serrsBayes (0.4-1)

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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) . 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.

Maintainer: Matt Moores
Author(s): 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]

License: GPL (>= 2) | file LICENSE

Uses: Matrix, Rcpp, truncnorm, testthat, knitr, rmarkdown

Released about 1 month ago.

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