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    "DPMGibbsN_parallel",
    "DPMGibbsN_SeqPrior",
    "DPMGibbsSkewN",
    "DPMGibbsSkewN_parallel",
    "DPMGibbsSkewT",
    "DPMGibbsSkewT_parallel",
    "DPMGibbsSkewT_SeqPrior",
    "DPMGibbsSkewT_SeqPrior_parallel",
    "DPMpost",
    "evalClustLoss",
    "Flimited",
    "Fmeasure_costC",
    "FmeasureC",
    "FmeasureC_no0",
    "invwishrnd",
    "lgamma_mv",
    "MAP_sNiW_mmEM",
    "MAP_sNiW_mmEM_vague",
    "MAP_sNiW_mmEM_weighted",
    "MLE_gamma",
    "MLE_NiW_mmEM",
    "MLE_sNiW",
    "MLE_sNiW_mmEM",
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    "mmNiWpdfC",
    "mmsNiWlogpdf",
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    "mvsnpdf",
    "mvstpdf",
    "mvtpdf",
    "NuMatParC",
    "plot_ConvDPM",
    "plot_DPM",
    "plot_DPMsn",
    "plot_DPMst",
    "postProcess.DPMMclust",
    "priormix",
    "rCRP",
    "rNiW",
    "rNNiW",
    "sample_alpha",
    "sampleClassC",
    "similarityMat",
    "similarityMat_nocostC",
    "similarityMatC",
    "traceEpsC",
    "update_SS",
    "update_SSsn",
    "update_SSst",
    "vclust2mcoclustC",
    "wishrnd"
  ],
  "_help": [
    {
      "page": "NPflow-package",
      "title": "Bayesian Nonparametrics for Automatic Gating of Flow Cytometry data",
      "topics": [
        "NPflow-package",
        "NPflow"
      ]
    },
    {
      "page": "burn.DPMMclust",
      "title": "Burning MCMC iterations from a Dirichlet Process Mixture Model.",
      "topics": [
        "burn.DPMMclust"
      ]
    },
    {
      "page": "cluster_est_binder",
      "title": "Point estimate of the partition for the Binder loss function",
      "topics": [
        "cluster_est_binder"
      ]
    },
    {
      "page": "cluster_est_Fmeasure",
      "title": "Point estimate of the partition using the F-measure as the cost function.",
      "topics": [
        "cluster_est_Fmeasure"
      ]
    },
    {
      "page": "cluster_est_Mbinder_norm",
      "title": "Point estimate of the partition using a modified Binder loss function",
      "topics": [
        "cluster_est_Mbinder_norm"
      ]
    },
    {
      "page": "cluster_est_pear",
      "title": "Gets a point estimate of the partition using posterior expected adjusted Rand index (PEAR)",
      "topics": [
        "cluster_est_pear"
      ]
    },
    {
      "page": "cytoScatter",
      "title": "Scatterplot of flow cytometry data",
      "topics": [
        "cytoScatter"
      ]
    },
    {
      "page": "DPMGibbsN",
      "title": "Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha",
      "topics": [
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      ]
    },
    {
      "page": "DPMGibbsN_parallel",
      "title": "Slice Sampling of the Dirichlet Process Mixture Model with a prior on alpha",
      "topics": [
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      ]
    },
    {
      "page": "DPMGibbsN_SeqPrior",
      "title": "Slice Sampling of Dirichlet Process Mixture of Gaussian distributions",
      "topics": [
        "DPMGibbsN_SeqPrior"
      ]
    },
    {
      "page": "DPMGibbsSkewN",
      "title": "Slice Sampling of Dirichlet Process Mixture of skew normal distributions",
      "topics": [
        "DPMGibbsSkewN"
      ]
    },
    {
      "page": "DPMGibbsSkewN_parallel",
      "title": "Parallel Implementation of Slice Sampling of Dirichlet Process Mixture of skew normal distributions",
      "topics": [
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      ]
    },
    {
      "page": "DPMGibbsSkewT",
      "title": "Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions",
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      ]
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    {
      "page": "DPMGibbsSkewT_parallel",
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    {
      "page": "DPMGibbsSkewT_SeqPrior",
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      "topics": [
        "DPMGibbsSkewT_SeqPrior"
      ]
    },
    {
      "page": "DPMGibbsSkewT_SeqPrior_parallel",
      "title": "Slice Sampling of Dirichlet Process Mixture of skew Student's t-distributions",
      "topics": [
        "DPMGibbsSkewT_SeqPrior_parallel"
      ]
    },
    {
      "page": "DPMpost",
      "title": "Posterior estimation for Dirichlet process mixture of multivariate (potentially skew) distributions models",
      "topics": [
        "DPMpost"
      ]
    },
    {
      "page": "evalClustLoss",
      "title": "ELoss of a partition point estimate compared to a gold standard",
      "topics": [
        "evalClustLoss"
      ]
    },
    {
      "page": "Flimited",
      "title": "Compute a limited F-measure",
      "topics": [
        "Flimited"
      ]
    },
    {
      "page": "Fmeasure_costC",
      "title": "Multiple cost computations with the F-measure as the loss function",
      "topics": [
        "Fmeasure_costC"
      ]
    },
    {
      "page": "FmeasureC",
      "title": "C++ implementation of the F-measure computation",
      "topics": [
        "FmeasureC"
      ]
    },
    {
      "page": "FmeasureC_no0",
      "title": "C++ implementation of the F-measure computation without the reference class 0",
      "topics": [
        "FmeasureC_no0"
      ]
    },
    {
      "page": "lgamma_mv",
      "title": "Multivariate log gamma function",
      "topics": [
        "lgamma_mv"
      ]
    },
    {
      "page": "MAP_sNiW_mmEM",
      "title": "EM MAP for mixture of sNiW",
      "topics": [
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        "MAP_sNiW_mmEM_vague",
        "MAP_sNiW_mmEM_weighted"
      ]
    },
    {
      "page": "MLE_gamma",
      "title": "MLE for Gamma distribution",
      "topics": [
        "MLE_gamma"
      ]
    },
    {
      "page": "MLE_NiW_mmEM",
      "title": "EM MLE for mixture of NiW",
      "topics": [
        "MLE_NiW_mmEM"
      ]
    },
    {
      "page": "MLE_sNiW",
      "title": "MLE for sNiW distributed observations",
      "topics": [
        "MLE_sNiW"
      ]
    },
    {
      "page": "MLE_sNiW_mmEM",
      "title": "EM MLE for mixture of sNiW",
      "topics": [
        "MLE_sNiW_mmEM"
      ]
    },
    {
      "page": "mmNiWpdf",
      "title": "multivariate Normal inverse Wishart probability density function for multiple inputs",
      "topics": [
        "mmNiWpdf"
      ]
    },
    {
      "page": "mmNiWpdfC",
      "title": "C++ implementation of multivariate Normal inverse Wishart probability density function for multiple inputs",
      "topics": [
        "mmNiWpdfC"
      ]
    },
    {
      "page": "mmsNiWlogpdf",
      "title": "Probability density function of multiple structured Normal inverse Wishart",
      "topics": [
        "mmsNiWlogpdf"
      ]
    },
    {
      "page": "mmsNiWpdfC",
      "title": "C++ implementation of multivariate structured Normal inverse Wishart probability density function for multiple inputs",
      "topics": [
        "mmsNiWpdfC"
      ]
    },
    {
      "page": "mmvnpdfC",
      "title": "C++ implementation of multivariate Normal probability density function for multiple inputs",
      "topics": [
        "mmvnpdfC"
      ]
    },
    {
      "page": "mmvsnpdfC",
      "title": "C++ implementation of multivariate skew Normal probability density function for multiple inputs",
      "topics": [
        "mmvsnpdfC"
      ]
    },
    {
      "page": "mmvstpdfC",
      "title": "C++ implementation of multivariate Normal probability density function for multiple inputs",
      "topics": [
        "mmvstpdfC"
      ]
    },
    {
      "page": "mmvtpdfC",
      "title": "C++ implementation of multivariate Normal probability density function for multiple inputs",
      "topics": [
        "mmvtpdfC"
      ]
    },
    {
      "page": "mvnlikC",
      "title": "C++ implementation of multivariate Normal probability density function for multiple inputs",
      "topics": [
        "mvnlikC"
      ]
    },
    {
      "page": "mvnpdf",
      "title": "multivariate-Normal probability density function",
      "topics": [
        "mvnpdf"
      ]
    },
    {
      "page": "mvnpdfC",
      "title": "C++ implementation of multivariate normal probability density function for multiple inputs",
      "topics": [
        "mvnpdfC"
      ]
    },
    {
      "page": "mvsnlikC",
      "title": "C++ implementation of multivariate skew normal likelihood function for multiple inputs",
      "topics": [
        "mvsnlikC"
      ]
    },
    {
      "page": "mvsnpdf",
      "title": "multivariate Skew-Normal probability density function",
      "topics": [
        "mvsnpdf"
      ]
    },
    {
      "page": "mvstlikC",
      "title": "C++ implementation of multivariate skew t likelihood function for multiple inputs",
      "topics": [
        "mvstlikC"
      ]
    },
    {
      "page": "mvstpdf",
      "title": "multivariate skew-t probability density function",
      "topics": [
        "mvstpdf"
      ]
    },
    {
      "page": "mvtpdf",
      "title": "multivariate Student's t-distribution probability density function",
      "topics": [
        "mvtpdf"
      ]
    },
    {
      "page": "NuMatParC",
      "title": "C++ implementation of similarity matrix computation using pre-computed distances",
      "topics": [
        "NuMatParC"
      ]
    },
    {
      "page": "plot_ConvDPM",
      "title": "Convergence diagnostic plots",
      "topics": [
        "plot_ConvDPM"
      ]
    },
    {
      "page": "plot_DPM",
      "title": "Plot of a Dirichlet process mixture of gaussian distribution partition",
      "topics": [
        "plot_DPM"
      ]
    },
    {
      "page": "plot_DPMsn",
      "title": "Plot of a Dirichlet process mixture of skew normal distribution partition",
      "topics": [
        "plot_DPMsn"
      ]
    },
    {
      "page": "plot_DPMst",
      "title": "Plot of a Dirichlet process mixture of skew t-distribution partition",
      "topics": [
        "plot_DPMst"
      ]
    },
    {
      "page": "postProcess.DPMMclust",
      "title": "Post-processing Dirichlet Process Mixture Models results to get a mixture distribution of the posterior locations",
      "topics": [
        "postProcess.DPMMclust"
      ]
    },
    {
      "page": "methods.summaryDPMMclust",
      "title": "Methods for a summary of a 'DPMMclust' object",
      "topics": [
        "plot.summaryDPMMclust",
        "print.summaryDPMMclust",
        "summaryDPMMclust"
      ]
    },
    {
      "page": "priormix",
      "title": "Construction of an Empirical based prior",
      "topics": [
        "priormix"
      ]
    },
    {
      "page": "rCRP",
      "title": "Generating cluster data from the Chinese Restaurant Process",
      "topics": [
        "rCRP"
      ]
    },
    {
      "page": "sample_alpha",
      "title": "Sampler for the concentration parameter of a Dirichlet process",
      "topics": [
        "sample_alpha"
      ]
    },
    {
      "page": "similarityMat",
      "title": "Computes the co-clustering (or similarity) matrix",
      "topics": [
        "similarityMat"
      ]
    },
    {
      "page": "similarityMat_nocostC",
      "title": "C++ implementation",
      "topics": [
        "similarityMat_nocostC"
      ]
    },
    {
      "page": "similarityMatC",
      "title": "C++ implementation",
      "topics": [
        "similarityMatC"
      ]
    },
    {
      "page": "summary.DPMMclust",
      "title": "Summarizing Dirichlet Process Mixture Models",
      "topics": [
        "summary.DPMMclust"
      ]
    },
    {
      "page": "vclust2mcoclustC",
      "title": "C++ implementation",
      "topics": [
        "vclust2mcoclustC"
      ]
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