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described in Wang et al., "A Statistical Approach of Background Removal and Spectrum Identification for SERS Data" (Unpublished).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 <jun.li at nd.edu> |
| 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: sabarsi_0.1.0.zip, r-release: sabarsi_0.1.0.zip, r-oldrel: sabarsi_0.1.0.zip |
| macOS binaries: | r-release: sabarsi_0.1.0.tgz, r-oldrel: sabarsi_0.1.0.tgz |
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