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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.5-0 |
rolling linux/jammy R-4.5 | serrsBayes_0.5-0.tar.gz |
1.1 MiB |
0.5-0 |
rolling linux/noble R-4.5 | serrsBayes_0.5-0.tar.gz |
1.1 MiB |
0.5-0 |
rolling source/ R- | serrsBayes_0.5-0.tar.gz |
915.8 KiB |
0.5-0 |
latest linux/jammy R-4.5 | serrsBayes_0.5-0.tar.gz |
1.1 MiB |
0.5-0 |
latest linux/noble R-4.5 | serrsBayes_0.5-0.tar.gz |
1.1 MiB |
0.5-0 |
latest source/ R- | serrsBayes_0.5-0.tar.gz |
915.8 KiB |
0.5-0 |
2026-04-26 source/ R- | serrsBayes_0.5-0.tar.gz |
915.8 KiB |
0.5-0 |
2026-04-23 source/ R- | serrsBayes_0.5-0.tar.gz |
915.8 KiB |
0.5-0 |
2026-04-09 windows/windows R-4.5 | serrsBayes_0.5-0.zip |
1.5 MiB |
0.5-0 |
2025-04-20 source/ R- | serrsBayes_0.5-0.tar.gz |
915.8 KiB |