sdnet: Soft-Discretization-Based Bayesian Network Inference

Fitting discrete Bayesian networks using soft-discretized data. Soft-discretization is based on mixture of normal distributions. Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence. For more information see Balov (2013) <doi:10.1186/1755-8794-6-S3-S1>.

Version: 2.4.1
Depends: R (≥ 3.0.2)
Imports: methods, stats, utils, graphics
Published: 2019-04-30
Author: Nikolay Balov
Maintainer: Nikolay Balov <nhbalov at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: sdnet results


Reference manual: sdnet.pdf
Package source: sdnet_2.4.1.tar.gz
Windows binaries: r-devel:, r-devel-gcc8:, r-release:, r-oldrel:
OS X binaries: r-release: sdnet_2.4.1.tgz, r-oldrel: sdnet_2.4.1.tgz
Old sources: sdnet archive


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