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:

Reference manual:

binaryRL.html , binaryRL.pdf

Downloads:

Package source:	binaryRL_0.9.7.tar.gz
Windows binaries:	r-devel: binaryRL_0.9.7.zip, r-release: binaryRL_0.9.7.zip, r-oldrel: binaryRL_0.9.7.zip
macOS binaries:	r-release (arm64): binaryRL_0.9.7.tgz, r-oldrel (arm64): binaryRL_0.9.0.tgz, r-release (x86_64): binaryRL_0.9.7.tgz, r-oldrel (x86_64): binaryRL_0.9.7.tgz
Old sources:	binaryRL archive

Linking:

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