mooresm/serrsBayes: Bayesian Modelling of Raman Spectroscopy
Version 0.3-13.9001

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.

Getting started

Package details

LicenseGPL (>= 2) | file LICENSE
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
mooresm/serrsBayes documentation built on Feb. 22, 2018, 1:02 p.m.