Package: fastcmprsk 1.24.5
fastcmprsk: Fine-Gray Regression via Forward-Backward Scan
In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net <doi:10.32614/RJ-2021-010>.
Authors:
fastcmprsk_1.24.5.tar.gz
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fastcmprsk_1.24.5.tgz(r-4.4-x86_64)fastcmprsk_1.24.5.tgz(r-4.4-arm64)fastcmprsk_1.24.5.tgz(r-4.3-x86_64)fastcmprsk_1.24.5.tgz(r-4.3-arm64)
fastcmprsk_1.24.5.tar.gz(r-4.5-noble)fastcmprsk_1.24.5.tar.gz(r-4.4-noble)
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fastcmprsk.pdf |fastcmprsk.html✨
fastcmprsk/json (API)
NEWS
| # Install 'fastcmprsk' in R: |
| install.packages('fastcmprsk', repos = c('https://erickawaguchi.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 months agofrom:56132fb3ca. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 13 2024 |
| R-4.5-win-x86_64 | OK | Sep 13 2024 |
| R-4.5-linux-x86_64 | OK | Sep 13 2024 |
| R-4.4-win-x86_64 | OK | Sep 13 2024 |
| R-4.4-mac-x86_64 | OK | Sep 13 2024 |
| R-4.4-mac-aarch64 | OK | Sep 13 2024 |
| R-4.3-win-x86_64 | OK | Sep 13 2024 |
| R-4.3-mac-x86_64 | OK | Sep 13 2024 |
| R-4.3-mac-aarch64 | OK | Sep 13 2024 |
Exports:CriskfastCrrfastCrrpsimulateTwoCauseFineGrayModelvarianceControl
Dependencies:codetoolsdynpredforeachiteratorslatticeMatrixsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Akaike's An Information Criterion | AIC.fcrr |
| Akaike's An Information Criterion | AIC.fcrrp |
| Extract coefficients from an "fcrr" object. | coef.fcrr |
| Extract coefficients from an "fcrrp" object. | coef.fcrrp |
| Confidence Intervals for Model Parameters | confint.fcrr |
| Create a Competing Risk Object | Crisk |
| Fast Fine-Gray Model Estimation | fastCrr |
| Penalized Fine-Gray Model Estimation via two-way linear scan | fastCrrp |
| Extract log-pseudo likelihood from an "fcrr" object. | logLik.fcrr |
| Extract log-pseudo likelihood from an "fcrrp" object. | logLik.fcrrp |
| Plots solution path for penalized methods | plot.fcrrp |
| Plots predicted cumulative incidence function | plot.predict.fcrr |
| Cumulative Incidence Function Estimation | predict.fcrr |
| Prints summary of a fcrr x | print.summary.fcrr |
| Simulate data from the Fine-Gray Model | simulateTwoCauseFineGrayModel |
| Summary method for fastCrr | summary.fcrr |
| Controls for Variance Calculation | varianceControl |
| Extract variance-covariance matrix from an "fcrr" object. | vcov.fcrr |
