spOccupancy 0.6.1

• Small changes in C++ code to pass CRAN additional issues

spOccupancy 0.6.0

• Incorporates new functionality to fit a non-spatial integrated multi-species occupancy model using the function intMsPGOcc(). This fits a single-season version of the “intgrated community occupancy model” from Doser et al. 2022. The function intMsPGOcc() should be considered experimental and is still under development. We have done adequate testing of the function and users can be confident the resulting estimates are correct. Rather, we consider this “experimental” because it lacks all the functionality currently supported for other spOccupancy model types. In particular, intMsPGOcc model objects do not currently work with ppcOcc() (posterior predictive checks), fitted() (generated fitted values), or k-fold cross-validation, and there may be specific data set situations that cause the function to break. Please contact us if you use the function and have any feedback or run into any problems. We are in active development of the associated spatial versions of the function (both without spatial factors and with spatial factors), as well as the above mentioned limitations. intMsPGOcc() does not currently support random effects in the detection models, which we are actively working on. See vignette("integratedMultispecies") for more details.
• New functionality to fit posthoc linear models to parameter estimates using the function postHocLM(). The function postHocLM() fits a basic linear (mixed) model to a response variable that is assumed to come from a previous model fit, and thus each value in the data response variable has a full set of posterior MCMC samples associated with it. While this function can be used for a variety of situations (including objects that don’t come from spOccupancy), postHocLM() may be particularly useful for use with multi-species occupancy models to explore associations between species-specific covariate effect estimates from a multi-species occupancy model with species-level covariates, while fully accounting for uncertainty in the estimates. A vignette displaying how to do this will be posted in the coming months, but see the documentation for the function for basic instructions on how to use the function.
• Updated sfMsPGOcc() to allow a half-t prior on the community-level variance parameters. See documentation for more information on how to specify this. All multi-species occupancy model fitting functions will eventually be updated to allow for this prior, which can be a less informative prior when sample sizes (i.e., the number of species in this case) is low.
• Updated intPGOcc() and spIntPGOcc() to remove an error that may occur if a data set only has site level detection covariates.
• Updated getSVCSamples() to eliminate errors that prevented the function from working under certain circumstances depending on which covariates in the design matrix were modelled as spatially-varying coefficients.
• Updated tPGOcc() and stPGOcc() to eliminate an error that occurred when trying to run these models with single-visit data sets.
• Added in the mis.spec.type and scale.param arguments to the simTOcc() function to simulate multi-season detection-nondetection data under varying forms of model mis-specification. See simTOcc() documentation for detials. Thanks to Sara Stoudt for her help with this.
• Updated a typo in the MCMC sampler documentation for multi-species occupancy models. Specifically, the T in the mean component of Equations 23 and 24 from the MCMC samplers vignette was incorrect, and instead is now correctly T\(^{-1}\). Similarly, Equations 9 and 10 were updated in the MCMC samplers for factor models vignette. Note that these were just typos in the vignettes, the underlying models are correct.
• Fixed a bug that prevented ppcOcc() from working when there were only site-level random effects on detection. This also sometimes caused problems with cross-validation functionality as well. Thanks to Jose Luis Mena for bringing this to my attention.

spOccupancy 0.5.2

spOccupancy v0.5.2 contains an important bug fix in the cross-validation functionality for single-season occupancy models with unbalanced sampling across replicates in the data set. Specifically, the reported cross-validation deviance metrics may be inaccurate when one or more sites had a detection history where a missing value came before a non-missing value. For example, if one or more sites had a detection history of c(NA, 1, 0, 0, 1), this would lead to the problem occurring, but this would not occur if all missing values were at the end of the detection history (e.g., c(1, 0, 0, 1, NA)). The affected functions include the following: PGOcc(), spPGOcc(), msPGOcc(), spMsPGOcc(), lfMsPGOcc(), sfMsPGOcc(), intPGOcc(), spIntPGOcc(). We strongly encourage users who have performed cross-validation with these models and unbalanced sampling across replicates in the manner described to rerun their analyses using v0.5.2. We apologize for any troubles this has caused.

spOccupancy 0.5.1

• Fixed issues with unicode text in the manual for passing CRAN checks on Windows
• Fixed a bug in the k-fold cross-validation for models that include unstructured random intercepts on the occupancy portion of the model. This bug could have led to inacurrate cross-validation metrics when comparing a model with the unstructured random effect and without the unstructured random effect. We strongly encourage users who have performed cross-validation under such a scenario to rerun their analyses using v0.5.1.

spOccupancy 0.5.0

spOccupancy v0.5.0 contains numerous substantial updates that provide new functionality, improved run times for models with unstructured random effects, an important bug fix for cross-validation with unstructured random effects under certain scenarios, and some other minor bug fixes. The changes include:

• New functionality for fitting spatially-varying coefficient occupancy models. The function svcPGOcc() fits a single-season spatially-varying coefficient model, and svcTPGOcc() fits a multi-season spatially-varying coefficient model. We also include the functions svcPGBinom() and svcTPGBinom() for fitting spatially-varying coefficient generalized linear models when ignoring imperfect detection. We also include the helper function getSVCSamples() to more easily extract the SVC samples from the resulting model objects if they are desired.
• Updated the underlying C++ code to reduce run times for models that include unstructured random intercepts.
• Added the k.fold.only argument to all model-fitting functions, which allows users to only perform k-fold cross-validation instead of having to run the model first with the entire data set.
• Adjusted how random intercepts in the detection model were being calculated, which resulted in unnecessary massive objects when fitting a model with a large number of random effect levels and spatial locations. See GitHub issue 14.
• Fixed a bug that prevented prediction from working for multi-species models when X.0 was supplied as a data frame and not a matrix. See GitHub issue 13.
• Fixed an error that occurred when the detection-nondetection data were specified in a specific way. See GitHub issue 12.

spOccupancy 0.4.0

• Major new functionality for fitting multi-season (i.e., spatio-temporal) single-species occupancy models using the functions tPGOcc() and stPGOcc().
• Fixed a bug in calculation of the detection probability values in fitted() functions for all spOccupancy model objects. See this Github issue for more details.
• Fixed an error that occurred when predicting for multi-species models and setting ignore.RE = TRUE.
  
• Fixed other small bugs that caused model fitting functions to break under specific circumstances.

spOccupancy 0.3.2

• Fixed a bug in waicOcc() for integrated models (intPGOcc() and spIntPGOcc()) that sometimes resulted in incorrect estimates of WAIC for data sets other than the first data set. We strongly encourage users who have used waicOcc() with an integrated model to rerun their analyses using v0.3.2.
• Fixed a bug introduced in v0.3.0 that sometimes resulted in incorrect predictions from a spatially-explicit model with non-spatial random effects in the occurrence portion of the model. We strongly encourage users who have used predict() on a spatially-explicit model with non-spatial random effects in the occurrence portion of the model to rerun their analyses using v0.3.2.
• Users can now specify a uniform prior on the spatial variance parameter instead of an inverse-Gamma prior. We also allow users to fix the value of the spatial variance parameter at the initial value. See the reference pages of spatially-explicit functions for more details.
• Slight changes in the information printed when fitting spatially-explicit models.
• Removed dependency on spBayes to pass CRAN checks.

spOccupancy 0.3.1

• Fixed two small problems with intPGOcc() and spIntPGOcc() that were accidentally introduced in v0.3.0. See this Github issue for more details.
• Adapted C/C++ code to properly handle characters strings when calling Fortran BLAS/LAPACK routines following the new requirements for R 4.2.0.

spOccupancy 0.3.0

spOccupancy Version 0.3.0 contains numerous substantial updates that provide new functionality, improved computational performance for model fitting and subsequent model checking/comparison, and minor bug fixes. The changes include:

• Additional functionality for fitting spatial and non-spatial multi-species occupancy models with residual species correlations (i.e., joint species distribution models with imperfect detection). See documentation for lfMsPGOcc() and sfMsPGOcc(). We also included the functions lfJSDM() and sfJSDM() which are more typical joint species distribution models that fail to explicitly account for imperfect detection.
• All single-species and multi-species models allow for unstructured random intercepts in both the occurrence and detection portions of the occupancy model. Prior to this version, random intercepts were not supported in the occurrence portion of spatially-explicit models.
• predict() functions for single-species and multi-species models now include the argument type, which allows for prediction of detection probability (type = 'detection') at a set of covariate values as well as predictions of occurrence (type = 'occupancy').
• All models are substantially faster than version 0.2.1. We improved performance by implementing a change in how we sample the latent Polya-Gamma variables in the detection component of the model. This results in substantial increases in speed for models where the number of replicates varies across sites. We additionally updated how non-spatial random effects were sampled, which also contributes to improved computational performance.
• All model fitting functions now include the object like.samples in the resulting model object, which contains model likelihood values needed for calculation of WAIC. This leads to much shorter run times for waicOcc() compared to previous versions.
• All fitted.*() functions now return both the fitted values and the estimated detection probability samples from a fitted spOccupancy model.
• Improved error handling for models with missing values and random effects.
• Added the argument ignore.RE to all predict() functions. If non-spatial random intercepts are included when fitting the model, setting ignore.RE = TRUE will yield predictions that ignore the values of the random effects. If ignore.RE = FALSE, the model will predict new values using the random intercepts for both sampled and non-sampled levels of the effects.
  
• Fixed a bug in the cross-validation component of all spOccupancy model fitting functions that occurred when random effects were included in the occurrence and/or detection component of the model.
• Fixed minor bug in simOcc() and simMsOcc() that prevented simulating data with multiple random intercepts on detection.
• Fixed minor bug in spatially-explicit models that resulted in an error when setting NNGP = FALSE and not specifying initial values for the spatial range parameter phi.
• Fixed a bug in the predict() functions for spMsPGOcc and spPGOcc objects that resulted in potentially inaccurate predictions when n.omp.threads > 1.

spOccupancy 0.2.1

• Minor changes related to arguments in C++ code in header files to pass CRAN additional issues.

spOccupancy 0.2.0

• Added an n.chains argument to all model-fitting functions for running multiple chains in sequence.
• Added posterior means, standard deviations, Gelman-Rubin diagnostic (Rhat) and Effective Sample Size (ESS) to summary displays for each model-fitting function.
• Fixed spatially-explicit predict functions to return occurrence probabilities at sampled sites instead of NAs.

spOccupancy 0.1.3

• Minor bug fixes related to memory allocation in C++ code.

spOccupancy 0.1.2

• This is the first release of spOccupancy.