Package: WINS 1.4.3
WINS: The R WINS Package
Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis, inverse probability of censoring weighting (IPCW) and inverse probability of treatment weighting (IPTW) analysis.
Authors:
WINS_1.4.3.tar.gz
WINS_1.4.3.zip(r-4.5)WINS_1.4.3.zip(r-4.4)WINS_1.4.3.zip(r-4.3)
WINS_1.4.3.tgz(r-4.5-any)WINS_1.4.3.tgz(r-4.4-any)WINS_1.4.3.tgz(r-4.3-any)
WINS_1.4.3.tar.gz(r-4.5-noble)WINS_1.4.3.tar.gz(r-4.4-noble)
WINS_1.4.3.tgz(r-4.4-emscripten)WINS_1.4.3.tgz(r-4.3-emscripten)
WINS.pdf |WINS.html✨
WINS/json (API)
| # Install 'WINS' in R: |
| install.packages('WINS', repos = c('https://cuiyingbeicheng.r-universe.dev', 'https://cloud.r-project.org')) |
- Z_t_con - Covariate history in the control group.
- Z_t_trt - Covariate history in the treatment group.
- data_binary - An example with three binary endpoints.
- data_continuous - An example with three continuous endpoints.
- data_mix - An example with a mixture of endpoint types.
- data_mix_stratum - An example with a mixture of endpoint types with three strata.
- data_tte - An example with three TTE endpoints.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:d3ef106387. Checks:1 OK, 7 WARNING. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Feb 12 2025 |
| R-4.5-win | WARNING | Feb 12 2025 |
| R-4.5-mac | WARNING | Feb 12 2025 |
| R-4.5-linux | WARNING | Feb 12 2025 |
| R-4.4-win | WARNING | Feb 12 2025 |
| R-4.4-mac | WARNING | Feb 12 2025 |
| R-4.3-win | WARNING | Feb 12 2025 |
| R-4.3-mac | WARNING | Feb 12 2025 |
Exports:partition_t.plotsim.datastat_t.plotwin.statwin.strategy.default
Dependencies:abindADGofTestbackportsbootbroomcarcarDataclicolorspacecopulacorrplotcowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragslgtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpcaPPpillarpkgconfigplyrpolynompsplinepurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasreshape2rlangrstatixscalesSparseMstablediststringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| An example with three binary endpoints. | data_binary |
| An example with three continuous endpoints. | data_continuous |
| An example with a mixture of endpoint types. | data_mix |
| An example with a mixture of endpoint types with three strata. | data_mix_stratum |
| An example with three TTE endpoints. | data_tte |
| Plot the Win Proportion over the Study Time. | partition_t.plot |
| Function for Data Simulation | sim.data |
| Plot Win Statistics over the Study Time. | stat_t.plot |
| Function to Calculate the Win Statistics | win.stat |
| The Default Win Strategy Function. | win.strategy.default |
| Covariate history in the control group. | Z_t_con |
| Covariate history in the treatment group. | Z_t_trt |
