Package: PCSinR 0.2.0

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; \doi{10.1037/0033-295X.88.5.375}), judgment and decision making (Glöckner & Betsch, 2008 \doi{10.1017/S1930297500002424}; Glöckner, Hilbig, & Jekel, 2014 \doi{10.1016/j.cognition.2014.08.017}), and several other fields. 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.

Authors:Felix Henninger [aut, cre], Daniel W. Heck [aut]

PCSinR_0.2.0.tar.gz
PCSinR_0.2.0.zip(r-4.7-any)PCSinR_0.2.0.zip(r-4.6-any)PCSinR_0.2.0.zip(r-4.5-any)
PCSinR_0.2.0.tgz(r-4.6-any)PCSinR_0.2.0.tgz(r-4.5-any)
PCSinR_0.2.0.tar.gz(r-4.7-any)PCSinR_0.2.0.tar.gz(r-4.6-any)
PCSinR_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
PCSinR/json (API)

# Install 'PCSinR' in R:
install.packages('PCSinR', repos = c('https://felixhenninger.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/felixhenninger/pcsinr/issues

On CRAN:

Conda:

3.40 score 5 stars 3 scripts 306 downloads 3 exports 0 dependencies

Last updated from:dbe708debe. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK92
source / vignettesOK160
linux-releaseOK94
macos-releaseOK133
macos-oldrelOK164
windows-develOK86
windows-releaseOK55
windows-oldrelOK54
wasm-releaseOK63

Exports:PCS_convergence_McCandRPCS_runPCS_run_from_interconnections

Dependencies: