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  "Package": "serrsBayes",
  "Type": "Package",
  "Title": "Bayesian Modelling of Raman Spectroscopy",
  "Version": "0.6-0",
  "Date": "2025-10-10",
  "Authors@R": "c(\nperson(\"Matt\", \"Moores\", role = c(\"aut\", \"cre\"), email = \"mmoores@gmail.com\", comment = c(ORCID = \"0000-0003-4531-3572\")),\nperson(\"Jake\", \"Carson\", role=\"aut\", email=\"Jake.Carson@warwick.ac.uk\", comment = c(ORCID = \"0000-0002-7896-0971\")),\nperson(\"Benjamin\", \"Moskowitz\", role=\"ctb\"),\nperson(\"Kirsten\", \"Gracie\", role=\"dtc\"),\nperson(\"Karen\", \"Faulds\", role=\"dtc\", comment = c(ORCID = \"0000-0002-5567-7399\")),\nperson(\"Mark\", \"Girolami\", role=\"aut\"),\nperson(\"Engineering and Physical Sciences Research Council\", role=\"fnd\", comment=\"EPSRC programme grant ref: EP/L014165/1\"),\nperson(\"University of Warwick\", role=\"cph\"))",
  "Description": "Sequential Monte Carlo (SMC) algorithms for fitting a\ngeneralised additive mixed model (GAMM) to surface-enhanced\nresonance Raman spectroscopy (SERRS), using the method of\nMoores et al. (2016) <arXiv:1604.07299>. Multivariate\nobservations of SERRS are highly collinear and lend themselves\nto a reduced-rank representation. The GAMM separates the SERRS\nsignal into three components: a sequence of Lorentzian,\nGaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;\nand additive white noise. The parameters of each component of\nthe model are estimated iteratively using SMC. The posterior\ndistributions of the parameters given the observed spectra are\nrepresented as a population of weighted particles.",
  "License": "GPL (>= 2) | file LICENSE",
  "URL": "https://github.com/mooresm/serrsBayes,\nhttps://mooresm.github.io/serrsBayes/",
  "BugReports": "https://github.com/mooresm/serrsBayes/issues",
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  "Repository": "https://mooresm.r-universe.dev",
  "Date/Publication": "2025-11-06 08:53:52 UTC",
  "RemoteUrl": "https://github.com/mooresm/serrsbayes",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-06-04 08:19:24 UTC",
    "User": "root"
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  "Author": "Matt Moores [aut, cre] (ORCID: <https://orcid.org/0000-0003-4531-3572>),\nJake Carson [aut] (ORCID: <https://orcid.org/0000-0002-7896-0971>),\nBenjamin Moskowitz [ctb],\nKirsten Gracie [dtc],\nKaren Faulds [dtc] (ORCID: <https://orcid.org/0000-0002-5567-7399>),\nMark Girolami [aut],\nEngineering and Physical Sciences Research Council [fnd] (EPSRC\nprogramme grant ref: EP/L014165/1),\nUniversity of Warwick [cph]",
  "Maintainer": "Matt Moores <mmoores@gmail.com>",
  "MD5sum": "6d4a18d6328a27d10961a98d01a37959",
  "_user": "mooresm",
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  "_created": "2026-06-04T08:19:24.000Z",
  "_published": "2026-06-04T08:26:49.452Z",
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    "description": "I work on Bayesian computational methods for image analysis and Raman spectroscopy. I'm Docent in Computational Statistics at LUT University, Finland.",
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  "_tags": [],
  "_topics": [
    "bayesian",
    "chemometrics",
    "raman",
    "sequential-monte-carlo",
    "spectroscopy",
    "cpp"
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  "_stars": 8,
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    "name": "Matt",
    "description": "I work on Bayesian computational methods for image analysis and Raman spectroscopy. I'm Docent in Computational Statistics at LUT University, Finland."
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  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/serrsBayes.html",
    "manual.pdf"
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      "date": "2018-02-14"
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    },
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      "date": "2021-06-07"
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    "computeLogLikelihood",
    "copyLogProposals",
    "effectiveSampleSize",
    "fitSpectraMCMC",
    "fitSpectraSMC",
    "fitVoigtIBIS",
    "fitVoigtPeaksSMC",
    "getBsplineBasis",
    "getVoigtParam",
    "marginalMetropolisUpdate",
    "mhUpdateVoigt",
    "mixedVoigt",
    "resampleParticles",
    "residualResampling",
    "reWeightParticles",
    "sumDexp",
    "sumDlogNorm",
    "sumDnorm",
    "weightedGaussian",
    "weightedLorentzian",
    "weightedMean",
    "weightedVariance"
  ],
  "_datasets": [
    {
      "name": "lsTamra",
      "title": "Surface-enhanced Raman spectram of tetramethylrhodamine+DNA (T20)",
      "object": "lsTamra",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "methanol",
      "title": "Raman spectrum of methanol (CH3OH)",
      "object": "methanol",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "result",
      "title": "SMC particles for TAMRA+DNA (T20)",
      "object": "result",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "result2",
      "title": "SMC particles for methanol (CH3OH)",
      "object": "result2",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    }
  ],
  "_help": [
    {
      "page": "computeLogLikelihood",
      "title": "Compute the log-likelihood.",
      "topics": [
        "computeLogLikelihood"
      ]
    },
    {
      "page": "copyLogProposals",
      "title": "Initialise the vector of Metropolis-Hastings proposals.",
      "topics": [
        "copyLogProposals"
      ]
    },
    {
      "page": "effectiveSampleSize",
      "title": "Compute the effective sample size (ESS) of the particles.",
      "topics": [
        "effectiveSampleSize"
      ]
    },
    {
      "page": "fitSpectraMCMC",
      "title": "Fit the model using Markov chain Monte Carlo.",
      "topics": [
        "fitSpectraMCMC"
      ]
    },
    {
      "page": "fitSpectraSMC",
      "title": "Fit the model using Sequential Monte Carlo (SMC).",
      "topics": [
        "fitSpectraSMC"
      ]
    },
    {
      "page": "fitVoigtIBIS",
      "title": "Fit the model with Voigt peaks using iterated batch importance sampling (IBIS).",
      "topics": [
        "fitVoigtIBIS"
      ]
    },
    {
      "page": "fitVoigtPeaksSMC",
      "title": "Fit the model with Voigt peaks using Sequential Monte Carlo (SMC).",
      "topics": [
        "fitVoigtPeaksSMC"
      ]
    },
    {
      "page": "getBsplineBasis",
      "title": "Compute cubic B-spline basis functions for the given wavenumbers.",
      "topics": [
        "getBsplineBasis"
      ]
    },
    {
      "page": "getVoigtParam",
      "title": "Compute the pseudo-Voigt mixing ratio for each peak.",
      "topics": [
        "getVoigtParam"
      ]
    },
    {
      "page": "lsTamra",
      "title": "Surface-enhanced Raman spectram of tetramethylrhodamine+DNA (T20)",
      "topics": [
        "lsTamra"
      ]
    },
    {
      "page": "marginalMetropolisUpdate",
      "title": "Update all of the parameters using a single Metropolis-Hastings step.",
      "topics": [
        "marginalMetropolisUpdate"
      ]
    },
    {
      "page": "methanol",
      "title": "Raman spectrum of methanol (CH3OH)",
      "topics": [
        "methanol"
      ]
    },
    {
      "page": "mhUpdateVoigt",
      "title": "Update the parameters of the Voigt peaks using marginal Metropolis-Hastings.",
      "topics": [
        "mhUpdateVoigt"
      ]
    },
    {
      "page": "mixedVoigt",
      "title": "Compute the spectral signature using Voigt peaks.",
      "topics": [
        "mixedVoigt"
      ]
    },
    {
      "page": "resampleParticles",
      "title": "Resample in place to avoid expensive copying of data structures, using a permutation of the ancestry vector.",
      "topics": [
        "resampleParticles"
      ]
    },
    {
      "page": "residualResampling",
      "title": "Compute an ancestry vector for residual resampling of the SMC particles.",
      "topics": [
        "residualResampling"
      ]
    },
    {
      "page": "result",
      "title": "SMC particles for TAMRA+DNA (T20)",
      "topics": [
        "result"
      ]
    },
    {
      "page": "result2",
      "title": "SMC particles for methanol (CH3OH)",
      "topics": [
        "result2"
      ]
    },
    {
      "page": "reWeightParticles",
      "title": "Update the importance weights of each particle.",
      "topics": [
        "reWeightParticles"
      ]
    },
    {
      "page": "serrsBayes",
      "title": "Bayesian modelling and quantification of Raman spectroscopy",
      "topics": [
        "serrsBayes"
      ]
    },
    {
      "page": "sumDexp",
      "title": "Sum log-likelihoods of i.i.d. exponential.",
      "topics": [
        "sumDexp"
      ]
    },
    {
      "page": "sumDlogNorm",
      "title": "Sum log-likelihoods of i.i.d. lognormal.",
      "topics": [
        "sumDlogNorm"
      ]
    },
    {
      "page": "sumDnorm",
      "title": "Sum log-likelihoods of Gaussian.",
      "topics": [
        "sumDnorm"
      ]
    },
    {
      "page": "weightedGaussian",
      "title": "Compute the spectral signature using Gaussian peaks.",
      "topics": [
        "weightedGaussian"
      ]
    },
    {
      "page": "weightedLorentzian",
      "title": "Compute the spectral signature using Lorentzian peaks.",
      "topics": [
        "weightedLorentzian"
      ]
    },
    {
      "page": "weightedMean",
      "title": "Compute the weighted arithmetic means of the particles.",
      "topics": [
        "weightedMean"
      ]
    },
    {
      "page": "weightedVariance",
      "title": "Compute the weighted variance of the particles.",
      "topics": [
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      "title": "Introducing serrsBayes",
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        "Computation",
        "Posterior Inference",
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