Source: ghmm Maintainer: Debian Med Packaging Team Uploaders: Steffen Moeller , Étienne Mollier 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.