mooresm/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.

Getting started

Package details

Maintainer
LicenseGPL (>= 2) | file LICENSE
Version0.3-15.9001
URL https://github.com/mooresm/serrsBayes https://mooresm.github.io/serrsBayes
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("mooresm/serrsBayes")
mooresm/serrsBayes documentation built on Aug. 26, 2018, 3:41 a.m.