  We develop some new strategies for building and fitting new flexible classes
of parametric capture-recapture models for closed populations which can be used
to address a better understanding of behavioural patterns. We first rely on a
conditional probability parameterization and review how to regard a large
subset of standard capture-recapture models as a suitable partitioning in
equivalence classes of the full set of conditional probability parameters. We
then propose the use of new suitable quantifications of the conditioning binary
partial capture histories as a device for enlarging the scope of flexible
behavioural models and also exploring the range of all possible partitions. We
show how one can easily find unconditional MLE of such models within a
generalized linear model framework. We illustrate the potential of our approach
with the analysis of some known datasets and a simulation study.
