v0.19.1

0.19.1

New features

• improved stability of LogNormalFitter
• Matplotlib for Python3 users are not longer forced to use 2.x.

API changes

• Important: we changed the parameterization of the PiecewiseExponential to the same as ExponentialFitter (from \lambda * t to t / \lambda).

v0.19.0

0.19.0

New features

• New regression model WeibullAFTFitter for fitting accelerated failure time models. Docs have been added to our documentation about how to use WeibullAFTFitter (spoiler: it's API is similar to the other regression models) and how to interpret the output.
• CoxPHFitter performance improvements (about 10%)
• CoxTimeVaryingFitter performance improvements (about 10%)

API changes

• Important: we changed the .hazards_ and .standard_errors_ on Cox models to be pandas Series (instead of Dataframes). This felt like a more natural representation of them. You may need to update your code to reflect this. See notes here: #636
• Important: we changed the .confidence_intervals_ on Cox models to be transposed. This felt like a more natural representation of them. You may need to update your code to reflect this. See notes here: #636
• Important: we changed the parameterization of the WeibullFitter and ExponentialFitter from \lambda * t to t / \lambda. This was for a few reasons: 1) it is a more common parameterization in literature, 2) it helps in convergence.
• Important: in models where we add an intercept (currently only AalenAdditiveModel), the name of the added column has been changed from baseline to _intercept
• Important: the meaning of alpha in all fitters has changed to be the standard interpretation of alpha in confidence intervals. That means that the default for alpha is set to 0.05 in the latest lifelines, instead of 0.95 in previous versions.

Bug Fixes

• Fixed a bug in the _log_likelihood_ property of ParametericUnivariateFitter models. It was showing the "average" log-likelihood (i.e. scaled by 1/n) instead of the total. It now displays the total.
• In model print_summarys, correct a label erroring. Instead of "Likelihood test", it should have read "Log-likelihood test".
• Fixed a bug that was too frequently rejecting the dtype of event columns.
• Fixed a calculation bug in the concordance index for stratified Cox models. Thanks @airanmehr!
• Fixed some Pandas <0.24 bugs.

v0.18.6

0.18.6

• some improvements to the output of check_assumptions. show_plots is turned to False by default now. It only shows rank and km p-values now.
• some performance improvements to qth_survival_time.

v0.18.5

0.18.5

• added new plotting methods to parametric univariate models: plot_survival_function, plot_hazard and plot_cumulative_hazard. The last one is an alias for plot.
• added new properties to parametric univarite models: confidence_interval_survival_function_, confidence_interval_hazard_, confidence_interval_cumulative_hazard_. The last one is an alias for confidence_interval_.
• Fixed some overflow issues with AalenJohansenFitter's variance calculations when using large datasets.
• Fixed an edgecase in AalenJohansenFitter that causing some datasets with to be jittered too often.
• Add a new kwarg to AalenJohansenFitter, calculate_variance that can be used to turn off variance calculations since this can take a long time for large datasets. Thanks @pzivich!

v0.18.4

0.18.4

• fixed confidence intervals in cumulative hazards for parametric univarite models. They were previously
  serverly depressed.
• adding left-truncation support to parametric univarite models with the entry kwarg in .fit

v0.18.3

0.18.3

• Some performance improvements to parametric univariate models.
• Suppressing some irrelevant NumPy and autograd warnings, so lifeline warnings are more noticeable.
• Improved some warning and error messages.

v0.18.2

0.18.2

• New univariate fitter PiecewiseExponentialFitter for creating a stepwise hazard model. See docs online.
• Ability to create novel parametric univariate models using the new ParametericUnivariateFitter super class. See docs online for how to do this.
• Unfortunately, parametric univariate fitters are not serializable with pickle. The library dill is still useable.
• Complete overhaul of all internals for parametric univariate fitters. Moved them all (most) to use autograd.
• LogNormalFitter no longer models log_sigma.

v0.18.1

0.18.1

• bug fixes in LogNormalFitter variance estimates
• improve convergence of LogNormalFitter. We now model the log of sigma internally, but still expose sigma externally.
• use the autograd lib to help with gradients.
• New LogLogisticFitter univariate fitter available.

v0.18.0

0.18.0

• LogNormalFitter is a new univariate fitter you can use.
• WeibullFitter now correctly returns the confidence intervals (previously returned only NaNs)
• WeibullFitter.print_summary() displays p-values associated with its parameters not equal to 1.0 - previously this was (implicitly) comparing against 0, which is trivially always true (the parameters must be greater than 0)
• ExponentialFitter.print_summary() displays p-values associated with its parameters not equal to 1.0 - previously this was (implicitly) comparing against 0, which is trivially always true (the parameters must be greater than 0)
• ExponentialFitter.plot now displays the cumulative hazard, instead of the survival function. This is to make it easier to compare to WeibullFitter and LogNormalFitter
• Univariate fitters' cumulative_hazard_at_times, hazard_at_times, survival_function_at_times return pandas Series now (use to be numpy arrays)
• remove alpha keyword from all statistical functions. This was never being used.
• Gone are astericks and dots in print_summary functions that represent signficance thresholds.
• In models' summary (including print_summary), the log(p) term has changed to -log2(p). This is known as the s-value. See https://lesslikely.com/statistics/s-values/
• introduce new statistical tests between univariate datasets: survival_difference_at_fixed_point_in_time_test,...
• new warning message when Cox models detects possible non-unique solutions to maximum likelihood.
• Generally: clean up lifelines exception handling. Ex: catch LinAlgError: Matrix is singular. and report back to the user advice.

v0.17.5

0.17.5

• more bugs in plot_covariate_groups fixed when using non-numeric strata.