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opam
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opam-version: "2.0"
bug-reports: "https://bitbucket.org/libra-tk/libra-tk/issues"
maintainer: [
"Daniel Lowd <lowd@cs.uoregon.edu>"
"Amirmohammad (Pedram) Rooshenas <pedram@cs.uoregon.edu>"
]
authors: [
"Daniel Lowd <lowd@cs.uoregon.edu>"
"Amirmohammad (Pedram) Rooshenas <pedram@cs.uoregon.edu>"
]
homepage: "http://libra.cs.uoregon.edu"
license: "BSD-2-clause"
tags: "clib:expat"
build: [
["ocaml" "setup.ml" "-configure" "--prefix" prefix]
["ocaml" "setup.ml" "-build"]
["ocaml" "setup.ml" "-doc"] {with-doc}
]
install: ["ocaml" "setup.ml" "-install"]
remove: [
["ocaml" "setup.ml" "-configure" "--prefix" prefix]
["ocaml" "setup.ml" "-uninstall"]
]
depends: [
"ocaml" {>= "4.00.0" & < "4.03.0"}
"base-unix"
"ocamlfind"
"ocaml-expat"
"ocamlbuild" {build}
]
synopsis: "Learning and inference with discrete probabilistic models"
description: """
The Libra Toolkit is a collection of algorithms for learning and
inference with discrete probabilistic models, including Bayesian
networks (BNs), Markov networks (MNs), dependency networks (DNs),
sum-product networks (SPNs), and arithmetic circuits (ACs). Compared
to other toolkits, Libra focuses more on structure learning,
especially for tractable models in which exact inference is efficient.
Each algorithm in Libra is implemented as a command-line program
suitable for interactive use or scripting, with consistent options and
file formats throughout the toolkit."""
url {
src: "http://libra.cs.uoregon.edu/libra-tk-1.1.2d.tar.gz"
checksum: [
"sha256=88948d298611f4139919e9ff974506912b07a2bef4944e5aeabd25007d72e0d9"
"md5=a53e35d844ba391d5053416696c48168"
]
}