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We have discussed rewriting CRPS and SCRPS functions to use better computation (probability weighted moment form). As new functions are needed, this has lead to thinking overall improvement of metric and score functions. Some thoughts
Keep the current loo for elpd, as it does not have arguments for the predictions and observations
a) Deprecate loo_predictive_metric and create new loo_predictive_measure, or
b) create new loo_predictive_score
(a) might be better if we change the output
Noa thinks we could drop loo_
I think we could drop predictive_ but should not drop loo_
For all measures, return a loo object with pointwise and estimates
With pointwise information, we can do model comparisons
Should we extend the current loo object or make a new object type?
One challenge might be the sub sampling loo, which increase the amount of
of work when implementing other measures
Extend loo_compare to work with other measures, as we know how to compute diff_se for all current
metrics and scores. Currently, loo_predictive_metric returns only estimate and se for one model
predictions
Or do we need a new function
Include MAE, RMSE, MSE, R2, ACC, balanced ACC, and Brie score to metrics
Include RPS, SRPS, CRPS, SCRPS, and log score to scores
When computing S?C?RPS or current metrics, maybe store function specific diagnostics?
All measures need psis object or log weights, and thus
should we always compute p_loo, too? Or should we compute measure specific value describing
amount of fitting?
I have a minimal function that computes loo versions of RPS, SRPS, CRPS, SCRPS and includes documentation for the equations with references (but no argument checking, and computes it only for one scalar y, etc)
This is related to issues #223, #220, and #201
We have discussed rewriting CRPS and SCRPS functions to use better computation (probability weighted moment form). As new functions are needed, this has lead to thinking overall improvement of metric and score functions. Some thoughts
b) create new loo_predictive_score
of work when implementing other measures
metrics and scores. Currently, loo_predictive_metric returns only estimate and se for one model
predictions
should we always compute p_loo, too? Or should we compute measure specific value describing
amount of fitting?
tagging @n-kall and @jgabry
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