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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) <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.
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Package details |
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| Author | Matt Moores [aut, cre] (<https://orcid.org/0000-0003-4531-3572>), Jake Carson [aut] (<https://orcid.org/0000-0002-7896-0971>), Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds [dtc] (<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] |
| Maintainer | Matt Moores <mmoores@gmail.com> |
| License | GPL (>= 2) | file LICENSE |
| Version | 0.5-0 |
| URL | https://github.com/mooresm/serrsBayes https://mooresm.github.io/serrsBayes/ |
| Package repository | View on CRAN |
| Installation |
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