Package: PTE 2.0
Adam Kapelner
PTE: Personalized Treatment Evaluator
We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?
Authors:
PTE_2.0.tar.gz
PTE_2.0.zip(r-4.7-x86_64)PTE_2.0.zip(r-4.7-arm64)PTE_2.0.zip(r-4.6-x86_64)PTE_2.0.zip(r-4.6-arm64)
PTE_2.0.tar.gz(r-4.7-arm64)PTE_2.0.tar.gz(r-4.7-x86_64)PTE_2.0.tar.gz(r-4.6-arm64)PTE_2.0.tar.gz(r-4.6-x86_64)
PTE_2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
PTE/json (API)
| # Install 'PTE' in R: |
| install.packages('PTE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kapelner/pte/issues
- continuous_example - Mock RCT data with a continuous endpoint
- survival_example - Mock RCT data with a survival endpoint
Last updated from:df28b99683. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 190 | ||
| linux-devel-x86_64 | OK | 245 | ||
| source / vignettes | OK | 287 | ||
| linux-release-arm64 | OK | 187 | ||
| linux-release-x86_64 | OK | 243 | ||
| windows-devel-arm64 | OK | 325 | ||
| windows-devel-x86_64 | OK | 312 | ||
| windows-release-arm64 | OK | 331 | ||
| windows-release-x86_64 | OK | 256 | ||
| wasm-release | OK | 127 |
Exports:PTE_bootstrap_inferenceselect_optimal_m_prop
Dependencies:backportscheckmateclicodetoolscpp11doParallelfarverforeachggplot2gluegtableisobanditeratorslabelinglatticelifecycleMatrixR6RColorBrewerRcppRcppEigenRcppNumericalrlangS7scalessurvivalvctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Mock RCT data with a continuous endpoint | continuous_example |
| Plots a summary of the bootstrap samples | plot.PTE_bootstrap_results |
| Prints a summary of the model to the console | print.PTE_bootstrap_results |
| Personalized Medicine Inference | PTE-package PTE |
| Bootstrap inference for a prespecified personalization / recommendation model | PTE_bootstrap_inference |
| Select an optimal m-out-of-n bootstrap resample size via the minimum volatility method | select_optimal_m_prop |
| Prints a summary of the model to the console | summary.PTE_bootstrap_results |
| Mock RCT data with a survival endpoint | survival_example |