Package: BayesDIP 0.1.1
BayesDIP: Bayesian Decreasingly Informative Priors for Early Termination Phase II Trials
Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials. See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>; Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.
Authors:
BayesDIP_0.1.1.tar.gz
BayesDIP_0.1.1.zip(r-4.5)BayesDIP_0.1.1.zip(r-4.4)BayesDIP_0.1.1.zip(r-4.3)
BayesDIP_0.1.1.tgz(r-4.4-any)BayesDIP_0.1.1.tgz(r-4.3-any)
BayesDIP_0.1.1.tar.gz(r-4.5-noble)BayesDIP_0.1.1.tar.gz(r-4.4-noble)
BayesDIP_0.1.1.tgz(r-4.4-emscripten)BayesDIP_0.1.1.tgz(r-4.3-emscripten)
BayesDIP.pdf |BayesDIP.html✨
BayesDIP/json (API)
NEWS
| # Install 'BayesDIP' in R: |
| install.packages('BayesDIP', repos = c('https://chenw10.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/chenw10/bayesdip/issues
Last updated 2 years agofrom:fa91ebb189. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 31 2024 |
| R-4.5-win | OK | Oct 31 2024 |
| R-4.5-linux | OK | Oct 31 2024 |
| R-4.4-win | OK | Oct 31 2024 |
| R-4.4-mac | OK | Oct 31 2024 |
| R-4.3-win | OK | Oct 31 2024 |
| R-4.3-mac | OK | Oct 31 2024 |
Exports:OneSampleBernoulliOneSampleBernoulli.DesignOneSampleNormal1OneSampleNormal1.DesignOneSampleNormal2OneSampleNormal2.DesignOneSamplePoissonOneSamplePoisson.DesignTwoSampleBernoulliTwoSampleBernoulli.Design
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| One sample Bernoulli model | OneSampleBernoulli |
| One sample Bernoulli model - Trial Design | OneSampleBernoulli.Design |
| One sample Normal model with one-parameter unknown, given variance | OneSampleNormal1 |
| One sample Normal model with one-parameter unknown, given variance | OneSampleNormal1.Design |
| One sample Normal model with two-parameter unknown - both mean and variance unknown | OneSampleNormal2 |
| One sample Normal model with two-parameter unknown - both mean and variance unknown | OneSampleNormal2.Design |
| One sample Poisson model | OneSamplePoisson |
| One sample Poisson model - Trial Design | OneSamplePoisson.Design |
| Two sample Bernoulli model | TwoSampleBernoulli |
| Two sample Bernoulli model - Trial Design | TwoSampleBernoulli.Design |
