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.

Version:	0.4-1
Depends:	R (≥ 3.5.0), Matrix, truncnorm, splines
Imports:	Rcpp (≥ 0.11.3), methods
LinkingTo:	Rcpp, RcppEigen
Suggests:	testthat, knitr, rmarkdown
Published:	2020-02-05
Author:	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]
Maintainer:	Matt Moores <mmoores at gmail.com>
BugReports:	https://github.com/mooresm/serrsBayes/issues
License:	GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL:	https://github.com/mooresm/serrsBayes, https://mooresm.github.io/serrsBayes
NeedsCompilation:	yes
Citation:	serrsBayes citation info
Materials:	README NEWS
CRAN checks:	serrsBayes results

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