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-[![packageversion](https://img.shields.io/badge/NNS%20version-10.9.3-blue.svg?style=flat-square)](https://github.com/OVVO-Financial/NNS/commits/NNS-Beta-Version)   [![Licence](https://img.shields.io/badge/licence-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html)
 
+[![packageversion](https://img.shields.io/badge/NNS%20version-10.9.3-blue.svg?style=flat-square)](https://github.com/OVVO-Financial/NNS/commits/NNS-Beta-Version)   [![Licence](https://img.shields.io/badge/licence-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html)
 
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 # NNS
-Nonlinear nonparametric statistics using partial moments.  Partial moments are the [elements of variance](https://www.linkedin.com/pulse/elements-variance-fred-viole) and [asymptotically approximate the area of f(x)](https://doi.org/10.2139/ssrn.2186471).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.
+Nonlinear nonparametric statistics using partial moments. Partial moments are the [elements of variance](https://www.linkedin.com/pulse/elements-variance-fred-viole) and [asymptotically approximate the area of f(x)](https://doi.org/10.2139/ssrn.2186471). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.
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 NNS offers: 
   - Numerical Integration & Numerical Differentiation