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serrsBayes  

Bayesian Modelling of Raman Spectroscopy
View on CRAN: Click here


Download and install serrsBayes package within the R console
Install from CRAN:
install.packages("serrsBayes")

Install from Github:
library("remotes")
install_github("cran/serrsBayes")

Install by package version:
library("remotes")
install_version("serrsBayes", "0.5-0")



Attach the package and use:
library("serrsBayes")
Maintained by
Matt Moores
[Scholar Profile | Author Map]
First Published: 2018-02-14
Latest Update: 2021-06-28
Description:
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.
How to cite:
Matt Moores (2018). serrsBayes: Bayesian Modelling of Raman Spectroscopy. R package version 0.5-0, https://cran.r-project.org/web/packages/serrsBayes. Accessed 26 Apr. 2025.
Previous versions and publish date:
0.3-10 (2018-02-14 19:46), 0.3-11 (2018-02-16 00:08), 0.3-12 (2018-02-18 21:44), 0.3-13 (2018-06-05 13:48), 0.4-0 (2019-04-29 13:30), 0.4-1 (2020-02-05 13:40), 0.4-2 (2021-06-07 08:10)
Other packages that cited serrsBayes R package
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Complete documentation for serrsBayes
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