binaryRL: Reinforcement Learning Tools for Two-Alternative Forced Choice
Tasks
Tools for building Rescorla-Wagner Models for Two-Alternative
Forced Choice tasks, commonly employed in psychological research.
Most concepts and ideas within this R package are referenced from
Sutton and Barto (2018) <ISBN:9780262039246>.
The package allows for the intuitive definition of RL models using simple
if-else statements and three basic models built into this R package are
referenced from
Niv et al. (2012)<doi:10.1523/JNEUROSCI.5498-10.2012>.
Our approach to constructing and evaluating these computational models
is informed by the guidelines proposed in
Wilson & Collins (2019) <doi:10.7554/eLife.49547>.
Example datasets included with the package are sourced from the work of
Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.
| Version: |
0.9.7 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
Rcpp, compiler, future, doFuture, foreach, doRNG, progressr |
| LinkingTo: |
Rcpp |
| Suggests: |
stats, GenSA, GA, DEoptim, pso, mlrMBO, mlr, ParamHelpers, smoof, lhs, DiceKriging, rgenoud, cmaes, nloptr |
| Published: |
2025-08-19 |
| DOI: |
10.32614/CRAN.package.binaryRL |
| Author: |
YuKi  [aut, cre] |
| Maintainer: |
YuKi <hmz1969a at gmail.com> |
| BugReports: |
https://github.com/yuki-961004/binaryRL/issues |
| License: |
GPL-3 |
| URL: |
https://yuki-961004.github.io/binaryRL/ |
| NeedsCompilation: |
yes |
| CRAN checks: |
binaryRL results |
Documentation:
Downloads:
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