serrsBayes: Bayesian Modelling of Raman Spectroscopy

Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <doi:10.48550/arXiv.1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.

Version: 0.5-0
Depends: R (≥ 3.5.0), Matrix, truncnorm, splines
Imports: Rcpp (≥ 0.11.3), methods
LinkingTo: Rcpp, RcppEigen
Suggests: testthat, knitr, rmarkdown, Hmisc
Published: 2021-06-28
DOI: 10.32614/CRAN.package.serrsBayes
Author: Matt Moores ORCID iD [aut, cre], Jake Carson ORCID iD [aut], Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds ORCID iD [dtc], Mark Girolami [aut], Engineering and Physical Sciences Research Council [fnd] (EPSRC programme grant ref: EP/L014165/1), University of Warwick [cph]
Maintainer: Matt Moores <mmoores at>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: yes
Citation: serrsBayes citation info
Materials: README NEWS
In views: ChemPhys
CRAN checks: serrsBayes results


Reference manual: serrsBayes.pdf
Vignettes: Introducing serrsBayes
Methanol example


Package source: serrsBayes_0.5-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): serrsBayes_0.5-0.tgz, r-oldrel (arm64): serrsBayes_0.5-0.tgz, r-release (x86_64): serrsBayes_0.5-0.tgz, r-oldrel (x86_64): serrsBayes_0.5-0.tgz
Old sources: serrsBayes archive


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