Merge remote-tracking branch 'origin/master'

README.md

@@ -73,7 +73,7 @@ PyNonpar.twosample.wilcoxon_mann_whitney_test(x, y, alternative="less", method =
 To calculate the sample size which is needed to detect a specific relative effect p with probability beta and type-I error
 alpha, the function'wilcoxon_mann_whitney_ssp' can be used. Here, prior information for one group is needed.
 The artificial data for the second group can be created by some interpretable effect, e.g. a location shift effect.
-For more information, see [3] or [4].
+For more information, see [1] or [3].
 
 ```Python
 import PyNonpar
@@ -193,6 +193,6 @@ PyNonpar.repeated_measures.kepner_robinson_test(data, time, subject, distributio
 [1] Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
 
 [2] Kepner, J. L., & Robinson, D. H. (1988). Nonparametric methods for detecting treatment effects in repeated-measures designs. Journal of the American Statistical Association, 83(402), 456-461.
-[3] Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear,
-[4] Happ, M., Bathke, A. C., & Brunner, E. (2019). Optimal sample size planning for the Wilcoxon‐Mann‐Whitney test. Statistics in medicine, 38(3), 363-375.
+
+[3] Happ, M., Bathke, A. C., & Brunner, E. (2019). Optimal sample size planning for the Wilcoxon‐Mann‐Whitney test. Statistics in medicine, 38(3), 363-375.
 

appveyor.yml

@@ -17,8 +17,9 @@ init:
 install:
   - conda config --set always_yes yes --set changeps1 no
   - conda config --add channels conda-forge
-  - conda update -q conda
   - conda install pip numpy scipy
+  - conda update -q conda
+  - "python -m pip install --upgrade pip"
   - "pip install -q pytest"
   - "pip install -qr requirements.txt"
   - "pip install ."

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup
 
 
-with open("README.md", "r") as fh:
+with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
 setup(