This package is considered a duplicate. The official version of this package is found at:https://czhong9106.r-universe.dev/BayesOrdDesign

Package: BayesOrdDesign 0.1.2

Chengxue Zhong

BayesOrdDesign: Bayesian Group Sequential Design for Ordinal Data

The proposed group-sequential trial design is based on Bayesian methods for ordinal endpoints, including three methods, the proportional-odds-model (PO)-based, non-proportional-odds-model (NPO)-based, and PO/NPO switch-model-based designs, which makes our proposed methods generic to be able to deal with various scenarios. Richard J. Barker, William A. Link (2013) <doi:10.1080/00031305.2013.791644>. Thomas A. Murray, Ying Yuan, Peter F. Thall, Joan H. Elizondo, Wayne L.Hofstetter (2018) <doi:10.1111/biom.12842>. Chengxue Zhong, Haitao Pan, Hongyu Miao (2021) <arxiv:2108.06568>.

Authors:Chengxue Zhong [aut, cre], Haitao Pan [aut], Hongyu Miao [aut]

BayesOrdDesign_0.1.2.tar.gz
BayesOrdDesign_0.1.2.tar.gz(r-4.5-noble)BayesOrdDesign_0.1.2.tar.gz(r-4.4-noble)
BayesOrdDesign_0.1.2.tgz(r-4.4-emscripten)BayesOrdDesign_0.1.2.tgz(r-4.3-emscripten)
BayesOrdDesign.pdf |BayesOrdDesign.html✨
BayesOrdDesign/json (API)

# Install 'BayesOrdDesign' in R:

install.packages('BayesOrdDesign', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC
c++– GNU Standard C++ Library v3

Datasets:

example.data - Clinical ordinal endpoints and treatments assignment for 200 patient

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 288 downloads 8 exports 89 dependencies

Last updated 2 years agofrom:481af78f02. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 18 2024
R-4.5-linux	OK	Nov 18 2024

Exports:Bayes_ord get_oc_NPO get_oc_PO get_oc_Switch rjmcmc_func ss_npo ss_po ss_switch

Dependencies:abind base64enc bigD bitops boot bslib cachem cli coda colorspace commonmark cpp11 curl digest dplyr evaluate expm fansi farver fastmap fontawesome fs generics ggplot2 glue gsDesign gt gtable highr htmltools htmlwidgets isoband jquerylib jsonlite juicyjuice knitr labeling lattice lifecycle madness magrittr markdown MASS Matrix matrixcalc memoise mgcv mime munsell mvtnorm nlme numDeriv ordinal pillar pkgconfig purrr R2jags r2rtf R2WinBUGS R6 rappdirs RColorBrewer Rcpp reactable reactR rjags rjmcmc rlang rmarkdown sass scales schoolmath stringi stringr superdiag tibble tidyr tidyselect tinytex ucminf utf8 V8 vctrs viridisLite withr xfun xml2 xtable yaml

Help page	Topics
Bayesian ordinal regression analysis Estimate the correlation coefficients of treatment variable, with and without the proportional odds assumption	Bayes_ord
Clinical ordinal endpoints and treatments assignment for 200 patient	example.data
Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption	get_oc_NPO
Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints with proportional odds assumption	get_oc_PO
Generate operating characteristics for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption.	get_oc_Switch
Perform reversible-jump MCMC post-process to select appropriate model between proportional odds (PO) model and non-proportional odds (NPO) model	rjmcmc_func
Determine the sample size for Bayesian two-stage trial design of ordinal endpoints without proportional odds assumption	ss_npo
Determine the sample size for Bayesian two-stage trial design of ordinal endpoints with proportional odds assumption	ss_po
Determine the sample size for Bayesian two-stage trial design for ordinal endpoints based on switch model	ss_switch

Package: BayesOrdDesign 0.1.2

BayesOrdDesign: Bayesian Group Sequential Design for Ordinal Data

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Help Manual

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