Source: pebl Section: python Priority: optional Maintainer: Debian Python Modules Team Uploaders: Miriam Ruiz , Yaroslav Halchenko Build-Depends: debhelper (>= 7), dh-buildinfo, quilt, python-all-dev, python-setuptools, python-sphinx, python-numpy, python-pydot, python-boto, dh-python, python-pkg-resources Standards-Version: 3.9.8 Vcs-Svn: svn://anonscm.debian.org/python-modules/packages/pebl/trunk/ Vcs-Browser: http://anonscm.debian.org/viewvc/python-modules/packages/pebl/trunk/ Homepage: https://github.com/abhik/pebl Package: python-pebl Architecture: any Depends: ${shlibs:Depends}, ${python:Depends}, ${misc:Depends}, python-numpy, python-pkg-resources Recommends: python-pydot, python-boto Provides: ${python:Provides} Suggests: python-pebl-dbg, python-pebl-doc Description: Python Environment for Bayesian Learning Pebl is a Python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: * Can learn with observational and interventional data * Handles missing values and hidden variables using exact and heuristic methods * Provides several learning algorithms; makes creating new ones simple * Has facilities for transparent parallel execution using several cluster/grid resources * Calculates edge marginals and consensus networks * Presents results in a variety of formats Package: python-pebl-dbg Section: debug Priority: extra Architecture: any Depends: python-pebl (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends} Description: Python Environment for Bayesian Learning - debug Pebl is a Python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. . This package contains the debugging symbols. Package: python-pebl-doc Section: doc Architecture: all Depends: ${misc:Depends} Recommends: python-pebl Description: Python Environment for Bayesian Learning - documentation Pebl is a Python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. . This package contains the documentation.