let's cut it here. So what's _not_ included in 0.15.0 is lognormal, a…

…nd robust to ctv models

CHANGELOG.md

@@ -1,7 +1,7 @@
 ### Changelogs
 
 #### 0.15.0
- - adding `robust` params to Cox models' `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.
+ - 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.
@@ -22,6 +22,7 @@
  - 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
  - fix for n > 2 groups in `multivariate_logrank_test` (again).

docs/Survival analysis with lifelines.rst

@@ -481,7 +481,7 @@ In lifelines, estimation is available using the ``WeibullFitter`` class:
 
 
 
-Other parametric models: Exponential and LogNormal
+Other parametric models: Exponential
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Similarly, there are other parametric models in lifelines. Generally, which parametric model to choose is determined by either knowledge of the distribution of durations, or some sort of model goodness-of-fit. Below are three parametric models of the same data. 
@@ -490,18 +490,15 @@ Similarly, there are other parametric models in lifelines. Generally, which para
 
     from lifelines import WeibullFitter
     from lifelines import ExponentialFitter
-    from lifelines import LogNormalFitter
   
     T = data['duration']
     E = data['observed']
 
     wf = WeibullFitter().fit(T, E, label='WeibullFitter')
     exf = ExponentialFitter().fit(T, E, label='ExponentalFitter')
-    lnf = LogNormalFitter().fit(T, E, label='LogNormalFitter')
 
     ax = wf.plot()
     ax = exf.plot(ax=ax)
-    ax = lnf.plot(ax=ax)
 
 
 Estimating hazard rates using Nelson-Aalen

tests/test_plotting.py

@@ -13,7 +13,6 @@
 from lifelines.generate_datasets import cumulative_integral
 
 
-@pytest.mark.plottest
 @pytest.mark.skipif("DISPLAY" not in os.environ, reason="requires display")
 class TestPlotting():