# Changelog¶

## 0.27.1 - 2022-03-15¶

### New features¶

• all fit_ methods now accept a fit_options dict that allows one to pass kwargs to the underlying fitting algorithm.

### API Changes¶

• step_size is removed from Cox models fit. See fit_options above.

### Bug fixes¶

• fixed Cox models when “trival” matrix was passed in (one with no covariates)

## 0.27.0 - 2022-03-15¶

Dropping Python3.6 support.

### Bug fixes¶

• Fix late entry in add_at_risk_counts.

### New features¶

• add_at_risk_counts has a new flag to determine to use start or end-of-period at risk counts.
• new column in fitter’s summary that display the number the parameter is being compared against.

### API Changes¶

• plot_lifetimes’s duration arg has the interpretation of “relative time the subject died (since birth)”, instead of the old “time observed for”. These interpretations are different when there is late entry.

## 0.26.4 - 2021-11-30¶

### New features¶

• adding weights to log rank functions

## 0.26.3 - 2021-09-16¶

### Bug fixes¶

• Fix using formulas with CoxPHFitter.score

## 0.26.2 - 2021-09-15¶

Error in v0.26.1 deployment

## 0.26.1 - 2021-09-15¶

### API Changes¶

• t_0 in logrank_test now will not remove data, but will instead censor all subjects that experience the event afterwards.
• update status column in lifelines.datasets.load_lung to be more standard coding: 0 is censored, 1 is event.

### Bug fixes¶

• Fix using formulas with AalenAdditiveFitter.predict_cumulative_hazard
• Fix using formulas with CoxPHFitter.score

## 0.26.0 - 2021-05-26¶

### New features¶

• .BIC_ is now present on fitted models.
• CoxPHFitter with spline baseline can accept pre-computed knot locations.
• Left censoring fitting in KaplanMeierFitter is now “expected”. That is, predict always predicts the survival function (as does every other model), confidence_interval_ is always the CI for the survival function (as does every other model), and so on. In summary: the API for estimates doesn’t change depending on what your censoring your dataset is.

### Bug fixes¶

• Fixed an annoying bug where at_risk-table label’s were not aligning properly when data spanned large ranges. See merging PR for details.
• Fixed a bug in find_best_parametric_model where the wrong BIC value was being computed.
• Fixed regression bug when using an array as a penalizer in Cox models.

## 0.25.11 - 2021-04-06¶

### Bug fixes¶

• Fix integer-valued categorical variables in regression model predictions.
• numpy > 1.20 is allowed.
• Bug fix in the elastic-net penalty for Cox models that wasn’t weighting the terms correctly.

## 0.25.10 - 2021-03-03¶

### New features¶

• Better appearance when using a single row to show in add_at_risk_table.

## 0.25.9 - 2021-02-04¶

Small bump in dependencies.

## 0.25.8 - 2021-01-22¶

Important: we dropped Patsy as our formula framework, and adopted Formulaic. Will the latter is less mature than Patsy, we feel the core capabilities are satisfactory and it provides new opportunities.

### New features¶

• Parametric models with formulas are able to be serialized now.
• a _scipy_callback function is available to use in fitting algorithms.

## 0.25.7 - 2020-12-09¶

### API Changes¶

• Adding cumulative_hazard_at_times to NelsonAalenFitter

### Bug fixes¶

• Fixed error in CoxPHFitter when entry time == event time.
• Fixed formulas in AFT interval censoring regression.
• Fixed concordance_index_ when no events observed
• Fixed label being overwritten in ParametricUnivariate models

## 0.25.6 - 2020-10-26¶

### New features¶

• Parametric Cox models can now handle left and interval censoring datasets.

### Bug fixes¶

• “improved” the output of add_at_risk_counts by removing a call to plt.tight_layout() - this works better when you are calling add_at_risk_counts on multiple axes, but it is recommended you call plt.tight_layout() at the very end of your script.
• Fix bug in KaplanMeierFitter’s interval censoring where max(lower bound) < min(upper bound).

## 0.25.5 - 2020-09-23¶

### API Changes¶

• check_assumptions now returns a list of list of axes that can be manipulated

### Bug fixes¶

• fixed error when using plot_partial_effects with categorical data in AFT models
• improved warning when Hessian matrix contains NaNs.
• fixed performance regression in interval censoring fitting in parametric models
• weights wasn’t being applied properly in NPMLE

## 0.25.4 - 2020-08-26¶

### New features¶

• New baseline estimator for Cox models: piecewise
• Performance improvements for parametric models log_likelihood_ratio_test() and print_summary()
• Better step-size defaults for Cox model -> more robust convergence.

### Bug fixes¶

• fix check_assumptions when using formulas.

## 0.25.3 - 2020-08-24¶

### New features¶

• survival_difference_at_fixed_point_in_time_test now accepts fitters instead of raw data, meaning that you can use this function on left, right or interval censored data.

### API Changes¶

• See note on survival_difference_at_fixed_point_in_time_test above.

### Bug fixes¶

• fix StatisticalResult printing in notebooks
• fix Python error when calling plot_covariate_groups
• fix dtype mismatches in plot_partial_effects_on_outcome.

## 0.25.2 - 2020-08-08¶

### New features¶

• Spline CoxPHFitter can now use strata.

### API Changes¶

• a small parameterization change of the spline CoxPHFitter. The linear term in the spline part was moved to a new Intercept term in the beta_.
• n_baseline_knots in the spline CoxPHFitter now refers to all knots, and not just interior knots (this was confusing to me, the author.). So add 2 to n_baseline_knots to recover the identical model as previously.

### Bug fixes¶

• fix splines CoxPHFitter with when predict_hazard was called.
• fix some exception imports I missed.
• fix log-likelihood p-value in splines CoxPHFitter

## 0.25.1 - 2020-08-01¶

### Bug fixes¶

• ok actually ship the out-of-sample calibration code
• fix labels=False in add_at_risk_counts
• allow for specific rows to be shown in add_at_risk_counts
• put patsy as a proper dependency.
• suppress some Pandas 1.1 warnings.

## 0.25.0 - 2020-07-27¶

### New features¶

• Formulas! lifelines now supports R-like formulas in regression models. See docs here.
• plot_covariate_group now can plot other y-values like hazards and cumulative hazards (default: survival function).
• CoxPHFitter now accepts late entries via entry_col.
• calibration.survival_probability_calibration now works with out-of-sample data.
• print_summary now accepts a column argument to filter down the displayed values. This helps with clutter in notebooks, latex, or on the terminal.
• add_at_risk_counts now follows the cool new KMunicate suggestions

### API Changes¶

• With the introduction of formulas, all models can be using formulas under the hood.
• For both custom regression models or non-AFT regression models, this means that you no longer need to add a constant column to your DataFrame (instead add a 1 as a formula string in the regressors dict). You may also need to remove the T and E columns from regressors. I’ve updated the models in the \examples folder with examples of this new model building.
• Unfortunately, if using formulas, your model will not be able to be pickled. This is a problem with an upstream library, and I hope to have it resolved in the near future.
• plot_covariate_groups has been deprecated in favour of plot_partial_effects_on_outcome.
• The baseline in plot_covariate_groups has changed from the mean observation (including dummy-encoded categorical variables) to median for ordinal (including continuous) and mode for categorical.
• Previously, lifelines used the label "_intercept" to when it added a constant column in regressions. To align with Patsy, we are now using "Intercept".
• In AFT models, ancillary_df kwarg has been renamed to ancillary. This reflects the more general use of the kwarg (not always a DataFrame, but could be a boolean or string now, too).
• Some column names in datasets shipped with lifelines have changed.
• The never used “lifelines.metrics” is deleted.
• With the introduction of formulas, plot_covariate_groups (now called plot_partial_effects_on_outcome) behaves differently for transformed variables. Users no longer need to add “derivatives” features, and encoding is done implicitly. See docs here.
• all exceptions and warnings have moved to lifelines.exceptions

### Bug fixes¶

• The p-value of the log-likelihood ratio test for the CoxPHFitter with splines was returning the wrong result because the degrees of freedom was incorrect.
• better print_summary logic in IDEs and Jupyter exports. Previously it should not be displayed.
• p-values have been corrected in the SplineFitter. Previously, the “null hypothesis” was no coefficient=0, but coefficient=0.01. This is now set to the former.
• fixed NaN bug in survival_table_from_events with intervals when no events would occur in a interval.

## 0.24.16 - 2020-07-09¶

### New features¶

• improved algorithm choice for large DataFrames for Cox models. Should see a significant performance boost.

### Bug fixes¶

• fixed utils.median_survival_time not accepting Pandas Series.

## 0.24.15 - 2020-07-07¶

### Bug fixes¶

• fixed an edge case in KaplanMeierFitter where a really late entry would occur after all other population had died.
• fixed plot in BreslowFlemingtonHarrisFitter
• fixed bug where using conditional_after and times in CoxPHFitter("spline") prediction methods would be ignored.

## 0.24.14 - 2020-07-02¶

### Bug fixes¶

• fixed a bug where using conditional_after and times in prediction methods would result in a shape error
• fixed a bug where score was not able to be used in splined CoxPHFitter
• fixed a bug where some columns would not be displayed in print_summary

## 0.24.13 - 2020-06-22¶

### Bug fixes¶

• fixed a bug where CoxPHFitter would ignore inputed alpha levels for confidence intervals
• fixed a bug where CoxPHFitter would fail with working with sklearn_adapter

## 0.24.12 - 2020-06-20¶

### New features¶

• improved convergence of GeneralizedGamma(Regression)Fitter.

## 0.24.11 - 2020-06-17¶

### New features¶

• new spline regression model CRCSplineFitter based on the paper “A flexible parametric accelerated failure time model” by Michael J. Crowther, Patrick Royston, Mark Clements.
• new survival probability calibration tool lifelines.calibration.survival_probability_calibration to help validate regression models. Based on “Graphical calibration curves and the integrated calibration index (ICI) for survival models” by P. Austin, F. Harrell, and D. van Klaveren.

### API Changes¶

• (and bug fix) scalar parameters in regression models were not being penalized by penalizer - we now penalizing everything except intercept terms in linear relationships.

## 0.24.10 - 2020-06-16¶

### New features¶

• New improvements when using splines model in CoxPHFitter - it should offer much better prediction and baseline-hazard estimation, including extrapolation and interpolation.

### API Changes¶

• Related to above: the fitted spline parameters are now available in the .summary and .print_summary methods.

### Bug fixes¶

• fixed a bug in initialization of some interval-censoring models -> better convergence.

## 0.24.9 - 2020-06-05¶

### New features¶

• Faster NPMLE for interval censored data
• New weightings available in the logrank_test: wilcoxon, tarone-ware, peto, fleming-harrington. Thanks @sean-reed
• new interval censored dataset: lifelines.datasets.load_mice

### Bug fixes¶

• Cleared up some mislabeling in plot_loglogs. Thanks @sean-reed!
• tuples are now able to be used as input in univariate models.

## 0.24.8 - 2020-05-17¶

### New features¶

• Non parametric interval censoring is now available, experimentally. Not all edge cases are fully checked, and some features are missing. Try it under KaplanMeierFitter.fit_interval_censoring

## 0.24.7 - 2020-05-17¶

### New features¶

• find_best_parametric_model can handle left and interval censoring. Also allows for more fitting options.
• AIC_ is a property on parametric models, and AIC_partial_ is a property on Cox models.
• penalizer in all regression models can now be an array instead of a float. This enables new functionality and better control over penalization. This is similar (but not identical) to penalty.factors in glmnet in R.
• some convergence tweaks which should help recent performance regressions.

## 0.24.6 - 2020-05-05¶

### New features¶

• At the cost of some performance, convergence is improved in many models.
• New lifelines.plotting.plot_interval_censored_lifetimes for plotting interval censored data - thanks @sean-reed!

### Bug fixes¶

• fixed bug where cdf_plot and qq_plot were not factoring in the weights correctly.

## 0.24.5 - 2020-05-01¶

### New features¶

• plot_lifetimes accepts pandas Series.

### Bug fixes¶

• Fixed important bug in interval censoring models. Users using interval censoring are strongly advised to upgrade.
• Improved at_risk_counts for subplots.
• More data validation checks for CoxTimeVaryingFitter

## 0.24.4 - 2020-04-13¶

### Bug fixes¶

• Improved stability of interval censoring in parametric models.
• setting a dataframe in ancillary_df works for interval censoring
• .score works for interval censored models

## 0.24.3 - 2020-03-25¶

### New features¶

• new logx kwarg in plotting curves
• PH models have compute_followup_hazard_ratios for simulating what the hazard ratio would be at previous times. This is useful because the final hazard ratio is some weighted average of these.

### Bug fixes¶

• Fixed error in HTML printer that was hiding concordance index information.

## 0.24.2 - 2020-03-15¶

### Bug fixes¶

• Fixed bug when no covariates were passed into CoxPHFitter. See #975
• Fixed error in StatisticalResult where the test name was not displayed correctly.
• Fixed a keyword bug in plot_covariate_groups for parametric models.

## 0.24.1 - 2020-03-05¶

### New features¶

• Stability improvements for GeneralizedGammaRegressionFitter and CoxPHFitter with spline estimation.

### Bug fixes¶

• Fixed bug with plotting hazards in NelsonAalenFitter.

## 0.24.0 - 2020-02-20¶

This version and future versions of lifelines no longer support py35. Pandas 1.0 is fully supported, along with previous versions. Minimum Scipy has been bumped to 1.2.0.

### New features¶

• CoxPHFitter and CoxTimeVaryingFitter has support for an elastic net penalty, which includes L1 and L2 regression.
• CoxPHFitter has new baseline survival estimation methods. Specifically, spline now estimates the coefficients and baseline survival using splines. The traditional method, breslow, is still the default however.
• Regression models have a new score method that will score your model against a dataset (ex: a testing or validation dataset). The default is to evaluate the log-likelihood, but also the concordance index can be chose.
• New MixtureCureFitter for quickly creating univariate mixture models.
• Univariate parametric models have a plot_density, density_at_times, and property density_ that computes the probability density function estimates.
• new dataset for interval regression involving C. Botulinum.
• new lifelines.fitters.mixins.ProportionalHazardMixin that implements proportional hazard checks.

### API Changes¶

• Models’ prediction method that return a single array now return a Series (use to return a DataFrame). This includes predict_median, predict_percentile, predict_expectation, predict_log_partial_hazard, and possibly others.
• The penalty in Cox models is now scaled by the number of observations. This makes it invariant to changing sample sizes. This change also make the penalty magnitude behave the same as any parametric regression model.
• score_ on models has been renamed concordance_index_
• models’ .variance_matrix_ is now a DataFrame.
• CoxTimeVaryingFitter no longer requires an id_col. It’s optional, and some checks may be done for integrity if provided.
• Significant changes to utils.k_fold_cross_validation.
• removed automatically adding inf from PiecewiseExponentialRegressionFitter.breakpoints and PiecewiseExponentialFitter.breakpoints
• tie_method was dropped from Cox models (it was always Efron anyways…)
• Mixins are moved to lifelines.fitters.mixins
• find_best_parametric_model evaluation kwarg has been changed to scoring_method.
• removed _score_ and path from Cox model.

### Bug fixes¶

• Fixed show_censors with KaplanMeierFitter.plot_cumulative_density see issue #940.
• Fixed error in "BIC" code path in find_best_parametric_model
• Fixed a bug where left censoring in AFT models was not converging well
• Cox models now incorporate any penalizers in their log_likelihood_

## 0.23.9 - 2020-01-28¶

### Bug fixes¶

• fixed important error when a parametric regression model would not assign the correct labels to fitted parameters’ variances. See more here: https://github.com/CamDavidsonPilon/lifelines/issues/931. Users of GeneralizedGammaRegressionFitter and any custom regression models should update their code as soon as possible.

## 0.23.8 - 2020-01-21¶

### Bug fixes¶

• fixed important error when a parametric regression model would not assign the correct labels to fitted parameters. See more here: https://github.com/CamDavidsonPilon/lifelines/issues/931. Users of GeneralizedGammaRegressionFitter and any custom regression models should update their code as soon as possible.

## 0.23.7 - 2020-01-14¶

Bug fixes for py3.5.

## 0.23.6 - 2020-01-07¶

### New features¶

• New univariate model, SplineFitter, that uses cubic splines to model the cumulative hazard.
• To aid users with selecting the best parametric model, there is a new lifelines.utils.find_best_parametric_model function that will iterate through the models and return the model with the lowest AIC (by default).
• custom parametric regression models can now do left and interval censoring.

## 0.23.5 - 2020-01-05¶

### New features¶

• New predict_hazard for parametric regression models.
• New lymph node cancer dataset, originally from H.F. for the German Breast Cancer Study Group (GBSG) (1994)

### Bug fixes¶

• fixes error thrown when converge of regression models fails.
• kwargs is now used in plot_covariate_groups
• fixed bug where large exponential numbers in print_summary were not being suppressed correctly.

## 0.23.4 - 2019-12-15¶

• Bug fix for PyPI

## 0.23.3 - 2019-12-11¶

### New features¶

• StatisticalResult.print_summary supports html output.

### Bug fixes¶

• fix import in printer.py
• fix html printing with Univariate models.

## 0.23.2 - 2019-12-07¶

### New features¶

• new lifelines.plotting.rmst_plot for pretty figures of survival curves and RMSTs.
• new variance calculations for lifelines.utils.resticted_mean_survival_time
• performance improvements on regression models’ preprocessing. Should make datasets with high number of columns more performant.

### Bug fixes¶

• fixed print_summary for AAF class.
• fixed repr for sklearn_adapter classes.
• fixed conditional_after in Cox model with strata was used.

## 0.23.1 - 2019-11-27¶

### New features¶

• new print_summary option style to print HTML, LaTeX or ASCII output
• performance improvements for CoxPHFitter - up to 30% performance improvements for some datasets.

### Bug fixes¶

• fixed bug where computed statistics were not being shown in print_summary for HTML output.
• fixed bug where “None” was displayed in models’ __repr__
• fixed bug in StatisticalResult.print_summary
• fixed bug when using print_summary with left censored models.
• lots of minor bug fixes.

## 0.23.0 - 2019-11-17¶

### New features¶

• new print_summary abstraction that allows HTML printing in Jupyter notebooks!
• silenced some warnings.

### Bug fixes¶

• The “comparison” value of some parametric univariate models wasn’t standard, so the null hypothesis p-value may have been wrong. This is now fixed.
• fixed a NaN error in confidence intervals for KaplanMeierFitter

### API Changes¶

• To align values across models, the column names for the confidence intervals in parametric univariate models summary have changed.
• Fixed typo in ParametricUnivariateFitter name.
• median_ has been removed in favour of median_survival_time_.
• left_censorship in fit has been removed in favour of fit_left_censoring.

## 0.22.10 - 2019-11-08¶

The tests were re-factored to be shipped with the package. Let me know if this causes problems.

### Bug fixes¶

• fixed error in plotting models with “lower” or “upper” was in the label name.
• fixed bug in plot_covariate_groups for AFT models when >1d arrays were used for values arg.

## 0.22.9 - 2019-10-30¶

### Bug fixes¶

• fixed predict_ methods in AFT models when timeline was not specified.
• fixed error in qq_plot
• fixed error when submitting a model in qth_survival_time
• CoxPHFitter now displays correct columns values when changing alpha param.

## 0.22.8 - 2019-10-06¶

### New features¶

• Serializing lifelines is better supported. Packages like joblib and pickle are now supported. Thanks @AbdealiJK!
• conditional_after now available in CoxPHFitter.predict_median
• Suppressed some unimportant warnings.

### Bug fixes¶

• fixed initial_point being ignored in AFT models.

## 0.22.7 - 2019-09-29¶

### New features¶

• new ApproximationWarning to tell you if the package is making an potentially mislead approximation.

### Bug fixes¶

• fixed a bug in parametric prediction for interval censored data.
• realigned values in print_summary.
• fixed bug in survival_difference_at_fixed_point_in_time_test

### API Changes¶

• utils.qth_survival_time no longer takes a cdf argument - users should take the compliment (1-cdf).
• Some previous StatisticalWarnings have been replaced by ApproximationWarning

## 0.22.6 - 2019-09-25¶

### New features¶

• conditional_after works for CoxPHFitter prediction models 😅

### API Changes¶

• CoxPHFitter.baseline_cumulative_hazard_’s column is renamed "baseline cumulative hazard" - previously it was "baseline hazard". (Only applies if the model has no strata.)
• utils.dataframe_interpolate_at_times renamed to utils.interpolate_at_times_and_return_pandas.

## 0.22.5 - 2019-09-20¶

### New features¶

• Improvements to the repr of models that takes into accounts weights.
• Better support for predicting on Pandas Series

### Bug fixes¶

• Fixed issue where fit_interval_censoring wouldn’t accept lists.
• Fixed an issue with AalenJohansenFitter failing to plot confidence intervals.

### API Changes¶

• _get_initial_value in parametric univariate models is renamed _create_initial_point

## 0.22.4 - 2019-09-04¶

### New features¶

• Some performance improvements to regression models.
• lifelines will avoid penalizing the intercept (aka bias) variables in regression models.
• new utils.restricted_mean_survival_time that approximates the RMST using numerical integration against survival functions.

### API changes¶

• KaplanMeierFitter.survival_function_‘s’ index is no longer given the name “timeline”.

### Bug fixes¶

• Fixed issue where concordance_index would never exit if NaNs in dataset.

## 0.22.3 - 2019-08-08¶

### New features¶

• model’s now expose a log_likelihood_ property.
• new conditional_after argument on predict_* methods that make prediction on censored subjects easier.
• new lifelines.utils.safe_exp to make exp overflows easier to handle.
• smarter initial conditions for parametric regression models.
• New regression model: GeneralizedGammaRegressionFitter

### API changes¶

• removed lifelines.utils.gamma - use autograd_gamma library instead.
• removed bottleneck as a dependency. It offered slight performance gains only in Cox models, and only a small fraction of the API was being used.

### Bug fixes¶

• AFT log-likelihood ratio test was not using weights correctly.
• corrected (by bumping) scipy and autograd dependencies
• convergence is improved for most models, and many exp overflow warnings have been eliminated.
• Fixed an error in the predict_percentile of LogLogisticAFTFitter. New tests have been added around this.

## 0.22.2 - 2019-07-25¶

### New features¶

• lifelines is now compatible with scipy>=1.3.0

### Bug fixes¶

• fixed printing error when using robust=True in regression models
• GeneralizedGammaFitter is more stable, maybe.
• lifelines was allowing old version of numpy (1.6), but this caused errors when using the library. The correctly numpy has been pinned (to 1.14.0+)

## 0.22.1 - 2019-07-14¶

### New features¶

• New univariate model, GeneralizedGammaFitter. This model contains many sub-models, so it is a good model to check fits.
• added a initial_point option in univariate parametric fitters.
• initial_point kwarg is present in parametric univariate fitters .fit
• event_table is now an attribute on all univariate fitters (if right censoring)
• improvements to lifelines.utils.gamma

### API changes¶

• In AFT models, the column names in confidence_intervals_ has changed to include the alpha value.
• In AFT models, some column names in .summary and .print_summary has changed to include the alpha value.
• In AFT models, some column names in .summary and .print_summary includes confidence intervals for the exponential of the value.

### Bug fixes¶

• when using censors_show in plotting functions, the censor ticks are now reactive to the estimate being shown.
• fixed an overflow bug in KaplanMeierFitter confidence intervals
• improvements in data validation for CoxTimeVaryingFitter

## 0.22.0 - 2019-07-03¶

### New features¶

• Ability to create custom parametric regression models by specifying the cumulative hazard. This enables new and extensions of AFT models.
• percentile(p) method added to univariate models that solves the equation p = S(t) for t
• for parametric univariate models, the conditional_time_to_event_ is now exact instead of an approximation.

### API changes¶

• In Cox models, the attribute hazards_ has been renamed to params_. This aligns better with the other regression models, and is more clear (what is a hazard anyways?)
• In Cox models, a new hazard_ratios_ attribute is available which is the exponentiation of params_.
• In Cox models, the column names in confidence_intervals_ has changed to include the alpha value.
• In Cox models, some column names in .summary and .print_summary has changed to include the alpha value.
• In Cox models, some column names in .summary and .print_summary includes confidence intervals for the exponential of the value.
• Significant changes to internal AFT code.
• A change to how fit_intercept works in AFT models. Previously one could set fit_intercept to False and not have to set ancillary_df - now one must specify a DataFrame.

### Bug fixes¶

• for parametric univariate models, the conditional_time_to_event_ is now exact instead of an approximation.
• fixed a name error bug in CoxTimeVaryingFitter.plot

## 0.21.5 - 2019-06-22¶

I’m skipping 0.21.4 version because of deployment issues.

### New features¶

• scoring_method now a kwarg on sklearn_adapter

### Bug fixes¶

• fixed an implicit import of scikit-learn. scikit-learn is an optional package.
• fixed visual bug that misaligned x-axis ticks and at-risk counts. Thanks @christopherahern!

## 0.21.3 - 2019-06-04¶

### New features¶

• include in lifelines is a scikit-learn adapter so lifeline’s models can be used with scikit-learn’s API. See documentation here.
• CoxPHFitter.plot now accepts a hazard_ratios (boolean) parameter that will plot the hazard ratios (and CIs) instead of the log-hazard ratios.
• CoxPHFitter.check_assumptions now accepts a columns parameter to specify only checking a subset of columns.

### Bug fixes¶

• covariates_from_event_matrix handle nulls better

## 0.21.2 - 2019-05-16¶

### New features¶

• New regression model: PiecewiseExponentialRegressionFitter is available. See blog post here: https://dataorigami.net/blogs/napkin-folding/churn
• Regression models have a new method log_likelihood_ratio_test that computes, you guessed it, the log-likelihood ratio test. Previously this was an internal API that is being exposed.

### API changes¶

• The default behavior of the predict method on non-parametric estimators (KaplanMeierFitter, etc.) has changed from (previous) linear interpolation to (new) return last value. Linear interpolation is still possible with the interpolate flag.
• removing _compute_likelihood_ratio_test on regression models. Use log_likelihood_ratio_test now.

## 0.21.1 - 2019-04-26¶

### New features¶

• users can provided their own start and stop column names in add_covariate_to_timeline
• PiecewiseExponentialFitter now allows numpy arrays as breakpoints

### API changes¶

• output of survival_table_from_events when collapsing rows to intervals now removes the “aggregate” column multi-index.

### Bug fixes¶

• fixed bug in CoxTimeVaryingFitter when ax is provided, thanks @j-i-l!

## 0.21.0 - 2019-04-12¶

### New features¶

• weights is now a optional kwarg for parametric univariate models.
• all univariate and multivariate parametric models now have ability to handle left, right and interval censored data (the former two being special cases of the latter). Users can use the fit_right_censoring (which is an alias for fit), fit_left_censoring and fit_interval_censoring.
• a new interval censored dataset is available under lifelines.datasets.load_diabetes

### API changes¶

• left_censorship on all univariate fitters has been deprecated. Please use the new api model.fit_left_censoring(...).
• invert_y_axis in model.plot(... has been removed.
• entries property in multivariate parametric models has a new Series name: entry

### Bug fixes¶

• lifelines was silently converting any NaNs in the event vector to True. An error is now thrown instead.
• Fixed an error that didn’t let users use Numpy arrays in prediction for AFT models

## 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

## 0.20.4 - 2019-03-27¶

### 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 *.

## 0.20.3 - 2019-03-23¶

### 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_.

## 0.20.2 - 2019-03-21¶

### 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

## 0.20.1 - 2019-03-16¶

• 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.

## 0.20.0 - 2019-03-05¶

• 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.

## 0.19.5 - 2019-02-26¶

### 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.

## 0.19.4 - 2019-02-25¶

### Bug fixes¶

• remove some bad print statements in CoxPHFitter.

## 0.19.3 - 2019-02-25¶

### 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.

## 0.19.2 - 2019-02-22¶

### 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.

## 0.19.1 - 2019-02-21¶

### 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).

## 0.19.0 - 2019-02-20¶

### 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: https://github.com/CamDavidsonPilon/lifelines/issues/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: https://github.com/CamDavidsonPilon/lifelines/issues/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.

## 0.18.6 - 2019-02-13¶

• 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.

## 0.18.5 - 2019-02-11¶

• 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!

## 0.18.4 - 2019-02-10¶

• 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

## 0.18.3 - 2019-02-07¶

• 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.

## 0.18.2 - 2019-02-05¶

• 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.

## 0.18.1 - 2019-02-02¶

• 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.

## 0.18.0 - 2019-01-31¶

• 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.

## 0.17.5 - 2019-01-25¶

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

## 0.17.4 -2019-01-25¶

• Fix bug in plot_covariate_groups that wasn’t allowing for strata to be used.
• change name of multicenter_aids_cohort_study to load_multicenter_aids_cohort_study
• groups is now called values in CoxPHFitter.plot_covariate_groups

## 0.17.3 - 2019-01-24¶

• Fix in compute_residuals when using schoenfeld and the minumum duration has only censored subjects.

## 0.17.2 2019-01-22¶

• Another round of serious performance improvements for the Cox models. Up to 2x faster for CoxPHFitter and CoxTimeVaryingFitter. This was mostly the result of using NumPy’s einsum to simplify a previous for loop. The downside is the code is more esoteric now. I’ve added comments as necessary though 🤞

## 0.17.1 - 2019-01-20¶

• adding bottleneck as a dependency. This library is highly-recommended by Pandas, and in lifelines we see some nice performance improvements with it too. (~15% for CoxPHFitter)
• There was a small bug in CoxPHFitter when using batch_mode that was causing coefficients to deviate from their MLE value. This bug eluded tests, which means that it’s discrepancy was less than 0.0001 difference. It’s fixed now, and even more accurate tests are added.
• Faster CoxPHFitter._compute_likelihood_ratio_test()
• Fixes a Pandas performance warning in CoxTimeVaryingFitter.
• Performances improvements to CoxTimeVaryingFitter.

## 0.17.0 - 2019-01-11¶

• corrected behaviour in CoxPHFitter where score_ was not being refreshed on every new fit.
• Reimplentation of AalenAdditiveFitter. There were significant changes to it:
• implementation is at least 10x faster, and possibly up to 100x faster for some datasets.
• memory consumption is way down
• removed the time-varying component from AalenAdditiveFitter. This will return in a future release.
• new print_summary
• weights_col is added
• nn_cumulative_hazard is removed (may add back)
• some plotting improvemnts to plotting.plot_lifetimes

## 0.16.3 - 2019-01-03¶

• More CoxPHFitter performance improvements. Up to a 40% reduction vs 0.16.2 for some datasets.

## 0.16.1 - 2019-01-01¶

• Fixed py2 division error in concordance method.

## 0.16.0 - 2019-01-01¶

• Drop Python 3.4 support.
• introduction of residual calculations in CoxPHFitter.compute_residuals. Residuals include “schoenfeld”, “score”, “delta_beta”, “deviance”, “martingale”, and “scaled_schoenfeld”.
• removes estimation namespace for fitters. Should be using from lifelines import xFitter now. Thanks @usmanatron
• removes predict_log_hazard_relative_to_mean from Cox model. Thanks @usmanatron
• StatisticalResult has be generalized to allow for multiple results (ex: from pairwise comparisons). This means a slightly changed API that is mostly backwards compatible. See doc string for how to use it.
• statistics.pairwise_logrank_test now returns a StatisticalResult object instead of a nasty NxN DataFrame 💗
• Display log(p-values) as well as p-values in print_summary. Also, p-values below thesholds will be truncated. The orignal p-values are still recoverable using .summary.
• Floats print_summary is now displayed to 2 decimal points. This can be changed using the decimal kwarg.
• removed standardized from Cox model plotting. It was confusing.
• visual improvements to Cox models .plot
• print_summary methods accepts kwargs to also be displayed.
• CoxPHFitter has a new human-readable method, check_assumptions, to check the assumptions of your Cox proportional hazard model.
• A new helper util to “expand” static datasets into long-form: lifelines.utils.to_episodic_format.
• CoxTimeVaryingFitter now accepts strata.

## 0.15.4¶

• bug fix for the Cox model likelihood ratio test when using non-trivial weights.

## 0.15.3 - 2018-12-18¶

• Only allow matplotlib less than 3.0.

## 0.15.2 - 2018-11-23¶

• API changes to plotting.plot_lifetimes
• cluster_col and strata can be used together in CoxPHFitter
• removed entry from ExponentialFitter and WeibullFitter as it was doing nothing.

## 0.15.1 - 2018-11-23¶

• Bug fixes for v0.15.0
• Raise NotImplementedError if the robust flag is used in CoxTimeVaryingFitter - that’s not ready yet.

## 0.15.0 - 2018-11-22¶

• 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.

## 0.14.6 - 2018-07-02¶

• fix for n > 2 groups in multivariate_logrank_test (again).
• fix bug for when event_observed column was not boolean.

## 0.14.5 - 2018-06-29¶

• fix for n > 2 groups in multivariate_logrank_test
• fix weights in KaplanMeierFitter when using a pandas Series.

## 0.14.4 - 2018-06-14¶

• Adds baseline_cumulative_hazard_ and baseline_survival_ to CoxTimeVaryingFitter. Because of this, new prediction methods are available.
• fixed a bug in add_covariate_to_timeline when using cumulative_sum with multiple columns.
• Added Likelihood ratio test to CoxPHFitter.print_summary and CoxTimeVaryingFitter.print_summary
• New checks in CoxTimeVaryingFitter that check for immediate deaths and redundant rows.
• New delay parameter in add_covariate_to_timeline
• removed two_sided_z_test from statistics

## 0.14.3 - 2018-05-24¶

• fixes a bug when subtracting or dividing two UnivariateFitters with labels.
• fixes an import error with using CoxTimeVaryingFitter predict methods.
• adds a column argument to CoxTimeVaryingFitter and CoxPHFitter plot method to plot only a subset of columns.

## 0.14.2 - 2018-05-18¶

• some quality of life improvements for working with CoxTimeVaryingFitter including new predict_ methods.

## 0.14.1 - 2018-04-01¶

• fixed bug with using weights and strata in CoxPHFitter
• fixed bug in using non-integer weights in KaplanMeierFitter
• Performance optimizations in CoxPHFitter for up to 40% faster completion of fit.
• even smarter step_size calculations for iterative optimizations.
• simple code optimizations & cleanup in specific hot spots.
• Performance optimizations in AalenAdditiveFitter for up to 50% faster completion of fit for large dataframes, and up to 10% faster for small dataframes.

## 0.14.0 - 2018-03-03¶

• adding plot_covariate_groups to CoxPHFitter to visualize what happens to survival as we vary a covariate, all else being equal.
• utils functions like qth_survival_times and median_survival_times now return the transpose of the DataFrame compared to previous version of lifelines. The reason for this is that we often treat survival curves as columns in DataFrames, and functions of the survival curve as index (ex: KaplanMeierFitter.survival_function_ returns a survival curve at time t).
• KaplanMeierFitter.fit and NelsonAalenFitter.fit accept a weights vector that can be used for pre-aggregated datasets. See this issue.
• Convergence errors now return a custom ConvergenceWarning instead of a RuntimeWarning
• New checks for complete separation in the dataset for regressions.

## 0.13.0 - 2017-12-22¶

• removes is_significant and test_result from StatisticalResult. Users can instead choose their significance level by comparing to p_value. The string representation of this class has changed aswell.
• CoxPHFitter and AalenAdditiveFitter now have a score_ property that is the concordance-index of the dataset to the fitted model.
• CoxPHFitter and AalenAdditiveFitter no longer have the data property. It was an almost duplicate of the training data, but was causing the model to be very large when serialized.
• Implements a new fitter CoxTimeVaryingFitter available under the lifelines namespace. This model implements the Cox model for time-varying covariates.
• Utils for creating time varying datasets available in utils.
• less noisy check for complete separation.
• removed datasets namespace from the main lifelines namespace
• CoxPHFitter has a slightly more intelligent (barely…) way to pick a step size, so convergence should generally be faster.
• CoxPHFitter.fit now has accepts a weight_col kwarg so one can pass in weights per observation. This is very useful if you have many subjects, and the space of covariates is not large. Thus you can group the same subjects together and give that observation a weight equal to the count. Altogether, this means a much faster regression.

## 0.12.0¶

• removes include_likelihood from CoxPHFitter.fit - it was not slowing things down much (empirically), and often I wanted it for debugging (I suppose others do too). It’s also another exit condition, so we many exit from the NR iterations faster.
• added step_size param to CoxPHFitter.fit - the default is good, but for extremely large or small datasets this may want to be set manually.
• added a warning to CoxPHFitter to check for complete seperation: https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/
• Additional functionality to utils.survival_table_from_events to bin the index to make the resulting table more readable.

## 0.11.3¶

• No longer support matplotlib 1.X
• Adding times argument to CoxPHFitter’s predict_survival_function and predict_cumulative_hazard to predict the estimates at, instead uses the default times of observation or censorship.
• More accurate prediction methods parametrics univariate models.

## 0.11.2¶

• Changing liscense to valilla MIT.
• Speed up NelsonAalenFitter.fit considerably.

## 0.11.1 - 2017-06-22¶

• Python3 fix for CoxPHFitter.plot.

## 0.11.0 - 2017-06-21¶

• fixes regression in KaplanMeierFitter.plot when using Seaborn and lifelines.
• introduce a new .plot function to a fitted CoxPHFitter instance. This plots the hazard coefficients and their confidence intervals.
• in all plot methods, the ix kwarg has been deprecated in favour of a new loc kwarg. This is to align with Pandas deprecating ix

## 0.10.1 - 2017-06-05¶

• fix in internal normalization for CoxPHFitter predict methods.

## 0.10.0¶

• corrected bug that was returning the wrong baseline survival and hazard values in CoxPHFitter when normalize=True.
• removed normalize kwarg in CoxPHFitter. This was causing lots of confusion for users, and added code complexity. It’s really nice to be able to remove it.
• correcting column name in CoxPHFitter.baseline_survival_
• CoxPHFitter.baseline_cumulative_hazard_ is always centered, to mimic R’s basehaz API.
• new predict_log_partial_hazards to CoxPHFitter

## 0.9.4¶

• adding plot_loglogs to KaplanMeierFitter
• added a (correct) check to see if some columns in a dataset will cause convergence problems.
• removing flat argument in plot methods. It was causing confusion. To replicate it, one can set ci_force_lines=True and show_censors=True.
• adding strata keyword argument to CoxPHFitter on initialization (ex: CoxPHFitter(strata=['v1', 'v2']). Why? Fitters initialized with strata can now be passed into k_fold_cross_validation, plus it makes unit testing strata fitters easier.
• If using strata in CoxPHFitter, access to strata specific baseline hazards and survival functions are available (previously it was a blended valie). Prediction also uses the specific baseline hazards/survivals.
• performance improvements in CoxPHFitter - should see at least a 10% speed improvement in fit.

## 0.9.2¶

• deprecates Pandas versions before 0.18.
• throw an error if no admissable pairs in the c-index calculation. Previously a NaN was returned.

## 0.9.1¶

• add two summary functions to Weibull and Exponential fitter, solves #224

## 0.9.0¶

• new prediction function in CoxPHFitter, predict_log_hazard_relative_to_mean, that mimics what R’s predict.coxph does.
• removing the predict method in CoxPHFitter and AalenAdditiveFitter. This is because the choice of predict_median as a default was causing too much confusion, and no other natual choice as a default was available. All other predict_ methods remain.
• Default predict method in k_fold_cross_validation is now predict_expectation

## 0.8.1 - 2015-08-01¶

• supports matplotlib 1.5.
• introduction of a param nn_cumulative_hazards in AalenAdditiveModel’s __init__ (default True). This parameter will truncate all non-negative cumulative hazards in prediction methods to 0.
• bug fixes including:
• fixed issue where the while loop in _newton_rhaphson would break too early causing a variable not to be set properly.
• scaling of smooth hazards in NelsonAalenFitter was off by a factor of 0.5.

## 0.8.0¶

• reorganized lifelines directories:
• moved test files out of main directory.
• moved utils.py into it’s own directory.
• moved all estimators fitters directory.
• added a at_risk column to the output of group_survival_table_from_events and survival_table_from_events
• added sample size and power calculations for statistical tests. See lifeline.statistics. sample_size_necessary_under_cph and lifelines.statistics. power_under_cph.
• fixed a bug when using KaplanMeierFitter for left-censored data.

## 0.7.1¶

• addition of a l2 penalizer to CoxPHFitter.
• dropped Fortran implementation of efficient Python version. Lifelines is pure python once again!
• addition of strata keyword argument to CoxPHFitter to allow for stratification of a single or set of categorical variables in your dataset.
• datetimes_to_durations now accepts a list as na_values, so multiple values can be checked.
• fixed a bug in datetimes_to_durations where fill_date was not properly being applied.
• Changed warning in datetimes_to_durations to be correct.
• refactor each fitter into it’s own submodule. For now, the tests are still in the same file. This will also not break the API.

## 0.7.0 - 2015-03-01¶

• allow for multiple fitters to be passed into k_fold_cross_validation.
• statistical tests in lifelines.statistics. now return a StatisticalResult object with properties like p_value, test_results, and summary.
• fixed a bug in how log-rank statistical tests are performed. The covariance matrix was not being correctly calculated. This resulted in slightly different p-values.
• WeibullFitter, ExponentialFitter, KaplanMeierFitter and BreslowFlemingHarringtonFitter all have a conditional_time_to_event_ property that measures the median duration remaining until the death event, given survival up until time t.

## 0.6.1¶

• addition of median_ property to WeibullFitter and ExponentialFitter.
• WeibullFitter and ExponentialFitter will use integer timelines instead of float provided by linspace. This is so if your work is to sum up the survival function (for expected values or something similar), it’s more difficult to make a mistake.

## 0.6.0 - 2015-02-04¶

• Inclusion of the univariate fitters WeibullFitter and ExponentialFitter.
• Removing BayesianFitter from lifelines.
• Added new penalization scheme to AalenAdditiveFitter. You can now add a smoothing penalizer that will try to keep subsequent values of a hazard curve close together. The penalizing coefficient is smoothing_penalizer.
• Changed penalizer keyword arg to coef_penalizer in AalenAdditiveFitter.
• new ridge_regression function in utils.py to perform linear regression with l2 penalizer terms.
• Matplotlib is no longer a mandatory dependency.
• .predict(time) method on univariate fitters can now accept a scalar (and returns a scalar) and an iterable (and returns a numpy array)
• In KaplanMeierFitter, epsilon has been renamed to precision.

## 0.5.1 - 2014-12-24¶

• New API for CoxPHFitter and AalenAdditiveFitter: the default arguments for event_col and duration_col. duration_col is now mandatory, and event_col now accepts a column, or by default, None, which assumes all events are observed (non-censored).
• Fix statistical tests.
• Allow negative durations in Fitters.
• New API in survival_table_from_events: min_observations is replaced by birth_times (default None).
• New API in CoxPHFitter for summary: summary will return a dataframe with statistics, print_summary() will print the dataframe (plus some other statistics) in a pretty manner.
• Adding “At Risk” counts option to univariate fitter plot methods, .plot(at_risk_counts=True), and the function lifelines.plotting.add_at_risk_counts.
• Fix bug Epanechnikov kernel.

## 0.5.0 - 2014-12-07¶

• move testing to py.test
• refactor tests into smaller files
• make test_pairwise_logrank_test_with_identical_data_returns_inconclusive a better test
• Alternate metrics can be used for k_fold_cross_validation.

## 0.4.4 - 2014-11-27¶

• Lots of improvements to numerical stability (but something things still need work)
• Additions to summary in CoxPHFitter.
• Make all prediction methods output a DataFrame
• Fixes bug in 1-d input not returning in CoxPHFitter
• Lots of new tests.

## 0.4.3 - 2014-07-23¶

• refactoring of qth_survival_times: it can now accept an iterable (or a scalar still) of probabilities in the q argument, and will return a DataFrame with these as columns. If len(q)==1 and a single survival function is given, will return a scalar, not a DataFrame. Also some good speed improvements.
• KaplanMeierFitter and NelsonAalenFitter now have a _label property that is passed in during the fit.
• KaplanMeierFitter/NelsonAalenFitter’s inital alpha value is overwritten if a new alpha value is passed in during the fit.
• New method for KaplanMeierFitter: conditional_time_to. This returns a DataFrame of the estimate: med(S(t | T>s)) - s, human readable: the estimated time left of living, given an individual is aged s.
• Adds option include_likelihood to CoxPHFitter fit method to save the final log-likelihood value.

## 0.4.2 - 2014-06-19¶

• Massive speed improvements to CoxPHFitter.
• Additional prediction method: predict_percentile is available on CoxPHFitter and AalenAdditiveFitter. Given a percentile, p, this function returns the value t such that S(t | x) = p. It is a generalization of predict_median.
• Additional kwargs in k_fold_cross_validation that will accept different prediction methods (default is predict_median).
• Bug fix in CoxPHFitter predict_expectation function.
• Correct spelling mistake in newton-rhapson algorithm.
• datasets now contains functions for generating the respective datasets, ex: generate_waltons_dataset.
• Bumping up the number of samples in statistical tests to prevent them from failing so often (this a stop-gap)
• pep8 everything

## 0.4.1.1¶

• Ability to specify default printing in statistical tests with the suppress_print keyword argument (default False).
• For the multivariate log rank test, the inverse step has been replaced with the generalized inverse. This seems to be what other packages use.
• Adding more robust cross validation scheme based on issue #67.
• fixing regression_dataset in datasets.

## 0.4.1 - 2014-06-11¶

• CoxFitter is now known as CoxPHFitter
• refactoring some tests that used redundant data from lifelines.datasets.
• Adding cross validation: in utils is a new k_fold_cross_validation for model selection in regression problems.
• Change CoxPHFitter’s fit method’s display_output to False.
• fixing bug in CoxPHFitter’s _compute_baseline_hazard that errored when sending Series objects to survival_table_from_events.
• CoxPHFitter’s fit now looks to columns with too low variance, and halts NR algorithm if a NaN is found.
• CoxFitter implements Cox Proportional Hazards model in lifelines.
• tests in the statistics module now prints the summary (and still return the regular values)
• new BaseFitter class is inherited from all fitters.