Bayesian Modelling of Raman Spectroscopy


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Documentation for package ‘serrsBayes’ version 0.3-13

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serrsBayes-package Bayesian modelling and quantification of Raman spectroscopy
computeLogLikelihood Compute the log-likelihood.
copyLogProposals Initialise the vector of Metropolis-Hastings proposals.
effectiveSampleSize Compute the effective sample size (ESS) of the particles.
fitSpectraMCMC Fit the model using Markov chain Monte Carlo.
fitSpectraSMC Fit the model using Sequential Monte Carlo (SMC).
fitVoigtPeaksSMC Fit the model with Voigt peaks using Sequential Monte Carlo (SMC).
getBsplineBasis Compute cubic B-spline basis functions for the given wavenumbers.
getVoigtParam Compute the pseudo-Voigt mixing ratio for each peak.
marginalMetropolisUpdate Update all of the parameters using a single Metropolis-Hastings step.
mhUpdateVoigt Update the parameters of the Voigt peaks using marginal Metropolis-Hastings.
mixedVoigt Compute the spectral signature using Voigt peaks.
resampleParticles Resample in place to avoid expensive copying of data structures, using a permutation of the ancestry vector.
residualResampling Compute an ancestry vector for residual resampling of the SMC particles.
result SMC particles for TAMRA+DNA (T20)
reWeightParticles Update the importance weights of each particle.
serrsBayes Bayesian modelling and quantification of Raman spectroscopy
sumDlogNorm Sum log-likelihoods of i.i.d. lognormal.
sumDnorm Sum log-likelihoods of Gaussian.
weightedGaussian Compute the spectral signature using Gaussian peaks.
weightedLorentzian Compute the spectral signature using Lorentzian peaks.
weightedMean Compute the weighted arithmetic means of the particles.
weightedVariance Compute the weighted variance of the particles.