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>.

An implementation of a regularized regression prediction and 
    empirical Bayes method to recover the true gene expression profile in 
    noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) 
    <doi:10.1038/s41592-018-0033-z> for more details.

Provides a toolbox that allows the user to implement accessibility related concepts.

The SAVVY (Survival Analysis for AdVerse Events with VarYing Follow-Up Times)
    project is a consortium of academic and pharmaceutical
    industry partners that aims to improve the analyses of adverse event (AE)
    data in clinical trials through the use of survival techniques appropriately
    dealing with varying follow-up times and competing events, see
    Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>.
    Although statistical methodologies have advanced,
    in AE analyses often the incidence proportion, the incidence density or a
    non-parametric Kaplan-Meier estimator are used, which either ignore censoring or
    competing events. This package contains functions to easily conduct the
    proposed improved AE analyses.

The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. Further description of the algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.