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catlearn

Formal Psychological Models of Categorization and Learning

Catlearn is an archive of formal models of categorization and learning, plus benchmark datasets to test them against. It's also an archive of simulations using those models. It's free and open source software ... and always will be.

Catlearn is a package for R, and is very easy to install within that environment. For a tutorial introduction to catlearn, and the Open Models initiative more generally, see Wills et al. (2017). There is also lots of information about catlearn on its website.

Installing the stable version

Make sure you're running the latest version of R and then type:

install.packages('catlearn')

and then

library(catlearn).

Installing the latest (unstable) version

If you're not sure what "unstable" means in this context, you probably want to install the stable version instead. If you're sure you want the unstable version, first install devtools and its dependencies. Then run:

devtools::install_github("ajwills72/catlearn")

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Version

Install

install.packages('catlearn')

Monthly Downloads

636

Version

0.8

License

GPL (>= 2)

Maintainer

Andy Wills

Last Published

September 16th, 2020

Functions in catlearn (0.8)

krus96exit

Simulation of AP krus96 with EXIT model
medin87train

Input representation of Exp. 1 in Medin et al. (1987) for models input-compatible with slpALCOVE or slpSUSTAIN.
catlearn-package

Formal Modeling for Psychology.
convertSUSTAIN

Convert nominal-dimension input representation into a 'padded' (slpSUSTAIN) format
act2probrat

Convert output activation to a rating of outcome probability
krus96train

Input representation of krus96 for models input-compatible with slpEXIT
nosof88exalcove

Simulation of CIRP nosof88 with ex-ALCOVE model
nosof88

Instantiation frequency CIRP
krus96

Inverse Base-rate Effect AP
homa76

Category breadth CIRP
nosof88train

Input representation of nosof88 for models input-compatible with slpALCOVE.
nosof94

Type I-VI category structure CIRP
nosof94exalcove

Simulation of CIRP nosof94 with ex-ALCOVE model
nosof94bnalcove

Simulation of CIRP nosof94 with BN-ALCOVE model
nosof88protoalcove

Simulation of CIRP nosof88 with proto-ALCOVE model
nosof88protoalcove_opt

Parameter optimization of proto-ALCOVE model with nosof88 CIRP
nosof88oat

Ordinal adequacy test for simulations of nosof88 CIRP
nosof88exalcove_opt

Parameter optimization of ex-ALCOVE model with nosof88 CIRP
nosof94sustain

Simulation of CIRP nosof94 with the SUSTAIN model
nosof94plot

Plot Nosofsky et al. (1994) data / simulations
shin92exalcove_opt

Parameter optimization of ex-ALCOVE model with shin92 CIRP
shin92exalcove

Simulation of CIRP shin92 with ex-ALCOVE model
shin92oat

Ordinal adequacy test for simulations of shin92 CIRP
shin92protoalcove

Simulation of CIRP shin92 with proto-ALCOVE model
nosof94exalcove_opt

Parameter optimization of ex-ALCOVE model with nosof94 CIRP
shin92protoalcove_opt

Parameter optimization of proto-ALCOVE model with shin92 CIRP
shin92train

Input representation of shin92 for models input-compatible with slpALCOVE.
nosof94oat

Ordinal adequacy test for simulations of nosof94 CIRP
nosof94train

Input representation of nosof94 for models input-compatible with slpALCOVE or slpSUSTAIN
shin92

Category size CIRP
slpRW

Rescorla-Wagner (1972) associative learning model.
slpMBMF

MB/MF reinforcement learning model
slpMack75

Mackintosh (1975) associative learning model
slpEXIT

EXIT Category Learning Model
slpLMSnet

Gluck & Bower (1988) network model
slpCOVIS

COVIS category learning model
slpDIVA

DIVA category learning model
slpALCOVE

ALCOVE category learning model
slpBM

Bush & Mosteller (1951) simple associative learning model
slpSUSTAIN

SUSTAIN Category Learning Model
ssecl

Sum of squared errors
thegrid

Ordinal adequacy results for all catlearn simulations
stsimGCM

Generalized Context Model