v0.20.5

0.20.5 - 2019-04-08

New features

• performance improvements for print_summary.

API changes

• utils.survival_events_from_table returns an integer weight vector as well as durations and censoring vector.
• in AalenJohansenFitter, the variance parameter is renamed to variance_ to align with the usual lifelines convention.

Bug fixes

• Fixed an error in the CoxTimeVaryingFitter's likelihood ratio test when using strata.
• Fixed some plotting bugs with AalenJohansenFitter

v0.20.4

0.20.4

New features

• left-truncation support in AFT models, using the entry_col kwarg in fit()
• generate_datasets.piecewise_exponential_survival_data for generating piecewise exp. data
• Faster print_summary for AFT models.

API changes

• Pandas is now correctly pinned to >= 0.23.0. This was always the case, but not specified in setup.py correctly.

Bug fixes

• Better handling for extremely large numbers in print_summary
• PiecewiseExponentialFitter is available with from lifelines import *.

v0.20.3

0.20.3

New features

• Now cumulative_density_ & survival_function_ are always present on a fitted KaplanMeierFitter.
• New attributes/methods on KaplanMeierFitter: plot_cumulative_density(), confidence_interval_cumulative_density_, plot_survival_function and confidence_interval_survival_function_.

v0.20.2

0.20.2

New features

• Left censoring is now supported in univariate parametric models: .fit(..., left_censorship=True). Examples are in the docs.
• new dataset: lifelines.datasets.load_nh4()
• Univariate parametric models now include, by default, support for the cumulative density function: .cumulative_density_, .confidence_interval_cumulative_density_, plot_cumulative_density(), cumulative_density_at_times(t).
• add a lifelines.plotting.qq_plot for univariate parametric models that handles censored data.

API changes

• plot_lifetimes no longer reverses the order when plotting. Thanks @vpolimenov!
• The C column in load_lcd dataset is renamed to E.

Bug fixes

• fixed a naming error in KaplanMeierFitter when left_censorship was set to True, plot_cumulative_density_() is now plot_cumulative_density().
• added some error handling when passing in timedeltas. Ideally, users don't pass in timedeltas, as the scale is ambiguous. However, the error message before was not obvious, so we do some conversion, warn the user, and pass it through.
• qth_survival_times for a truncated CDF would return np.inf if the q parameter was below the truncation limit. This should have been -np.inf

v0.20.1

0.20.1

• Some performance improvements to CoxPHFitter (about 30%). I know it may seem silly, but we are now about the same or slighty faster than the Cox model in R's survival package (for some testing datasets and some configurations). This is a big deal, because 1) lifelines does more error checking prior, 2) R's cox model is written in C, and we are still pure Python/NumPy, 3) R's cox model has decades of development.
• suppressed unimportant warnings

API changes

• Previously, lifelines always added a 0 row to cph.baseline_hazard_, even if there were no event at this time. This is no longer the case. A 0 will still be added if there is a duration (observed or not) at 0 occurs however.

v0.20.0

Changelog

0.20.0

• Starting with 0.20.0, only Python3 will be supported. Over 75% of recent installs where Py3.
• Updated minimum dependencies, specifically Matplotlib and Pandas.

New features

• smarter initialization for AFT models which should improve convergence.

API changes

• inital_beta in Cox model's .fit is now initial_point.
• initial_point is now available in AFT models and CoxTimeVaryingFitter
• the DataFrame confidence_intervals_ for univariate models is transposed now (previous parameters where columns, now parameters are rows).

Bug fixes

• Fixed a bug with plotting and check_assumptions.

v0.19.5

0.19.5

New features

• plot_covariate_group can accept multiple covariates to plot. This is useful for columns that have implicit correlation like polynomial features or categorical variables.
• Convergence improvements for AFT models.

v0.19.4

0.19.4

Bug fixes

• remove some bad print statements in CoxPHFitter.

v0.19.3

0.19.3

New features

• new AFT models: LogNormalAFTFitter and LogLogisticAFTFitter.
• AFT models now accept a weights_col argument to fit.
• Robust errors (sandwich errors) are now avilable in AFT models using the robust=True kwarg in fit.
• Performance increase to print_summary in the CoxPHFitter and CoxTimeVaryingFitter model.

v0.19.2

0.19.2

New features

• ParametricUnivariateFitters, like WeibullFitter, have smoothed plots when plotting (vs stepped plots)

Bug fixes

• The ExponentialFitter log likelihood value was incorrect - inference was correct however.
• Univariate fitters are more flexiable and can allow 2-d and DataFrames as inputs.