Update to include pip install instructions.

README.md

@@ -4,20 +4,19 @@
 
 [Read our documentation!](https://pylift.readthedocs.io/en/latest/)
 
-**pylift** is an uplift library that provides, primarily, (1) fast uplift
-modeling implementations and (2) evaluation tools. While other packages and
-more exact methods exist to model uplift, **pylift** is designed to be quick,
-flexible, and effective. **pylift** heavily leverages the optimizations of
-other packages -- namely, `xgboost`, `sklearn`, `pandas`, `matplotlib`,
-`numpy`, and `scipy`. The primary method currently implemented is the
-Transformed Outcome proxy method (Athey 2015).
+**pylift** is an uplift library that provides, primarily, (1) fast uplift modeling implementations and (2) evaluation tools (`UpliftEval` class). While other packages and more exact methods exist to model uplift, **pylift** is designed to be quick, flexible, and effective. **pylift** heavily leverages the optimizations of other packages -- namely, `xgboost`, `sklearn`, `pandas`, `matplotlib`, `numpy`, and `scipy`. The primary method currently implemented is the Transformed Outcome proxy method (Athey 2015).
+
+This branch is a fork from [github.com/wayfair/pylift](https://github.com/wayfair/pylift), and is actively being maintained.
+
+## Installation
+This version of pylift can be installed through pypi:
+`pip install pylift`
 
 ## License
 Licensed under the BSD-2-Clause by the authors.
 
 ## Reference
-Athey, S., & Imbens, G. W. (2015). Machine learning methods for estimating
-heterogeneous causal effects. stat, 1050(5).
+Athey, S., & Imbens, G. W. (2015). Machine learning methods for estimating heterogeneous causal effects. stat, 1050(5).
 
 Gutierrez, P., & Gérardy, J. Y. (2017). Causal Inference and Uplift Modelling: A Review of the Literature. In International Conference on Predictive Applications and APIs (pp. 1-13).