CjsModel: Cormack-Jolly-Seber model class
In r-glennie/openpopscr: Open Population Spatial Capture-Recapture

Cormack-Jolly-Seber model: fits model, formats inference, and simulates from fitted model.

form: a named list of formulae for each parameter (~1 for constant)
scr_data: a ScrData object
start: a named list of starting values
num_cores (optional, default = 1): number of processors cores to use in parallelised code
print: (defualt TRUE) if TRUE then useful output is printed

Methods include:

get_par(name, j, k): returns value of parameter "name" for detector j on occasion k (if j, k omitted, then returns value(s) for all)
set_par(par): can change the parameter the model uses. Note, the model will simulate data using this parameter, but will only present inference based on the maximum likelihood estimates.
set_mle(mle, V, llk): sets model at maximum likelihood with parameters mle, covariance matrix V, and likelihood value llk
calc_initial_distribution(): computes initial distribution over life states (alive, dead)
calc_tpms(): returns list of transition probability matrix for each occasion
calc_pr_capture(): returns array where (i,k,m) is probability of capture record on occasion k for individual i given activity centre at mesh point m
calc_llk(): compute log-likelihood at current parameter values
fit: fit the model by obtaining the maximum likelihood estimates
simulate(): simulate ScrData object from fitted model
par(): return current parameter of the model
mle(): return maximum likelihood estimates for the fitted model
data(): return ScrData that the model is fit to
estimates(): return estimates in a easy to extract list
cov_matrix(): return variance-covariance matrix from fitted model (on working scale)
mle_llk(): return log-likelihood value of maximum likelihood estimates