Package: DTAplots 1.0.2.5
DTAplots: Creates Plots Accompanying Bayesian Diagnostic Test Accuracy Meta-Analyses
Function to create forest plots. Functions to use posterior samples from Bayesian bivariate meta-analysis model, Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis model or Bayesian latent class (LC) meta-analysis model to create Summary Receiver Operating Characteristic (SROC) plots using methods described by Harbord et al (2007)<doi:10.1093/biostatistics/kxl004>.
Authors:
DTAplots_1.0.2.5.tar.gz
DTAplots_1.0.2.5.zip(r-4.5)DTAplots_1.0.2.5.zip(r-4.4)DTAplots_1.0.2.5.zip(r-4.3)
DTAplots_1.0.2.5.tgz(r-4.4-any)DTAplots_1.0.2.5.tgz(r-4.3-any)
DTAplots_1.0.2.5.tar.gz(r-4.5-noble)DTAplots_1.0.2.5.tar.gz(r-4.4-noble)
DTAplots_1.0.2.5.tgz(r-4.4-emscripten)DTAplots_1.0.2.5.tgz(r-4.3-emscripten)
DTAplots.pdf |DTAplots.html✨
DTAplots/json (API)
NEWS
| # Install 'DTAplots' in R: |
| install.packages('DTAplots', repos = c('https://ianschiller.r-universe.dev', 'https://cloud.r-project.org')) |
- Anti_CCP - Anti-CCP dataset
- Xpert - Xpert dataset
- posterior_samples_Bivariate - Samples of the posterior distributions obtained from a Bayesian bivariate meta-analysis model
- posterior_samples_HSROC - Samples of the posterior distributions obtained from a Bayesian HSROC meta-analysis model
- posterior_samples_LC - Samples of the posterior distributions obtained from a Bayesian latent class (LC) meta-analysis model
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:f64b1442ab. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 01 2024 |
| R-4.5-win | OK | Nov 01 2024 |
| R-4.5-linux | OK | Nov 01 2024 |
| R-4.4-win | NOTE | Nov 01 2024 |
| R-4.4-mac | NOTE | Nov 01 2024 |
| R-4.3-win | NOTE | Nov 01 2024 |
| R-4.3-mac | NOTE | Nov 01 2024 |
Exports:ForestSROC_rjags
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Anti-CCP dataset | Anti_CCP |
| Forest plot for sensitivity and specificity | Forest |
| Samples of the posterior distributions obtained from a Bayesian bivariate meta-analysis model | posterior_samples_Bivariate |
| Samples of the posterior distributions obtained from a Bayesian HSROC meta-analysis model | posterior_samples_HSROC |
| Samples of the posterior distributions obtained from a Bayesian latent class (LC) meta-analysis model | posterior_samples_LC |
| A function to create a summary plot in Receiver Operating Characteristic (ROC) space | SROC_rjags |
| Xpert dataset | Xpert |
