• adding robust params to CoxPHFitter's fit. This enables atleast i) using non-integer weights in the model (these could be sampling weights like IPTW), and ii) mis-specified models (ex: non-proportional hazards). Under the hood it's a sandwich estimator. This does not handle ties, so if there are high number of ties, results may significantly differ from other software.
• standard_errors_ is now a property on fitted CoxPHFitter which describes the standard errors of the coefficients.
• variance_matrix_ is now a property on fitted CoxPHFitter which describes the variance matrix of the coefficients.
• new criteria for convergence of CoxPHFitter and CoxTimeVaryingFitter called the Newton-decrement. Tests show it is as accurate (w.r.t to previous coefficients) and typically shaves off a single step, resulting in generally faster convergence. See https://www.cs.cmu.edu/~pradeepr/convexopt/Lecture_Slides/Newton_methods.pdf. Details about the Newton-decrement are added to the show_progress statements.
• Minimum suppport for scipy is 1.0
• Convergence errors in models that use Newton-Rhapson methods now throw a ConvergenceError, instead of a ValueError (the former is a subclass of the latter, however).
• AalenAdditiveModel raises ConvergenceWarning instead of printing a warning.
• KaplanMeierFitter now has a cumulative plot option. Example kmf.plot(invert_y_axis=True)
• a weights_col option has been added to CoxTimeVaryingFitter that allows for time-varying weights.
• WeibullFitter has a new show_progress param and additional information if the convergence fails.
• CoxPHFitter, ExponentialFitter, WeibullFitter and CoxTimeVaryFitter method print_summary is updated with new fields.
• WeibullFitter has renamed the incorrect _jacobian to _hessian_.
• variance_matrix_ is now a property on fitted WeibullFitter which describes the variance matrix of the parameters.
• The default WeibullFitter().timeline has changed from integers between the min and max duration to n floats between the max and min durations, where n is the number of observations.
• Performance improvements for CoxPHFitter (~20% faster)
• Performance improvements for CoxTimeVaryingFitter (~100% faster)
• In Python3, Univariate models are now serialisable with pickle. Thanks @dwilson1988 for the contribution. For Python2, dill is still the preferred method.
• baseline_cumulative_hazard_ (and derivatives of that) on CoxPHFitter now correctly incorporate the weights_col.
• Fixed a bug in KaplanMeierFitter when late entry times lined up with death events. Thanks @pzivich
• Adding cluster_col argument to CoxPHFitter so users can specify groups of subjects/rows that may be correlated.
• Shifting the "signficance codes" for p-values down an order of magnitude. (Example, p-values between 0.1 and 0.05 are not noted at all and p-values between 0.05 and 0.1 are noted with ., etc.). This deviates with how they are presented in other software. There is an argument to be made to remove p-values from lifelines altogether (become the changes you want to see in the world lol), but I worry that people could compute the p-values by hand incorrectly, a worse outcome I think. So, this is my stance. P-values between 0.1 and 0.05 offer very little information, so they are removed. There is a growing movement in statistics to shift "signficant" findings to p-values less than 0.01 anyways.
• New fitter for cumulative incidence of multiple risks AalenJohansenFitter. Thanks @pzivich! See "Methodologic Issues When Estimating Risks in Pharmacoepidemiology" for a nice overview of the model.