Beta version of python library: survivalstan

[Jacqueline Buros Novik]

We're working on a python package to implement several common Survival models, called survivalstan. The package includes Stan code for models and provides helper functions to support the modeling workflow (preparing data, posterior-predictive checks, etc).  

The repo also includes some worked examples, which are best viewed using nbviewer.  The Test PEM survival model with simulated data.ipynb contains the most explanatory text.

It's very much a work in progress, but posting here in case it's helpful. Welcome your feedback / collaboration / etc. Many thanks in advance!

[Bob Carpenter]

Thanks. The Jupyter notebooks look great and helpfully
explain the survival models (which aren't covered in the
manual yet).

- Bob

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[Mike Lawrence]

Beautiful! I'm teaching a class using Stan and a student asked yesterday about how to handle survival data, so this is perfect timing. Thanks!

--
Mike Lawrence
Graduate Student
Department of Psychology & Neuroscience
Dalhousie University

~ Certainty is (possibly) folly ~

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[Jacqueline Buros Novik]

No problem, happy to hear it's useful. I will be expanding some of the notebooks, so welcome feedback on how to make them more informative.

There are several examples of survival models using Stan on github, such as : 

   - https://github.com/to-mi/stan-survival-shrinkage/blob/master/example.R

   - https://github.com/stan-dev/example-models/blob/master/bugs_examples/vol1/leuk/leuk.stan

 

On Wednesday, November 16, 2016 at 2:16:10 PM UTC-5, Mike Lawrence wrote:

Beautiful! I'm teaching a class using Stan and a student asked yesterday about how to handle survival data, so this is perfect timing. Thanks!

--
Mike Lawrence
Graduate Student
Department of Psychology & Neuroscience
Dalhousie University

~ Certainty is (possibly) folly ~

On Wed, Nov 16, 2016 at 1:48 PM, Jacqueline Buros Novik <jacki...@gmail.com> wrote:

We're working on a python package to implement several common Survival models, called survivalstan. The package includes Stan code for models and provides helper functions to support the modeling workflow (preparing data, posterior-predictive checks, etc).  

The repo also includes some worked examples, which are best viewed using nbviewer.  The Test PEM survival model with simulated data.ipynb contains the most explanatory text.

It's very much a work in progress, but posting here in case it's helpful. Welcome your feedback / collaboration / etc. Many thanks in advance!

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