boundedHill: Bayesian Logistic Regression with Lower and Upper Asymptotes
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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
BayesLogit, graphics, grDevices, posterior, stats |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2026-10-21 |
| DOI: |
10.32614/CRAN.package.boundedHill (may not be active yet) |
| Author: |
Minji Lee [aut, cre, cph],
Chang Hyeong Lee [aut, cph],
Ryung S. Kim [aut, cph] |
| Maintainer: |
Minji Lee <leeminji at unist.ac.kr> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Language: |
en-US |
| Citation: |
boundedHill citation info |
| Materials: |
README |
| CRAN checks: |
boundedHill results |
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