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  "Title": "Generalized Linear Density Ratio Models",
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  "Description": "Fits a generalized linear density ratio model (GLDRM). A\nGLDRM is a semiparametric generalized linear model. In contrast\nto a GLM, which assumes a particular exponential family\ndistribution, the GLDRM uses a semiparametric likelihood to\nestimate the reference distribution. The reference distribution\nmay be any discrete, continuous, or mixed exponential family\ndistribution. The model parameters, which include both the\nregression coefficients and the cdf of the unspecified\nreference distribution, are estimated by maximizing a\nsemiparametric likelihood. Regression coefficients are\nestimated with no loss of efficiency, i.e. the asymptotic\nvariance is the same as if the true exponential family\ndistribution were known. Huang (2014)\n<doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012)\n<doi:10.1093/biomet/asr075>. Rathouz and Gao (2008)\n<doi:10.1093/biostatistics/kxn030>.",
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  "Repository": "https://cran.r-universe.dev",
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      "title": "Fits a generalized linear density ratio model (GLDRM)",
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