Julian Gehring

Yes, I agree that would be nice to have. I had that idea of parallelising the bootstrapping on my mind for quite a while, but both going through `pmap` or...

Nice suggestion! I'll need to do more reading on the Bayesian bootstrap - it could be a good addition to the existing sampling methods. If I understand it correctly, this...

Thanks for taking a shot at implement this. Redefining the `bootstrap` method to take a data tuple seems like a big chance to make the multi-distribution happen. I guess that...

Please ignore what I said about the `Vector`s earlier - I misunderstood what you were trying to achieve and my comment wasn't a suitable solution. I really like the idea...

What if we used your `bootstrap(statistic::Function, data::Tuple, sampling::BasicSampling)` function and then define the current "standard" bootstrap function as ```julia bootstrap(statistic::Function, data, sampling::BasicSampling) = bootstrap(statistic, tuple(data), sampling) ``` Wouldn't that avoid...

Thank you, this looks very nice! It might take some time to test and review the changes - I'll try to finish it as soon as I can.

I'm happy to explore together how we can bring the two packages closer together. How well can the functionality of `DependentBootstrap` be mapped onto the `Bootstrap` API ( `bootstrap`, `confint`,...

Sorry for the radio silence, I have been off the grid for a while. Your ideas sound very good to me. I'm in favour of working around `Requires` if possible....

Nice idea, that would definitely be a useful feature to support!

As a "workaround", one could currently get the same in multiple steps: ```julia bs1 = bootstrap(x->mean(x), xs, BasicSampling(1000)) bs2 = bootstrap(y->mean(y), ys, BasicSampling(1000)) z = straps(bs1)[1] - straps(bs2)[1] ```