SCR with random thinning samplers marginalizing out latent individual IDs. Poisson observation model only. To speed up computation, I use the approach of Herliansyah et al. (2024, section 4.3) in the custom N/z and activity center updates.
https://link.springer.com/article/10.1007/s13253-023-00598-3
These models use N-prior data augmentation: https://github.com/benaug/SCR-N-Prior-Data-Augmentation
There are 4 types of models:
- Single session
- Multisession
- Single session with density covariates and habitat mask
- Multisession with density covariates and habitat mask
Random thinning models that allow observation models other than Poisson and/or categorical partial IDs can be found here:
https://github.com/benaug/RandomThinIDCov
These are more limited (e.g., no habitat mask, density covariates), but can be modified.
Analogous repositories for different combinations of marked and unmarked data types can be found here:
Unmarked SCR: https://github.com/benaug/Unmarked-SCR-Marginal
Spatial mark-resight: https://github.com/benaug/Spatial-Mark-Resight-Marginal
SCR with integrated occupancy data: https://github.com/benaug/SCR_Dcov_IntegratedOccupancy