binaryRL: Reinforcement Learning Tools for Two-Alternative Forced ChoiceTasks
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: | 0.9.8 |
| 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: | 2025-10-28 |
| 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 |
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