Matthijs Hollanders
Matthijs Hollanders
Thanks Daniel! That did the trick. I'm going to get into nimbleFunctions for real!
Thanks for the prompt response and tips, @bob-carpenter, I have made the changes to my code for efficiency! FYI, my HMM is a bit more complex but it's good to...
Hey, firstly, yes I did catch the bugs. For my model, I firstly just generate new TPMs for each individual because there are very often individual-by-time varying covariates, such as...
Hi, you're correct. -28.865, 153.565 still gives the wrong results. date lat lon sunrise sunset 1 2023-09-15 -28.865 153.565 2023-09-14 09:46:16 2023-09-14 21:39:09
Any chance this could be addressed? I think accounting for this is making formulating complex HMMs on the log scale a bit more verbose than it needs to be.
Hi @jsocolar, sounds good. For the single season model, the key data structures are (1) the number of detections per site, primary occasion, and secondary occasion and (2) the waiting...
Yes. The idea is that there's an overall occupancy state of site $i \in 1:I$ in season $k \in 1:K$; I'll just write it out for $K=1$ for now. Then,...
Hi Jacob, I'm having a crack at steps 1--3 as outlined above. Pardon me that I'm a bit slow to understand all of this, as I've never worked on an...
Hey, thanks for the help. traceback() gives me the following: ``` > fit$loo() Error in if (varx == 0) { : missing value where TRUE/FALSE needed > traceback() 8: posterior::autocovariance(sims[,...
Hey @jgabry, I don't get the error with `r_eff = FALSE`. Thanks so much!