Without using covariates (i.e., with capture probabilities assumed flat over the covariate space), select the best log-linear model for the marginal contingency table of capture pattern counts.

1 2 | ```
flat.IC(pop, models = make.hierarchical.term.sets(k = attributes(dt)$k),
rasch = FALSE, ic = "AICc", adjust = FALSE, averaging = FALSE)
``` |

`pop` |
A data.frame containing CRC data as output of |

`models` |
A list of models – or an expression that returns a
list of models – to be considered in local model search. The default is |

`rasch` |
Logical: Should the Rasch model (most basic version, Darroch
et. al. 1993) be considered, in addition to standard models? |

`ic` |
Character string specifying the information criterion to use for model selection. Currently AIC, AICc, BIC, and BICpi are implemented. |

`adjust` |
Logical: Should we adjust the cells as in Evans and Bonett (1995)? |

`averaging` |
Logical: Should we use model averaging based on the information criterion scores? |

`pred` |
The point estimate of the population size |

`form` |
The log-linear terms in the chosen model |

Zach Kurtz

Fienberg SE (1972). "The Multiple Recapture Census for Closed
Populations and Incomplete $2^k$ Contingency Tables." *Biometrika*,
**59**(3), pp. 591.

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