Update README.md

README.md

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 ## Overview
 
-Many systems across nature and society have the capacity to undergo an abrupt and profound change in their dynamics. From a dynamical systemes perspective, these changes corresopond to bifurcations, which carry some generic features that can be picked up on in time series data ([Scheffer et al. 2009](https://www.nature.com/articles/nature08227)). Two commonly used metrics include variance and lag-1 autocorrelation, though there exist many others (see e.g. [Clements & Ozgul 2018](https://onlinelibrary.wiley.com/doi/full/10.1111/ele.12948)). More recently, deep learning methods have been developed to provide early warning signals, whilst also signalling the type of bifurcation approaching [(Bury et al. 2021)](https://www.pnas.org/doi/10.1073/pnas.2106140118).
+Many systems across nature and society have the capacity to undergo an abrupt and profound change in their dynamics. From a dynamical systemes perspective, these events are often associated with the crossing of a bifurcation. Early warning signals (EWS) for bifurcations are therefore in high demand. Two commonly used EWS for bifurcations are variance and lag-1 autocorrelation, that are expected to increase prior to many bifurcations due to critical slowing down ([Scheffer et al. 2009](https://www.nature.com/articles/nature08227)). There now exist a wealth of other EWS based on changes in time series dynamics that are expected to occur prior to bifurcations (see e.g. [Clements & Ozgul 2018](https://onlinelibrary.wiley.com/doi/full/10.1111/ele.12948)). More recently, deep learning classifiers have trained and applied to detect bifurcations, with promising results ([Bury et al. 2021](https://www.pnas.org/doi/10.1073/pnas.2106140118)).
 
-The goal of this Python package is to provide a user-friendly toolbox for computing early warning signals in time series data. It complements an existing early warning signals package in R ([Dakos et al. 2012](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041010)). We hope that having an early warning signal toolbox in Python will allow for additional testing, and appeal to those who primarily work in Python. I will try to keep it updated with the latest methods.
+The goal of this Python package is to provide a an accessible toolbox for computing, analysing and visulaising EWS in time series data. It complements an existing EWS package in R ([Dakos et al. 2012](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041010)). We hope that having an EWS toolbox in Python will allow for additional testing, and appeal to those who primarily work in Python.
 
 Current functionality of *ewstools* includes