Published December 22, 2017
| Version v0.13.0
Software
Open
CamDavidsonPilon/lifelines: v0.13
Authors/Creators
- Cameron Davidson-Pilon1
- Jonas Kalderstam2
- Ben Kuhn3
- Andrew Fiore-Gartland4
- Luis Moneda
- Alex Parij
- Kyle Stark
- Steven Anton5
- Lilian Besson6
- Jona7
- Harsh Gadgil8
- Dave Golland
- Sean Hussey9
- Andreas Klintberg
- akkineniramesh
- Niels Bantilan10
- Nick Furlotte
- Nick Evans1
- Matt Braymer-Hayes11
- Lukasz12
- Jonathan Séguin13
- Jeff Rose14
- Isaac Slavitt
- Eric Martin
- Eduardo Ochoa
- Dylan Albrecht
- dhuynh15
- Daniel Chen16
- Chris Fournier1
- André F. Rendeiro17
- 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_significantandtest_resultfromStatisticalResult. Users can instead choose their significance level by comparing top_value. The string representation of this class has changed aswell. CoxPHFitterandAalenAdditiveFitternow have ascore_property that is the concordance-index of the dataset to the fitted model.CoxPHFitterandAalenAdditiveFitterno longer have thedataproperty. It was an almost duplicate of the training data, but was causing the model to be very large when serialized.- Implements a new fitter
CoxTimeVaryingFitteravailable under thelifelinesnamespace. 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
datasetsnamespace from the mainlifelinesnamespace CoxPHFitterhas a slightly more intelligent (barely...) way to pick a step size, so convergence should generally be faster.CoxPHFitter.fitnow has accepts aweight_colkwarg 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
Files
(3.7 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/CamDavidsonPilon/lifelines/tree/v0.13.0 (URL)