Abstract
We describe some developments in the P OPAN system for the analysis of mark-recapture data from Jolly-Seber (JS) type experiments. The latest version, P OPAN-6, adopts the Design Matrix approach for specifying constraints and then uses it in the constrained maximization of the likelihood. We describe how this is done and the difference it makes to convergence and parameter identifiability over the constraint contrast-equation methods used in P OPAN-5. Then we show how the SIMULATE capabilities of P OPAN can be used to explore the properties of estimates, including their identifiability, precision, and robustness to model misspecification or capture heterogeneity.
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