Package: SAutomata
Type: Package
Title: Inference and Learning in Stochastic Automata
Version: 0.1.0
Authors@R: c(
    person("Muhammad Kashif", "Hanif",  role = c("cre","aut"),
    email = "mkashifhanif@gcuf.edu.pk"),
    person("Muhammad Umer", "Sarwar", role = "aut", email = "Mumersarwar@gcuf.edu.pk"),
    person("Rehman", "Ahmad", role = "aut", email = "rehman.ahmad777@gmail.com"),
    person("Zeeshan", "Ahmad", role = "aut", email = "zeeshan.cs822@gmail.com "),
    person("Karl-Heinz", "Zimmermann", role = "aut", email = "k.zimmermann@tuhh.de")
    )
Maintainer: Muhammad Kashif Hanif <mkashifhanif@gcuf.edu.pk>
Description: 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>.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 2.0.0)
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2026-09-27 09:34:07 UTC; root
Author: Muhammad Kashif Hanif [cre, aut], Muhammad Umer Sarwar [aut],
        Rehman Ahmad [aut], Zeeshan Ahmad [aut], Karl-Heinz Zimmermann
        [aut]
Repository: https://cran.r-universe.dev
Date/Publication: 2018-11-02 17:00:03 UTC
RemoteUrl: https://github.com/cran/SAutomata
RemoteRef: HEAD
RemoteSha: a3a3572abe1b35c97b2249493cc9700bd60e45d8