I am interested in using survival models together with the tidymodels package. I have come across the 'censored' package, which works in conjunction with tidymodels. However, I need to tune the model parameters and use it together with the 'recipes' package. Additionally, I believe that 'censored' does not currently support XGBoost models. Is there a plan to implement parameter tuning in the 'censored' package?
We have tuning and several other aspects of modeling event time data implemented. We are currently doing a lot of testing and documentation right now. The code is mostly in the Github versions of several packages.
I hope to publish a blog post before we go to the posit conference in September that has examples, installation instructions, and a request for people to test it out.
For xgboost, there is a pull requisition in censored. As with all things xgboost, they go about structuring the data in the weirdest way so that needs a lot of work to implement (we might just rewrite it).
Thank you, Max, for the prompt response.
I have been working with a model to predict the kidney transplant waiting list in São Paulo, Brazil. In this model, I used Cox regression. (A machine learning prediction model for waiting time to kidney transplant)
Currently, I am in the process of predicting kidney transplants across all Brazilian states. To achieve this goal, I intend to employ tidymodels and the censored package. I am hopeful that these implementations will be operational soon.
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