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Simulates outcomes of an updated version of the latent state reinforcement learning model originally described in Cochran and Cisler (2019) <doi:10.1371/journal.pcbi.1007331>. The package is designed to create results under all reasonable experiment setups, including different reinforcement schedules, number of cues, number of phases, and number of options per trial. Participants can be simulated using either fixed parameters or parameters drawn from a distribution.
| Version: | 1.0.0 |
| Published: | 2026-09-17 |
| DOI: | 10.32614/CRAN.package.latentState |
| Author: | Martin Benada [aut, cre] |
| Maintainer: | Martin Benada <martinibenada at gmail.com> |
| License: | GPL (≥ 3) |
| URL: | https://osf.io/2whcu |
| NeedsCompilation: | no |
| CRAN checks: | latentState results |
| Reference manual: | latentState.html , latentState.pdf |
| Package source: | latentState_1.0.0.tar.gz |
| Windows binaries: | r-devel: latentState_1.0.0.zip, r-release: not available, r-oldrel: latentState_1.0.0.zip |
| macOS binaries: | r-release (arm64): latentState_1.0.0.tgz, r-oldrel (arm64): latentState_1.0.0.tgz, r-release (x86_64): latentState_1.0.0.tgz, r-oldrel (x86_64): latentState_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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