Analytic CRPS formulas tracking issue

Issue to track implementation of analytical CRPS formulas, as implemented in scoringRules.

[x] beta (betainc missing in torch)
[x] binomial (betainc missing in torch)
[x] exponential
[x] exponential point mass variant
[x] gamma
[x] gen. extreme value (expi missing in torch)
[x] gen. Pareto
[x] hypergeometric
[x] Laplace
[x] log-Laplace
[x] log-logistic
[x] log-normal
[x] logistic
[x] logistic truncated
[x] logistic censored
[x] logistic point mass variant
[x] mixture of normals (Ready for PR)
[x] negative binomial (hypergeometric missing in jax, tensorflow and torch)
[x] normal
[x] normal truncated
[x] normal censored
[x] normal point mass variant
[x] Poisson
[x] Student’s t (hypergeometric missing in jax, tensorflow and torch)
[x] Student’s t truncated (hypergeometric missing in jax, tensorflow and torch)
[x] Student's t censored (hypergeometric missing in jax, tensorflow and torch)
[x] Student's t point mass variant (hypergeometric missing in jax, tensorflow and torch)
[x] two-piece exponential
[x] two-piece normal
[x] uniform

Notes:

gamma, beta and bessel functions not supported in numba (affects beta, gamma, gev, poisson, t, generalised t, negative binomial, binomial, hypergeometric, and log-logistic distribution).
regularised incomplete beta function not supported in torch (affects beta, log-logistic, binomial, hypergeometric, negative binomial, and generalised t distribution)
hypergeometric function not supported in torch or tensorflow (affects t and negative binomial distribution)
exponential integral not supported in torch (affects gev distribution)
hypergeometric cdf needed

frazane / scoringrules