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  "Title": "Fitting Semi-Parametric Generalized log-Gamma Regression Models",
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  "Description": "Set of tools to fit a linear multiple or semi-parametric\nregression models with the possibility of non-informative\nrandom right or left censoring. Under this setup, the\nlocalization parameter of the response variable distribution is\nmodeled by using linear multiple regression or semi-parametric\nfunctions, whose non-parametric components may be approximated\nby natural cubic spline or P-splines. The supported\ndistribution for the model error is a generalized log-gamma\ndistribution which includes the generalized extreme value and\nstandard normal distributions as important special cases.\nInference is based on likelihood, penalized likelihood and\nbootstrap methods. Lastly, some numerical and graphical devices\nfor diagnostic of the fitted models are offered.",
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