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  "Description": "Quantile-based estimators (Q-estimators) fit any\nparametric distribution through its quantile function, by\nminimizing the integrated loss of quantile regression. They are\nusually more robust than maximum likelihood estimators, and\nallow for censored and truncated data. The package implements\nuser-defined quantile functions, built-in families (normal,\ngamma, continuous Poisson, uniform), linear models and\nproportional hazards models. The method is described in Sottile\nand Frumento (2022) <doi:10.1016/j.csda.2022.107471>.",
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  "Author": "Gianluca Sottile [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9347-7251>), Paolo Frumento [aut]",
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      "title": "Baseline Cumulative Hazard of a Qcoxph Model",
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      "title": "Inverse of a Quantile Function",
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      "title": "Diagnostic Plot of Q-Estimators",
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      "title": "Predictions from a Qcoxph Model",
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      "title": "Predictions from Q-Estimators",
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      "title": "Predictions from Qlm Models",
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    },
    {
      "page": "Qcoxph",
      "title": "Q-Estimation of Proportional Hazards Regression Models",
      "topics": [
        "Qcoxph"
      ]
    },
    {
      "page": "Qest",
      "title": "Quantile-Based Estimators (Q-Estimators)",
      "topics": [
        "Qest"
      ]
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        "Qest.control"
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      "title": "Family Objects for Qest",
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        "Qgamma",
        "Qnorm",
        "Qpois",
        "Qunif"
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      "title": "Q-Estimation of Linear Regression Models",
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        "Qlm.fit"
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        "print.summary.Qlm",
        "summary.Qest",
        "summary.Qlm"
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    },
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        "1. The Q-estimator",
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