Author: Trevor Davis.  Some documentation and unit tests ported from
Allen Day's ``getopt`` package.  Based on the optparse Python
library by the Python Software Foundation.  Contributions from
Depends: R (>= 2.9.0), methods, getopt (>= 1.17)

Author: Trevor L Davis. Some documentation and examples ported from
Allen Day's ``getopt`` package. Some documentation from the
optparse Python module by the Python Software Foundation.
Contributions from Steve Lianoglou and Jim Nikelski.
Copyright: Copyright (c) 2010-2012 Trevor L Davis Copyright (c) 2011
Jim Nikelski Copyright (c) 2010 Steve Lianoglou Copyright (c)
2008 Allen Day Copyright (c) 2001-2009 Python Software

Allen Day's getopt package. Some documentation from the
Description: A command line parser inspired by Python's 'optparse'
library to be used with Rscript to write "#!" shebang scripts

Author: Trevor L Davis. Some documentation and examples ported from Allen Day's
getopt package. Some documentation from the optparse Python module by
the Python Software Foundation. Contributions from Steve Lianoglou and Jim
Copyright: Copyright (c) 2010-2013 Trevor L Davis Copyright (c) 2011
Description: A command line parser inspired by Python's 'optparse' library to
be used with Rscript to write "#!" shebang scripts that accept short and

getopt package. Some documentation from the optparse Python module by the
Python Software Foundation. Contributions from Steve Lianoglou, Jim

Nikelski, Kirill Müller, and Peter Humburg.

Nikelski, Kirill Müller, Peter Humburg, and Rich FitzJohn.

Suggests: knitr (>= 1.15.19), stringr, testthat

Suggests: covr, knitr (>= 1.15.19), stringr, testthat

Package: optpart
Version: 2.0-1
Date: 2010-01-04
Title: Optimal partitioning of similarity relations
Author: David W. Roberts <droberts@montana.edu>
Maintainer: David W. Roberts <droberts@montana.edu>
Depends: cluster, labdsv, MASS, plotrix
Description: The package contains a set of algorithms for creating
partitions and coverings of objects largely based on operations
on similarity relations (or matrices). There are several
iterative re-assigmnent algorithms optimizing different
goodness-of-clustering criteria.  In addition, there are
covering algorithms "clique" which derives maximal cliques, and
"maxpact" which creates a covering of maximally compact sets.
Graphical analyses and conversion routines are also included.
License: GPL (>= 2)
URL: http://ecology.msu.montana.edu/labdsv/R
Packaged: 2010-01-08 12:28:36 UTC; root
Repository: CRAN
Date/Publication: 2010-01-08 17:04:20