{
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  "Title": "Scoring Rules for Parametric and Simulated Distribution\nForecasts",
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  "Description": "Dictionary-like reference for computing scoring rules in a\nwide range of situations. Covers both parametric forecast\ndistributions (such as mixtures of Gaussians) and distributions\ngenerated via simulation. Further details can be found in the\npackage vignettes <doi:10.18637/jss.v090.i12>,\n<doi:10.18637/jss.v110.i08>.",
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    "clogs_sample",
    "crps",
    "crps_2pexp",
    "crps_2pnorm",
    "crps_beta",
    "crps_binom",
    "crps_clogis",
    "crps_cnorm",
    "crps_ct",
    "crps_exp",
    "crps_expM",
    "crps_gamma",
    "crps_gev",
    "crps_gpd",
    "crps_gtclogis",
    "crps_gtcnorm",
    "crps_gtct",
    "crps_hyper",
    "crps_lapl",
    "crps_llapl",
    "crps_llogis",
    "crps_lnorm",
    "crps_logis",
    "crps_mixnorm",
    "crps_mixnorm_int",
    "crps_nbinom",
    "crps_norm",
    "crps_pois",
    "crps_sample",
    "crps_t",
    "crps_tlogis",
    "crps_tnorm",
    "crps_tt",
    "crps_unif",
    "crps.numeric",
    "dss_beta",
    "dss_exp",
    "dss_gamma",
    "dss_gev",
    "dss_gpd",
    "dss_lapl",
    "dss_llapl",
    "dss_llogis",
    "dss_lnorm",
    "dss_logis",
    "dss_mixnorm",
    "dss_moments",
    "dss_nbinom",
    "dss_norm",
    "dss_pois",
    "dss_sample",
    "dss_t",
    "dss_unif",
    "es_sample",
    "ess_moments",
    "f2pexp",
    "f2pnorm",
    "fexp",
    "fgev",
    "fgpd",
    "flapl",
    "fllapl",
    "fllogis",
    "flogis",
    "fmixnorm",
    "fnorm",
    "ft",
    "get_weight_func",
    "gradcrps_clogis",
    "gradcrps_cnorm",
    "gradcrps_ct",
    "gradcrps_logis",
    "gradcrps_norm",
    "gradcrps_t",
    "gradcrps_tlogis",
    "gradcrps_tnorm",
    "gradcrps_tt",
    "hesscrps_clogis",
    "hesscrps_cnorm",
    "hesscrps_ct",
    "hesscrps_logis",
    "hesscrps_norm",
    "hesscrps_t",
    "hesscrps_tlogis",
    "hesscrps_tnorm",
    "hesscrps_tt",
    "ints_quantiles",
    "ints_sample",
    "logs",
    "logs_2pexp",
    "logs_2pnorm",
    "logs_beta",
    "logs_binom",
    "logs_exp",
    "logs_exp2",
    "logs_gamma",
    "logs_gev",
    "logs_gpd",
    "logs_hyper",
    "logs_lapl",
    "logs_llapl",
    "logs_llogis",
    "logs_lnorm",
    "logs_logis",
    "logs_mixnorm",
    "logs_nbinom",
    "logs_norm",
    "logs_pois",
    "logs_sample",
    "logs_t",
    "logs_tlogis",
    "logs_tnorm",
    "logs_tt",
    "logs_unif",
    "logs.numeric",
    "mmds_sample",
    "owcrps_sample",
    "owes_sample",
    "owmmds_sample",
    "owvs_sample",
    "qs_quantiles",
    "qs_sample",
    "rps_probs",
    "run_casestudy",
    "run_mcstudy",
    "twcrps_sample",
    "twes_sample",
    "twmmds_sample",
    "twvs_sample",
    "vs_sample"
  ],
  "_datasets": [
    {
      "name": "gdp",
      "title": "Data and forecasts for US GDP growth",
      "object": "gdp",
      "file": "gdp.rda",
      "class": [
        "data.frame"
      ],
      "fields": [
        "dt",
        "vint",
        "val"
      ],
      "rows": 33616,
      "table": true,
      "tojson": true
    },
    {
      "name": "gdp_mcmc",
      "title": "Data and forecasts for US GDP growth",
      "object": "gdp_mcmc",
      "file": "gdp_mcmc.rda",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "ar_ms",
      "title": "Bayesian analysis of a Markov Switching autoregressive model",
      "topics": [
        "ar_ms"
      ]
    },
    {
      "page": "crps.numeric",
      "title": "Continuous Ranked Probability Score for Parametric Forecast Distributions",
      "topics": [
        "crps.numeric"
      ]
    },
    {
      "page": "gdp_mcmc",
      "title": "Data and forecasts for US GDP growth",
      "topics": [
        "gdp",
        "gdp_mcmc"
      ]
    },
    {
      "page": "get_weight_func",
      "title": "Default Weight and Chaining Functions",
      "topics": [
        "get_weight_func"
      ]
    },
    {
      "page": "logs.numeric",
      "title": "Logarithmic Score for Parametric Forecast Distributions",
      "topics": [
        "logs.numeric"
      ]
    },
    {
      "page": "plot.casestudy",
      "title": "Plot the output of run_casestudy",
      "topics": [
        "plot.casestudy"
      ]
    },
    {
      "page": "plot.mcstudy",
      "title": "Plot the output of run_mcstudy",
      "topics": [
        "plot.mcstudy"
      ]
    },
    {
      "page": "print.casestudy",
      "title": "Simple print method for object of class casestudy",
      "topics": [
        "print.casestudy"
      ]
    },
    {
      "page": "print.mcstudy",
      "title": "Simple print function for object of class mcstudy",
      "topics": [
        "print.mcstudy"
      ]
    },
    {
      "page": "rps_probs",
      "title": "Ranked Probability Score",
      "topics": [
        "rps_probs"
      ]
    },
    {
      "page": "run_casestudy",
      "title": "Run the case study in KLTG (2021), or a smaller version thereof",
      "topics": [
        "run_casestudy"
      ]
    },
    {
      "page": "run_mcstudy",
      "title": "Run the Monte Carlo study by KLTG (2021), or a smaller version thereof",
      "topics": [
        "run_mcstudy"
      ]
    },
    {
      "page": "scores",
      "title": "Generic Scoring Rule Calculation",
      "topics": [
        "crps",
        "logs",
        "scores"
      ]
    },
    {
      "page": "scores_2pexp",
      "title": "Calculating scores for the two-piece-exponential distribution",
      "topics": [
        "crps_2pexp",
        "logs_2pexp",
        "scores_2pexp"
      ]
    },
    {
      "page": "scores_2pnorm",
      "title": "Calculating scores for the two-piece-normal distribution",
      "topics": [
        "crps_2pnorm",
        "logs_2pnorm",
        "scores_2pnorm"
      ]
    },
    {
      "page": "scores_beta",
      "title": "Calculating scores for the beta distribution",
      "topics": [
        "crps_beta",
        "dss_beta",
        "logs_beta",
        "scores_beta"
      ]
    },
    {
      "page": "scores_binom",
      "title": "Calculating scores for the binomial distribution",
      "topics": [
        "crps_binom",
        "logs_binom",
        "scores_binom"
      ]
    },
    {
      "page": "scores_exp",
      "title": "Calculating scores for the exponential distribution",
      "topics": [
        "crps_exp",
        "crps_expM",
        "dss_exp",
        "logs_exp",
        "logs_exp2",
        "scores_exp"
      ]
    },
    {
      "page": "scores_gamma",
      "title": "Calculating scores for the gamma distribution",
      "topics": [
        "crps_gamma",
        "dss_gamma",
        "logs_gamma",
        "scores_gamma"
      ]
    },
    {
      "page": "scores_gev",
      "title": "Calculating scores for the generalized extreme value distribution",
      "topics": [
        "crps_gev",
        "dss_gev",
        "logs_gev",
        "scores_gev"
      ]
    },
    {
      "page": "scores_gpd",
      "title": "Calculating scores for the generalized Pareto distribution",
      "topics": [
        "crps_gpd",
        "dss_gpd",
        "logs_gpd",
        "scores_gpd"
      ]
    },
    {
      "page": "scores_hyper",
      "title": "Calculating scores for the hypergeometric distribution",
      "topics": [
        "crps_hyper",
        "logs_hyper",
        "scores_hyper"
      ]
    },
    {
      "page": "scores_lapl",
      "title": "Calculating scores for the Laplace distribution",
      "topics": [
        "crps_lapl",
        "dss_lapl",
        "logs_lapl",
        "scores_lapl"
      ]
    },
    {
      "page": "scores_llapl",
      "title": "Calculating scores for the log-Laplace distribution",
      "topics": [
        "crps_llapl",
        "dss_llapl",
        "logs_llapl",
        "scores_llapl"
      ]
    },
    {
      "page": "scores_llogis",
      "title": "Calculating scores for the log-logistic distribution",
      "topics": [
        "crps_llogis",
        "dss_llogis",
        "logs_llogis",
        "scores_llogis"
      ]
    },
    {
      "page": "scores_lnorm",
      "title": "Calculating scores for the log-normal distribution",
      "topics": [
        "crps_lnorm",
        "dss_lnorm",
        "logs_lnorm",
        "scores_lnorm"
      ]
    },
    {
      "page": "scores_logis",
      "title": "Calculating scores for the logistic distribution",
      "topics": [
        "crps_clogis",
        "crps_gtclogis",
        "crps_logis",
        "crps_tlogis",
        "dss_logis",
        "gradcrps_clogis",
        "gradcrps_logis",
        "gradcrps_tlogis",
        "hesscrps_clogis",
        "hesscrps_logis",
        "hesscrps_tlogis",
        "logs_logis",
        "logs_tlogis",
        "scores_logis"
      ]
    },
    {
      "page": "scores_mixnorm",
      "title": "Calculating scores for a mixture of normal distributions.",
      "topics": [
        "crps_mixnorm",
        "crps_mixnorm_int",
        "dss_mixnorm",
        "logs_mixnorm",
        "scores_mixnorm"
      ]
    },
    {
      "page": "scores_moments",
      "title": "Scoring Rules for a Vector of Moments",
      "topics": [
        "dss_moments",
        "ess_moments",
        "scores_moments"
      ]
    },
    {
      "page": "scores_nbinom",
      "title": "Calculating scores for the negative binomial distribution",
      "topics": [
        "crps_nbinom",
        "dss_nbinom",
        "logs_nbinom",
        "scores_nbinom"
      ]
    },
    {
      "page": "scores_norm",
      "title": "Calculating scores for the normal distribution",
      "topics": [
        "crps_cnorm",
        "crps_gtcnorm",
        "crps_norm",
        "crps_tnorm",
        "dss_norm",
        "gradcrps_cnorm",
        "gradcrps_norm",
        "gradcrps_tnorm",
        "hesscrps_cnorm",
        "hesscrps_norm",
        "hesscrps_tnorm",
        "logs_norm",
        "logs_tnorm",
        "scores_norm"
      ]
    },
    {
      "page": "scores_pois",
      "title": "Calculating scores for the Poisson distribution",
      "topics": [
        "crps_pois",
        "dss_pois",
        "logs_pois",
        "scores_pois"
      ]
    },
    {
      "page": "scores_quantiles",
      "title": "Quantile and interval scores",
      "topics": [
        "ints_quantiles",
        "ints_sample",
        "qs_quantiles",
        "qs_sample",
        "scores_quantiles"
      ]
    },
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