Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known.

Version: | 1.4 |

Depends: | R (≥ 3.2.2) |

Imports: | stats (≥ 3.2.2) |

Suggests: | testthat (≥ 1.0.2) |

Published: | 2017-12-06 |

Author: | Michael Wurm [aut, cre], Paul Rathouz [aut] |

Maintainer: | Michael Wurm <wurm at uwalumni.com> |

License: | MIT + file LICENSE |

NeedsCompilation: | no |

CRAN checks: | gldrm results |

Reference manual: | gldrm.pdf |

Package source: | gldrm_1.4.tar.gz |

Windows binaries: | r-devel: gldrm_1.4.zip, r-release: gldrm_1.4.zip, r-oldrel: gldrm_1.4.zip |

OS X El Capitan binaries: | r-release: gldrm_1.4.tgz |

OS X Mavericks binaries: | r-oldrel: gldrm_1.4.tgz |

Old sources: | gldrm archive |

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