Hi,

Version 0.5 of the 'catlearn' package is now available.

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.

New to version 0.5 are implementations of Nosofsky's Generalized
Context Model, Daw/Gillan's hybrid model-based model-free learning
model, and the Bush & Mosteller model.

In addition, most archived simulations now also record the process of
parameter optimization they used; and the Rescorla-Wagner model
implementation now runs much faster. The DIVA model implementation had
a minor update. The ALCOVE and COVIS model implementations remain in
the same good state as version 0.4.

Catlearn is a package for R, and is very easy to install within that
environment. Make sure you're running the latest version of R
(particularly important for Windows users), and then type:

install.packages('catlearn')

and then

library(catlearn).

For a tutorial introduction to catlearn, and the Open Models
initiative more generally, see Wills et al. (2017).

Thanks to all the contributors to catlearn, and particular thanks to
Lenard Dome, Garrett Honke, Rene Schegelmilch, and Stuart Spicer, who
all contributed new code for the version 0.5 release.

All the best

Andy

Wills, A.J., O'Connell, G., Edmunds, C.E.R., & Inkster,
A.B.(2017). Progress in modeling through distributed collaboration:
Concepts, tools, and category-learning examples. The Psychology of
Learning and Motivation