Highlight that pypfilt implements several filters

doc/index.rst

@@ -2,16 +2,14 @@ Bootstrap particle filter for Python
 ====================================
 
 Welcome to the pypfilt_ documentation.
-This package implements a bootstrap particle filter that can be used for
-recursive Bayesian estimation and forecasting.
+This package implements several particle filter methods that can be used for recursive Bayesian estimation and forecasting.
 
 If there is a system or process that can be:
 
 * Described (modelled) with mathematical equations; and
 * Measured repeatedly in some (noisy) way.
 
-Then you can use pypfilt_ to estimate the state and/or parameters of this
-system.
+Then you can use pypfilt_ to estimate the state and/or parameters of this system.
 
 .. figure:: getting-started/lorenz63_forecast_regularised.png
    :width: 100%
@@ -38,16 +36,12 @@ The :doc:`settings/index` guide
    Explains the settings that can be defined in a scenario file.
 
 The :doc:`api/index`
-   Presents the details of each pypfilt_ component and is likely only of
-   interest if you need to develop your own components.
+   Presents the details of each pypfilt_ component and is likely only of interest if you need to develop your own components.
 
 License
 -------
 
-The code is distributed under the terms of the BSD 3-Clause license (see
-``LICENSE``), and the documentation is distributed under the terms of the
-`Creative Commons BY-SA 4.0 license
-<http://creativecommons.org/licenses/by-sa/4.0/>`_.
+The code is distributed under the terms of the BSD 3-Clause license (see ``LICENSE``), and the documentation is distributed under the terms of the `Creative Commons BY-SA 4.0 license <http://creativecommons.org/licenses/by-sa/4.0/>`_.
 
 .. _user-docs: