SAutomata: Inference and Learning in Stochastic Automata

Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.

Version: 0.1.0
Depends: R (≥ 2.0.0)
Published: 2018-11-02
Author: Muhammad Kashif Hanif [cre, aut], Muhammad Umer Sarwar [aut], Rehman Ahmad [aut], Zeeshan Ahmad [aut], Karl-Heinz Zimmermann [aut]
Maintainer: Muhammad Kashif Hanif <mkashifhanif at gcuf.edu.pk>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: SAutomata results

Downloads:

Reference manual: SAutomata.pdf
Package source: SAutomata_0.1.0.tar.gz
Windows binaries: r-devel: SAutomata_0.1.0.zip, r-release: SAutomata_0.1.0.zip, r-oldrel: SAutomata_0.1.0.zip
OS X binaries: r-release: SAutomata_0.1.0.tgz, r-oldrel: SAutomata_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=SAutomata to link to this page.