ic.fit: Select and fit an LLM
In lllcrc: Local Log-linear Models for Capture-Recapture

Use an information criterion to select a local log-linear model

1 2	ic.fit(densi, models, N, ic, averaging = averaging, normalized = TRUE, rasch = FALSE)

`densi`	A matrix with one row and 2^k-1 column containing cell counts or empirical cell probabilities corresponding to all the possible capture patterns.
`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 `make.hierarchical.term.sets()`.
`N`	If you multiply `densi` by `N` and then sum over the resulting vector, you should get the effective sample size.
`ic`	The information criterion, such as AIC, AICc, BIC, or BICpi.
`averaging`	Logical: TRUE means that we use information criterion scores to do model averaging.
`normalized`	Logical: TRUE means that beta0 will be adjusted so that the log-linear model corresponds to cell probabilities instead of expected cell counts.
`rasch`	Logical: TRUE means that the Rasch model is a candidate.