# Compute an IC for several LLMs

### Description

Given several sets of log-linear terms, compute the IC corresponding to each model.

### Usage

1 | ```
ic.all(models, ddat, ic, normalized = normalized)
``` |

### Arguments

`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 |

`ddat` |
The log-linear design matrix. |

`ic` |
The information criterion, such as AIC, AICc, BIC, or BICpi. |

`normalized` |
Logical: TRUE means that beta0 will be adjusted so that the log-linear model corresponds to cell probabilities instead of expected cell counts. |

### Value

A matrix with as many rows as there are entries in `models`

.
The columns contain the point estimates of the population size, the
information criterion scores, and the information criterion weights for all
the models, which sum to one

### Author(s)

Zach Kurtz