v0.10

This release adds the ties argument to sksurv.linear_model.CoxPHSurvivalAnalysis to choose between Breslow’s and Efron’s likelihood in the presence of tied event times. Moreover, sksurv.compare.compare_survival() has been added, which implements the log-rank hypothesis test for comparing the survival function of 2 or more groups.

Enhancements

• Update API doc of predict function of boosting estimators (#75).
• Clarify documentation for GradientBoostingSurvivalAnalysis (#78).
• Implement Efron’s likelihood for handling tied event times.
• Implement log-rank test for comparing survival curves.
• Add support for scipy 1.3.1 (#66).

Bug fixes

• Re-add baseline_survival_ and cum_baseline_hazard_ attributes to sksurv.linear_model.CoxPHSurvivalAnalysis (#76).

v0.9

This release adds support for sklearn 0.21 and pandas 0.24.

Enhancements

• Add reference to IPCRidge (#65).
• Use scipy.special.comb instead of deprecated scipy.misc.comb.
• Add support for pandas 0.24 and drop support for 0.20.
• Add support for scikit-learn 0.21 and drop support for 0.20 (#71).
• Explain use of intercept in ComponentwiseGradientBoostingSurvivalAnalysis (#68)
• Bump Eigen to 3.3.7.

Bug fixes

• Disallow scipy 1.3.0 due to scipy regression (#66).

v0.8

Enhancements

• Add sksurv.linear_model.CoxnetSurvivalAnalysis.predict_survival_function
  and sksurv.linear_model.CoxnetSurvivalAnalysis.predict_cumulative_hazard_function
  (#46).
• Add sksurv.nonparametric.SurvivalFunctionEstimator
  and sksurv.nonparametric.CensoringDistributionEstimator that
  wrap sksurv.nonparametric.kaplan_meier_estimator and provide
  a predict_proba method for evaluating the estimated function on
  test data.
• Implement censoring-adjusted C-statistic proposed by Uno et al. (2011)
  in sksurv.metrics.concordance_index_ipcw.
• Add estimator of cumulative/dynamic AUC of Uno et al. (2007)
  in sksurv.metrics.cumulative_dynamic_auc.
• Add flchain dataset (see sksurv.datasets.load_flchain).

Bug fixes

• The tied_time return value of sksurv.metrics.concordance_index_censored
  now correctly reflects the number of comparable pairs that share the same time
  and that are used in computing the concordance index.
• Fix a bug in sksurv.metrics.concordance_index_censored where a
  pair with risk estimates within tolerance was counted both as
  concordant and tied.

v0.7

This release adds support for Python 3.7 and sklearn 0.20.

Changes:

• Add support for sklearn 0.20 (#48).
• Migrate to py.test (#50).
• Explicitly request ECOS solver for sksurv.svm.MinlipSurvivalAnalysis and sksurv.svm.HingeLossSurvivalSVM.
• Add support for Python 3.7 (#49).
• Add support for cvxpy >=1.0.
• Add support for numpy 1.15.

v0.6.0

This release adds support for numpy 1.14 and pandas up to 0.23. In addition, the new class sksurv.util.Surv makes it easier to construct a structured array from numpy arrays, lists, or a pandas data frame.

Changes:

• Support numpy 1.14 and pandas 0.22, 0.23 (#36).
• Enable support for cvxopt with Python 3.5+ on Windows (requires cvxopt >=1.1.9).
• Add max_iter parameter to sksurv.svm.MinlipSurvivalAnalysis and sksurv.svm.HingeLossSurvivalSVM.
• Fix score function of sksurv.svm.NaiveSurvivalSVM to use concordance index.
• sksurv.linear_model.CoxnetSurvivalAnalysis now throws an exception if coefficients get too large (#47).
• Add sksurv.util.Surv class to ease constructing a structured array (#26).

v0.5

This release adds support for scikit-learn 0.19 and pandas 0.21. In turn, support for older versions is dropped, namely Python 3.4, scikit-learn 0.18, and pandas 0.18.

v0.4

This release adds sksurv.linear_model.CoxnetSurvivalAnalysis which implements an efficient algorithm to fit Cox's proportional hazards model with LASSO, ridge, and elastic net penalty. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.

v0.3

This release adds predict_survival_function and predict_cumulative_hazard_function to sksurv.linear_model.CoxPHSurvivalAnalysis, which return the survival function and cumulative hazard function using Breslow's estimator.

Moreover, it fixes a build error on Windows (#3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.

v0.2

This release adds support for Python 3.6, and pandas 0.19 and 0.20.