Edited DESCR in the case of:

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4 files changed, 31 insertions, 57 deletions

diff --git a/math/R-ncdf/DESCR b/math/R-ncdf/DESCR
index f2658375a57..f9fff4c466b 100644
--- a/math/R-ncdf/DESCR
+++ b/math/R-ncdf/DESCR
@@ -5,4 +5,4 @@ files can be opened and data sets read in easily.  It is also easy to
 create new netCDF dimensions, variables, and files, or manipulate
 existing netCDF files.  This interface provides considerably more
 functionality than the old "netCDF" package for R, and is not
-compatible with the old "netCDF" package for R.  
+compatible with the old "netCDF" package for R.
diff --git a/math/arpack/DESCR b/math/arpack/DESCR
index ded552c3db2..3205bcda348 100644
--- a/math/arpack/DESCR
+++ b/math/arpack/DESCR
@@ -1,35 +1,23 @@
-ARPACK is a collection of Fortran77 subroutines designed to solve large
-scale eigenvalue problems.
-
-The package is designed to compute a few eigenvalues and corresponding
-eigenvectors of a general n by n matrix A. It is most appropriate for large
-sparse or structured matrices A where structured means that a matrix-vector
-product w <- Av requires order n rather than the usual order n**2 floating
-point operations. This software is based upon an algorithmic variant of the
-Arnoldi process called the Implicitly Restarted Arnoldi Method (IRAM). When
-the matrix A is symmetric it reduces to a variant of the Lanczos process
-called the Implicitly Restarted Lanczos Method (IRLM). These variants may be
-viewed as a synthesis of the Arnoldi/Lanczos process with the Implicitly
-Shifted QR technique that is suitable for large scale problems. For many
-standard problems, a matrix factorization is not required. Only the action
-of the matrix on a vector is needed.  ARPACK software is capable of solving
-large scale symmetric, nonsymmetric, and generalized eigenproblems from
-significant application areas. The software is designed to compute a few (k)
-eigenvalues with user specified features such as those of largest real part
-or largest magnitude.  Storage requirements are on the order of n*k locations.
-No auxiliary storage is required. A set of Schur basis vectors for the desired
-k-dimensional eigen-space is computed which is numerically orthogonal to working
-precision. Numerically accurate eigenvectors are available on request.
-
-Important Features:
-
-    o  Reverse Communication Interface.
-    o  Single and Double Precision Real Arithmetic Versions for Symmetric,
-       Non-symmetric, Standard or Generalized Problems.
-    o  Single and Double Precision Complex Arithmetic Versions for Standard
-       or Generalized Problems.
-    o  Routines for Banded Matrices - Standard or Generalized Problems.
-    o  Routines for The Singular Value Decomposition.
-    o  Example driver routines that may be used as templates to implement
-       numerous Shift-Invert strategies for all problem types, data types
-       and precision.
+ARPACK is a collection of Fortran77 subroutines designed to solve large scale
+eigenvalue problems. The package is designed to compute a few eigenvalues and
+corresponding eigenvectors of a general n by n matrix A. It is most appropriate
+for large sparse or structured matrices A where structured means that a
+matrix-vector product w <- Av requires order n rather than the usual order n**2
+floating point operations. This software is based upon an algorithmic variant of
+the Arnoldi process called the Implicitly Restarted Arnoldi Method (IRAM). When
+the matrix A is symmetric it reduces to a variant of the Lanczos process called
+the Implicitly Restarted Lanczos Method (IRLM). These variants may be viewed as
+a synthesis of the Arnoldi/Lanczos process with the Implicitly Shifted QR
+technique that is suitable for large scale problems. For many standard problems,
+a matrix factorization is not required. Only the action of the matrix on a
+vector is needed. ARPACK software is capable of solving large scale symmetric,
+nonsymmetric, and generalized eigenproblems from significant application areas.
+The software is designed to compute a few (k) eigenvalues with user specified
+features such as those of largest real part or largest magnitude.  Storage
+requirements are on the order of n*k locations. No auxiliary storage is
+required. A set of Schur basis vectors for the desired k-dimensional eigen-space
+is computed which is numerically orthogonal to working precision. Numerically accurate eigenvectors are available on request. Important Features:
+ o Reverse Communication Interface.
+ o Single and Double Precision Real Arithmetic Versions for Symmetric,
+   Non-symmetric, Standard or Generalized Problems.
+   ...and more!
diff --git a/math/eigen2/DESCR b/math/eigen2/DESCR
index 38332b7f76d..f6a63080ae5 100644
--- a/math/eigen2/DESCR
+++ b/math/eigen2/DESCR
@@ -8,24 +8,17 @@ related algorithms. It is:
   o both plain matrices/vectors and abstract expressions.
   o both column-major (the default) and row-major matrix storage.
   o both basic matrix/vector manipulation and many more advanced, specialized
-    modules providing algorithms for linear algebra, geometry, quaternions,
-    or advanced array manipulation.
+    modules providing algorithms for linear algebra, geometry, quaternions, or
+    advanced array manipulation.
 * Fast.
   o Expression templates allow to intelligently remove temporaries and enable
     lazy evaluation, when that is appropriate -- Eigen takes care of this
     automatically and handles aliasing too in most cases.
   o Explicit vectorization is performed for the SSE (2 and later) and AltiVec
-    instruction sets, with graceful fallback to non-vectorized code.
-    Expression templates allow to perform these optimizations globally for
-    whole expressions.
+    instruction sets, with graceful fallback to non-vectorized code. Expression
+    templates allow to perform these optimizations globally for whole
+    expressions.
   o With fixed-size objects, dynamic memory allocation is avoided, and the
     loops are unrolled when that makes sense.
   o For large matrices, special attention is paid to cache-friendliness.
-* Elegant. The API is extremely clean and expressive, thanks to expression
-  templates. Implementing an algorithm on top of Eigen feels like just copying
-  pseudocode. You can use complex expressions and still rely on Eigen to
-  produce optimized code: there is no need for you to manually decompose
-  expressions into small steps.
-* Compiler-friendy. Eigen has very reasonable compilation times at least with
-  GCC, compared to other C++ libraries based on expression templates and heavy
-  metaprogramming. Eigen is also standard C++ and supports various compilers.
+...and more!
diff --git a/math/eigen3/DESCR b/math/eigen3/DESCR
index 60e78c89451..b6dbd3900c7 100644
--- a/math/eigen3/DESCR
+++ b/math/eigen3/DESCR
@@ -21,11 +21,4 @@ related algorithms. It is:
   o With fixed-size objects, dynamic memory allocation is avoided, and the
     loops are unrolled when that makes sense.
   o For large matrices, special attention is paid to cache-friendliness.
-* Elegant. The API is extremely clean and expressive, thanks to expression
-  templates. Implementing an algorithm on top of Eigen feels like just copying
-  pseudocode. You can use complex expressions and still rely on Eigen to
-  produce optimized code: there is no need for you to manually decompose
-  expressions into small steps.
-* Compiler-friendy. Eigen has very reasonable compilation times at least with
-  GCC, compared to other C++ libraries based on expression templates and heavy
-  metaprogramming. Eigen is also standard C++ and supports various compilers.
+...and more!