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@@ -9,7 +9,7 @@ This package is currently in very active development. An addition ten to fifteen
 
 The documentation is divided into the [user guide](docs/userguide.md), the [contribution guide](docs/contributing.md) (including developer documentation) and [API documentation](docs/api.md).
 
-'scores' is a modular scoring package containing mathematical functions that can be used for the verification, evaluation, and optimisation of models, as well as other statistical functions. It is primarily aiming to support the geoscience and earth system community. Scores is focused on supporting xarray datatypes for earth system data. Other data formats such as Pandas and Iris can be easily be converted to xarray objects to utilise `scores`. It has wider potential application in machine learning and domains other than meteorology, geoscience and weather but primarily supports those fields. It aims to be compatible with geopandas, pangeo and work with NetCDF4, Zarr, and hd5 data sources among others.
+'scores' is a modular scoring package containing mathematical functions that can be used for the verification, evaluation, and optimisation of models, as well as other statistical functions. It is primarily aiming to support the geoscience and earth system community. Scores is focused on supporting xarray datatypes for earth system data. Other data formats such as Pandas and Iris can be easily be converted to xarray objects to utilise `scores`. It has wider potential application in machine learning and domains other than meteorology, geoscience and weather but primarily supports those fields. It aims to be compatible with geopandas, pangeo and work with NetCDF4, Zarr, and hd5 data sources among others. To use `scores` with GRIB data, install [cfgrib](https://github.com/ecmwf/cfgrib) into your python environment and use `engine='cfgrib'` when opening a grib file with xarray.
 
 All of the scores and metrics in this package have undergone a thorough statistical and scientific review.
 
@@ -39,7 +39,7 @@ array(2.)
 
 'scores' is a modular scoring package containing verification metrics, error functions, training scores and other statistical functions. It is primarily aiming to support the geoscience and earth system community. Scores is focused on supporting xarray and pandas datatypes for earth system data. It has wider potential application in machine learning and domains other than meteorology, geoscience and weather but primarily supports those fields. It aims to be compatible with geopandas, pangeo and work with NetCDF4 and hdf5 data sources among others.
 
-'scores' includes novel scores not commonly found elsewhere (e.g. FIRM and FlipFlip index), complex scores (CRPS, Diebold Mariano) and more common scores (MAE, RMSE). Scores provides its own implementations where relevant to avoid extensive dependencies, and its roadmap includes a comprehensive implementation of optimised, reviewed and useful set of scoring functions for verification, statistics, optimisation and machine learning.
+'scores' includes novel scores not commonly found elsewhere (e.g. FIRM and FlipFlip index), complex scores (CRPS), more common scores (MAE, RMSE) and statistical tests (such as the Diebold Mariano test). Scores provides its own implementations where relevant to avoid extensive dependencies, and its roadmap includes a comprehensive implementation of optimised, reviewed and useful set of scoring functions for verification, statistics, optimisation and machine learning.
 
 All of the scores and metrics in this package have undergone a thorough statistical and scientific review.