catlearn v0.8
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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.
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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")
Functions in catlearn
| Name | Description | |
| 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 | |
| No Results! | ||
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Details
| Type | Package |
| Date | 2020-09-16 |
| Encoding | UTF-8 |
| License | GPL (>= 2) |
| LinkingTo | Rcpp, RcppArmadillo |
| LazyData | true |
| NeedsCompilation | yes |
| Packaged | 2020-09-16 09:07:13 UTC; andy |
| Repository | CRAN |
| Date/Publication | 2020-09-16 09:50:07 UTC |
| imports | doParallel , dplyr , foreach , Rcpp (>= 1.0.0) , tidyr |
| depends | R (>= 3.5) |
| linkingto | RcppArmadillo |
| suggests | testthat |
| Contributors | Andy Wills, Lenard Dome, Charlotte Edmunds, Garrett Honke, Angus Inkster, Stuart Spicer, Ren<c3><a9> Schlegelmilch |
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