Continuous‐time capture–recapture in closed populations

Summary The standard approach to fitting capture–recapture data collected in continuous time involves arbitrarily forcing the data into a series of distinct discrete capture sessions. We show how continuous‐time models can be fitted as easily as discrete‐time alternatives. The likelihood is factored so that efficient Markov chain Monte Carlo algorithms can be implemented for Bayesian estimation, available online in the R package ctime . We consider goodness‐of‐fit tests for behavior and heterogeneity effects as well as implementing models that allow for such effects.