Inference and Learning in Stochastic Automata R package SAutomata version 0.1.0 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>. Software Depends: R (>= 2.0.0) Muhammad Kashif Hanif <mkashifhanif@gcuf.edu.pk> Comprehensive R Archive Network (CRAN) Muhammad Kashif Hanif [cre, aut], Muhammad Umer Sarwar [aut], Rehman Ahmad [aut], Zeeshan Ahmad [aut], Karl-Heinz Zimmermann [aut] GPL (>= 3) 2018-11-02 application/tgz https://CRAN.R-project.org/package=SAutomata doi:10.32614/CRAN.package.SAutomata