datafsm: Estimating Finite State Machine Models from Data

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 ORCID iD [cre, aut]
Maintainer: Jonathan M. Gilligan <jonathan.gilligan at>
License: MIT + file LICENSE
NeedsCompilation: yes
Language: en-US
Citation: datafsm citation info
Materials: NEWS
CRAN checks: datafsm results


Reference manual: datafsm.pdf
Vignettes: Introduction to datafsm
Example with real data


Package source: datafsm_0.2.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): datafsm_0.2.4.tgz, r-oldrel (arm64): datafsm_0.2.4.tgz, r-release (x86_64): datafsm_0.2.4.tgz, r-oldrel (x86_64): datafsm_0.2.4.tgz
Old sources: datafsm archive


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