Reinforcement Learning Tools for Two-Alternative Forced Choice Tasks R package binaryRL version 0.9.9 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>. Software 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 YuKi <hmz1969a@gmail.com> Comprehensive R Archive Network (CRAN) YuKi [aut, cre] (ORCID: <https://orcid.org/0009-0000-1378-1318>) GPL-3 2026-01-08 application/tgz https://CRAN.R-project.org/package=binaryRL doi:10.32614/CRAN.package.binaryRL