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) doi:10.48550/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.
- Version0.5-0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- LicenseLICENSE
- Needs compilation?Yes
- serrsBayes citation info
- Last release06/28/2021
Documentation
Team
Matt Moores
Jake Carson
Benjamin Moskowitz
Show author detailsRolesContributorKirsten Gracie
Show author detailsRolesdtcKaren Faulds
Mark Girolami
Show author detailsRolesAuthorEngineering and Physical Sciences Research Council
Show author detailsRolesfndUniversity of Warwick
Show author detailsRolesCopyright holder
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- Depends2 packages
- Imports1 package
- Suggests4 packages
- Linking To2 packages