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Published December 22, 2017 | Version v0.13.0

CamDavidsonPilon/lifelines: v0.13

  • 1. Shopify
  • 2. @neo4j
  • 3. Wave
  • 4. Fred Hutchinson Cancer Research Center
  • 5. ID Analytics
  • 6. ENS de Cachan - Paris Saclay University
  • 7. Berlin Institute for Medical Systems Biology
  • 8. Bell
  • 9. Ampion, Inc.
  • 10. @arenadotio
  • 11. @econorthwest
  • 12. Axelspace
  • 13. IRIC | Plateforme de bioinformatique
  • 14. ThinkTopic
  • 15. @Microsoft
  • 16. Virginia Tech - @bi-sdal - GBCB
  • 17. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences

Description

0.13.0
  • removes is_significant and test_result from StatisticalResult. Users can instead choose their significance level by comparing to p_value. The string representation of this class has changed aswell.
  • CoxPHFitter and AalenAdditiveFitter now have a score_ property that is the concordance-index of the dataset to the fitted model.
  • CoxPHFitter and AalenAdditiveFitter no longer have the data property. It was an almost duplicate of the training data, but was causing the model to be very large when serialized.
  • Implements a new fitter CoxTimeVaryingFitter available under the lifelines namespace. This model implements the Cox model for time-varying covariates.
  • Utils for creating time varying datasets available in utils.
  • less noisy check for complete separation.
  • removed datasets namespace from the main lifelines namespace
  • CoxPHFitter has a slightly more intelligent (barely...) way to pick a step size, so convergence should generally be faster.
  • CoxPHFitter.fit now has accepts a weight_col kwarg so one can pass in weights per observation. This is very useful if you have many subjects, and the space of covariates is not large. Thus you can group the same subjects together and give that observation a weight equal to the count. Altogether, this means a much faster regression.

Files

CamDavidsonPilon/lifelines-v0.13.0.zip

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