Automatic generation of finite state machine models of dynamic decision-making that both have strong predictive power and are interpretable in human terms. We use an efficient model representation and a genetic algorithm-based estimation process to generate simple deterministic approximations that explain most of the structure of complex stochastic processes. We have applied the software to empirical data, and demonstrated it's ability to recover known data-generating processes by simulating data with agent-based models and correctly deriving the underlying decision models for multiple agent models and degrees of stochasticity.
Version: 0.2.4 Depends: R (≥ 4.0), methods (≥ 4.0), stats (≥ 4.0) Imports: caret (≥ 6.0), GA (≥ 3.2), Rcpp (≥ 1.0) LinkingTo: Rcpp (≥ 1.0) Suggests: doParallel (≥ 1.0), foreach (≥ 1.5), testthat (≥ 3.0), diagram (≥ 1.6), knitr (≥ 1.33), rmarkdown (≥ 2.8), pander (≥ 0.6), dplyr (≥ 1.0), tidyr (≥ 1.0), purrr (≥ 0.3) Published: 2021-05-29 DOI: 10.32614/CRAN.package.datafsm Author: John J. Nay [aut], Jonathan M. Gilligan [cre, aut] Maintainer: Jonathan M. Gilligan <jonathan.gilligan at vanderbilt.edu> BugReports: https://github.com/jonathan-g/datafsm/issues License: MIT + file LICENSE URL: https://jonathan-g.github.io/datafsm/, https://github.com/jonathan-g/datafsm NeedsCompilation: yes Language: en-US Citation: datafsm citation info Materials: NEWS CRAN checks: datafsm results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=datafsm to link to this page.
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