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    "read.ensemble.ts",
    "read.sa.output",
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    "runModule.get.results",
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    "runModule.run.sensitivity.analysis",
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      "page": "compute_sobol_indices",
      "title": "Compute Sobol indices from a finished PEcAn run",
      "topics": [
        "compute_sobol_indices"
      ]
    },
    {
      "page": "ensemble.filename",
      "title": "Generate ensemble filenames",
      "topics": [
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    {
      "page": "ensemble.ts",
      "title": "Plots an ensemble time-series from PEcAn for the selected target variable",
      "topics": [
        "ensemble.ts"
      ]
    },
    {
      "page": "flux.uncertainty",
      "title": "Calculate parameters for heteroskedastic flux uncertainty",
      "topics": [
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      ]
    },
    {
      "page": "generate_joint_ensemble_design",
      "title": "Generate joint ensemble design for parameter sampling Creates a joint ensemble design that maintains parameter correlations across all sites in a multi-site run. This function generates sample indices that are shared across sites to ensure consistent parameter sampling.",
      "topics": [
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      ]
    },
    {
      "page": "generate_OAT_SA_design",
      "title": "Generate One-At-a-Time (OAT) input design for sensitivity analysis",
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    {
      "page": "get_parameter_samples",
      "title": "Generate parameter samples from priors and MCMC posteriors (pure function)",
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      "page": "get.change",
      "title": "Get delta between sequential flux datapoints",
      "topics": [
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    },
    {
      "page": "get.coef.var",
      "title": "Get coefficient of variance",
      "topics": [
        "get.coef.var"
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      "page": "get.elasticity",
      "title": "Generic function for the elasticity",
      "topics": [
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    },
    {
      "page": "get.ensemble.samples",
      "title": "Get parameter values used in ensemble",
      "topics": [
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      "title": "Get g_i(phi_i)",
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      "page": "get.parameter.samples",
      "title": "Convert priors / MCMC samples to parameter sample chains",
      "topics": [
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      "page": "get.results",
      "title": "Read model output and save parsed results for sensitivity and ensemble analyses",
      "topics": [
        "get.results"
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    },
    {
      "page": "get.sensitivity",
      "title": "Calculate the sensitivity of a function at the median",
      "topics": [
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    {
      "page": "input.ens.gen",
      "title": "Generate an ensemble of samples from one input",
      "topics": [
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    },
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      "page": "kurtosis",
      "title": "Calculates the excess kurtosis of a vector",
      "topics": [
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    },
    {
      "page": "plot_flux_uncertainty",
      "title": "Plot fit for heteroskedastic flux uncertainty",
      "topics": [
        "plot_flux_uncertainty"
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    },
    {
      "page": "plot_sensitivities",
      "title": "Plot functions and quantiles used in sensitivity analysis",
      "topics": [
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    },
    {
      "page": "plot_sensitivity",
      "title": "Sensitivity plot",
      "topics": [
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    {
      "page": "plot_variance_decomposition",
      "title": "Variance Decomposition Plots",
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      "page": "read.ameriflux.L2",
      "title": "Read Ameriflux L2 Data",
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      "title": "Reads output from model ensemble",
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      "title": "Run sensitivity analysis on finished model runs",
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      "title": "Run ensemble analyses across all sites in settings",
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      "page": "runModule.run.sensitivity.analysis",
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      "title": "Spline estimate of univariate relationship between parameter value and model output",
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      "page": "sd.var",
      "title": "Calculates the standard deviation of the variance estimate",
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      "page": "sensitivity.analysis",
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      "page": "sensitivity.filename",
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      "page": "spline.ensemble",
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      "page": "spline.truncate",
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