Bayesian Modelling of Raman Spectroscopy R package serrsBayes version 0.5-0 CRAN Task View: ChemPhys (https://CRAN.R-project.org/view=ChemPhys) 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) <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. Software Depends: R (>= 3.5.0), Matrix, truncnorm, splines Imports: Rcpp (>= 0.11.3), methods LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, rmarkdown, Hmisc Matt Moores <mmoores@gmail.com> Comprehensive R Archive Network (CRAN) Matt Moores [aut, cre] (ORCID: <https://orcid.org/0000-0003-4531-3572>), Jake Carson [aut] (ORCID: <https://orcid.org/0000-0002-7896-0971>), Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds [dtc] (ORCID: <https://orcid.org/0000-0002-5567-7399>), Mark Girolami [aut], Engineering and Physical Sciences Research Council [fnd] (EPSRC programme grant ref: EP/L014165/1), University of Warwick [cph] GPL (>= 2) file LICENSE (https://CRAN.R-project.org/package=serrsBayes/LICENSE) 2021-06-28 application/tgz https://CRAN.R-project.org/package=serrsBayes doi:10.32614/CRAN.package.serrsBayes