Source: haskell-statistics
Priority: optional
Section: misc
Maintainer: Clifford Beshers <beshers@seereason.com>
Build-Depends: debhelper (>= 7.0),
               haskell-devscripts (>= 0.6.15+nmu7),
               hscolour,
               cdbs,
               ghc6 (>= 6.8),
               ghc6-prof,
               libghc6-erf-prof,
               libghc6-uvector-prof (>= 0.1.0.4),
               libghc6-uvector-algorithms-prof (>= 0.2)
Build-Depends-Indep: ghc6-doc,
                     haddock,
                     haskell-erf-doc,
                     haskell-uvector-doc (>= 0.1.0.4),
                     haskell-uvector-algorithms-doc (>= 0.2)
Standards-Version: 3.8.1
Homepage: http://darcs.serpentine.com/statistics

Package: libghc6-statistics-dev
Architecture: any
Section: haskell
Depends: ${haskell:Depends},
         ${misc:Depends}
Description: A library of statistical types, data, and functions
 This library provides a number of common functions and types useful
 in statistics.  Our focus is on high performance, numerical
 robustness, and use of good algorithms.  Where possible, we provide
 references to the statistical literature.
 .
 The library's facilities can be divided into three broad categories:
 .
 Working with widely used discrete and continuous probability
 distributions.  (There are dozens of exotic distributions in use; we
 focus on the most common.)
 .
 Computing with sample data: quantile estimation, kernel density
 estimation, bootstrap methods, and autocorrelation analysis.
 .
 Random variate generation under several different distributions.
 .
  Author: Bryan O'Sullivan <bos@serpentine.com>
  Upstream-Maintainer: Bryan O'Sullivan <bos@serpentine.com>
 .
 This package contains the normal library files.

Package: libghc6-statistics-prof
Architecture: any
Section: haskell
Depends: ${haskell:Depends},
         ${misc:Depends},
         libghc6-statistics-dev
Description: A library of statistical types, data, and functions
 This library provides a number of common functions and types useful
 in statistics.  Our focus is on high performance, numerical
 robustness, and use of good algorithms.  Where possible, we provide
 references to the statistical literature.
 .
 The library's facilities can be divided into three broad categories:
 .
 Working with widely used discrete and continuous probability
 distributions.  (There are dozens of exotic distributions in use; we
 focus on the most common.)
 .
 Computing with sample data: quantile estimation, kernel density
 estimation, bootstrap methods, and autocorrelation analysis.
 .
 Random variate generation under several different distributions.
 .
  Author: Bryan O'Sullivan <bos@serpentine.com>
  Upstream-Maintainer: Bryan O'Sullivan <bos@serpentine.com>
 .
 This package contains the libraries compiled with profiling enabled.

Package: haskell-statistics-doc
Architecture: all
Section: doc
Depends: ${haskell:Depends},
         ${misc:Depends},
         ghc6-doc
Description: A library of statistical types, data, and functions
 This library provides a number of common functions and types useful
 in statistics.  Our focus is on high performance, numerical
 robustness, and use of good algorithms.  Where possible, we provide
 references to the statistical literature.
 .
 The library's facilities can be divided into three broad categories:
 .
 Working with widely used discrete and continuous probability
 distributions.  (There are dozens of exotic distributions in use; we
 focus on the most common.)
 .
 Computing with sample data: quantile estimation, kernel density
 estimation, bootstrap methods, and autocorrelation analysis.
 .
 Random variate generation under several different distributions.
 .
  Author: Bryan O'Sullivan <bos@serpentine.com>
  Upstream-Maintainer: Bryan O'Sullivan <bos@serpentine.com>
 .
 This package contains the documentation files.