diff --git a/README.md b/README.md
index 667eba7..107864a 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# pyLandau [![Build Status](https://travis-ci.org/SiLab-Bonn/pyLandau.svg?branch=master)](https://travis-ci.org/SiLab-Bonn/pyLandau) [![Build Status](https://ci.appveyor.com/api/projects/status/github/SiLab-Bonn/pyLandau)](https://ci.appveyor.com/project/DavidLP/pyLandau)
+# pylandau [![Build Status](https://travis-ci.org/SiLab-Bonn/pyLandau.svg?branch=master)](https://travis-ci.org/SiLab-Bonn/pyLandau) [![Build Status](https://ci.appveyor.com/api/projects/status/github/SiLab-Bonn/pyLandau)](https://ci.appveyor.com/project/DavidLP/pyLandau)
 A simple [Landau](http://en.wikipedia.org/wiki/Landau_distribution) definition to be used in Python, since no common package (Scipy, Numpy, ...) provides this. Also a fast Landau + Gauss convolution is offered, that is usefull for fitting energy losses of charged particles in matter. The Landau is approximated according to  [Computer Phys. Comm. 31 (1984) 97-111](http://dx.doi.org/10.1016/0010-4655(84)90085-7) and the implementation is from [CERN ROOT Mathlibs] (https://project-mathlibs.web.cern.ch/project-mathlibs/sw/html/PdfFuncMathCore_8cxx_source.html).
 
 # Installation
@@ -15,7 +15,7 @@ python setup.py develop
 
 import numpy as np
 
-import pyLandau
+import pylandau
 
 x = np.arange(0, 100, 0.01)