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PCSinR: Parallel Constraint Satisfaction Networks in R

Parallel Constraint Satisfaction (PCS) models are an increasingly common class of models in Psychology, with applications to reading and word recognition (McClelland & Rumelhart, 1981), judgment and decision making (Glöckner & Betsch, 2008; Glöckner, Hilbig, & Jekel, 2014), and several other fields (e.g. Read, Vanman, & Miller, 1997). In each of these fields, they provide a quantitative model of psychological phenomena, with precise predictions regarding choice probabilities, decision times, and often the degree of confidence. This package provides the necessary functions to create and simulate basic Parallel Constraint Satisfaction networks within R.

Version: 0.1.0
Depends: R (≥ 3.3.1)
Suggests: testthat
Published: 2016-10-19
DOI: 10.32614/CRAN.package.PCSinR
Author: Felix Henninger [aut, cre]
Maintainer: Felix Henninger <mailbox at felixhenninger.com>
License: GPL (≥ 3)
URL: https://github.com/felixhenninger/PCSinR
NeedsCompilation: no
Materials: README NEWS
CRAN checks: PCSinR results

Documentation:

Reference manual: PCSinR.pdf

Downloads:

Package source: PCSinR_0.1.0.tar.gz
Windows binaries: r-devel: PCSinR_0.1.0.zip, r-release: PCSinR_0.1.0.zip, r-oldrel: PCSinR_0.1.0.zip
macOS binaries: r-release (arm64): PCSinR_0.1.0.tgz, r-oldrel (arm64): PCSinR_0.1.0.tgz, r-release (x86_64): PCSinR_0.1.0.tgz, r-oldrel (x86_64): PCSinR_0.1.0.tgz

Linking:

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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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