Select LLLMs for each row of the input data.

1 | ```
apply.ic.fit(ydens, models, ess, mct, ic, cell.adj, averaging, loud = TRUE)
``` |

`ydens` |
A matrix with 2^k-1 columns, one for each capture pattern. Each row sums to 1; these are empirical capture pattern probabilities. |

`models` |
A list of character vectors, with each vector containing
column names from the associated log-linear design matrix.
For example, see the output of |

`ess` |
A vector of effective sample sizes, one for each row of ydens. |

`mct` |
The number of population units that were observed for each row of ydens. |

`ic` |
The chosen information criterion. Currently implemented: "AIC", "AICc", "BIC", "BICpi". |

`cell.adj` |
Logical: TRUE means that the cell adjustment of Evans and Bonet (1995) is applied. |

`averaging` |
Logical: TRUE means that the information criterion weights are used to do model averaging, locally. |

`loud` |
Logical: TRUE means that the progress is noted by printing the number of the row of ydens currently being processed. |

See Kurtz (2013). Each row of `ydens`

corresponds to a covariate
vector, and contains a local average of multinomial capture pattern outcomes
across nearby points. `apply.ic.fit`

applies the function
`ic.fit`

at each row. The vector of local effective sample sizes is
crucial, and is specified in the `ess`

argument.

`lll` |
An object of class "lllcrc" |

Zach Kurtz

Kurtz (2013)

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