- 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.