It would be good think about the future of this package, whether it is worth maintaing and perhaps even developing further and if yes what additional functionality we might want.
My understanding is that there is no other package that does what this one does (optimising mixtures of predictive distributions for CRPS and creating a mixture forecast), and that this is a valid approach that is worth doing further research on / testing its predictive ability. As a minimum, I therefore think that it is worth fixing the failing checks and spending a bit of time on bringing the package back to life with some tests etc. and give it a unique name (#4).
Additionally we could support convolutions via optimising the WIS as currently implemented in https://github.com/epiforecasts/qra and promised there.
If we wanted to go further it should be possible to support optimisation for any score available in https://github.com/epiforecasts/scoringutils and create mixtures and convolutions from that. This would require more development work to support different represenations of forecasts and require some method for optimisation under constraints (e.g. sum of weights = 1) which is difficult to to reliably in the general case. One approach would be to re-implement the scores in stan as is done with CRPS optimisation at the moment but it's not clear to me if there is value in that.
So as a possible destination I think a package that tightly integrates with scoringutils to do general optimisation whilst drawing on better methods where available (e.g. the stan model here for sample-based CRPS optimisation and https://github.com/ryantibs/quantgen for quantile-based WIS optimisation) would be of value if there is no other package that can do the job.
It would be good think about the future of this package, whether it is worth maintaing and perhaps even developing further and if yes what additional functionality we might want.
My understanding is that there is no other package that does what this one does (optimising mixtures of predictive distributions for CRPS and creating a mixture forecast), and that this is a valid approach that is worth doing further research on / testing its predictive ability. As a minimum, I therefore think that it is worth fixing the failing checks and spending a bit of time on bringing the package back to life with some tests etc. and give it a unique name (#4).
Additionally we could support convolutions via optimising the WIS as currently implemented in https://github.com/epiforecasts/qra and promised there.
If we wanted to go further it should be possible to support optimisation for any score available in https://github.com/epiforecasts/scoringutils and create mixtures and convolutions from that. This would require more development work to support different represenations of forecasts and require some method for optimisation under constraints (e.g. sum of weights = 1) which is difficult to to reliably in the general case. One approach would be to re-implement the scores in
stanas is done with CRPS optimisation at the moment but it's not clear to me if there is value in that.So as a possible destination I think a package that tightly integrates with
scoringutilsto do general optimisation whilst drawing on better methods where available (e.g. thestanmodel here for sample-based CRPS optimisation and https://github.com/ryantibs/quantgen for quantile-based WIS optimisation) would be of value if there is no other package that can do the job.