move.HSMM.mllk.full: Compute negative log likelihood of HSMM using all parameters
In benaug/move.HMM: Fit HMM and HSMM animal movement models

This function computes the negative log likelihood of the hidden Markov model. using all parameters, untransformed. It is used to get the covariance matrix of the fitted model.

1 2	move.HSMM.mllk.full(parvect, obs, PDFs, CDFs, skeleton, nstates, m1, ini, useRcpp = FALSE)

`parvect`	The vector of parameters to be estimated
`obs`	A n x ndist matrix of data. If ndist=1, obs must be a n x 1 matrix.
`PDFs`	A list of PDFs for the dwell time and ndist observation distributions.
`CDFs`	A list of CDFs for the dwell time and ndist observation distributions.
`skeleton`	A list with the original parameter structure used to reassemble parvect
`nstates`	Number of hidden states
`m1`	a vector of length nstates that specifies how many states will be used to approximate each state of the HSMM (see Langrock and Zuchinni 2011)
`ini`	numeric value that specifies how the initial state distribution is calculated. 0 sets the initial distribution to the stationary distribution. If this matrix is not invertible, 1 sets the initial distribution for each state within each state agreggate to 1/m(state).
`useRcpp`	Logical indicating whether or not to use Rcpp.