Merge pull request #1075 from CamDavidsonPilon/v0.24.15

V0.24.15
CamDavidsonPilon · Jul 7, 2020 · 88a37ed · 88a37ed
2 parents b6d12cb + 3a2f45e
commit 88a37ed
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diff --git a/.travis.yml b/.travis.yml
@@ -7,10 +7,10 @@ python:
    - "3.8"
 env:
   matrix:
-    - export PANDAS_VERSION=0.23.4     && export NUMPY_VERSION=1.19.0
-    - export PANDAS_VERSION=0.25.3     && export NUMPY_VERSION=1.19.0
-    - export NUMPY_VERSION=1.15.4      && export PANDAS_VERSION=1.0.5
-    - export NUMPY_VERSION=1.19.0      && export PANDAS_VERSION=1.0.5
+    - PANDAS_VERSION=0.23.4  && NUMPY_VERSION=1.19.0
+    - PANDAS_VERSION=0.25.3  && NUMPY_VERSION=1.19.0
+    - NUMPY_VERSION=1.15.4   && PANDAS_VERSION=1.0.5
+    - NUMPY_VERSION=1.19.0   && PANDAS_VERSION=1.0.5
 before_install:
   - ls
   # - sudo apt-get update

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,5 +1,13 @@
 ## Changelog
 
+#### 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

diff --git a/Makefile b/Makefile
@@ -3,7 +3,7 @@ ifeq ($(TRAVIS), true)
 		pip install -r reqs/travis-requirements.txt
 		pip install pandas==${PANDAS_VERSION}
 		pip install numpy==${NUMPY_VERSION}
-		pip list --local
+		pip freeze --local
 else
 		pip install -r reqs/dev-requirements.txt
 		pre-commit install

diff --git a/README.md b/README.md
@@ -11,13 +11,13 @@
 [What is survival analysis and why should I learn it?](http://lifelines.readthedocs.org/en/latest/Survival%20Analysis%20intro.html)
  Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer *why do events occur now versus later* under uncertainty (where *events* might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like *what factors might influence deaths?*
 
-But outside of medicine and actuarial science, there are many other interesting and exciting applications of this survival analysis. For example:
+But outside of medicine and actuarial science, there are many other interesting and exciting applications of survival analysis. For example:
 - SaaS providers are interested in measuring subscriber lifetimes, or time to some first action
 - inventory stock out is a censoring event for true "demand" of a good.
 - sociologists are interested in measuring political parties' lifetimes, or relationships, or marriages
 - A/B tests to determine how long it takes different groups to perform an action.
 
-*lifelines* is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using *lifelines*, please leave an Issue and let us know your thoughts on the library.
+*lifelines* is a pure Python implementation of the best parts of survival analysis.
 
 
 ## Documentation and intro to survival analysis