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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.
Version: | 0.5-0 |
Depends: | R (≥ 3.5.0), Matrix, truncnorm, splines |
Imports: | Rcpp (≥ 0.11.3), methods |
LinkingTo: | Rcpp, RcppEigen |
Suggests: | testthat, knitr, rmarkdown, Hmisc |
Published: | 2021-06-28 |
DOI: | 10.32614/CRAN.package.serrsBayes |
Author: | Matt Moores [aut, cre], Jake Carson [aut], Benjamin Moskowitz [ctb], Kirsten Gracie [dtc], Karen Faulds [dtc], Mark Girolami [aut], Engineering and Physical Sciences Research Council [fnd] (EPSRC programme grant ref: EP/L014165/1), University of Warwick [cph] |
Maintainer: | Matt Moores <mmoores at gmail.com> |
BugReports: | https://github.com/mooresm/serrsBayes/issues |
License: | GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE] |
URL: | https://github.com/mooresm/serrsBayes, https://mooresm.github.io/serrsBayes/ |
NeedsCompilation: | yes |
Citation: | serrsBayes citation info |
Materials: | README NEWS |
In views: | ChemPhys |
CRAN checks: | serrsBayes results |
Reference manual: | serrsBayes.pdf |
Vignettes: |
Introducing serrsBayes Methanol example |
Package source: | serrsBayes_0.5-0.tar.gz |
Windows binaries: | r-devel: serrsBayes_0.5-0.zip, r-release: serrsBayes_0.5-0.zip, r-oldrel: serrsBayes_0.5-0.zip |
macOS binaries: | r-release (arm64): serrsBayes_0.5-0.tgz, r-oldrel (arm64): serrsBayes_0.5-0.tgz, r-release (x86_64): serrsBayes_0.5-0.tgz, r-oldrel (x86_64): serrsBayes_0.5-0.tgz |
Old sources: | serrsBayes archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.