Fix typos in documentation (#1635)

* Always capitalize Eigen

* Fix spelling

docs/advanced/cast/eigen.rst

@@ -37,7 +37,7 @@ that maps into the source ``numpy.ndarray`` data: this requires both that the
 data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
 data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is
 ``double``); and that the storage is layout compatible.  The latter limitation
 ``double``); and that the storage is layout compatible.  The latter limitation
 is discussed in detail in the section below, and requires careful
 is discussed in detail in the section below, and requires careful
-consideration: by default, numpy matrices and eigen matrices are *not* storage
+consideration: by default, numpy matrices and Eigen matrices are *not* storage
 compatible.
 compatible.
 
 
 If the numpy matrix cannot be used as is (either because its types differ, e.g.
 If the numpy matrix cannot be used as is (either because its types differ, e.g.
@@ -226,7 +226,7 @@ order.
 Failing rather than copying
 Failing rather than copying
 ===========================
 ===========================
 
 
-The default behaviour when binding ``Eigen::Ref<const MatrixType>`` eigen
+The default behaviour when binding ``Eigen::Ref<const MatrixType>`` Eigen
 references is to copy matrix values when passed a numpy array that does not
 references is to copy matrix values when passed a numpy array that does not
 conform to the element type of ``MatrixType`` or does not have a compatible
 conform to the element type of ``MatrixType`` or does not have a compatible
 stride layout.  If you want to explicitly avoid copying in such a case, you
 stride layout.  If you want to explicitly avoid copying in such a case, you
@@ -289,13 +289,13 @@ will be passed as such a column vector.  If not, but the Eigen type constraints
 will accept a row vector, it will be passed as a row vector.  (The column
 will accept a row vector, it will be passed as a row vector.  (The column
 vector takes precedence when both are supported, for example, when passing a
 vector takes precedence when both are supported, for example, when passing a
 1D numpy array to a MatrixXd argument).  Note that the type need not be
 1D numpy array to a MatrixXd argument).  Note that the type need not be
-expicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an
+explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an
 Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix.
 Eigen ``Matrix<double, Dynamic, 5>``: you would end up with a 1x5 Eigen matrix.
 Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix.
 Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix.
 
 
-When returning an eigen vector to numpy, the conversion is ambiguous: a row
+When returning an Eigen vector to numpy, the conversion is ambiguous: a row
 vector of length 4 could be returned as either a 1D array of length 4, or as a
 vector of length 4 could be returned as either a 1D array of length 4, or as a
-2D array of size 1x4.  When encoutering such a situation, pybind11 compromises
+2D array of size 1x4.  When encountering such a situation, pybind11 compromises
 by considering the returned Eigen type: if it is a compile-time vector--that
 by considering the returned Eigen type: if it is a compile-time vector--that
 is, the type has either the number of rows or columns set to 1 at compile
 is, the type has either the number of rows or columns set to 1 at compile
 time--pybind11 converts to a 1D numpy array when returning the value.  For
 time--pybind11 converts to a 1D numpy array when returning the value.  For