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: 1.3.0 Depends: R (≥ 3.5.0) Imports: grDevices, methods, stats, utils Suggests: knitr, markdown, Matrix, tinytest Published: 2025-02-25 DOI: 10.32614/CRAN.package.alkahest Author: Nicolas Frerebeau [aut, cre], Brice Lebrun [art] (Logo designer), Université Bordeaux Montaigne [fnd], CNRS [fnd] Maintainer: Nicolas Frerebeau <nicolas.frerebeau at u-bordeaux-montaigne.fr> BugReports: https://codeberg.org/tesselle/alkahest/issues License: GPL (≥ 3) URL: https://codeberg.org/tesselle/alkahest, https://packages.tesselle.org/alkahest/ NeedsCompilation: no Citation: alkahest citation info Materials: README, NEWS CRAN checks: alkahest results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=alkahest to link to this page.
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