bmm v0.4.0

New features

• add a check for the sdmSimple model if the data is sorted by predictors. This leads to much faster sampling. The user can control the default behavior with the sort_data argument (#72)
• the mixture3p and imm models now require that when set size is used as a predictor, the intercept must be suppressed. This is because set size 1 otherwise causes problems - there can be no contribution of non_target responses when there is set size 1, and it is not meaningful to estimate an intercept for parameters that involve non_target responses (#96).
• add postprocessing methods for sdmSimple to allow for pp_check(), conditional_effects and bridgesampling usage with the model (#30)
• add informed default priors for all models. You can always use the get_model_prior() function to see the default priors for a model
• add a new function set_default_prior for developers, which allows them to more easily set default priors on new models regardless of the user-specified formula
• you can now specify variables for models via regular expressions rather than character vectors (#102)
• you can now view and set all bmm global options via bmm_options(). See ?bmm_options for more information
• add a startup message upon loading the package

Bug fixes

• fix a bug in the mixture3p and IMM models which caused an error when intercept was not supressed and set size was used as predictor
• update() now works properly with bmmfit objects (#95)
• fix a bug in the sort_data check which caused an error when using grouped covariance structure in random effects across different parameters

Other changes

• brms is now loaded automatically when loading bmm with library(bmm)