latentState: Simulate Outcomes of a Latent State Reinforcement Learning Model

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 (may not be active yet)
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

Documentation:

Reference manual:

latentState.html , latentState.pdf

Downloads:

Package source:	latentState_1.0.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, 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

Linking:

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