Noa Kallioinen

Hi, you might have some luck with using the generic moment matching functions from https://github.com/topipa/iwmm You'll need to manually specify the target function or importance weight function but it should...

Yes, it is the same underlying mechanism, just generic (i.e. not tied to importance weights for leave-one-out posteriors). Given a `log_ratio_fun`, the `moment_match` function will return transformed draws and importance...

A solution to this could also help with what @topipa and I are trying to do with iwmm, see topipa/iwmm/issues/4 We've been thinking about the best way to handle moment...

In the separate_scaling branch of priorsense I have implemented the selection of priors and likelihood contributions in the case that lprior and log_lik are arrays. Currently the user needs to...

I've been using these new methods a fair amount lately for implementing cmdstanr support in the [iwmm package](https://github.com/topipa/iwmm). In addition to extending a big thanks to @andrjohns for these methods,...

I think the better workflow is to do all mutations to the draws beforehand (i.e. adding the theta) and then the sensitivity analysis. Two options that might offer a better...

I'll need to work on how to do this kind of workflow with moment-matching though

This seems to no longer occur in ggplot 3.5.0

Good questions, thanks for looking into this! I don't know the motivation for the horseshoe partitioning, but we did discuss the R2D2 and I think we concluded that the R2...

Thanks for opening this issue. I completely agree that current way predictions are handled is far from ideal. If there's only one predicted variable (for example R2), it should work...