Package: catlearn 1.0.1

catlearn: Formal Psychological Models of Categorization and Learning

Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.

Authors:Andy Wills, Lenard Dome, Charlotte Edmunds, Garrett Honke, Angus Inkster, René Schlegelmilch, Stuart Spicer

catlearn_1.0.1.tar.gz
catlearn_1.0.1.zip(r-4.5)catlearn_1.0.1.zip(r-4.4)catlearn_1.0.1.zip(r-4.3)
catlearn_1.0.1.tgz(r-4.5-x86_64)catlearn_1.0.1.tgz(r-4.5-arm64)catlearn_1.0.1.tgz(r-4.4-x86_64)catlearn_1.0.1.tgz(r-4.4-arm64)catlearn_1.0.1.tgz(r-4.3-x86_64)catlearn_1.0.1.tgz(r-4.3-arm64)
catlearn_1.0.1.tar.gz(r-4.5-noble)catlearn_1.0.1.tar.gz(r-4.4-noble)
catlearn.pdf |catlearn.html✨
catlearn/json (API)

# Install 'catlearn' in R:
install.packages('catlearn', repos = c('https://ajwills72.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ajwills72/catlearn/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • homa76 - Category breadth CIRP
  • krus96 - Inverse Base-rate Effect AP
  • nosof88 - Instantiation frequency CIRP
  • nosof94 - Type I-VI category structure CIRP
  • shin92 - Category size CIRP
  • thegrid - Ordinal adequacy results for all catlearn simulations

On CRAN:

categorizationcognitive-scienceformal-modelslearninglearning-theoryopen-modelsopen-sciencepsychologycpp

5.24 score 25 stars 46 scripts 495 downloads 39 exports 27 dependencies

Last updated 2 months agofrom:c3f56cac6b. Checks:4 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 14 2025
R-4.5-win-x86_64NOTEFeb 14 2025
R-4.5-mac-x86_64NOTEFeb 14 2025
R-4.5-mac-aarch64NOTEFeb 14 2025
R-4.5-linux-x86_64NOTEFeb 14 2025
R-4.4-win-x86_64NOTEFeb 14 2025
R-4.4-mac-x86_64NOTEFeb 14 2025
R-4.4-mac-aarch64NOTEFeb 14 2025
R-4.3-win-x86_64OKFeb 14 2025
R-4.3-mac-x86_64OKFeb 14 2025
R-4.3-mac-aarch64OKFeb 14 2025

Exports:act2probratconvertSUSTAINkrus96exitkrus96trainmedin87trainnosof88exalcovenosof88exalcove_optnosof88oatnosof88protoalcovenosof88protoalcove_optnosof88trainnosof94bnalcovenosof94exalcovenosof94exalcove_optnosof94oatnosof94plotnosof94sustainnosof94trainshin92exalcoveshin92exalcove_optshin92oatshin92protoalcoveshin92protoalcove_optshin92trainslpALCOVEslpBMslpCOVISslpDGCMslpDIVAslpEXITslpLMSnetslpMack75slpMBMFslpNNCAGslpNNRASslpRWslpSUSTAINsseclstsimGCM

Dependencies:clicodetoolscpp11doParalleldplyrfansiforeachgenericsglueiteratorslifecyclemagrittrpillarpkgconfigpurrrR6RcppRcppArmadillorlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Readme and manuals

Help Manual

Help pageTopics
Formal Modeling for Psychology.catlearn-package
Convert output activation to a rating of outcome probabilityact2probrat
Convert nominal-dimension input representation into a 'padded' (slpSUSTAIN) formatconvertSUSTAIN
Category breadth CIRPhoma76
Inverse Base-rate Effect APkrus96
Simulation of AP krus96 with EXIT modelkrus96exit
Input representation of krus96 for models input-compatible with slpEXITkrus96train
Input representation of Exp. 1 in Medin et al. (1987) for models input-compatible with slpALCOVE or slpSUSTAIN.medin87train
Instantiation frequency CIRPnosof88
Simulation of CIRP nosof88 with ex-ALCOVE modelnosof88exalcove
Parameter optimization of ex-ALCOVE model with nosof88 CIRPnosof88exalcove_opt
Ordinal adequacy test for simulations of nosof88 CIRPnosof88oat
Simulation of CIRP nosof88 with proto-ALCOVE modelnosof88protoalcove
Parameter optimization of proto-ALCOVE model with nosof88 CIRPnosof88protoalcove_opt
Input representation of nosof88 for models input-compatible with slpALCOVE.nosof88train
Type I-VI category structure CIRPnosof94
Simulation of CIRP nosof94 with BN-ALCOVE modelnosof94bnalcove
Simulation of CIRP nosof94 with ex-ALCOVE modelnosof94exalcove
Parameter optimization of ex-ALCOVE model with nosof94 CIRPnosof94exalcove_opt
Ordinal adequacy test for simulations of nosof94 CIRPnosof94oat
Plot Nosofsky et al. (1994) data / simulationsnosof94plot
Simulation of CIRP nosof94 with the SUSTAIN modelnosof94sustain
Input representation of nosof94 for models input-compatible with slpALCOVE or slpSUSTAINnosof94train
Category size CIRPshin92
Simulation of CIRP shin92 with ex-ALCOVE modelshin92exalcove
Parameter optimization of ex-ALCOVE model with shin92 CIRPshin92exalcove_opt
Ordinal adequacy test for simulations of shin92 CIRPshin92oat
Simulation of CIRP shin92 with proto-ALCOVE modelshin92protoalcove
Parameter optimization of proto-ALCOVE model with shin92 CIRPshin92protoalcove_opt
Input representation of shin92 for models input-compatible with slpALCOVE.shin92train
ALCOVE category learning modelslpALCOVE
Bush & Mosteller (1951) simple associative learning modelslpBM
COVIS category learning modelslpCOVIS
Similarity-Dissimilarity Generalized Context Model (DGCM)slpDGCM
DIVA category learning modelslpDIVA
EXIT Category Learning ModelslpEXIT
Gluck & Bower (1988) network modelslpLMSnet
Mackintosh (1975) associative learning modelslpMack75
MB/MF reinforcement learning modelslpMBMF
A Neural Network with Competitive Attentional Gating (NNCAG)slpNNCAG
A Neural Network with Rapid Attentional Shifts (NNRAS)slpNNRAS
Rescorla-Wagner (1972) associative learning model.slpRW
SUSTAIN Category Learning ModelslpSUSTAIN
Sum of squared errorsssecl
Generalized Context ModelstsimGCM
Ordinal adequacy results for all catlearn simulationsthegrid