bmm 1.2.0

New models

• Add the Memory Measurement Model (Oberauer & Lewandowsky, 2019) and its generalization as the Multinomial Measurement Model for categorical decision tasks as new model class m3 with three versions: simple span (ss), complex span (cs), and custom. For details, see the article on the bmm website (#237). Thanks to @GidonFrischkorn and @chenyu-psy

New features

• Updates to the bmf2bf S3 methods for more flexible translation of bmmformulas into brmsformulas (#227).
• New function apply_links adds link functions to all non-linear formulas in a bmmformula object.
• New example data set oberauer_lewandowsky_2019_e1 for exploring the m3 model.
• The file_refit argument of the bmm function now accepts character strings like brms. A warning is given when "on_change" is specified, as this is not currently implemented for bmmodels (#228).
• New function rejection_sampling

Bug fixes

• Fix conflict in setting default priors when model parameters were transformed in a non-linear formula (#232).
• Allow a NULL formula (formula(NULL)) to be added to a bmmformula for consistentcy with brms (#264)
• Improve error messages when attempting to construct bmmformulas without a left-hand-side variable

Documentation

• Add documentation to the continuous reproduction task article for pre-processing half-circular stimulus spaces when using bmmodels of the circular model class (#229, #233).
• New online article to accompany the m3 model

Other changes

• vectorize k2sd() function for improved performance
• various internal refactorings (#246, #242)
• dplyr, magrittr and tidyr dependencies are now optional (#240)
• new contributor - Chenyun Li (chenyu-psy) for his work on the m3 model