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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 |
| 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 |
| Reference manual: | boundedHill.html , boundedHill.pdf |
| Package source: | boundedHill_0.1.0.tar.gz |
| Windows binaries: | r-devel: boundedHill_0.1.0.zip, r-release: boundedHill_0.1.0.zip, r-oldrel: boundedHill_0.1.0.zip |
| macOS binaries: | r-release (arm64): boundedHill_0.1.0.tgz, r-oldrel (arm64): boundedHill_0.1.0.tgz, r-release (x86_64): boundedHill_0.1.0.tgz, r-oldrel (x86_64): boundedHill_0.1.0.tgz |
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