diff --git a/CHANGELOG.md b/CHANGELOG.md
index cfb61340c..599a70999 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,17 @@
 ## Changelog
 
 
+#### 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
diff --git a/docs/Changelog.rst b/docs/Changelog.rst
index c8525f28a..df861f95a 100644
--- a/docs/Changelog.rst
+++ b/docs/Changelog.rst
@@ -1,9 +1,30 @@
 Changelog
 ---------
 
+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.
+
+.. _section-1:
+
 0.24.8 - 2020-05-17
 ^^^^^^^^^^^^^^^^^^^
 
+.. _new-features-1:
+
 New features
 ''''''''''''
 
@@ -11,12 +32,12 @@ New features
    Not all edge cases are fully checked, and some features are missing.
    Try it under ``KaplanMeierFitter.fit_interval_censoring``
 
-.. _section-1:
+.. _section-2:
 
 0.24.7 - 2020-05-17
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-1:
+.. _new-features-2:
 
 New features
 ''''''''''''
@@ -32,12 +53,12 @@ New features
 -  some convergence tweaks which should help recent performance
    regressions.
 
-.. _section-2:
+.. _section-3:
 
 0.24.6 - 2020-05-05
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-2:
+.. _new-features-3:
 
 New features
 ''''''''''''
@@ -47,25 +68,27 @@ New features
 -  New ``lifelines.plotting.plot_interval_censored_lifetimes`` for
    plotting interval censored data - thanks @sean-reed!
 
+.. _bug-fixes-1:
+
 Bug fixes
 '''''''''
 
 -  fixed bug where ``cdf_plot`` and ``qq_plot`` were not factoring in
    the weights correctly.
 
-.. _section-3:
+.. _section-4:
 
 0.24.5 - 2020-05-01
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-3:
+.. _new-features-4:
 
 New features
 ''''''''''''
 
 -  ``plot_lifetimes`` accepts pandas Series.
 
-.. _bug-fixes-1:
+.. _bug-fixes-2:
 
 Bug fixes
 '''''''''
@@ -75,12 +98,12 @@ Bug fixes
 -  Improved ``at_risk_counts`` for subplots.
 -  More data validation checks for ``CoxTimeVaryingFitter``
 
-.. _section-4:
+.. _section-5:
 
 0.24.4 - 2020-04-13
 ^^^^^^^^^^^^^^^^^^^
 
-.. _bug-fixes-2:
+.. _bug-fixes-3:
 
 Bug fixes
 '''''''''
@@ -89,12 +112,12 @@ Bug fixes
 -  setting a dataframe in ``ancillary_df`` works for interval censoring
 -  ``.score`` works for interval censored models
 
-.. _section-5:
+.. _section-6:
 
 0.24.3 - 2020-03-25
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-4:
+.. _new-features-5:
 
 New features
 ''''''''''''
@@ -104,7 +127,7 @@ New features
    the hazard ratio would be at previous times. This is useful because
    the final hazard ratio is some weighted average of these.
 
-.. _bug-fixes-3:
+.. _bug-fixes-4:
 
 Bug fixes
 '''''''''
@@ -112,12 +135,12 @@ Bug fixes
 -  Fixed error in HTML printer that was hiding concordance index
    information.
 
-.. _section-6:
+.. _section-7:
 
 0.24.2 - 2020-03-15
 ^^^^^^^^^^^^^^^^^^^
 
-.. _bug-fixes-4:
+.. _bug-fixes-5:
 
 Bug fixes
 '''''''''
@@ -129,12 +152,12 @@ Bug fixes
 -  Fixed a keyword bug in ``plot_covariate_groups`` for parametric
    models.
 
-.. _section-7:
+.. _section-8:
 
 0.24.1 - 2020-03-05
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-5:
+.. _new-features-6:
 
 New features
 ''''''''''''
@@ -142,14 +165,14 @@ New features
 -  Stability improvements for GeneralizedGammaRegressionFitter and
    CoxPHFitter with spline estimation.
 
-.. _bug-fixes-5:
+.. _bug-fixes-6:
 
 Bug fixes
 '''''''''
 
 -  Fixed bug with plotting hazards in NelsonAalenFitter.
 
-.. _section-8:
+.. _section-9:
 
 0.24.0 - 2020-02-20
 ^^^^^^^^^^^^^^^^^^^
@@ -158,7 +181,7 @@ 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-6:
+.. _new-features-7:
 
 New features
 ''''''''''''
@@ -208,7 +231,7 @@ API Changes
    to ``scoring_method``.
 -  removed ``_score_`` and ``path`` from Cox model.
 
-.. _bug-fixes-6:
+.. _bug-fixes-7:
 
 Bug fixes
 '''''''''
@@ -221,12 +244,12 @@ Bug fixes
 -  Cox models now incorporate any penalizers in their
    ``log_likelihood_``
 
-.. _section-9:
+.. _section-10:
 
 0.23.9 - 2020-01-28
 ^^^^^^^^^^^^^^^^^^^
 
-.. _bug-fixes-7:
+.. _bug-fixes-8:
 
 Bug fixes
 '''''''''
@@ -237,12 +260,12 @@ Bug fixes
    of ``GeneralizedGammaRegressionFitter`` and any custom regression
    models should update their code as soon as possible.
 
-.. _section-10:
+.. _section-11:
 
 0.23.8 - 2020-01-21
 ^^^^^^^^^^^^^^^^^^^
 
-.. _bug-fixes-8:
+.. _bug-fixes-9:
 
 Bug fixes
 '''''''''
@@ -253,19 +276,19 @@ Bug fixes
    ``GeneralizedGammaRegressionFitter`` and any custom regression models
    should update their code as soon as possible.
 
-.. _section-11:
+.. _section-12:
 
 0.23.7 - 2020-01-14
 ^^^^^^^^^^^^^^^^^^^
 
 Bug fixes for py3.5.
 
-.. _section-12:
+.. _section-13:
 
 0.23.6 - 2020-01-07
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-7:
+.. _new-features-8:
 
 New features
 ''''''''''''
@@ -279,12 +302,12 @@ New features
 -  custom parametric regression models can now do left and interval
    censoring.
 
-.. _section-13:
+.. _section-14:
 
 0.23.5 - 2020-01-05
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-8:
+.. _new-features-9:
 
 New features
 ''''''''''''
@@ -293,7 +316,7 @@ New features
 -  New lymph node cancer dataset, originally from *H.F. for the German
    Breast Cancer Study Group (GBSG) (1994)*
 
-.. _bug-fixes-9:
+.. _bug-fixes-10:
 
 Bug fixes
 '''''''''
@@ -303,26 +326,26 @@ Bug fixes
 -  fixed bug where large exponential numbers in ``print_summary`` were
    not being suppressed correctly.
 
-.. _section-14:
+.. _section-15:
 
 0.23.4 - 2019-12-15
 ^^^^^^^^^^^^^^^^^^^
 
 -  Bug fix for PyPI
 
-.. _section-15:
+.. _section-16:
 
 0.23.3 - 2019-12-11
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-9:
+.. _new-features-10:
 
 New features
 ''''''''''''
 
 -  ``StatisticalResult.print_summary`` supports html output.
 
-.. _bug-fixes-10:
+.. _bug-fixes-11:
 
 Bug fixes
 '''''''''
@@ -330,12 +353,12 @@ Bug fixes
 -  fix import in ``printer.py``
 -  fix html printing with Univariate models.
 
-.. _section-16:
+.. _section-17:
 
 0.23.2 - 2019-12-07
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-10:
+.. _new-features-11:
 
 New features
 ''''''''''''
@@ -347,7 +370,7 @@ New features
 -  performance improvements on regression models’ preprocessing. Should
    make datasets with high number of columns more performant.
 
-.. _bug-fixes-11:
+.. _bug-fixes-12:
 
 Bug fixes
 '''''''''
@@ -356,12 +379,12 @@ Bug fixes
 -  fixed repr for ``sklearn_adapter`` classes.
 -  fixed ``conditional_after`` in Cox model with strata was used.
 
-.. _section-17:
+.. _section-18:
 
 0.23.1 - 2019-11-27
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-11:
+.. _new-features-12:
 
 New features
 ''''''''''''
@@ -371,7 +394,7 @@ New features
 -  performance improvements for ``CoxPHFitter`` - up to 30% performance
    improvements for some datasets.
 
-.. _bug-fixes-12:
+.. _bug-fixes-13:
 
 Bug fixes
 '''''''''
@@ -383,12 +406,12 @@ Bug fixes
 -  fixed bug when using ``print_summary`` with left censored models.
 -  lots of minor bug fixes.
 
-.. _section-18:
+.. _section-19:
 
 0.23.0 - 2019-11-17
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-12:
+.. _new-features-13:
 
 New features
 ''''''''''''
@@ -397,7 +420,7 @@ New features
    Jupyter notebooks!
 -  silenced some warnings.
 
-.. _bug-fixes-13:
+.. _bug-fixes-14:
 
 Bug fixes
 '''''''''
@@ -419,7 +442,7 @@ API Changes
 -  ``left_censorship`` in ``fit`` has been removed in favour of
    ``fit_left_censoring``.
 
-.. _section-19:
+.. _section-20:
 
 0.22.10 - 2019-11-08
 ^^^^^^^^^^^^^^^^^^^^
@@ -427,7 +450,7 @@ API Changes
 The tests were re-factored to be shipped with the package. Let me know
 if this causes problems.
 
-.. _bug-fixes-14:
+.. _bug-fixes-15:
 
 Bug fixes
 '''''''''
@@ -437,12 +460,12 @@ Bug fixes
 -  fixed bug in plot_covariate_groups for AFT models when >1d arrays
    were used for values arg.
 
-.. _section-20:
+.. _section-21:
 
 0.22.9 - 2019-10-30
 ^^^^^^^^^^^^^^^^^^^
 
-.. _bug-fixes-15:
+.. _bug-fixes-16:
 
 Bug fixes
 '''''''''
@@ -454,12 +477,12 @@ Bug fixes
 -  ``CoxPHFitter`` now displays correct columns values when changing
    alpha param.
 
-.. _section-21:
+.. _section-22:
 
 0.22.8 - 2019-10-06
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-13:
+.. _new-features-14:
 
 New features
 ''''''''''''
@@ -469,19 +492,19 @@ New features
 -  ``conditional_after`` now available in ``CoxPHFitter.predict_median``
 -  Suppressed some unimportant warnings.
 
-.. _bug-fixes-16:
+.. _bug-fixes-17:
 
 Bug fixes
 '''''''''
 
 -  fixed initial_point being ignored in AFT models.
 
-.. _section-22:
+.. _section-23:
 
 0.22.7 - 2019-09-29
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-14:
+.. _new-features-15:
 
 New features
 ''''''''''''
@@ -489,7 +512,7 @@ New features
 -  new ``ApproximationWarning`` to tell you if the package is making an
    potentially mislead approximation.
 
-.. _bug-fixes-17:
+.. _bug-fixes-18:
 
 Bug fixes
 '''''''''
@@ -508,19 +531,19 @@ API Changes
 -  Some previous ``StatisticalWarnings`` have been replaced by
    ``ApproximationWarning``
 
-.. _section-23:
+.. _section-24:
 
 0.22.6 - 2019-09-25
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-15:
+.. _new-features-16:
 
 New features
 ''''''''''''
 
 -  ``conditional_after`` works for ``CoxPHFitter`` prediction models 😅
 
-.. _bug-fixes-18:
+.. _bug-fixes-19:
 
 Bug fixes
 '''''''''
@@ -536,12 +559,12 @@ API Changes
 -  ``utils.dataframe_interpolate_at_times`` renamed to
    ``utils.interpolate_at_times_and_return_pandas``.
 
-.. _section-24:
+.. _section-25:
 
 0.22.5 - 2019-09-20
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-16:
+.. _new-features-17:
 
 New features
 ''''''''''''
@@ -550,7 +573,7 @@ New features
    weights.
 -  Better support for predicting on Pandas Series
 
-.. _bug-fixes-19:
+.. _bug-fixes-20:
 
 Bug fixes
 '''''''''
@@ -567,12 +590,12 @@ API Changes
 -  ``_get_initial_value`` in parametric univariate models is renamed
    ``_create_initial_point``
 
-.. _section-25:
+.. _section-26:
 
 0.22.4 - 2019-09-04
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-17:
+.. _new-features-18:
 
 New features
 ''''''''''''
@@ -591,7 +614,7 @@ API changes
 -  ``KaplanMeierFitter.survival_function_``\ ‘s’ index is no longer
    given the name “timeline”.
 
-.. _bug-fixes-20:
+.. _bug-fixes-21:
 
 Bug fixes
 '''''''''
@@ -599,12 +622,12 @@ Bug fixes
 -  Fixed issue where ``concordance_index`` would never exit if NaNs in
    dataset.
 
-.. _section-26:
+.. _section-27:
 
 0.22.3 - 2019-08-08
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-18:
+.. _new-features-19:
 
 New features
 ''''''''''''
@@ -628,7 +651,7 @@ API changes
    gains only in Cox models, and only a small fraction of the API was
    being used.
 
-.. _bug-fixes-21:
+.. _bug-fixes-22:
 
 Bug fixes
 '''''''''
@@ -640,19 +663,19 @@ Bug fixes
 -  Fixed an error in the ``predict_percentile`` of
    ``LogLogisticAFTFitter``. New tests have been added around this.
 
-.. _section-27:
+.. _section-28:
 
 0.22.2 - 2019-07-25
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-19:
+.. _new-features-20:
 
 New features
 ''''''''''''
 
 -  lifelines is now compatible with scipy>=1.3.0
 
-.. _bug-fixes-22:
+.. _bug-fixes-23:
 
 Bug fixes
 '''''''''
@@ -663,12 +686,12 @@ Bug fixes
    errors when using the library. The correctly numpy has been pinned
    (to 1.14.0+)
 
-.. _section-28:
+.. _section-29:
 
 0.22.1 - 2019-07-14
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-20:
+.. _new-features-21:
 
 New features
 ''''''''''''
@@ -696,7 +719,7 @@ API changes
    ``.print_summary`` includes confidence intervals for the exponential
    of the value.
 
-.. _bug-fixes-23:
+.. _bug-fixes-24:
 
 Bug fixes
 '''''''''
@@ -706,12 +729,12 @@ Bug fixes
 -  fixed an overflow bug in ``KaplanMeierFitter`` confidence intervals
 -  improvements in data validation for ``CoxTimeVaryingFitter``
 
-.. _section-29:
+.. _section-30:
 
 0.22.0 - 2019-07-03
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-21:
+.. _new-features-22:
 
 New features
 ''''''''''''
@@ -745,7 +768,7 @@ API changes
    could set ``fit_intercept`` to False and not have to set
    ``ancillary_df`` - now one must specify a DataFrame.
 
-.. _bug-fixes-24:
+.. _bug-fixes-25:
 
 Bug fixes
 '''''''''
@@ -754,21 +777,21 @@ Bug fixes
    is now exact instead of an approximation.
 -  fixed a name error bug in ``CoxTimeVaryingFitter.plot``
 
-.. _section-30:
+.. _section-31:
 
 0.21.5 - 2019-06-22
 ^^^^^^^^^^^^^^^^^^^
 
 I’m skipping 0.21.4 version because of deployment issues.
 
-.. _new-features-22:
+.. _new-features-23:
 
 New features
 ''''''''''''
 
 -  ``scoring_method`` now a kwarg on ``sklearn_adapter``
 
-.. _bug-fixes-25:
+.. _bug-fixes-26:
 
 Bug fixes
 '''''''''
@@ -778,12 +801,12 @@ Bug fixes
 -  fixed visual bug that misaligned x-axis ticks and at-risk counts.
    Thanks @christopherahern!
 
-.. _section-31:
+.. _section-32:
 
 0.21.3 - 2019-06-04
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-23:
+.. _new-features-24:
 
 New features
 ''''''''''''
@@ -797,19 +820,19 @@ New features
 -  ``CoxPHFitter.check_assumptions`` now accepts a ``columns`` parameter
    to specify only checking a subset of columns.
 
-.. _bug-fixes-26:
+.. _bug-fixes-27:
 
 Bug fixes
 '''''''''
 
 -  ``covariates_from_event_matrix`` handle nulls better
 
-.. _section-32:
+.. _section-33:
 
 0.21.2 - 2019-05-16
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-24:
+.. _new-features-25:
 
 New features
 ''''''''''''
@@ -833,17 +856,17 @@ API changes
 -  removing ``_compute_likelihood_ratio_test`` on regression models. Use
    ``log_likelihood_ratio_test`` now.
 
-.. _bug-fixes-27:
+.. _bug-fixes-28:
 
 Bug fixes
 '''''''''
 
-.. _section-33:
+.. _section-34:
 
 0.21.1 - 2019-04-26
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-25:
+.. _new-features-26:
 
 New features
 ''''''''''''
@@ -860,19 +883,19 @@ API changes
 -  output of ``survival_table_from_events`` when collapsing rows to
    intervals now removes the “aggregate” column multi-index.
 
-.. _bug-fixes-28:
+.. _bug-fixes-29:
 
 Bug fixes
 '''''''''
 
 -  fixed bug in CoxTimeVaryingFitter when ax is provided, thanks @j-i-l!
 
-.. _section-34:
+.. _section-35:
 
 0.21.0 - 2019-04-12
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-26:
+.. _new-features-27:
 
 New features
 ''''''''''''
@@ -897,7 +920,7 @@ API changes
 -  ``entries`` property in multivariate parametric models has a new
    Series name: ``entry``
 
-.. _bug-fixes-29:
+.. _bug-fixes-30:
 
 Bug fixes
 '''''''''
@@ -907,12 +930,12 @@ Bug fixes
 -  Fixed an error that didn’t let users use Numpy arrays in prediction
    for AFT models
 
-.. _section-35:
+.. _section-36:
 
 0.20.5 - 2019-04-08
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-27:
+.. _new-features-28:
 
 New features
 ''''''''''''
@@ -929,7 +952,7 @@ API changes
 -  in ``AalenJohansenFitter``, the ``variance`` parameter is renamed to
    ``variance_`` to align with the usual lifelines convention.
 
-.. _bug-fixes-30:
+.. _bug-fixes-31:
 
 Bug fixes
 '''''''''
@@ -938,12 +961,12 @@ Bug fixes
    test when using strata.
 -  Fixed some plotting bugs with ``AalenJohansenFitter``
 
-.. _section-36:
+.. _section-37:
 
 0.20.4 - 2019-03-27
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-28:
+.. _new-features-29:
 
 New features
 ''''''''''''
@@ -962,7 +985,7 @@ 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-31:
+.. _bug-fixes-32:
 
 Bug fixes
 '''''''''
@@ -971,12 +994,12 @@ Bug fixes
 -  ``PiecewiseExponentialFitter`` is available with
    ``from lifelines import *``.
 
-.. _section-37:
+.. _section-38:
 
 0.20.3 - 2019-03-23
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-29:
+.. _new-features-30:
 
 New features
 ''''''''''''
@@ -989,12 +1012,12 @@ New features
    ``plot_survival_function`` and
    ``confidence_interval_survival_function_``.
 
-.. _section-38:
+.. _section-39:
 
 0.20.2 - 2019-03-21
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-30:
+.. _new-features-31:
 
 New features
 ''''''''''''
@@ -1018,7 +1041,7 @@ API changes
    @vpolimenov!
 -  The ``C`` column in ``load_lcd`` dataset is renamed to ``E``.
 
-.. _bug-fixes-32:
+.. _bug-fixes-33:
 
 Bug fixes
 '''''''''
@@ -1034,7 +1057,7 @@ Bug fixes
    the q parameter was below the truncation limit. This should have been
    ``-np.inf``
 
-.. _section-39:
+.. _section-40:
 
 0.20.1 - 2019-03-16
 ^^^^^^^^^^^^^^^^^^^
@@ -1058,7 +1081,7 @@ API changes
    This is no longer the case. A 0 will still be added if there is a
    duration (observed or not) at 0 occurs however.
 
-.. _section-40:
+.. _section-41:
 
 0.20.0 - 2019-03-05
 ^^^^^^^^^^^^^^^^^^^
@@ -1067,7 +1090,7 @@ API changes
    recent installs where Py3.
 -  Updated minimum dependencies, specifically Matplotlib and Pandas.
 
-.. _new-features-31:
+.. _new-features-32:
 
 New features
 ''''''''''''
@@ -1087,19 +1110,19 @@ API changes
    transposed now (previous parameters where columns, now parameters are
    rows).
 
-.. _bug-fixes-33:
+.. _bug-fixes-34:
 
 Bug fixes
 '''''''''
 
 -  Fixed a bug with plotting and ``check_assumptions``.
 
-.. _section-41:
+.. _section-42:
 
 0.19.5 - 2019-02-26
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-32:
+.. _new-features-33:
 
 New features
 ''''''''''''
@@ -1109,24 +1132,24 @@ New features
    features or categorical variables.
 -  Convergence improvements for AFT models.
 
-.. _section-42:
+.. _section-43:
 
 0.19.4 - 2019-02-25
 ^^^^^^^^^^^^^^^^^^^
 
-.. _bug-fixes-34:
+.. _bug-fixes-35:
 
 Bug fixes
 '''''''''
 
 -  remove some bad print statements in ``CoxPHFitter``.
 
-.. _section-43:
+.. _section-44:
 
 0.19.3 - 2019-02-25
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-33:
+.. _new-features-34:
 
 New features
 ''''''''''''
@@ -1138,12 +1161,12 @@ New features
 -  Performance increase to ``print_summary`` in the ``CoxPHFitter`` and
    ``CoxTimeVaryingFitter`` model.
 
-.. _section-44:
+.. _section-45:
 
 0.19.2 - 2019-02-22
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-34:
+.. _new-features-35:
 
 New features
 ''''''''''''
@@ -1151,7 +1174,7 @@ New features
 -  ``ParametricUnivariateFitters``, like ``WeibullFitter``, have
    smoothed plots when plotting (vs stepped plots)
 
-.. _bug-fixes-35:
+.. _bug-fixes-36:
 
 Bug fixes
 '''''''''
@@ -1161,12 +1184,12 @@ Bug fixes
 -  Univariate fitters are more flexiable and can allow 2-d and
    DataFrames as inputs.
 
-.. _section-45:
+.. _section-46:
 
 0.19.1 - 2019-02-21
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-35:
+.. _new-features-36:
 
 New features
 ''''''''''''
@@ -1183,12 +1206,12 @@ API changes
    ``PiecewiseExponential`` to the same as ``ExponentialFitter`` (from
    ``\lambda * t`` to ``t / \lambda``).
 
-.. _section-46:
+.. _section-47:
 
 0.19.0 - 2019-02-20
 ^^^^^^^^^^^^^^^^^^^
 
-.. _new-features-36:
+.. _new-features-37:
 
 New features
 ''''''''''''
@@ -1227,7 +1250,7 @@ API changes
    means that the *default* for alpha is set to 0.05 in the latest
    lifelines, instead of 0.95 in previous versions.
 
-.. _bug-fixes-36:
+.. _bug-fixes-37:
 
 Bug Fixes
 '''''''''
@@ -1244,7 +1267,7 @@ Bug Fixes
    models. Thanks @airanmehr!
 -  Fixed some Pandas <0.24 bugs.
 
-.. _section-47:
+.. _section-48:
 
 0.18.6 - 2019-02-13
 ^^^^^^^^^^^^^^^^^^^
@@ -1254,7 +1277,7 @@ Bug Fixes
    ``rank`` and ``km`` p-values now.
 -  some performance improvements to ``qth_survival_time``.
 
-.. _section-48:
+.. _section-49:
 
 0.18.5 - 2019-02-11
 ^^^^^^^^^^^^^^^^^^^
@@ -1275,7 +1298,7 @@ Bug Fixes
    that can be used to turn off variance calculations since this can
    take a long time for large datasets. Thanks @pzivich!
 
-.. _section-49:
+.. _section-50:
 
 0.18.4 - 2019-02-10
 ^^^^^^^^^^^^^^^^^^^
@@ -1285,7 +1308,7 @@ Bug Fixes
 -  adding left-truncation support to parametric univarite models with
    the ``entry`` kwarg in ``.fit``
 
-.. _section-50:
+.. _section-51:
 
 0.18.3 - 2019-02-07
 ^^^^^^^^^^^^^^^^^^^
@@ -1295,7 +1318,7 @@ Bug Fixes
    warnings are more noticeable.
 -  Improved some warning and error messages.
 
-.. _section-51:
+.. _section-52:
 
 0.18.2 - 2019-02-05
 ^^^^^^^^^^^^^^^^^^^
@@ -1311,7 +1334,7 @@ Bug Fixes
    Moved them all (most) to use ``autograd``.
 -  ``LogNormalFitter`` no longer models ``log_sigma``.
 
-.. _section-52:
+.. _section-53:
 
 0.18.1 - 2019-02-02
 ^^^^^^^^^^^^^^^^^^^
@@ -1322,7 +1345,7 @@ Bug Fixes
 -  use the ``autograd`` lib to help with gradients.
 -  New ``LogLogisticFitter`` univariate fitter available.
 
-.. _section-53:
+.. _section-54:
 
 0.18.0 - 2019-01-31
 ^^^^^^^^^^^^^^^^^^^
@@ -1359,7 +1382,7 @@ Bug Fixes
    ``LinAlgError: Matrix is singular.`` and report back to the user
    advice.
 
-.. _section-54:
+.. _section-55:
 
 0.17.5 - 2019-01-25
 ^^^^^^^^^^^^^^^^^^^
@@ -1367,7 +1390,7 @@ Bug Fixes
 -  more bugs in ``plot_covariate_groups`` fixed when using non-numeric
    strata.
 
-.. _section-55:
+.. _section-56:
 
 0.17.4 -2019-01-25
 ^^^^^^^^^^^^^^^^^^
@@ -1379,7 +1402,7 @@ Bug Fixes
 -  ``groups`` is now called ``values`` in
    ``CoxPHFitter.plot_covariate_groups``
 
-.. _section-56:
+.. _section-57:
 
 0.17.3 - 2019-01-24
 ^^^^^^^^^^^^^^^^^^^
@@ -1387,7 +1410,7 @@ Bug Fixes
 -  Fix in ``compute_residuals`` when using ``schoenfeld`` and the
    minumum duration has only censored subjects.
 
-.. _section-57:
+.. _section-58:
 
 0.17.2 2019-01-22
 ^^^^^^^^^^^^^^^^^
@@ -1398,7 +1421,7 @@ Bug Fixes
    ``for`` loop. The downside is the code is more esoteric now. I’ve
    added comments as necessary though 🤞
 
-.. _section-58:
+.. _section-59:
 
 0.17.1 - 2019-01-20
 ^^^^^^^^^^^^^^^^^^^
@@ -1415,7 +1438,7 @@ Bug Fixes
 -  Fixes a Pandas performance warning in ``CoxTimeVaryingFitter``.
 -  Performances improvements to ``CoxTimeVaryingFitter``.
 
-.. _section-59:
+.. _section-60:
 
 0.17.0 - 2019-01-11
 ^^^^^^^^^^^^^^^^^^^
@@ -1436,7 +1459,7 @@ Bug Fixes
 
 -  some plotting improvemnts to ``plotting.plot_lifetimes``
 
-.. _section-60:
+.. _section-61:
 
 0.16.3 - 2019-01-03
 ^^^^^^^^^^^^^^^^^^^
@@ -1444,7 +1467,7 @@ Bug Fixes
 -  More ``CoxPHFitter`` performance improvements. Up to a 40% reduction
    vs 0.16.2 for some datasets.
 
-.. _section-61:
+.. _section-62:
 
 0.16.2 - 2019-01-02
 ^^^^^^^^^^^^^^^^^^^
@@ -1455,14 +1478,14 @@ Bug Fixes
    has lots of duplicate times. See
    https://github.com/CamDavidsonPilon/lifelines/issues/591
 
-.. _section-62:
+.. _section-63:
 
 0.16.1 - 2019-01-01
 ^^^^^^^^^^^^^^^^^^^
 
 -  Fixed py2 division error in ``concordance`` method.
 
-.. _section-63:
+.. _section-64:
 
 0.16.0 - 2019-01-01
 ^^^^^^^^^^^^^^^^^^^
@@ -1498,7 +1521,7 @@ Bug Fixes
    ``lifelines.utils.to_episodic_format``.
 -  ``CoxTimeVaryingFitter`` now accepts ``strata``.
 
-.. _section-64:
+.. _section-65:
 
 0.15.4
 ^^^^^^
@@ -1506,14 +1529,14 @@ Bug Fixes
 -  bug fix for the Cox model likelihood ratio test when using
    non-trivial weights.
 
-.. _section-65:
+.. _section-66:
 
 0.15.3 - 2018-12-18
 ^^^^^^^^^^^^^^^^^^^
 
 -  Only allow matplotlib less than 3.0.
 
-.. _section-66:
+.. _section-67:
 
 0.15.2 - 2018-11-23
 ^^^^^^^^^^^^^^^^^^^
@@ -1524,7 +1547,7 @@ Bug Fixes
 -  removed ``entry`` from ``ExponentialFitter`` and ``WeibullFitter`` as
    it was doing nothing.
 
-.. _section-67:
+.. _section-68:
 
 0.15.1 - 2018-11-23
 ^^^^^^^^^^^^^^^^^^^
@@ -1533,7 +1556,7 @@ Bug Fixes
 -  Raise NotImplementedError if the ``robust`` flag is used in
    ``CoxTimeVaryingFitter`` - that’s not ready yet.
 
-.. _section-68:
+.. _section-69:
 
 0.15.0 - 2018-11-22
 ^^^^^^^^^^^^^^^^^^^
@@ -1604,7 +1627,7 @@ Bug Fixes
    When Estimating Risks in Pharmacoepidemiology” for a nice overview of
    the model.
 
-.. _section-69:
+.. _section-70:
 
 0.14.6 - 2018-07-02
 ^^^^^^^^^^^^^^^^^^^
@@ -1612,7 +1635,7 @@ Bug Fixes
 -  fix for n > 2 groups in ``multivariate_logrank_test`` (again).
 -  fix bug for when ``event_observed`` column was not boolean.
 
-.. _section-70:
+.. _section-71:
 
 0.14.5 - 2018-06-29
 ^^^^^^^^^^^^^^^^^^^
@@ -1620,7 +1643,7 @@ Bug Fixes
 -  fix for n > 2 groups in ``multivariate_logrank_test``
 -  fix weights in KaplanMeierFitter when using a pandas Series.
 
-.. _section-71:
+.. _section-72:
 
 0.14.4 - 2018-06-14
 ^^^^^^^^^^^^^^^^^^^
@@ -1637,7 +1660,7 @@ Bug Fixes
 -  New ``delay`` parameter in ``add_covariate_to_timeline``
 -  removed ``two_sided_z_test`` from ``statistics``
 
-.. _section-72:
+.. _section-73:
 
 0.14.3 - 2018-05-24
 ^^^^^^^^^^^^^^^^^^^
@@ -1649,7 +1672,7 @@ Bug Fixes
 -  adds a ``column`` argument to ``CoxTimeVaryingFitter`` and
    ``CoxPHFitter`` ``plot`` method to plot only a subset of columns.
 
-.. _section-73:
+.. _section-74:
 
 0.14.2 - 2018-05-18
 ^^^^^^^^^^^^^^^^^^^
@@ -1657,7 +1680,7 @@ Bug Fixes
 -  some quality of life improvements for working with
    ``CoxTimeVaryingFitter`` including new ``predict_`` methods.
 
-.. _section-74:
+.. _section-75:
 
 0.14.1 - 2018-04-01
 ^^^^^^^^^^^^^^^^^^^
@@ -1675,7 +1698,7 @@ Bug Fixes
    faster completion of ``fit`` for large dataframes, and up to 10%
    faster for small dataframes.
 
-.. _section-75:
+.. _section-76:
 
 0.14.0 - 2018-03-03
 ^^^^^^^^^^^^^^^^^^^
@@ -1697,7 +1720,7 @@ Bug Fixes
    of a ``RuntimeWarning``
 -  New checks for complete separation in the dataset for regressions.
 
-.. _section-76:
+.. _section-77:
 
 0.13.0 - 2017-12-22
 ^^^^^^^^^^^^^^^^^^^
@@ -1726,7 +1749,7 @@ Bug Fixes
    group the same subjects together and give that observation a weight
    equal to the count. Altogether, this means a much faster regression.
 
-.. _section-77:
+.. _section-78:
 
 0.12.0
 ^^^^^^
@@ -1743,7 +1766,7 @@ Bug Fixes
 -  Additional functionality to ``utils.survival_table_from_events`` to
    bin the index to make the resulting table more readable.
 
-.. _section-78:
+.. _section-79:
 
 0.11.3
 ^^^^^^
@@ -1755,7 +1778,7 @@ Bug Fixes
    observation or censorship.
 -  More accurate prediction methods parametrics univariate models.
 
-.. _section-79:
+.. _section-80:
 
 0.11.2
 ^^^^^^
@@ -1763,14 +1786,14 @@ Bug Fixes
 -  Changing liscense to valilla MIT.
 -  Speed up ``NelsonAalenFitter.fit`` considerably.
 
-.. _section-80:
+.. _section-81:
 
 0.11.1 - 2017-06-22
 ^^^^^^^^^^^^^^^^^^^
 
 -  Python3 fix for ``CoxPHFitter.plot``.
 
-.. _section-81:
+.. _section-82:
 
 0.11.0 - 2017-06-21
 ^^^^^^^^^^^^^^^^^^^
@@ -1784,14 +1807,14 @@ Bug Fixes
    of a new ``loc`` kwarg. This is to align with Pandas deprecating
    ``ix``
 
-.. _section-82:
+.. _section-83:
 
 0.10.1 - 2017-06-05
 ^^^^^^^^^^^^^^^^^^^
 
 -  fix in internal normalization for ``CoxPHFitter`` predict methods.
 
-.. _section-83:
+.. _section-84:
 
 0.10.0
 ^^^^^^
@@ -1806,7 +1829,7 @@ Bug Fixes
    mimic R’s ``basehaz`` API.
 -  new ``predict_log_partial_hazards`` to ``CoxPHFitter``
 
-.. _section-84:
+.. _section-85:
 
 0.9.4
 ^^^^^
@@ -1829,7 +1852,7 @@ Bug Fixes
 -  performance improvements in ``CoxPHFitter`` - should see at least a
    10% speed improvement in ``fit``.
 
-.. _section-85:
+.. _section-86:
 
 0.9.2
 ^^^^^
@@ -1838,7 +1861,7 @@ Bug Fixes
 -  throw an error if no admissable pairs in the c-index calculation.
    Previously a NaN was returned.
 
-.. _section-86:
+.. _section-87:
 
 0.9.1
 ^^^^^
@@ -1846,7 +1869,7 @@ Bug Fixes
 -  add two summary functions to Weibull and Exponential fitter, solves
    #224
 
-.. _section-87:
+.. _section-88:
 
 0.9.0
 ^^^^^
@@ -1862,7 +1885,7 @@ Bug Fixes
 -  Default predict method in ``k_fold_cross_validation`` is now
    ``predict_expectation``
 
-.. _section-88:
+.. _section-89:
 
 0.8.1 - 2015-08-01
 ^^^^^^^^^^^^^^^^^^
@@ -1879,7 +1902,7 @@ Bug Fixes
    -  scaling of smooth hazards in NelsonAalenFitter was off by a factor
       of 0.5.
 
-.. _section-89:
+.. _section-90:
 
 0.8.0
 ^^^^^
@@ -1898,7 +1921,7 @@ Bug Fixes
    ``lifelines.statistics. power_under_cph``.
 -  fixed a bug when using KaplanMeierFitter for left-censored data.
 
-.. _section-90:
+.. _section-91:
 
 0.7.1
 ^^^^^
@@ -1917,7 +1940,7 @@ Bug Fixes
 -  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.
 
-.. _section-91:
+.. _section-92:
 
 0.7.0 - 2015-03-01
 ^^^^^^^^^^^^^^^^^^
@@ -1936,7 +1959,7 @@ Bug Fixes
    duration remaining until the death event, given survival up until
    time t.
 
-.. _section-92:
+.. _section-93:
 
 0.6.1
 ^^^^^
@@ -1948,7 +1971,7 @@ Bug Fixes
    your work is to sum up the survival function (for expected values or
    something similar), it’s more difficult to make a mistake.
 
-.. _section-93:
+.. _section-94:
 
 0.6.0 - 2015-02-04
 ^^^^^^^^^^^^^^^^^^
@@ -1971,7 +1994,7 @@ Bug Fixes
 -  In ``KaplanMeierFitter``, ``epsilon`` has been renamed to
    ``precision``.
 
-.. _section-94:
+.. _section-95:
 
 0.5.1 - 2014-12-24
 ^^^^^^^^^^^^^^^^^^
@@ -1992,7 +2015,7 @@ Bug Fixes
    ``lifelines.plotting.add_at_risk_counts``.
 -  Fix bug Epanechnikov kernel.
 
-.. _section-95:
+.. _section-96:
 
 0.5.0 - 2014-12-07
 ^^^^^^^^^^^^^^^^^^
@@ -2005,7 +2028,7 @@ Bug Fixes
 -  add test for summary()
 -  Alternate metrics can be used for ``k_fold_cross_validation``.
 
-.. _section-96:
+.. _section-97:
 
 0.4.4 - 2014-11-27
 ^^^^^^^^^^^^^^^^^^
@@ -2017,7 +2040,7 @@ Bug Fixes
 -  Fixes bug in 1-d input not returning in CoxPHFitter
 -  Lots of new tests.
 
-.. _section-97:
+.. _section-98:
 
 0.4.3 - 2014-07-23
 ^^^^^^^^^^^^^^^^^^
@@ -2038,7 +2061,7 @@ Bug Fixes
 -  Adds option ``include_likelihood`` to CoxPHFitter fit method to save
    the final log-likelihood value.
 
-.. _section-98:
+.. _section-99:
 
 0.4.2 - 2014-06-19
 ^^^^^^^^^^^^^^^^^^
@@ -2058,7 +2081,7 @@ Bug Fixes
    from failing so often (this a stop-gap)
 -  pep8 everything
 
-.. _section-99:
+.. _section-100:
 
 0.4.1.1
 ^^^^^^^
@@ -2071,7 +2094,7 @@ Bug Fixes
 -  Adding more robust cross validation scheme based on issue #67.
 -  fixing ``regression_dataset`` in ``datasets``.
 
-.. _section-100:
+.. _section-101:
 
 0.4.1 - 2014-06-11
 ^^^^^^^^^^^^^^^^^^
@@ -2090,7 +2113,7 @@ Bug Fixes
 -  Adding a Changelog.
 -  more sanitizing for the statistical tests =)
 
-.. _section-101:
+.. _section-102:
 
 0.4.0 - 2014-06-08
 ^^^^^^^^^^^^^^^^^^
diff --git a/lifelines/datasets/__init__.py b/lifelines/datasets/__init__.py
index 9c41d05f4..c0673ca20 100644
--- a/lifelines/datasets/__init__.py
+++ b/lifelines/datasets/__init__.py
@@ -557,3 +557,14 @@ def load_c_botulinum_lag_phase(**kwargs):
 
     """
     return _load_dataset("c_botulinum_lag_phase.csv", **kwargs)
+
+
+def load_mice(**kwargs):
+    """
+    A dataset of interval-censored observations of mice tumors in two different environments.
+
+    References
+    -----------
+    Hoel D. and Walburg, H.,(1972), Statistical analysis of survival experiments, The Annals of Statistics, 18, 1259-1294
+    """
+    return _load_dataset("mice.csv", **kwargs)
diff --git a/lifelines/datasets/mice.csv b/lifelines/datasets/mice.csv
new file mode 100644
index 000000000..b8f34ed9b
--- /dev/null
+++ b/lifelines/datasets/mice.csv
@@ -0,0 +1,145 @@
+"","l","u","grp"
+"1",0,381,"ce"
+"2",0,477,"ce"
+"3",0,485,"ce"
+"4",0,515,"ce"
+"5",0,539,"ce"
+"6",0,563,"ce"
+"7",0,565,"ce"
+"8",0,582,"ce"
+"9",0,603,"ce"
+"10",0,616,"ce"
+"11",0,624,"ce"
+"12",0,650,"ce"
+"13",0,651,"ce"
+"14",0,656,"ce"
+"15",0,659,"ce"
+"16",0,672,"ce"
+"17",0,679,"ce"
+"18",0,698,"ce"
+"19",0,702,"ce"
+"20",0,709,"ce"
+"21",0,723,"ce"
+"22",0,731,"ce"
+"23",0,775,"ce"
+"24",0,779,"ce"
+"25",0,795,"ce"
+"26",0,811,"ce"
+"27",0,839,"ce"
+"28",45,Inf,"ce"
+"29",198,Inf,"ce"
+"30",215,Inf,"ce"
+"31",217,Inf,"ce"
+"32",257,Inf,"ce"
+"33",262,Inf,"ce"
+"34",266,Inf,"ce"
+"35",371,Inf,"ce"
+"36",431,Inf,"ce"
+"37",447,Inf,"ce"
+"38",454,Inf,"ce"
+"39",459,Inf,"ce"
+"40",475,Inf,"ce"
+"41",479,Inf,"ce"
+"42",484,Inf,"ce"
+"43",500,Inf,"ce"
+"44",502,Inf,"ce"
+"45",503,Inf,"ce"
+"46",505,Inf,"ce"
+"47",508,Inf,"ce"
+"48",516,Inf,"ce"
+"49",531,Inf,"ce"
+"50",541,Inf,"ce"
+"51",553,Inf,"ce"
+"52",556,Inf,"ce"
+"53",570,Inf,"ce"
+"54",572,Inf,"ce"
+"55",575,Inf,"ce"
+"56",577,Inf,"ce"
+"57",585,Inf,"ce"
+"58",588,Inf,"ce"
+"59",594,Inf,"ce"
+"60",600,Inf,"ce"
+"61",601,Inf,"ce"
+"62",608,Inf,"ce"
+"63",614,Inf,"ce"
+"64",616,Inf,"ce"
+"65",632,Inf,"ce"
+"66",632,Inf,"ce"
+"67",638,Inf,"ce"
+"68",642,Inf,"ce"
+"69",642,Inf,"ce"
+"70",642,Inf,"ce"
+"71",644,Inf,"ce"
+"72",644,Inf,"ce"
+"73",647,Inf,"ce"
+"74",647,Inf,"ce"
+"75",653,Inf,"ce"
+"76",659,Inf,"ce"
+"77",660,Inf,"ce"
+"78",662,Inf,"ce"
+"79",663,Inf,"ce"
+"80",667,Inf,"ce"
+"81",667,Inf,"ce"
+"82",673,Inf,"ce"
+"83",673,Inf,"ce"
+"84",677,Inf,"ce"
+"85",689,Inf,"ce"
+"86",693,Inf,"ce"
+"87",718,Inf,"ce"
+"88",720,Inf,"ce"
+"89",721,Inf,"ce"
+"90",728,Inf,"ce"
+"91",760,Inf,"ce"
+"92",762,Inf,"ce"
+"93",773,Inf,"ce"
+"94",777,Inf,"ce"
+"95",815,Inf,"ce"
+"96",886,Inf,"ce"
+"97",0,546,"ge"
+"98",0,609,"ge"
+"99",0,692,"ge"
+"100",0,692,"ge"
+"101",0,710,"ge"
+"102",0,752,"ge"
+"103",0,773,"ge"
+"104",0,781,"ge"
+"105",0,782,"ge"
+"106",0,789,"ge"
+"107",0,808,"ge"
+"108",0,810,"ge"
+"109",0,814,"ge"
+"110",0,842,"ge"
+"111",0,846,"ge"
+"112",0,851,"ge"
+"113",0,871,"ge"
+"114",0,873,"ge"
+"115",0,876,"ge"
+"116",0,888,"ge"
+"117",0,888,"ge"
+"118",0,890,"ge"
+"119",0,894,"ge"
+"120",0,896,"ge"
+"121",0,911,"ge"
+"122",0,913,"ge"
+"123",0,914,"ge"
+"124",0,914,"ge"
+"125",0,916,"ge"
+"126",0,921,"ge"
+"127",0,921,"ge"
+"128",0,926,"ge"
+"129",0,936,"ge"
+"130",0,945,"ge"
+"131",0,1008,"ge"
+"132",412,Inf,"ge"
+"133",524,Inf,"ge"
+"134",647,Inf,"ge"
+"135",648,Inf,"ge"
+"136",695,Inf,"ge"
+"137",785,Inf,"ge"
+"138",814,Inf,"ge"
+"139",817,Inf,"ge"
+"140",851,Inf,"ge"
+"141",880,Inf,"ge"
+"142",913,Inf,"ge"
+"143",942,Inf,"ge"
+"144",986,Inf,"ge"
diff --git a/lifelines/fitters/__init__.py b/lifelines/fitters/__init__.py
index 8457fb3be..3383afaa7 100644
--- a/lifelines/fitters/__init__.py
+++ b/lifelines/fitters/__init__.py
@@ -93,6 +93,7 @@ def _update_docstrings(self):
         self.__class__.divide.__doc__ = self.divide.__doc__.format(self._estimate_name, self._class_name)
         self.__class__.predict.__doc__ = self.predict.__doc__.format(self._class_name)
         self.__class__.plot.__doc__ = _plot_estimate.__doc__.format(self._class_name, self._estimate_name)
+        return
 
     def plot(self, **kwargs):
         """
@@ -882,6 +883,7 @@ def _fit(
         n = len(utils.coalesce(*Ts))
 
         if event_observed is not None:
+            event_observed = np.asarray(event_observed)
             utils.check_nans_or_infs(event_observed)
 
         self.event_observed = np.asarray(event_observed, dtype=int) if event_observed is not None else np.ones(n)
diff --git a/lifelines/fitters/coxph_fitter.py b/lifelines/fitters/coxph_fitter.py
index fdcbcd75d..5c56ae25e 100644
--- a/lifelines/fitters/coxph_fitter.py
+++ b/lifelines/fitters/coxph_fitter.py
@@ -1965,7 +1965,7 @@ def score(self, df: pd.DataFrame, scoring_method: str = "log_likelihood") -> flo
         """
 
         if self.baseline_estimation_method != "breslow":
-            return NotImplementedError("Only breslow implemented atm.")
+            raise NotImplementedError("Only breslow implemented atm.")
 
         df = df.copy()
 
diff --git a/lifelines/fitters/kaplan_meier_fitter.py b/lifelines/fitters/kaplan_meier_fitter.py
index 83c0991a5..9e36e69a9 100644
--- a/lifelines/fitters/kaplan_meier_fitter.py
+++ b/lifelines/fitters/kaplan_meier_fitter.py
@@ -1,5 +1,4 @@
 # -*- coding: utf-8 -*-
-import functools
 import warnings
 import numpy as np
 import pandas as pd
@@ -125,10 +124,10 @@ def fit_interval_censoring(
         label=None,
         alpha=None,
         ci_labels=None,
-        show_progress=False,
         entry=None,
         weights=None,
-        tol=1e-7,
+        tol: float = 1e-5,
+        show_progress: bool = False,
     ) -> "KaplanMeierFitter":
         """
         Fit the model to a interval-censored dataset using non-parametric MLE. This estimator is
@@ -168,6 +167,10 @@ def fit_interval_censoring(
               if providing a weighted dataset. For example, instead
               of providing every subject as a single element of `durations` and `event_observed`, one could
               weigh subject differently.
+          tol: float, optional
+            minimum difference in log likelihood changes for iterative algorithm.
+          show_progress: bool, optional
+            display information during fitting.
 
         Returns
         -------
@@ -203,12 +206,11 @@ def fit_interval_censoring(
 
         self._label = coalesce(label, self._label, "NPMLE_estimate")
 
-        results = npmle(self.lower_bound, self.upper_bound, verbose=show_progress)
+        results = npmle(self.lower_bound, self.upper_bound, verbose=show_progress, tol=tol)
         self.survival_function_ = reconstruct_survival_function(*results, self.timeline, label=self._label).loc[self.timeline]
         self.cumulative_density_ = 1 - self.survival_function_
 
         self._median = median_survival_times(self.survival_function_)
-        self.percentile = functools.partial(qth_survival_time, model_or_survival_function=self.survival_function_)
 
         """
         self.confidence_interval_ = npmle_compute_confidence_intervals(self.lower_bound, self.upper_bound, self.survival_function_, self.alpha)
@@ -291,8 +293,11 @@ def _fit(
           self with new properties like ``survival_function_``, ``plot()``, ``median_survival_time_``
 
         """
+        durations = np.asarray(durations)
         self._check_values(durations)
+
         if event_observed is not None:
+            event_observed = np.asarray(event_observed)
             self._check_values(event_observed)
 
         self._label = coalesce(label, self._label, "KM_estimate")
@@ -345,7 +350,6 @@ def _fit(
         self.__estimate = getattr(self, primary_estimate_name)
         self.confidence_interval_ = self._bounds(cumulative_sq_[:, None], alpha, ci_labels)
         self._median = median_survival_times(self.survival_function_)
-        self.percentile = functools.partial(qth_survival_time, model_or_survival_function=self.survival_function_)
         self._cumulative_sq_ = cumulative_sq_
 
         setattr(self, "confidence_interval_" + primary_estimate_name, self.confidence_interval_)
diff --git a/lifelines/fitters/nelson_aalen_fitter.py b/lifelines/fitters/nelson_aalen_fitter.py
index 7d12c030d..e0d9e3449 100644
--- a/lifelines/fitters/nelson_aalen_fitter.py
+++ b/lifelines/fitters/nelson_aalen_fitter.py
@@ -105,8 +105,10 @@ def fit(
           self, with new properties like ``cumulative_hazard_``.
 
         """
+        durations = np.asarray(durations)
         check_nans_or_infs(durations)
         if event_observed is not None:
+            event_observed = np.asarray(event_observed)
             check_nans_or_infs(event_observed)
 
         if weights is not None:
diff --git a/lifelines/fitters/npmle.py b/lifelines/fitters/npmle.py
index dd2447507..5686fb5eb 100644
--- a/lifelines/fitters/npmle.py
+++ b/lifelines/fitters/npmle.py
@@ -13,6 +13,7 @@
 from numpy.linalg import norm
 import pandas as pd
 from lifelines.utils import ConvergenceWarning
+from typing import *
 
 interval = namedtuple("Interval", ["left", "right"])
 
@@ -26,7 +27,7 @@ def __init__(self):
         self.min = np.inf
         self.max = -np.inf
 
-    def add(self, value):
+    def add(self, value: float):
         if value > self.max:
             self.max = value
         if value < self.min:
@@ -37,23 +38,45 @@ def __iter__(self):
         yield self.max
 
 
-def E_step_M_step(observation_intervals, p_old, turnbull_interval_lookup, weights):
+def temper(i: int, optimize) -> float:
+    if optimize:
+        return 0.9 * (2 * np.arctan(i / 100) / np.pi) + 1
+    else:
+        return 1.0
 
+
+def E_step_M_step(observation_intervals, p_old, turnbull_interval_lookup, weights, i, optimize) -> np.ndarray:
+    """
+    See [1], but also modifications.
+
+    References
+    -----------
+    1. Clifford Anderson-Bergman (2016): An efficient implementation of the
+     EMICM algorithm for the interval censored NPMLE, Journal of Computational and Graphical
+     Statistics, DOI: 10.1080/10618600.2016.1208616
+    """
     N = 0
-    p_new = np.zeros_like(p_old)
+    m = np.zeros_like(p_old)
+    P = cumulative_sum(p_old)
     for observation_interval, w in zip(observation_intervals, weights):
         # find all turnbull intervals, t, that are contained in (ol, or). Call this set T
         # the denominator is sum of p_old[T] probabilities
         # the numerator is p_old[t]
-
         min_, max_ = turnbull_interval_lookup[observation_interval]
-        p_new[min_ : max_ + 1] += w * p_old[min_ : max_ + 1] / p_old[min_ : max_ + 1].sum()
+        m[min_ : max_ + 1] += w / (P[max_ + 1] - P[min_]).sum()
         N += w
 
-    return p_new / N
+    p_new = p_old * (m / N) ** temper(i, optimize)
+    p_new /= p_new.sum()
+    return p_new
+
+
+def cumulative_sum(p: np.ndarray) -> np.ndarray:
+    # return np.insert(p, 0, 0).cumsum()
+    return np.concatenate((np.zeros(1), p)).cumsum()
 
 
-def create_turnbull_intervals(left, right):
+def create_turnbull_intervals(left, right) -> List[interval]:
     """
     obs are []
     turnbulls are []
@@ -72,14 +95,16 @@ def create_turnbull_intervals(left, right):
     return intervals
 
 
-def is_subset(query_interval, super_interval):
+def is_subset(query_interval: interval, super_interval: interval) -> bool:
     """
     assumes query_interval is [], and super_interval is (]
     """
     return super_interval.left <= query_interval.left and query_interval.right <= super_interval.right
 
 
-def create_turnbull_lookup(turnbull_intervals, observation_intervals):
+def create_turnbull_lookup(
+    turnbull_intervals: List[interval], observation_intervals: List[interval]
+) -> Dict[interval, List[interval]]:
 
     turnbull_lookup = defaultdict(min_max)
 
@@ -95,27 +120,41 @@ def create_turnbull_lookup(turnbull_intervals, observation_intervals):
     return {o: list(s) for o, s in turnbull_lookup.items()}
 
 
-def check_convergence(p_new, p_old, tol, i, verbose=False):
+def check_convergence(
+    p_new: np.ndarray,
+    p_old: np.ndarray,
+    turnbull_lookup: Dict[interval, List[interval]],
+    weights: np.ndarray,
+    tol: float,
+    i: int,
+    verbose=False,
+) -> bool:
+    old_ll = log_likelihood(p_old, turnbull_lookup, weights)
+    new_ll = log_likelihood(p_new, turnbull_lookup, weights)
+    delta = new_ll - old_ll
     if verbose:
-        print("Iteration %d: delta: %.6f" % (i, norm(p_new - p_old)))
-    if norm(p_new - p_old) < tol:
+        print("Iteration %d " % i)
+        print("   delta log-likelihood: %.10f" % delta)
+        print("   log-like:             %.6f" % log_likelihood(p_new, turnbull_lookup, weights))
+    if (delta < tol) and (delta >= 0):
         return True
     return False
 
 
-def create_observation_intervals(obs):
+def create_observation_intervals(obs) -> List[interval]:
     return [interval(l, r) for l, r in obs]
 
 
-def odds(p):
-    return p / (1 - p)
+def log_odds(p: np.ndarray) -> np.ndarray:
+    return np.log(p) - np.log(1 - p)
 
 
-def probs(o):
+def probs(log_odds: np.ndarray) -> np.ndarray:
+    o = np.exp(log_odds)
     return o / (o + 1)
 
 
-def npmle(left, right, tol=1e-5, weights=None, verbose=False, max_iter=1e5):
+def npmle(left, right, tol=1e-7, weights=None, verbose=False, max_iter=1e5, optimize=False, fit_method="em"):
     """
     left and right are closed intervals.
     TODO: extend this to open-closed intervals.
@@ -135,6 +174,54 @@ def npmle(left, right, tol=1e-5, weights=None, verbose=False, max_iter=1e5):
     observation_intervals = create_observation_intervals(unique_obs)
     turnbull_lookup = create_turnbull_lookup(turnbull_intervals, observation_intervals)
 
+    if fit_method == "em":
+        p = expectation_maximization_fit(
+            observation_intervals, turnbull_intervals, turnbull_lookup, weights, tol, max_iter, optimize, verbose
+        )
+    elif fit_method == "scipy":
+        p = scipy_minimize_fit(turnbull_lookup, turnbull_intervals, weights, tol, verbose)
+
+    return p, turnbull_intervals
+
+
+def scipy_minimize_fit(turnbull_interval_lookup, turnbull_intervals, weights, tol, verbose):
+    import autograd.numpy as anp
+    from autograd import value_and_grad
+    from scipy.optimize import minimize
+
+    def cumulative_sum(p):
+        return anp.concatenate((anp.zeros(1), p)).cumsum()
+
+    def negative_log_likelihood(p, turnbull_interval_lookup, weights):
+        P = cumulative_sum(p)
+        ix = anp.array(list(turnbull_interval_lookup.values()))
+        return -(weights * anp.log(P[ix[:, 1] + 1] - P[ix[:, 0]])).sum()
+
+    def con(p):
+        return p.sum() - 1
+
+    # initialize to equal weight
+    T = len(turnbull_intervals)
+    p = 1 / T * np.ones(T)
+
+    cons = {"type": "eq", "fun": con}
+    results = minimize(
+        value_and_grad(negative_log_likelihood),
+        args=(turnbull_interval_lookup, weights),
+        x0=p,
+        bounds=[(0, 1)] * T,
+        jac=True,
+        constraints=cons,
+        tol=tol,
+        options={"disp": verbose},
+    )
+    return results.x
+
+
+def expectation_maximization_fit(
+    observation_intervals, turnbull_intervals, turnbull_lookup, weights, tol, max_iter, optimize, verbose
+):
+
     # convergence init
     converged = False
     i = 0
@@ -144,18 +231,17 @@ def npmle(left, right, tol=1e-5, weights=None, verbose=False, max_iter=1e5):
     p = 1 / T * np.ones(T)
 
     while (not converged) and (i < max_iter):
-        p_new = E_step_M_step(observation_intervals, p, turnbull_lookup, weights)
-        converged = check_convergence(p_new, p, tol, i, verbose=verbose)
+        new_p = E_step_M_step(observation_intervals, p, turnbull_lookup, weights, i, optimize)
+        converged = check_convergence(new_p, p, turnbull_lookup, weights, tol, i, verbose=verbose)
 
         # find alpha that maximizes ll using a line search
-        best_alpha, best_p, best_ll = None, None, -np.inf
-        delta = odds(p_new) - odds(p)
+        best_p, best_ll = None, -np.inf
+        delta = log_odds(new_p) - log_odds(p)
         for alpha in np.array([1.0, 1.25, 1.95]):
-            p_temp = probs(odds(p) + alpha * delta)
-            ll_temp = log_likelihood(observation_intervals, p_temp, turnbull_lookup, weights)
+            p_temp = probs(log_odds(p) + alpha * delta)
+            ll_temp = log_likelihood(p_temp, turnbull_lookup, weights)
             if best_ll < ll_temp:
                 best_ll = ll_temp
-                best_alpha = alpha
                 best_p = p_temp
 
         p = best_p
@@ -165,19 +251,18 @@ def npmle(left, right, tol=1e-5, weights=None, verbose=False, max_iter=1e5):
     if i >= max_iter:
         warnings.warn("Exceeded max iterations", ConvergenceWarning)
 
-    return p, turnbull_intervals
-
+    return p
 
-def log_likelihood(observation_intervals, p, turnbull_interval_lookup, weights):
 
-    ll = 0
-    for observation_interval, w in zip(observation_intervals, weights):
-        min_, max_ = turnbull_interval_lookup[observation_interval]
-        ll += w * np.log(p[min_ : max_ + 1].sum())
-    return ll
+def log_likelihood(p: np.ndarray, turnbull_interval_lookup, weights) -> float:
+    P = cumulative_sum(p)
+    ix = np.array(list(turnbull_interval_lookup.values()))
+    return (weights * np.log(P[ix[:, 1] + 1] - P[ix[:, 0]])).sum()
 
 
-def reconstruct_survival_function(probabilities, turnbull_intervals, timeline=None, label="NPMLE"):
+def reconstruct_survival_function(
+    probabilities: np.ndarray, turnbull_intervals: List[interval], timeline=None, label="NPMLE"
+) -> pd.DataFrame:
 
     if timeline is None:
         timeline = []
diff --git a/lifelines/tests/test_estimation.py b/lifelines/tests/test_estimation.py
index 5ce38510f..39cfb0c61 100644
--- a/lifelines/tests/test_estimation.py
+++ b/lifelines/tests/test_estimation.py
@@ -528,7 +528,7 @@ def test_ci_is_not_all_nan(self, positive_sample_lifetimes, univariate_fitters):
                 pass
             assert not (pd.isnull(fitter.confidence_interval_)).all().all()
 
-    def test_lists_as_input(self, positive_sample_lifetimes, univariate_fitters):
+    def test_lists_and_tuples_as_input(self, positive_sample_lifetimes, univariate_fitters):
         T, C = positive_sample_lifetimes
         for f in univariate_fitters:
             fitter = f()
@@ -536,21 +536,29 @@ def test_lists_as_input(self, positive_sample_lifetimes, univariate_fitters):
             if isinstance(fitter, NelsonAalenFitter):
                 with_array = fitter.fit(T, C).cumulative_hazard_
                 with_list = fitter.fit(list(T), list(C)).cumulative_hazard_
+                with_tuple = fitter.fit(tuple(T), tuple(C)).cumulative_hazard_
                 assert_frame_equal(with_list, with_array)
+                assert_frame_equal(with_tuple, with_array)
 
             else:
                 with_array = fitter.fit(T, C).survival_function_
                 with_list = fitter.fit(list(T), list(C)).survival_function_
+                with_tuple = fitter.fit(tuple(T), tuple(C)).survival_function_
                 assert_frame_equal(with_list, with_array)
+                assert_frame_equal(with_tuple, with_array)
 
                 if isinstance(fitter, ParametricUnivariateFitter):
                     with_array = fitter.fit_interval_censoring(T, T + 1, (T == T + 1)).survival_function_
                     with_list = fitter.fit_interval_censoring(list(T), list(T + 1), list((T == T + 1))).survival_function_
+                    with_tuple = fitter.fit_interval_censoring(tuple(T), tuple(T + 1), tuple((T == T + 1))).survival_function_
                     assert_frame_equal(with_list, with_array)
+                    assert_frame_equal(with_tuple, with_array)
 
                     with_array = fitter.fit_left_censoring(T, C).survival_function_
                     with_list = fitter.fit_left_censoring(list(T), list(C)).survival_function_
+                    with_tuple = fitter.fit_left_censoring(tuple(T), tuple(C)).survival_function_
                     assert_frame_equal(with_list, with_array)
+                    assert_frame_equal(with_tuple, with_array)
 
     def test_subtraction_function(self, positive_sample_lifetimes, univariate_fitters):
         T2 = np.arange(1, 50)
diff --git a/lifelines/tests/test_npmle.py b/lifelines/tests/test_npmle.py
index c7da5e2b6..7ec38125a 100644
--- a/lifelines/tests/test_npmle.py
+++ b/lifelines/tests/test_npmle.py
@@ -2,7 +2,9 @@
 import pytest
 from lifelines.fitters.npmle import npmle, is_subset, create_turnbull_intervals, interval, reconstruct_survival_function
 from numpy import testing as npt
+from lifelines.datasets import load_mice
 import numpy as np
+import pandas as pd
 
 
 def test_is_subset():
@@ -33,13 +35,13 @@ def test_create_turnbull_intervals():
 
 def test_npmle():
     left, right = [1, 8, 8, 7, 7, 17, 37, 46, 46, 45], [7, 8, 10, 16, 14, np.inf, 44, np.inf, np.inf, np.inf]
-    npt.assert_allclose(npmle(left, right)[0], np.array([0.16667016, 0.33332984, 0.125, 0.375]), rtol=1e-4)
+    npt.assert_allclose(npmle(left, right, verbose=True)[0], np.array([0.16667016, 0.33332984, 0.125, 0.375]), rtol=1e-4)
 
 
 def test_npmle_with_weights_is_identical_if_uniform_weights():
     left, right = [1, 8, 8, 7, 7, 17, 37, 46, 46, 45], [7, 8, 10, 16, 14, np.inf, 44, np.inf, np.inf, np.inf]
     weights = 2 * np.ones_like(right)
-    npt.assert_allclose(npmle(left, right)[0], np.array([0.16667016, 0.33332984, 0.125, 0.375]), rtol=1e-4)
+    npt.assert_allclose(npmle(left, right, verbose=True)[0], np.array([0.16667016, 0.33332984, 0.125, 0.375]), rtol=1e-4)
 
 
 def test_npmle_with_weights():
@@ -61,3 +63,17 @@ def test_sf_doesnt_return_nans():
     npt.assert_allclose(results[0], [0.5, 0.5])
     sf = reconstruct_survival_function(*results, timeline=[6, 7, 8, 16, 20])
     assert not np.isnan(sf.values).any()
+
+
+def test_mice_and_optimization_flag():
+    df = load_mice()
+    results = npmle(df["l"], df["u"], verbose=True, optimize=True)
+    npt.assert_allclose(results[0][0], 1 - 0.8571429, rtol=1e-4)
+    npt.assert_allclose(results[0][-1], 0.166667, rtol=1e-4)
+
+
+def test_mice_scipy():
+    df = load_mice()
+    results = npmle(df["l"], df["u"], verbose=True, fit_method="scipy")
+    npt.assert_allclose(results[0][0], 1 - 0.8571429, rtol=1e-4)
+    npt.assert_allclose(results[0][-1], 0.166667, rtol=1e-4)
diff --git a/lifelines/utils/__init__.py b/lifelines/utils/__init__.py
index f5cf5e47b..49e412c3c 100644
--- a/lifelines/utils/__init__.py
+++ b/lifelines/utils/__init__.py
@@ -918,7 +918,7 @@ def _preprocess_inputs(durations, event_observed, timeline, entry, weights):
     if event_observed is None:
         event_observed = np.ones(n, dtype=int)
     else:
-        event_observed = np.asarray(event_observed).reshape((n,)).copy().astype(int)
+        event_observed = np.asarray(event_observed).reshape((n,)).astype(int)
 
     if entry is not None:
         entry = np.asarray(entry).reshape((n,))
diff --git a/lifelines/version.py b/lifelines/version.py
index f20f10eee..f656938bc 100644
--- a/lifelines/version.py
+++ b/lifelines/version.py
@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 from __future__ import unicode_literals
 
-__version__ = "0.24.8"
+__version__ = "0.24.9"