Package: boundedHill 0.1.0
Minji Lee
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.
Authors:
boundedHill_0.1.0.tar.gz
boundedHill_0.1.0.zip(r-4.7-any)boundedHill_0.1.0.zip(r-4.6-any)
boundedHill_0.1.0.tar.gz(r-4.7-any)boundedHill_0.1.0.tar.gz(r-4.6-any)
boundedHill_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
boundedHill/json (API)
| # Install 'boundedHill' in R: |
| install.packages('boundedHill', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:71e496d4cb. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 101 | ||
| source / vignettes | OK | 171 | ||
| linux-release | OK | 105 | ||
| windows-devel | OK | 61 | ||
| windows-release | OK | 73 | ||
| wasm-release | OK | 104 |
Exports:bh_beta_momentsbh_controlbh_diagnosebh_fitbh_log_likbh_posterior_drawsbh_posterior_predictbh_priorbounded_probabilityrbounded_binom
Dependencies:abindbackportsBayesLogitcheckmateclidistributionalgenericsgluelifecyclemagrittrmatrixStatsnumDerivpillarpkgconfigposteriorrlangtensorAtibbleutf8vctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Beta shapes from a prior mean and standard deviation | bh_beta_moments |
| Control Gibbs sampling | bh_control print.bh_control |
| Diagnose computation and data-range information | bh_diagnose print.bh_diagnostic |
| Fit Bayesian bounded logistic regression | bh_fit |
| Pointwise log-likelihood draws | bh_log_lik |
| Extract posterior draws | bh_posterior_draws |
| Draw from the posterior predictive distribution | bh_posterior_predict |
| Specify priors for bounded logistic regression | bh_prior print.bh_prior |
| Bounded logistic probabilities | bounded_probability |
| Plot a bounded logistic regression fit | plot.bounded_reg |
| Methods for bounded logistic regression fits | bounded_reg-methods coef.bounded_reg fitted.bounded_reg predict.bounded_reg print.bounded_reg print.summary.bounded_reg simulate.bounded_reg summary.bounded_reg |
| Generate bounded binomial outcomes | rbounded_binom |