Actually those were removed from RMark awhile back and put into a package called marked that is on CRAN. All of my code is open access and available at
https://github.com/jlaake.
The marked package now contains 2 implementations of the CJS model. The first is based on Shirley Pledger's hierarchical approach for handling 0 tail of the capture history. This is implemented in FORTRAN within the package but the R code is also shown in the help file for cjs.lnl (?cjs.lnl) except that it will not handle loss on captures. The FORTRAN code does handle loss on captures. That approach is also implemented in an ADMB TPL (c++ like) file and if you set use.admb=TRUE then it will use ADMB code. This allows fitting mixed effect models but this is in its infancy somewhat.
The second approach and what I consider to be the best approach uses hidden Markov models (HMM). The reason I think it is the best approach is because it easily extends to much more complicated models. I've not used it but from what I understand it is the basis of Esurge. See help ?HMMLikelihood and associated model-specific functions. Also, see
http://www.afsc.noaa.gov/Publications/ProcRpt/PR2013-04.pdf which provides code for a purely R implementation of HMM. I use a FORTRAN version in marked because it is faster than pure R version. For a citation for marked software see
http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12065/abstract.
Please, do not run off and start using marked over MARK! I recommend sticking with MARK and RMark (if it suits your needs) unless there is something in marked that isn't in MARK or you have models with lots of individual covariates and need to speed up the execution. I don't have the same infrastructure that MARK does, nor will you get the same level of documentation and help. I am adding new models for my open purposes that may also be useful for others at some point.
But you are certainly welcome to explore and use the code as you see fit. Just don't come to me if you modify the code and it no longer works. I recommend you focus your efforts on the HMM approach.
regards --jeff