Pre-Processing XY Data from Experimental Methods R package alkahest version 1.3.0 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. Software Depends: R (>= 3.5.0) Imports: grDevices, methods, stats, utils Suggests: knitr, markdown, Matrix, tinytest Nicolas Frerebeau <nicolas.frerebeau@u-bordeaux-montaigne.fr> Comprehensive R Archive Network (CRAN) Nicolas Frerebeau [aut, cre] (ORCID: <https://orcid.org/0000-0001-5759-4944>), Brice Lebrun [art] (ORCID: <https://orcid.org/0000-0001-7503-8685>, Logo designer), Université Bordeaux Montaigne [fnd] (ROR: <https://ror.org/03pbgwk21>), CNRS [fnd] (ROR: <https://ror.org/02feahw73>) GPL (>= 3) 2025-02-25 application/tgz https://CRAN.R-project.org/package=alkahest doi:10.32614/CRAN.package.alkahest