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TBFmultinomial
View on CRAN: Click
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Download and install TBFmultinomial package within the R console
Install from CRAN:
install.packages("TBFmultinomial")
Install from Github:
library("remotes")
install_github("cran/TBFmultinomial") Install by package version:
library("remotes")
install_version("TBFmultinomial", "0.1.3") Attach the package and use:
library("TBFmultinomial")
Maintained by
Rachel Heyard
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-11-02
Latest Update: 2018-10-12
Description:
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>].
How to cite:
Rachel Heyard (2017). TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes. R package version 0.1.3, https://cran.r-project.org/web/packages/TBFmultinomial. Accessed 04 Jun. 2026.
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Complete documentation for TBFmultinomial
Functions, R codes and Examples using
the TBFmultinomial R package
Some associated functions: AIC_BIC_based_marginalLikelihood . CSVS . PIPs_by_landmarking . PMP-class . PMP . TBF . TBF_ingredients . TBFmultinomial-package . VAP_data . all_formulas . as.data.frame . model_priors . plot_CSVS . postInclusionProb . sample_multinomial .
Some associated R codes: AIC_BIC_based_marginalLikelihood.R . CSVS.R . CSVS_plot.R . M.R . PIPs_by_landmarking.R . PMP-class.R . PMP-methods.R . PMP.R . TBF.R . TBF_ingredients.R . TBFmultinomial-package.R . VAP_data.R . all_formulas.R . model_priors.R . nullModel_formula.R . postInclusionProb.R . sample_multinomial.R . Full TBFmultinomial package functions and examples
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