Title: Sequential Bayesian Design for Accelerated Life Tests
Description: The planning of accelerated life tests by using sequential Bayesian design, proposed by Lee et al. (2018) <doi:10.1080/00401706.2018.1437475>.

Title: Sequence and Latent Process Detector
Description: 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.
Depends: R (>= 3.4.0), Rcpp (>= 1.0.3), eventdataR

Title: Sequential Experimental Design via Matching on-the-Fly
Description: Generates the following sequential two-arm experimental designs:
(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>.
Depends: R6, checkmate, doParallel, R (>= 3.6.3)