Description Usage Arguments Value Examples

This function optimizes a model using information critera as a decision rule. This solves many of the problems related to model selection based on hypothesis tests, see also `opt.h`

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In finite samples, the AIC is known to favor large models. The corrected AIC is a slightly stricter measure.

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`model` |
The model to be optimized. Supports "lm" for the linear probability model, "logit" for the logistic probability model, and "probit" for the probit model. |

`Y` |
The binary response variable. |

`X` |
A dataframe with collumns of exogenous regressors. |

`KLIC` |
information criterion to be used, "AIC" or "AICc", See also |

`returntype` |
"model", "data", or "colnames" |

`tracelevel` |
level of printing. |

`memorymanagement` |
logical, indicating whether memory should be more actively managed. |

"model", "data", or "colnames", to be specified in returnype.

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