bmm 0.2.0

A major restructuring of the package to support stable and generalizable development of future models (#41, #43).

• Refactor the fit_model() function to be generic and independent of the model being fit (#20)
• Transform models to be S3 objects. Deprecate model_type argument in fit_model(). Models must now be specified with S3 functions passed to argument model rather than model names as strings passed to argument model_type (#41)
• Improve user interface to fit_model() ensures package stability and future development. Model specific arguments are now passed to the model functions as named arguments (e.g. mixture3p(non_targets, setsize)). This allows for a more flexible and intuitive way to specify model arguments. Passing model specific arguments directly to the fit_model() function is now deprecated (#43).
• Add S3 methods for checking the data, formula, model and priors (#41)

New features

• New model available - The Signal Discrimination Model by Oberauer (2023) for visual working memory continuous reproduction tasks. See ?sdmSimple. The current version does not take into account non-target activation
• Add ability to extract information about the default priors in bmm models with get_model_prior() (#53)
• Add ability to generate stan code and stan data for each model with get_model_stancode() and get_model_standata() (#81)
• Add distribution functions for the Signal Discrimination Model. See ?SDM for usage (#27)
• Add distribution functions for likelihood (e.g. dimm()) and random variate generation rimm()) for all models in the package. Remove deprecated gen_3p_data() and gen_imm_data() functions (#69)
• Two new datasets available: ZhangLuck_2008 and OberauerLin_2017 (#22)
• Add ability to generate a template file for adding new models to the package with use_model_template() (for developers) (#39)
• View currently supported models with new function supported_models(). Currently supported models are: mixture2p(), mixture3p(), IMMabc(), IMMbsc(), IMMfull()
• Add softmax and invsoftmax functions

Documentation

• Website for the development version of the package is now available at https://venpopov.github.io/bmm/dev/ (#18)
• Add vignettes for each model to the website at https://venpopov.github.io/bmm/dev/articles/
• Add a detailed developer's guide to the website at https://venpopov.github.io/bmm/dev/dev-notes (#21)
• Improve README with more detailed information about the package's goals and its models (#21)
• Improve documentation of model functions. You can now get help on each model by typing ?model_name into your console. For example, calling the information on the full version of the Interference Measurement Model would look like this: ?IMMfull

Bug Fixes

• Change default prior on log(kappa) to Normal(2,1) for the mixture3p() model (#15)

Other changes

• Save bmm package version in the brmsfit object for reproducibility - e.g. fit$version$bmm (#88)
• Add extensive unit testing
• Add information about each model such as domain, task, name, version, citation, requirements and parameters (#42)