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Merge pull request #280 from stan-dev/update_scrps_ref
update scrps ref and improve doc
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R/crps.R

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#'
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#' The `crps()` and `scrps()` functions and their `loo_*()` counterparts can be
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#' used to compute the continuously ranked probability score (CRPS) and scaled
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#' CRPS (SCRPS) (see Bolin and Wallin, 2022). CRPS is a proper scoring rule, and
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#' CRPS (SCRPS) (as defined by Bolin and Wallin, 2023). CRPS is a proper scoring rule, and
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#' strictly proper when the first moment of the predictive distribution is
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#' finite. Both can be expressed in terms of samples form the predictive
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#' distribution. See e.g. Gneiting and Raftery (2007) for a comprehensive
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#' discussion on CRPS.
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#' distribution. See, for example, a paper by Gneiting and Raftery (2007)
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#' for a comprehensive discussion on CRPS.
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#'
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#' To compute (S)CRPS, the user needs to provide two sets of draws, `x` and
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#' `x2`, from the predictive distribution. This is due to the fact that formulas
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#'
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#' @return A list containing two elements: `estimates` and `pointwise`.
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#' The former reports estimator and standard error and latter the pointwise
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#' values.
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#' values. Following Bolin & Wallin (2023), a larger value is better.
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#'
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#' @examples
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#' \dontrun{
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#' }
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#'
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#' @references
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#' Bolin, D., & Wallin, J. (2022). Local scale invariance and robustness of
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#' proper scoring rules. arXiv. \doi{10.48550/arXiv.1912.05642}
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#' Bolin, D., & Wallin, J. (2023). Local scale invariance and robustness of
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#' proper scoring rules. Statistical Science, 38(1):140-159.
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#'
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#' Gneiting, T., & Raftery, A. E. (2007). Strictly Proper Scoring Rules,
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#' Prediction, and Estimation. Journal of the American Statistical Association,

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