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Source: ghmm
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Steffen Moeller <moeller@debian.org>,
Étienne Mollier <emollier@debian.org>
Section: science
Priority: optional
Build-Depends: dpkg-dev (>= 1.22.5), debhelper-compat (= 13),
dh-sequence-python3,
d-shlibs (>= 0.106~),
python3-dev,
python3-setuptools,
pkgconf,
libxml2-dev,
libgsl-dev,
liblapacke-dev,
zlib1g-dev,
swig
Standards-Version: 4.7.0
Vcs-Browser: https://salsa.debian.org/med-team/ghmm
Vcs-Git: https://salsa.debian.org/med-team/ghmm.git
Homepage: https://sourceforge.net/p/ghmm/wiki/Home/
Rules-Requires-Root: no
Package: ghmm
Architecture: any
Depends: ${shlibs:Depends},
${misc:Depends},
${python3:Depends},
python3,
libghmm1t64
Recommends: libopenblas0 | libblis4
Description: General Hidden-Markov-Model library - tools
The General Hidden Markov Model Library (GHMM) is a C library with
additional Python3 bindings implementing a wide range of types of
Hidden Markov Models and algorithms: discrete, continuous emissions,
basic training, HMM clustering, HMM mixtures.
.
This package contains some tools using the library.
Package: libghmm-dev
Architecture: any
Section: libdevel
Depends: ${shlibs:Depends},
${misc:Depends},
libghmm1t64 (>= ${source:Upstream-Version}),
libghmm1t64 (<< ${source:Upstream-Version}+1)
Description: General Hidden-Markov-Model library - header files
The General Hidden Markov Model Library (GHMM) is a C library with
additional Python3 bindings implementing a wide range of types of
Hidden Markov Models and algorithms: discrete, continuous emissions,
basic training, HMM clustering, HMM mixtures.
.
Header files and static library to compile against the library.
Package: libghmm1t64
Provides: ${t64:Provides}
Replaces: libghmm1
Conflicts: libghmm1 (<< ${source:Version})
Architecture: any
Section: libs
Depends: ${shlibs:Depends},
${misc:Depends},
python3
Description: General Hidden-Markov-Model library
The General Hidden Markov Model Library (GHMM) is a C library with
additional Python3 bindings implementing a wide range of types of
Hidden Markov Models and algorithms: discrete, continuous emissions,
basic training, HMM clustering, HMM mixtures.
.
The dynamic library.
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