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.
Package details |
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Maintainer | |
License | GPL (>= 2) | file LICENSE |
Version | 0.5-0 |
URL | https://github.com/mooresm/serrsBayes https://mooresm.github.io/serrsBayes/ |
Package repository | View on GitHub |
Installation |
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