Package: TBFmultinomial 0.1.3
TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].
Authors:
TBFmultinomial_0.1.3.tar.gz
TBFmultinomial_0.1.3.zip(r-4.5)TBFmultinomial_0.1.3.zip(r-4.4)TBFmultinomial_0.1.3.zip(r-4.3)
TBFmultinomial_0.1.3.tgz(r-4.4-any)TBFmultinomial_0.1.3.tgz(r-4.3-any)
TBFmultinomial_0.1.3.tar.gz(r-4.5-noble)TBFmultinomial_0.1.3.tar.gz(r-4.4-noble)
TBFmultinomial_0.1.3.tgz(r-4.4-emscripten)TBFmultinomial_0.1.3.tgz(r-4.3-emscripten)
TBFmultinomial.pdf |TBFmultinomial.html✨
TBFmultinomial/json (API)
| # Install 'TBFmultinomial' in R: |
| install.packages('TBFmultinomial', repos = c('https://rachelhey.r-universe.dev', 'https://cloud.r-project.org')) |
- VAP_data - Data on VAP acquistion in one ICU
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 6 years agofrom:b616719172. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 29 2024 |
| R-4.5-win | OK | Oct 29 2024 |
| R-4.5-linux | OK | Oct 29 2024 |
| R-4.4-win | OK | Oct 29 2024 |
| R-4.4-mac | OK | Oct 29 2024 |
| R-4.3-win | OK | Oct 29 2024 |
| R-4.3-mac | OK | Oct 29 2024 |
Exports:AIC_BIC_based_marginalLikelihoodCSVSmodel_priorsPIPs_by_landmarkingplot_CSVSPMPpostInclusionProbsample_multinomialTBFTBF_ingredients
Dependencies:cligluelifecyclemagrittrnnetplotrixrlangstringistringrvctrsVGAM
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Objective Bayesian variable selection for multinomial regression and discrete time-to-event models with competing risks | TBFmultinomial-package TBFmultinomial |
| Marginal likelihoods based on AIC or BIC | AIC_BIC_based_marginalLikelihood |
| Formulas of all the candidate models | all_formulas |
| Convert a PMP object into a data frame | as.data.frame.PMP |
| Cause-specific variable selection (CSVS) | CSVS |
| Prior model probability | model_priors |
| Posterior inclusion probabilities (PIPs) by landmarking | PIPs_by_landmarking |
| Plot a CSVS object | plot_CSVS |
| Posterior model probability | PMP |
| Class for PMP objects | PMP-class |
| Posterior inclusion probability (PIP) | postInclusionProb |
| Samples from a PMP object | sample_multinomial |
| Test-based Bayes factor | TBF |
| Ingredients to calculate the TBF | TBF_ingredients |
| Data on VAP acquistion in one ICU | VAP_data |
