Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->censored <a href='https://censored.tidymodels.org'><img src='man/figures/logo.png' align="right" height="139" alt="a pixelated version of the parsnip logo with a black censoring bar" /></a>
<!-- badges: start --> <!-- badges: end -->censored
is a parsnip extension
package which provides engines for various models for censored
regression and survival analysis.
Installation
You can install the released version of censored from CRAN with:
install.packages("censored")
And the development version from GitHub with:
# install.packages("pak")
pak::pak("tidymodels/censored")
Available models, engines, and prediction types
censored provides engines for the models in the following table. For examples, please see Fitting and Predicting with censored.
The time to event can be predicted with type = "time"
, the survival
probability with type = "survival"
, the linear predictor with
type = "linear_pred"
, the quantiles of the event time distribution
with type = "quantile"
, and the hazard with type = "hazard"
.
model | engine | time | survival | linear_pred | raw | quantile | hazard |
---|---|---|---|---|---|---|---|
bag_tree | rpart | ✔ | ✔ | ✖ | ✖ | ✖ | ✖ |
boost_tree | mboost | ✔ | ✔ | ✔ | ✖ | ✖ | ✖ |
decision_tree | rpart | ✔ | ✔ | ✖ | ✖ | ✖ | ✖ |
decision_tree | partykit | ✔ | ✔ | ✖ | ✖ | ✖ | ✖ |
proportional_hazards | survival | ✔ | ✔ | ✔ | ✖ | ✖ | ✖ |
proportional_hazards | glmnet | ✔ | ✔ | ✔ | ✔ | ✖ | ✖ |
rand_forest | partykit | ✔ | ✔ | ✖ | ✖ | ✖ | ✖ |
rand_forest | aorsf | ✔ | ✔ | ✖ | ✖ | ✖ | ✖ |
survival_reg | survival | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ |
survival_reg | flexsurv | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ |
survival_reg | flexsurvspline | ✔ | ✔ | ✔ | ✖ | ✔ | ✔ |
Contributing
This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
-
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on Posit Community.
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If you think you have encountered a bug, please submit an issue.
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Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
-
Check out further details on contributing guidelines for tidymodels packages and how to get help.