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_.


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


  • 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.


  • 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.


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.


Bug fixes

  • remove some bad print statements in CoxPHFitter.


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.


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.


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).


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:
  • 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:
  • 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.


  • 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.


  • 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!


  • 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


  • 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.


  • 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.


  • 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.


  • 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
  • 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.


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


  • 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


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


  • 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 🤞


  • 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.


  • 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


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



  • Fixed py2 division error in concordance method.


  • 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.


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


  • Only allow matplotlib less than 3.0.


  • 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.


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


  • 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 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.


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


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


  • 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


  • 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.


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


  • 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.


  • 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).
  • and 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.


  • 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.
  • 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.



  • 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.


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


  • Python3 fix for CoxPHFitter.plot.


  • 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


  • fix in internal normalization for CoxPHFitter predict methods.


  • 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


  • 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.


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


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


  • 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


  • 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.


  • reorganized lifelines directories:
    • moved test files out of main directory.
    • moved 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.


  • 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.


  • 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.


  • 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.


  • 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 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.


  • 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.


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


  • 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.


  • 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.


  • 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

  • Ability to specify default printing in statsitical 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.


  • 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.
  • Adding a Changelog.
  • more sanitizing for the statistical tests =)


  • CoxFitter implements Cox Proportional Hazards model in lifelines.
  • lifelines moves the wheels distributions.
  • tests in the statistics module now prints the summary (and still return the regular values)
  • new BaseFitter class is inherited from all fitters.