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Fits a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
| Version: | 1.0.1 |
| Imports: | mco, stats |
| Suggests: | RUnit |
| Published: | 2018-02-13 |
| DOI: | 10.32614/CRAN.package.pdmod |
| Author: | Chloe Bracis |
| Maintainer: | Chloe Bracis <cbracis at uw.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | pdmod results |
| Reference manual: | pdmod.pdf |
| Vignettes: |
pdmod |
| Package source: | pdmod_1.0.1.tar.gz |
| Windows binaries: | r-devel: pdmod_1.0.1.zip, r-release: pdmod_1.0.1.zip, r-oldrel: pdmod_1.0.1.zip |
| macOS binaries: | r-release (arm64): pdmod_1.0.1.tgz, r-oldrel (arm64): pdmod_1.0.1.tgz, r-release (x86_64): pdmod_1.0.1.tgz, r-oldrel (x86_64): pdmod_1.0.1.tgz |
| Old sources: | pdmod archive |
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These binaries (installable software) and packages are in development.
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