more for v0.8.0 updat

NEWS.md

@@ -1,6 +1,6 @@
 # spOccupancy 0.8.0
 
-+ All model fitting functions have a new function `parallel.chains` that allows chains to be run in parallel. If set to `TRUE`, the `n.chains` will be run in parallel. Note that this is different from `n.omp.threads`, which is used for *within-chain* parallelization for spatial models (`n.omp.threads` does not do anything for non-spatial models). Note that I do not recommend using both `parallel.chains` and `n.omp.threads` if fitting a spatial model, as it will actually result in substantial slowing of the model relative to even a model with no paralelization. For the vast majority of users, using `parallel.chains` will be fastest. Generally, using `parallel.chains` will be faster than using `n.omp.threads` for spatial models, however for very large data sets (e.g., tens of thousands of locations) `n.omp.threads` may be faster. The truly fastest way to run a spatial model in `spOccupancy` is to separately run chains in different R sessions, where each chain uses `n.omp.threads` to implement within-chain parallelization. I hope to put together a short vignette on this at some point. 
++ All model fitting functions have a new function `parallel.chains` that allows chains to be run in parallel. If set to `TRUE`, the `n.chains` will be run in parallel. Note that this is different from `n.omp.threads`, which is used for *within-chain* parallelization for spatial models (`n.omp.threads` does not do anything for non-spatial models). Note that I do not recommend using both `parallel.chains` and `n.omp.threads` if fitting a spatial model, as it will actually result in substantial slowing of the model relative to even a model with no paralelization. For the vast majority of users, using `parallel.chains` will be fastest. Generally, using `parallel.chains` will be faster than using `n.omp.threads` for spatial models, however for very large data sets (e.g., tens of thousands of locations) `n.omp.threads` may be faster. The truly fastest way to run a spatial model in `spOccupancy` is to separately run chains in different R sessions, where each chain uses `n.omp.threads` to implement within-chain parallelization. Note that results for a model won't be exactly the same if it's run in parallel vs. sequence as a result of (1) random seeds being different and (2) the sequential runs using the previous tuning value as the starting tuning value for the subsequent run of the model. So, in theory if you give really bad tuning variances to start off, the sequential model may lead to slightly faster convergence (in terms of number of iterations needed and not actual time). Note that generally using `parallel.chains` does not result in computational improvements when using a full Gaussian process, but it will give substantial improvements with all other models (NNGP models or non-spatial models).
 + New functionality for fitting multi-season, single-species integrated occupancy models. The function `tIntPGOcc()` fits a non-spatial multi-season integrated occupancy model, `stIntPGOcc()` fits a spatial multi-season integrated occupancy model, and `svcTIntPGOcc()` fits a spatially-varying coefficient multi-season occupancy model. Random intercepts are supported in both the occurrence and detection formulas for both model types.  
 + Added in functionality for both occupancy and detection random intercepts in single-species single-season integrated models (`intPGOcc()` and `spIntPGOcc()`) using `lme4` syntax (e.g., `(1 | observer)` for a random effect of observer).
 + `simTIntPGOcc()` is a new function that allows simulation of single-species multi-season detection-nondetection data from multiple data sources.
@@ -13,6 +13,7 @@
 + Added the `include.w` argument in the `predict()` function for `lfMsPGOcc()` models that enables predicting without the latent factors. This also allows prediction to occur without needing to supply the coordinates, which is useful when generating conditional probability plots.
 + Updated `lfJSDM()` to give an error more quickly when there are memory limitations.
 + Fixed a bug in all multi-season, multi-species models that caused the model to crash upon initialization of the MCMC algorithm when data were supplied in a way such that for a given data set, the maximum number of times a specific site was sampled was less than the total number of "replicate periods" (i.e., the fourth dimension of the data list). This may happen when the "replicates" are structured as specific time periods (i.e., weeks, years) instead of a specific "replicate". Thanks to José Ribeiro for bringing this to my attention. 
++ Fixed a bug in multi-species cross-validation that could cause an error when using a smaller number of threads for cross-validation compared to the number of folds used.
 
 # spOccupancy 0.7.6