Package: latentState 1.0.0
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.
Authors:
latentState_1.0.0.tar.gz
latentState_1.0.0.zip(r-4.7-any)latentState_1.0.0.zip(r-4.6-any)latentState_1.0.0.zip(r-4.5-any)
latentState_1.0.0.tgz(r-4.6-any)latentState_1.0.0.tgz(r-4.5-any)
latentState_1.0.0.tar.gz(r-4.7-any)latentState_1.0.0.tar.gz(r-4.6-any)
latentState_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
latentState/json (API)
| # Install 'latentState' in R: |
| install.packages('latentState', repos = c('https://martin2002-ux.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:d3a3ccd257. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 126 | ||
| source / vignettes | OK | 135 | ||
| linux-release | OK | 121 | ||
| macos-release | OK | 182 | ||
| macos-oldrel | OK | 178 | ||
| windows-devel | OK | 48 | ||
| windows-release | OK | 53 | ||
| windows-oldrel | OK | 55 | ||
| wasm-release | OK | 91 |
Exports:make_worldplot_comparerun
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| latentState: Simulate Outcomes of a Latent State Reinforcement Learning Model | latentState-package latentState |
| Build a fixed, non-randomized trial world for use with 'run()' | make_world |
| Compare latent-state learning results across groups | plot_compare |
| Simulate latent-state reinforcement learning across participants | run |