  Traditional analyses of capture-recapture data are based on likelihood
functions that explicitly integrate out all missing data. We use a complete
data likelihood (CDL) to show how a wide range of capture-recapture models can
be easily fitted using readily available software JAGS/BUGS even when there are
individual-specific time-varying covariates. The models we describe extend
those that condition on first capture to include abundance parameters, or
parameters related to abundance, such as population size, birth rates or
lifetime. The use of a CDL means that any missing data, including uncertain
individual covariates, can be included in models without the need for
customized likelihood functions. This approach also facilitates modeling
processes of demographic interest rather than the complexities caused by
non-ignorable missing data. We illustrate using two examples, (i) open
population modeling in the presence of a censored time-varying individual
covariate in a full robust-design, and (ii) full open population multi-state
modeling in the presence of a partially observed categorical variable.
