Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surfaceenhanced 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 reducedrank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudoVoigt peaks; a smoothlyvarying 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.
Package details 


Author  Matt Moores [aut, cre] (<https://orcid.org/0000000345313572>), Jake Carson [aut] (<https://orcid.org/0000000278960971>), Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds [dtc] (<https://orcid.org/0000000255677399>), 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  0.41 
URL  https://github.com/mooresm/serrsBayes https://mooresm.github.io/serrsBayes 
Package repository  View on CRAN 
Installation 
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