Faster CRPS calculations for sample distributions

[robjhyndman]

When the distribution is stored as a sample, a much faster way to compute CRPS is to use eq (3) from https://cran.r-project.org/web/packages/scoringRules/vignettes/article.pdf

[mitchelloharawild]

scoringRules::crps(y = 3, family = "normal", mean = 0, sd = 1)
#> [1] 2.436575
dist <- distributional::dist_normal(0, 1)
fabletools::CRPS(dist, 3)
#> [1] 2.436575
fabletools::CRPS(dist_sample(generate(dist, 10000)), 3)
#> [1] 2.43802

scoringRules::crps(y = 1, family = "unif", min = 2, max = 4.4, lmass = 0, umass = 0)
#> [1] 1.8
dist <- distributional::dist_uniform(2, 4.4)
fabletools::CRPS(dist, 1)
#> [1] 1.800799
fabletools::CRPS(dist_sample(generate(dist, 10000)), 1)
#> [1] 1.800534

^{Created on 2020-07-12 by the reprex package (v0.3.0)}

[robjhyndman]

Hmm. Must have been something else making fable go slow in my code.

[mitchelloharawild]

Do you have a workflow MRE I can optimise performance for?