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  "Description": "Fits Bayesian logistic regression models in which the\nsuccess probability rises or falls between a lower and an upper\nasymptote instead of between 0 and 1, for binomial outcomes\nincluding binary data. The success probability equals the lower\nasymptote plus the difference between the two asymptotes\nmultiplied by the logistic function of a linear predictor. The\nlower and upper asymptotes can be either estimated or fixed.\nWith a single predictor such as log dose, this is the\nfour-parameter logistic (Hill) model. Posterior computation is\na full Gibbs sampler that requires no tuning.",
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      "title": "Methods for bounded logistic regression fits",
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        "predict.bounded_reg",
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