1
Depends: R (≥ 3.5.0)
Imports: grDevices, methods, stats, utils
Suggests: knitr, Matrix, rmarkdown, tinytest
Published: 2023-06-13
DOI: 10.32614/CRAN.package.alkahest
Author: Nicolas Frerebeau ORCID iD [aut, cre] (Université Bordeaux Montaigne), Brice Lebrun ORCID iD [ctb]
Maintainer: Nicolas Frerebeau <nicolas.frerebeau at>
License: GPL (≥ 3)
NeedsCompilation: no
Citation: alkahest citation info
Materials: README NEWS
CRAN checks: alkahest results


Reference manual: alkahest.pdf
Vignettes: Bibliography


Package source: alkahest_1.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): alkahest_1.1.1.tgz, r-oldrel (arm64): alkahest_1.1.1.tgz, r-release (x86_64): alkahest_1.1.1.tgz, r-oldrel (x86_64): alkahest_1.1.1.tgz
Old sources: alkahest archive


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