move.HSMM.CI: Add confidence intervals to a move.HSMM object
In benaug/move.HMM: Fit HMM and HSMM animal movement models

move.HSMM.CI is used to add confidence intervals to a move.HSMM object. Current options are parametric bootstrap percentile CIs or CIs calculated from the finite differences Hessian. Bootstrapping utilizes parallel processing on a local host. For each bootstrap sample, NAs in the original data are inserted in the simulated data in the same positions. CI's based on the finite differences Hessian should generally be treated skeptically unless you've done a coverage analysis for your model with the amount of data you have. Bootstrap samples are stored in move.HSMM$store boot so they can be inspected or combined with more bootstrap samples.

1 2	move.HSMM.CI(move.HSMM, CI = "boot", alpha = 0.05, B = 100, cores = 2, stepm, iterlim, useRcpp = FALSE)

`move.HSMM`	A fitted move.HSMM object.
`CI`	A character determining which type of CI is to be calculated. Current options are "FD" for the finitie differences Hessian and "boot" for parametric bootstrapping and percentile CIs.
`alpha`	Type I error rate for CIs. alpha=0.05 for 95 percent CIs
`B`	Number of bootstrap resamples
`cores`	Number of cores to be used in parallell bootstrapping
`stepm`	a positive scalar which gives the maximum allowable scaled step length. stepm is used to prevent steps which would cause the optimization function to overflow, to prevent the algorithm from leaving the area of interest in parameter space, or to detect divergence in the algorithm. stepm would be chosen small enough to prevent the first two of these occurrences, but should be larger than any anticipated reasonable step.
`iterlim`	a positive integer specifying the maximum number of iterations to be performed before the nlm is terminated.
`useRcpp`	Logical indicating whether or not to use Rcpp.