Package: boundedHill
Type: Package
Title: Bayesian Logistic Regression with Lower and Upper Asymptotes
Version: 0.1.0
Authors@R: c(
    person("Minji", "Lee", email = "leeminji@unist.ac.kr",
           role = c("aut", "cre", "cph")),
    person("Chang Hyeong", "Lee", role = c("aut", "cph")),
    person("Ryung S.", "Kim", role = c("aut", "cph")))
Description: Fits Bayesian logistic regression models in which the
        success probability rises or falls between a lower and an upper
        asymptote instead of between 0 and 1, for binomial outcomes
        including binary data. The success probability equals the lower
        asymptote plus the difference between the two asymptotes
        multiplied by the logistic function of a linear predictor. The
        lower and upper asymptotes can be either estimated or fixed.
        With a single predictor such as log dose, this is the
        four-parameter logistic (Hill) model. Posterior computation is
        a full Gibbs sampler that requires no tuning.
License: GPL (>= 3)
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
Imports: BayesLogit, graphics, grDevices, posterior, stats
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-10-21 20:55:34 UTC; root
Author: Minji Lee [aut, cre, cph], Chang Hyeong Lee [aut, cph], Ryung
        S. Kim [aut, cph]
Maintainer: Minji Lee <leeminji@unist.ac.kr>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-21 14:40:08 UTC
RemoteUrl: https://github.com/cran/boundedHill
RemoteRef: HEAD
RemoteSha: 71e496d4cbc6be179b7cdfb1ecd3154483fc2380