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  "Title": "Markov Chain Monte Carlo Small Area Estimation",
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  "Description": "Fit multi-level models with possibly correlated random\neffects using Markov Chain Monte Carlo simulation. Such models\nallow smoothing over space and time and are useful in, for\nexample, small area estimation.",
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    "vfac",
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    {
      "page": "mcmcsae-package",
      "title": "Markov Chain Monte Carlo Small Area Estimation",
      "topics": [
        "mcmcsae-package",
        "mcmcsae"
      ]
    },
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      "page": "acceptance_rates",
      "title": "Return Metropolis-Hastings acceptance rates",
      "topics": [
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      ]
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      "title": "Convert a dc_summary object to a data.frame",
      "topics": [
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      ]
    },
    {
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      "topics": [
        "as.data.frame.mcdraws_summary"
      ]
    },
    {
      "page": "bayesR2",
      "title": "Compute a Bayesian measure of percentage of explained variance",
      "topics": [
        "bayesR2"
      ]
    },
    {
      "page": "binomial_control",
      "title": "Set computational options for the sampling algorithms",
      "topics": [
        "binomial_control"
      ]
    },
    {
      "page": "brt",
      "title": "Create a model component object for a BART (Bayesian Additive Regression Trees) component in the linear predictor",
      "topics": [
        "brt"
      ]
    },
    {
      "page": "CG_control",
      "title": "Set options for the conjugate gradient (CG) sampler",
      "topics": [
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    },
    {
      "page": "chol_control",
      "title": "Set options for Cholesky decomposition",
      "topics": [
        "chol_control"
      ]
    },
    {
      "page": "cMVN_control",
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      "topics": [
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      ]
    },
    {
      "page": "combine_chains",
      "title": "Combine multiple mcdraws objects into a single one by combining their chains",
      "topics": [
        "combine_chains"
      ]
    },
    {
      "page": "combine_iters",
      "title": "Combine multiple mcdraws objects into a single one by combining their draws",
      "topics": [
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      ]
    },
    {
      "page": "computeDesignMatrix",
      "title": "Compute a list of design matrices for all terms in a model formula, or based on a sampler environment",
      "topics": [
        "computeDesignMatrix"
      ]
    },
    {
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      "title": "Correlation factor structures in generic model components",
      "topics": [
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        "correlation",
        "custom",
        "iid",
        "RW1",
        "RW2",
        "season",
        "spatial",
        "splines"
      ]
    },
    {
      "page": "create_cMVN_sampler",
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      "topics": [
        "create_cMVN_sampler"
      ]
    },
    {
      "page": "create_sampler",
      "title": "Create a sampler object",
      "topics": [
        "create_sampler"
      ]
    },
    {
      "page": "create_TMVN_sampler",
      "title": "Set up a sampler object for sampling from a possibly truncated and degenerate multivariate normal distribution",
      "topics": [
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      ]
    },
    {
      "page": "f_binomial",
      "title": "Specify a binomial sampling distribution",
      "topics": [
        "f_binomial"
      ]
    },
    {
      "page": "f_gamma",
      "title": "Specify a Gamma sampling distribution",
      "topics": [
        "f_gamma"
      ]
    },
    {
      "page": "f_gaussian",
      "title": "Specify a Gaussian sampling distribution",
      "topics": [
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      ]
    },
    {
      "page": "f_gaussian_gamma",
      "title": "Specify a Gaussian-Gamma sampling distribution",
      "topics": [
        "f_gaussian_gamma"
      ]
    },
    {
      "page": "f_multi",
      "title": "Specify a multi-response sampling distribution",
      "topics": [
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      ]
    },
    {
      "page": "f_multinomial",
      "title": "Specify a multinomial sampling distribution",
      "topics": [
        "f_multinomial"
      ]
    },
    {
      "page": "f_negbinomial",
      "title": "Specify a negative binomial sampling distribution",
      "topics": [
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      ]
    },
    {
      "page": "f_poisson",
      "title": "Specify a Poisson sampling distribution",
      "topics": [
        "f_poisson"
      ]
    },
    {
      "page": "f_student_t",
      "title": "Specify a Student-t sampling distribution",
      "topics": [
        "f_student_t"
      ]
    },
    {
      "page": "gaussian_control",
      "title": "Set computational options for the sampling algorithms",
      "topics": [
        "gaussian_control"
      ]
    },
    {
      "page": "gen",
      "title": "Create a model component object for a generic random effects component in the linear predictor",
      "topics": [
        "gen"
      ]
    },
    {
      "page": "gen_control",
      "title": "Set computational options for the sampling algorithms used for a 'gen' model component",
      "topics": [
        "gen_control"
      ]
    },
    {
      "page": "generate_data",
      "title": "Generate a data vector according to a model",
      "topics": [
        "generate_data"
      ]
    },
    {
      "page": "get_draw",
      "title": "Extract a list of parameter values for a single draw",
      "topics": [
        "get_draw"
      ]
    },
    {
      "page": "glreg",
      "title": "Create a model object for group-level regression effects within a generic random effects component.",
      "topics": [
        "glreg"
      ]
    },
    {
      "page": "GMRF_structure",
      "title": "Set up a GMRF structure for a generic model component",
      "topics": [
        "GMRF_structure"
      ]
    },
    {
      "page": "labels",
      "title": "Get and set the variable labels of a draws component object for a vector-valued parameter",
      "topics": [
        "labels",
        "labels.dc",
        "labels<-"
      ]
    },
    {
      "page": "matrix-vector",
      "title": "Fast matrix-vector multiplications",
      "topics": [
        "%m*v%",
        "crossprod_mv",
        "matrix-vector"
      ]
    },
    {
      "page": "maximize_log_lh_p",
      "title": "Maximise the log-likelihood or log-posterior as defined by a sampler closure",
      "topics": [
        "maximize_log_lh_p"
      ]
    },
    {
      "page": "mc_offset",
      "title": "Create a model component object for an offset, i.e. fixed, non-parametrised term in the linear predictor",
      "topics": [
        "mc_offset"
      ]
    },
    {
      "page": "MCMC-diagnostics",
      "title": "Compute MCMC diagnostic measures",
      "topics": [
        "MCMC-diagnostics",
        "n_eff",
        "R_hat"
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    },
    {
      "page": "MCMC-object-conversion",
      "title": "Convert a draws component object to another format",
      "topics": [
        "as.array.dc",
        "as.matrix.dc",
        "MCMC-object-conversion",
        "to_draws_array",
        "to_mcmc"
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    },
    {
      "page": "mcmcsae_example",
      "title": "Generate artificial data according to an additive spatio-temporal model",
      "topics": [
        "mcmcsae_example"
      ]
    },
    {
      "page": "MCMCsim",
      "title": "Run a Markov Chain Monte Carlo simulation",
      "topics": [
        "MCMCsim"
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    },
    {
      "page": "mec",
      "title": "Create a model component object for a regression (fixed effects) component in the linear predictor with measurement errors in quantitative covariates",
      "topics": [
        "mec"
      ]
    },
    {
      "page": "model_matrix",
      "title": "Compute possibly sparse model matrix",
      "topics": [
        "model_matrix"
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    },
    {
      "page": "model-information-criteria",
      "title": "Compute DIC, WAIC and leave-one-out cross-validation model measures",
      "topics": [
        "compute_DIC",
        "compute_WAIC",
        "loo.mcdraws",
        "model-information-criteria",
        "waic.mcdraws"
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    },
    {
      "page": "multinomial_control",
      "title": "Set computational options for the sampling algorithms",
      "topics": [
        "multinomial_control"
      ]
    },
    {
      "page": "n_chains-n_draws-n_vars",
      "title": "Get the number of chains, samples per chain or the number of variables in a simulation object",
      "topics": [
        "n_chains",
        "n_chains-n_draws-n_vars",
        "n_draws",
        "n_vars"
      ]
    },
    {
      "page": "negbinomial_control",
      "title": "Set computational options for the sampling algorithms",
      "topics": [
        "negbinomial_control"
      ]
    },
    {
      "page": "par_names",
      "title": "Get the parameter names from an mcdraws object",
      "topics": [
        "par_names"
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    },
    {
      "page": "plot_coef",
      "title": "Plot a set of model coefficients or predictions with uncertainty intervals based on summaries of simulation results or other objects.",
      "topics": [
        "plot_coef"
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    },
    {
      "page": "plot.dc",
      "title": "Trace, density and autocorrelation plots for (parameters of a) draws component (dc) object",
      "topics": [
        "plot.dc"
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    },
    {
      "page": "plot.mcdraws",
      "title": "Trace, density and autocorrelation plots",
      "topics": [
        "plot.mcdraws"
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    },
    {
      "page": "poisson_control",
      "title": "Set computational options for the sampling algorithms",
      "topics": [
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    },
    {
      "page": "posterior-moments",
      "title": "Get means or standard deviations of parameters from the MCMC output in an mcdraws object",
      "topics": [
        "get_means",
        "get_sds",
        "posterior-moments"
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    },
    {
      "page": "pr_beta",
      "title": "Create an object representing beta prior distributions",
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