Source: haskell-statistics
Maintainer: Debian Haskell Group
Uploaders:
Joachim Breitner ,
Priority: optional
Section: haskell
Rules-Requires-Root: no
Build-Depends:
cdbs,
debhelper (>= 10),
ghc (>= 8),
ghc-prof,
haskell-devscripts (>= 0.13),
libghc-aeson-dev (>= 0.6.0.0),
libghc-aeson-prof,
libghc-base-orphans-dev (>= 0.6),
libghc-base-orphans-dev (<< 0.8),
libghc-base-orphans-prof,
libghc-erf-dev,
libghc-erf-prof,
libghc-math-functions-dev (>= 0.1.7),
libghc-math-functions-prof,
libghc-monad-par-dev (>= 0.3.4),
libghc-monad-par-prof,
libghc-mwc-random-dev (>= 0.13.0.0),
libghc-mwc-random-prof,
libghc-primitive-dev (>= 0.3),
libghc-primitive-prof,
libghc-vector-algorithms-dev (>= 0.4),
libghc-vector-algorithms-prof,
libghc-vector-binary-instances-dev (>= 0.2.1),
libghc-vector-binary-instances-prof,
libghc-vector-th-unbox-dev,
libghc-vector-th-unbox-prof,
Build-Depends-Indep: ghc-doc,
libghc-aeson-doc,
libghc-base-orphans-doc,
libghc-erf-doc,
libghc-math-functions-doc,
libghc-monad-par-doc,
libghc-mwc-random-doc,
libghc-primitive-doc,
libghc-vector-algorithms-doc,
libghc-vector-binary-instances-doc,
libghc-vector-th-unbox-doc,
Standards-Version: 4.1.4
Homepage: https://github.com/bos/statistics
Vcs-Browser: https://salsa.debian.org/haskell-team/DHG_packages/tree/master/p/haskell-statistics
Vcs-Git: https://salsa.debian.org/haskell-team/DHG_packages.git [p/haskell-statistics]
Package: libghc-statistics-dev
Architecture: any
Depends:
${haskell:Depends},
${misc:Depends},
${shlibs:Depends},
Recommends:
${haskell:Recommends},
Suggests:
${haskell:Suggests},
Provides:
${haskell:Provides},
Description: A library of statistical types, data, and functions${haskell:ShortBlurb}
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, regression and autocorrelation analysis.
.
Random variate generation under several different distributions.
.
${haskell:Blurb}
Package: libghc-statistics-prof
Architecture: any
Depends:
${haskell:Depends},
${misc:Depends},
${shlibs:Depends},
Recommends:
${haskell:Recommends},
Suggests:
${haskell:Suggests},
Provides:
${haskell:Provides},
Description: A library of statistical types, data, and functions${haskell:ShortBlurb}
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.
.
${haskell:Blurb}
Package: libghc-statistics-doc
Architecture: all
Section: doc
Depends:
${haskell:Depends},
${misc:Depends},
Recommends:
${haskell:Recommends},
Suggests:
${haskell:Suggests},
Description: A library of statistical types, data, and functions${haskell:ShortBlurb}
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.
.
${haskell:Blurb}