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wip/py-pebl,
Python Environment for Bayesian Learning
Branch: CURRENT,
Version: 1.0.2,
Package name: py312-pebl-1.0.2,
Maintainer: jihbed.researchPebl 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 formas
Required to run:[
devel/py-setuptools] [
math/py-numpy] [
devel/py-nose] [
lang/python37]
Required to build:[
pkgtools/cwrappers]
Master sites:
RMD160: 0d6ef1e18416c27cddb5c5c44099c3c3b6425ab6
Filesize: 2431.508 KB
Version history: (Expand)
- (2024-09-19) Updated to version: py312-pebl-1.0.2
- (2024-09-19) Package has been reborn
- (2024-09-15) Package deleted from pkgsrc
- (2023-02-13) Package has been reborn
- (2023-02-13) Updated to version: py310-pebl-1.0.2
- (2021-10-08) Updated to version: py39-pebl-1.0.2
CVS history: (Expand)
2012-10-07 15:57:25 by Aleksej Saushev | Files touched by this commit (211) |
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
|
2011-10-28 19:41:43 by Kamel Derouiche | Files touched by this commit (4) | |
Log message:
Import py27-pebl-1.0.2 as wip/py-pebl.
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 formas
|