                        Changes in version 0.8.2                        

  - Created a new function simFPOcc() that simulates data for the
    false-positive occupancy model of Chambert et al. (2018).
  - Fixed multiple bugs in prediction code that led to wrong predictions
    with specific combinations of non-spatial random effects (#58).
  - Fixed bug in prediction for NNGP = FALSE and an indexing problem
    with spMsPGOcc that could lead to scrambled output (#58).
  - Fixed a variety of small errors in prediction functions that led to
    function crashes (#58).
  - Fixed minor defects in multiple simulation functions (#59).

                 Changes in version 0.8.1 (2026-08-21)                  

  - Fixed a bug in stMsPGOcc() and svcTMsPGOcc() that prevented the
    models from running when there was only 1 within-season replicate.
    Thanks to Kevin Lafferty for bringing this to my attention.
  - Fixed a bug in cross-validation of integrated models that led to the
    function crashing in certain situations, particularly on Windows
    machines. Thanks to Frederico Martins for bringing this to my
    attention.
  - Fixed a bug in the predict() function for svcPGOcc() (e.g.,
    predict.svcPGOcc()) when using the grid.index functionality. When
    using predict(), a warning message would be displayed saying that
    there was a mismatch in the lengths of different vectors. This was a
    problem that resulted in inaccurate predictions when using
    svcPGOcc() and specifying the resolution of the SVCs as something
    higher than the point locations using the grid.index functionality.
    If you did this, I strongly encourage you to rerun the functions
    with v0.8.1, and I apologize for the inconvience this caused. Thanks
    very much to Ben Goldstein for pointing this out. Note that this bug
    did not affect results if using svcPGOcc() without specifying
    grid.index in the data list (which is likely the vast majority of
    instances for when svcPGOcc() is used).
  - Fixed bug in prediction when a data frame was supplied to the X.0
    argument (PR 54, thanks to Juan Vargas Soto for pointing this out).
  - Updated contact email in function docs.
  - Fixed an error that occurred in very specific situations when
    running multi-season models. If all covariates in either the
    detection and/or occupancy portion of the model were site-level
    covariates, they were all supplied to the data list as a vector, and
    at least two of the covariates were of a different type (e.g.,
    double vs. character) then the covariates would all get converted to
    a single type. This usually resulted in the situation when a
    continuous covariate was converted to a categorical covariate, which
    was then evident in the resulting model output. Thanks to José
    Ribeiro for bringing this to my attention (issue #49.

                 Changes in version 0.8.0 (2024-12-14)                  

  - For queries on anything related to spOccupancy (and spAbundance),
    please use the new spOccupancy/spAbundance mailing list.
  - 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. I am behind
    on adding vignettes for some of the newer functionality (sorry!),
    but adding a vignette for this new functionality is on my todo list.
    If interested in using these functions and you're having problems
    fitting them, please send your questions to the mailing list.
  - 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.
  - Updated simMsIntPGOcc() to now include simulation of data sets with
    spatially-varying coefficients and unstructured random effects on
    both occurrence and detection.
  - Fixed a bug in the k-fold cross-validation for spatial integrated
    occupancy models (NNGP models only) that could lead to incorrect
    model deviance results under certain situations depending on how the
    spatial coordinates were ordered on the user-side relative to how
    they are re-ordered when fitting the model. If using spIntPGOcc()
    with NNGP = TRUE and using cross-validation, I suggest re-running
    the analysis. Apologies for the inconvenience.
  - Added in a residuals() function to extract occupancy and detection
    residuals following the approach of Wilson et al. (2019) for
    single-season, single-species occupancy models (functions PGOcc(),
    spPGOcc(), and svcPGOcc()). I'm hoping to implement this for all
    model functions and improve GoF functionality. If anyone has any
    interest in helping out with this, then please let me know!
  - waicOcc() for integrated single-species models is now substantially
    faster.
  - updateMCMC() now works with lfJSDM.
  - Fixed a bug in updateMCMC() that prevented it from working with
    spAbundance::msAbund() when there were random effects in the model.
    Also added the save.fitted argument to updateMCMC() to allow it to
    work with msAbund() and not save the replicate/fitted data values in
    cases where the amount of RAM is an important consideration.
  - 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.

                 Changes in version 0.7.6 (2024-04-19)                  

  - Fixed a memory problem in the saving of the tuning values for
    svcTPGOcc models that required updating v0.7.3 on CRAN to pass
    valgrind checks, as well as a memory leak in the calculation of the
    nearest neighbors, and a small problem with the DESCRIPTION file for
    including on CRAN.

                        Changes in version 0.7.5                        

  - See v0.7.6

                        Changes in version 0.7.4                        

  - See v0.7.5

                 Changes in version 0.7.3 (2024-03-28)                  

  - Fixed a problem that could arise when calculating Rhat in all models
    when running multiple chains (but usually only happened in
    multispecies models) when there was a high amount of correlation
    between parameter estimates. This would lead to the model running
    completely, but then failing after all chains have been run. This
    most often occurred when fitting a multispecies model with a lot of
    rare species. Thanks to Marc Kery for bringing this to my attention.
  - Added in a check at the top of all model fitting functions to return
    an error when the number of posterior samples saved based on the
    MCMC criteria (n.batch, batch.length, n.samples, n.burn, n.thin,
    n.chains) are specified in a way that leads to a non-integer value.
    In such situations, models would previously run and return without
    an error, but sometimes the last posterior sample in any given chain
    could have widely inaccurate values, or values that prevented
    subsequent functions from working. Thanks to Wendy Leuenberger and
    Colin Swider for bringing this to my attention.
  - Added in functionality for fitting spatially-explicit models where
    the spatial random effects (or spatially varying coefficients) are
    not specified at the individual site, but rather are specified at a
    larger spatial resolution. This is accomplished using a new
    component of the data list supplied to model fitting functions
    called grid.index. This is useful for data sets where there is some
    sort of nested structuring among the data collection protocol, such
    that you may wish to specify the spatial random effects at a lower
    resolution than each individual location. Further, it can be
    particularly useful for SVC models where you only want to specify
    nonstationarity at a lower spatial resolution (e.g., across a set of
    grid cells). This is currently implemented for the following
    functions: spPGOcc, sfMsPGOcc, stMsPGOcc, stPGOcc, svcPGOcc,
    svcTMsPGOcc, svcTPGOcc. See the documentation for a given model
    function for how to specify this. I am hoping to eventually write up
    a small example that shows how to do this, but for now documentation
    is fairly limited to just the manual pages for each function. Feel
    free to contact me if you want to use this functionality and have
    any questions.
  - Added in the updateMCMC() function. This function is in active
    development, but it will ultimately allow for all spOccupancy and
    spAbundance model objects to be updated with additional MCMC
    samples, instead of having to completely rerun an MCMC analysis if
    adequate burn-in/convergence was not reached. It currently works for
    the function sfJSDM() in spOccupancy and msAbund() in spAbundance.
  - Added in the ability to specify independent priors for the
    species-level regression coefficients for two functions: svcTMsPGOcc
    and sfJSDM. This is done by setting the tags independent.betas and
    independent.alphas to TRUE. This will fix the values of the
    community-level mean and variance parameters to the initial values
    specified in inits. This is equivalent to setting an independent
    Gaussian prior for each of the species-specific regression
    coefficients, which may potentially be useful in certian situations
    where the assumption of normality in the distribution of the
    species-level effects is not well met. This functionality will
    eventually be incorporated for all multi-species models.
  - Fixed a bug in intMsPGOcc() 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 third 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".
    This was previously fixed in all other model fitting functions.
  - Wrote a new "vignette" (really more of a blog post) on some
    recommendations to help improve interpretability of inferences in
    SVC models.
  - Fixed a few typos in the MCMC sampler vignettes for factor models
    and SVC models.
  - Fixed a bug that prevented cross-validation from working properly in
    multi-species models when setting k.fold.only = TRUE. Thanks to Zack
    Steel for pointing this out.
  - Fixed a typo in the generation of initial values for latent
    unstructured random effects in all model functions. The typo had no
    major ramifications, if anything it would have just led to slower
    convergence, as it resulted in very large (or very small) initial
    values for the latent random effects that are not really viable on
    the logit scale.

                 Changes in version 0.7.2 (2023-11-01)                  

  - Added in functionality for using the plot() function to generate
    simple traceplots using spOccupancy model objects. Details can be
    found in the help page (e.g., for spPGOcc models, type ?plot.spPGOcc
    in the console).
  - Not an update to the package, but a new vignette has been posted on
    testing model identifiability using spOccupancy. Thanks to Sara
    Stoudt for writing this!
  - Added in the ability to fit lfJSDM() without residual species
    correlations by setting n.factors = 0. This is a model analogous to
    msPGOcc(), but without the detection component.
  - Added in the shared.spatial argument to sfJSDM(). If set to TRUE,
    this argument estimates a common spatial process for all species
    instead of using the default spatial factor modeling approach.
  - Fixed a bug in predict.svcTMsPGOcc() when same variable was used for
    a fixed and random effect (e.g., if including a linear year trend
    and also an unstructured random intercept for year). Thanks to Liam
    Kendall for pointing this out.

                 Changes in version 0.7.1 (2023-08-31)                  

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

                 Changes in version 0.7.0 (2023-08-16)                  

spOccupancy v0.7.0 contains a variety of substantial updates, most
notably functionality for fitting non-spatial and spatial multi-species
multi-season occupancy models, as well as multi-species
spatially-varying coefficient models. There are also a variety of
smaller bug fixes/additional error handling that will help eliminate
some common hard-to-interpret errors that users encountered.

  - New functionality for fitting multi-species, multi-season occupancy
    models. The function tMsPGOcc() fits non-spatial, multi-season,
    multi-species occupancy models, and the function stMsPGOcc() fits
    spatial, multi-season occupancy models. The spatially-explicit
    function also inherently accounts for species correlations with a
    spatial factor modeling approach (e.g., they are joint species
    distribution models with imperfect detection and species
    correlations). See Doser et al. 2023 for statistical details on the
    spatial factor modeling approach. A vignette will be posted that
    details fitting these models in depth in the coming months, but the
    syntax is essentially a combination of multi-species models (e.g.
    msPGOcc(), sfMsPGOcc()) and multi-season single-species models
    (i.e., tPGOcc() and stPGOcc()), so the recommendations provided in
    the vignettes for those models is applicable for these models as
    well.
  - New functionality for multi-species spatially-varying coefficient
    occupancy models for single-season (svcMsPGOcc()) and multi-season
    (svcTMsPGOcc()) models. These approaches use a spatial factor
    modeling approach for each of the SVCs to make the models relatively
    computationally efficient. The functions inherently account for
    species correlations. The vignette on spatially-varying coefficients
    provides an example for svcMsPGOcc(), with an example for
    svcTMsPGOcc() coming soon.
  - The function simTMsOcc() simulates multi-season, multi-species
    occupancy models.
  - Updated getSVCSamples() to now work with multi-species
    spatially-varying coefficient models.
  - Added in a new check in all spatially-explicit models to see if all
    the spatial coordinates in the data$coords object were unique, as
    this is a requirement for spOccupancy spatially-explicit models. In
    previous versions, this resulted in an error of c++ error: dpotrf
    failed, or something along those lines, which was a common source of
    confusion.
  - Updated all model fitting functions to avoid running for a long
    time, just to eventually crash. Now, if trying to run models and
    save an object that is too large for memory, R should crash at the
    beginning. This occurred in situations where the n.burn argument was
    greater than 0 and/or n.thin was greater than 1. Thanks to Alex
    Bacjz for bringing this to my attention.
  - Added in the by.sp argument to waicOcc() to allow users to calculate
    WAIC separately for individual species in all multi-species model
    types in spOccupancy.
  - Minor updates to multiple vignettes to reflect changes since their
    original versions.

                 Changes in version 0.6.1 (2023-03-11)                  

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

                 Changes in version 0.6.0 (2023-03-03)                  

  - 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.

                 Changes in version 0.5.2 (2022-12-21)                  

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.

                 Changes in version 0.5.1 (2022-12-08)                  

  - 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.

                 Changes in version 0.5.0 (2022-11-16)                  

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.

                 Changes in version 0.4.0 (2022-07-13)                  

  - 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.

                 Changes in version 0.3.2 (2022-05-21)                  

  - 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.

                 Changes in version 0.3.1 (2022-04-13)                  

  - 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.

                 Changes in version 0.3.0 (2022-03-29)                  

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.

                 Changes in version 0.2.1 (2022-01-07)                  

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

                 Changes in version 0.2.0 (2021-12-19)                  

  - 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.

                 Changes in version 0.1.3 (2021-11-25)                  

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

                 Changes in version 0.1.2 (2021-11-11)                  

  - This is the first release of spOccupancy.
