glmmboot 0.6.0

Removing previously deprecated functions (deprecated as of 0.4.0)
Adding conditional evaluation for tests and the vignette (and the readme) for glmmTMB, in case it's not present / installable on a given system (as it's only a suggested package)
Removing call. = FALSE for errors and warnings
adding future.seed = TRUE to future.apply::future_lapply call for better RNG
Minor vignette and README copy edits

glmmboot 0.5.1

if parallelism = "parallel" but num_cores is left as NULL, we default to using parallel::detectCores() - 1L cores. This was in fact the documented behaviour previously, just sadly not the actual behaviour
bootstrap_model (and internal function bootstrap_runner) accepts an argument future_packages that will be passed along to future.apply::future.lapply; this is needed for futures that don't share memory, because the required global isn't visible (when using S3 generics...). This is also passed along to README/vignettes/tests etc.
Some documentation edits, shouldn't affect functionality
sampling message shown by default in interactive settings
fixing all incorrect uses of class (new matrix class is the biggest change) - mostly was in tests!

API changes:

BootGlmm is now bootstrap_model as the primary exported function
BootCI is now bootstrap_ci
Both of these functions should now work smoothly if your model returns a list of matrix coefficients; it will perform the bootstrap sampling on all of them
You basically have to supply base_data to bootstrap_model; to reflect this, it's now the second argument, prior to resamples
parallel functionality is improve, now supporting the future.apply::future_lapply backend (and some slightly more robust versions of e.g. parallel::mclapply if desired)
Added code coverage

For general narrowness bias, now resampling k-1 levels of random effects / n-1 rows of regular data (depending on existence of random effects). This can be turned off with narrowness_avoid = FALSE in BootGlmm.

Added a warning if data not supplied explicitly
Changed two-sided p-value calculation to more tightly respect the fact that the bootstrap t-values intentionally are not forced to be symmetric.