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 [aut, cre], Jake Carson [aut], Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds [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 gmail.com> BugReports: https://github.com/mooresm/serrsBayes/issues License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE] URL: https://github.com/mooresm/serrsBayes, https://mooresm.github.io/serrsBayes/ NeedsCompilation: yes Citation: serrsBayes citation info Materials: README NEWS In views: ChemPhys CRAN checks: serrsBayes results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=serrsBayes to link to this page.
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