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 


Maintainer  
License  GPL (>= 2)  file LICENSE 
Version  0.41 
URL  https://github.com/mooresm/serrsBayes https://mooresm.github.io/serrsBayes 
Package repository  View on GitHub 
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