Documentation Fixes

docs/userguide/kriging.rst

@@ -163,7 +163,7 @@ dot product of the :math:`\gamma_i` and the :math:`a_i` part.
 
 That might look a bit complicated at first, but we have calculated almost everything.
 The last matrix are the `variances` that we calculated in the last step.
-The first matrix is of same shape as the sqaureform distance matrix calculated in
+The first matrix is of same shape as the squareform distance matrix calculated in
 the very beginning. All we need to do is to map the variogram model on it and
 solve the system for the matrix of factors :math:`a_1 \ldots a_5`.
 In Python, there are several strategies how you could solve this problem.

docs/userguide/variogram.rst

@@ -553,8 +553,7 @@ That's a commonly used, very fast least squares implementation.
 However, sometimes it fails to find good parameters, as it is
 unbounded and *searching* an invalid parameter space.
 The default for :class:`Variogram <skgstat.Variogram>` is
-Trust-Region Reflective (TRF), which is also the default for
-:class:`Variogram <skgstat.Variogram>`. It uses a valid parameter space as bounds
+Trust-Region Reflective (TRF). It uses a valid parameter space as bounds
 and therefore won't fail in finding parameters.
 You can, however, switch to Levenberg-Marquardt
 by setting the :class:`Variogram.fit_method <skgstat.Variogram.fit_method>`

setup.py

@@ -31,6 +31,12 @@ def classifiers():
       description='Geostatistical expansion in the scipy style',
       long_description=readme(),
       long_description_content_type='text/x-rst',
+      project_urls={
+          "Documentation": "https://scikit-gstat.readthedocs.io",
+          "Source": "https://github.com/scikit-gstat/scikit-gstat",
+          "Tracker": "https://github.com/scikit-gstat/scikit-gstat/issues",
+      },
+      url="https://github.com/scikit-gstat/scikit-gstat",
       classifiers=classifiers(),
       install_requires=requirements(),
       test_suite='nose.collector',