fitVoigtPeaksSMC: Fit the model with Voigt peaks using Sequential Monte Carlo...
In mooresm/serrsBayes: Bayesian Modelling of Raman Spectroscopy

Fit the model with Voigt peaks using Sequential Monte Carlo (SMC).

fitVoigtPeaksSMC(
  wl,
  spc,
  lPriors,
  conc = rep(1, nrow(spc)),
  npart = 10000,
  rate = 0.9,
  mcAR = 0.234,
  mcSteps = 20,
  minESS = npart/2,
  destDir = NA,
  minPart = npart
)

`wl`	Vector of `nwl` wavenumbers at which the spetra are observed.
`spc`	`n_y * nwl` Matrix of observed Raman spectra.
`lPriors`	List of hyperparameters for the prior distributions.
`conc`	Vector of `n_y` nanomolar (nM) dye concentrations for each observation.
`npart`	number of SMC particles to use for the importance sampling distribution.
`rate`	the target rate of reduction in the effective sample size (ESS).
`mcAR`	target acceptance rate for the MCMC kernel
`mcSteps`	number of iterations of the MCMC kernel
`minESS`	minimum effective sample size, below which the particles are resampled.
`destDir`	destination directory to save intermediate results (for long-running computations)
`minPart`	target number of unique particles for the MCMC iterations

wavenumbers <- seq(200,600,by=10)
spectra <- matrix(nrow=1, ncol=length(wavenumbers))
peakLocations <- c(300,500)
peakAmplitude <- c(10000,4000)
peakScale <- c(10, 15)
signature <- weightedLorentzian(peakLocations, peakScale, peakAmplitude, wavenumbers)
baseline <- 1000*cos(wavenumbers/200) + 2*wavenumbers
spectra[1,] <- signature + baseline + rnorm(length(wavenumbers),0,200)
lPriors <- list(scaG.mu=log(11.6) - (0.4^2)/2, scaG.sd=0.4, scaL.mu=log(11.6) - (0.4^2)/2,
   scaL.sd=0.4, bl.smooth=5, bl.knots=20, loc.mu=peakLocations, loc.sd=c(5,5),
   beta.mu=c(5000,5000), beta.sd=c(5000,5000), noise.sd=200, noise.nu=4)
## Not run: 
result <- fitVoigtPeaksSMC(wavenumbers, spectra, lPriors, npart=50, mcSteps=1)

## End(Not run)