datafsm: Estimating Finite State Machine Models from Data

Our method automatically generates 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: 0.2.0
Depends: R (≥ 3.1.1), methods, stats
Imports: caret, GA, Rcpp
LinkingTo: Rcpp
Suggests: doParallel, foreach, testthat, diagram, knitr
Published: 2017-06-17
Author: Nay John J. [aut], Gilligan Jonathan M. [cre, aut]
Maintainer: Gilligan Jonathan M. <jonathan.gilligan at vanderbilt.edu>
BugReports: https://github.com/jonathan-g/datafsm/issues
License: MIT + file LICENSE
URL: https://github.com/jonathan-g/datafsm
NeedsCompilation: yes
Citation: datafsm citation info
Materials: README NEWS
CRAN checks: datafsm results

Downloads:

Reference manual: datafsm.pdf
Vignettes: Introduction to datafsm
Package source: datafsm_0.2.0.tar.gz
Windows binaries: r-devel: datafsm_0.2.0.zip, r-release: datafsm_0.2.0.zip, r-oldrel: datafsm_0.2.0.zip
OS X El Capitan binaries: r-release: datafsm_0.2.0.tgz
OS X Mavericks binaries: r-oldrel: datafsm_0.2.0.tgz
Old sources: datafsm archive

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