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serrsBayes
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]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.serrsBayes . https://github.com/mooresm/serrsBayes/issues . https://github.com/mooresm/serrsBayes . https://mooresm.github.io/serrsBayes/ . serrsBayes citation info . serrsBayes results . serrsBayes.pdf . Introducing serrsBayes . serrsBayes_0.5-0.tar.gz . serrsBayes_0.5-0.zip . serrsBayes_0.5-0.zip . serrsBayes_0.5-0.zip . serrsBayes_0.5-0.tgz . serrsBayes_0.5-0.tgz . serrsBayes_0.5-0.tgz . serrsBayes_0.5-0.tgz . serrsBayes_0.5-0.tgz . serrsBayes_0.5-0.tgz . serrsBayes archive . https://CRAN.R-project.org/package=serrsBayes .
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 29 Mar. 2025.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for serrsBayes
Functions, R codes and Examples using
the serrsBayes R package
Some associated functions: computeLogLikelihood . copyLogProposals . effectiveSampleSize . fitSpectraMCMC . fitSpectraSMC . fitVoigtIBIS . fitVoigtPeaksSMC . getBsplineBasis . getVoigtParam . lsTamra . marginalMetropolisUpdate . methanol . mhUpdateVoigt . mixedVoigt . reWeightParticles . resampleParticles . residualResampling . result . result2 . serrsBayes . sumDexp . sumDlogNorm . sumDnorm . weightedGaussian . weightedLorentzian . weightedMean . weightedVariance .
Some associated R codes: RcppExports.R . data.R . fitSpectraMCMC.R . fitSpectraSMC.R . fitVoigtIBIS.R . fitVoigtPeaksSMC.R . getBsplineBasis.R . serrsBayes.R . Full serrsBayes package functions and examples
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