sabarsi: Background Removal and Spectrum Identification for SERS Data

Implements a new approach 'SABARSI' described in Wang et al., "A Statistical Approach of Background Removal and Spectrum Identification for SERS Data" (Unpublished). Sabarsi forms a pipeline for SERS (surface-enhanced Raman scattering) data analysis including background removal, signal detection, signal integration, and cross-experiment comparison. The background removal algorithm, the very first step of SERS data analysis, takes into account the change of background shape.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: stats (≥ 3.5.0)
Suggests: knitr, rmarkdown (≥ 1.13)
Published: 2019-08-08
Author: Li Jun [cre], Wang Chuanqi [aut]
Maintainer: Li Jun < at>
License: GPL-3
NeedsCompilation: no
CRAN checks: sabarsi results


Reference manual: sabarsi.pdf
Vignettes: sabarsi
Package source: sabarsi_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sabarsi_0.1.0.tgz, r-release (x86_64): sabarsi_0.1.0.tgz, r-oldrel: sabarsi_0.1.0.tgz


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