Package: multiRL Version: 0.4.5 Title: Reinforcement Learning Tools for Multi-Armed Bandit Description: A flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the 'binaryRL' package, 'multiRL' modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) . Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters. Authors@R: c( person( given = "YuKi", role = c("aut", "cre"), email = "hmz1969a@gmail.com", comment = c(ORCID = "0009-0000-1378-1318") ), person( given = "Xinyu", role = "aut", email = "xinyu000328@gmail.com", comment = c(ORCID = "0009-0004-4974-9191") ) ) Maintainer: YuKi URL: https://yuki-961004.github.io/multiRL/ BugReports: https://github.com/yuki-961004/multiRL/issues License: GPL-3 Encoding: UTF-8 LazyData: TRUE ByteCompile: TRUE RoxygenNote: 7.3.3 Depends: R (>= 4.1.0) Imports: methods, utils, Rcpp, compiler, future, doFuture, foreach, doRNG, progressr, ggplot2, scales, grDevices LinkingTo: Rcpp Suggests: stats, GenSA, GA, DEoptim, pso, mlrMBO, mlr, ParamHelpers, smoof, lhs, DiceKriging, rgenoud, cmaes, nloptr, abc, pls, reticulate, keras, keras3 Repository: https://yuki-961004.r-universe.dev Date/Publication: 2026-05-30 11:56:09 UTC RemoteUrl: https://github.com/yuki-961004/multirl RemoteRef: HEAD RemoteSha: 326ab9225f276af02ccce03046713c8f8be8e09c NeedsCompilation: yes Packaged: 2026-07-04 13:02:21 UTC; root Author: YuKi [aut, cre] (ORCID: ), Xinyu [aut] (ORCID: )