Package: OBsMD 12.0
Marta Nai Ruscone
OBsMD: Objective Bayesian Model Discrimination in Follow-Up Designs
Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.
Authors:
OBsMD_12.0.tar.gz
OBsMD_12.0.zip(r-4.7-x86_64)OBsMD_12.0.zip(r-4.7-arm64)OBsMD_12.0.zip(r-4.6-x86_64)OBsMD_12.0.zip(r-4.6-arm64)
OBsMD_12.0.tar.gz(r-4.7-arm64)OBsMD_12.0.tar.gz(r-4.7-x86_64)OBsMD_12.0.tar.gz(r-4.6-arm64)OBsMD_12.0.tar.gz(r-4.6-x86_64)
OBsMD_12.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
OBsMD/json (API)
| # Install 'OBsMD' in R: |
| install.packages('OBsMD', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- BM86.data - Data sets in Box and Meyer
- BM93.e1.data - Example 1 data in Box and Meyer
- BM93.e2.data - Example 2 data in Box and Meyer
- BM93.e3.data - Example 3 data in Box and Meyer
- MetalCutting - Data sets in Edwards, Weese and Palmer
- OBsMD.es5 - OBsMD.es5
- PB12Des - 12-run Plackett-Burman Design Matrix
- Reactor.data - Reactor Experiment Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:6a588c5f0d. Checks:9 OK, 1 WARNING. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 101 | ||
| linux-devel-x86_64 | OK | 123 | ||
| source / vignettes | OK | 210 | ||
| linux-release-arm64 | OK | 93 | ||
| linux-release-x86_64 | OK | 113 | ||
| windows-devel-arm64 | OK | 185 | ||
| windows-devel-x86_64 | OK | 89 | ||
| windows-release-arm64 | WARNING | 196 | ||
| windows-release-x86_64 | OK | 71 | ||
| wasm-release | OK | 93 |
Exports:combinationsOBsProbOMD
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Objective Bayesian Model Discrimination in Follow-Up Designs | OBsMD-package OBsMD |
| Data sets in Box and Meyer (1986) | BM86.data |
| Example 1 data in Box and Meyer (1993) | BM93.e1.data |
| Example 2 data in Box and Meyer (1993) | BM93.e2.data |
| Example 3 data in Box and Meyer (1993) | BM93.e3.data |
| Enumerate the Combinations of the Elements of a Vector | combinations |
| Data sets in Edwards, Weese and Palmer (2014) | MetalCutting |
| OBsMD.es5 | OBsMD.es5 |
| Objective Posterior Probabilities from Bayesian Screening Experiments | OBsProb |
| Objective Model Discrimination (OMD) in Follow-Up Experiments | OMD |
| 12-run Plackett-Burman Design Matrix | PB12Des |
| Plotting of Posterior Probabilities from Objective Bayesian Design | plot.OBsProb |
| Printing Objective Posterior Probabilities from Bayesian Design | print.OBsProb |
| Print Optimal OMD Follow-Up Experiments | print.OMD |
| Reactor Experiment Data | Reactor.data |
| Summary of Posterior Probabilities from Objective Bayesian Design | summary.OBsProb |
| Summary of Optimal OMD Follow-Up Experiments | summary.OMD |