Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences.
    Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) <doi:10.1109/ACCESS.2019.2955245>.
    This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.

Generates the following sequential two-arm experimental designs:
    (1) completely randomized (Bernoulli)
    (2) balanced completely randomized
    (3) Efron's (1971) Biased Coin
    (4) Atkinson's (1982) Covariate-Adjusted Biased Coin
    (5) Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly
    (6) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Naive)
    (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Stepwise)
    and also provides the following types of inference:
    (1) estimation (with both Z-style estimators and OLS estimators), 
    (2) frequentist testing (via asymptotic distribution results and via employing the nonparametric randomization test) and
    (3) frequentist confidence intervals (only under the superpopulation sampling assumption currently). Details can be found
    in our publication: Kapelner and Krieger "A Matching Procedure for Sequential Experiments that Iteratively Learns which 
    Covariates Improve Power" (2020) <arXiv:2010.05980>.

Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.