add DV as variable to model() call & add bmm_formula

.Rbuildignore

@@ -14,3 +14,5 @@
 ^Meta$
 ^dev_utils$
 ^index.md$
+^tests/internal-.*$
+^tests/internal$

.gitignore

@@ -8,3 +8,5 @@ docs
 inst/doc
 /doc/
 /Meta/
+/tests/internal/cmdstanr_output/
+/tests/internal/*cache*

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: bmm
 Title: Easy and Accesible Bayesian Measurement Models using 'brms'
-Version: 0.2.2.9000
+Version: 0.3.0
 Authors@R: c(
     person("Vencislav", "Popov", , "[email protected]", role = c("aut", "cre", "cph")),
     person("Gidon", "Frischkorn", , "[email protected]", role = c("aut", "cph")))
@@ -25,7 +25,8 @@ Suggests:
     mixtur,
     ggthemes,
     cowplot,
-    stringr
+    stringr,
+    remotes
 Config/testthat/edition: 3
 Imports: 
     brms,

NAMESPACE

@@ -1,6 +1,10 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method("+",bmmformula)
+S3method("[",bmmformula)
+S3method(bmf2bf,bmmmodel)
 S3method(check_data,IMMspatial)
+S3method(check_data,bmmmodel)
 S3method(check_data,default)
 S3method(check_data,nontargets)
 S3method(check_data,vwm)
@@ -10,15 +14,23 @@ S3method(configure_model,IMMfull)
 S3method(configure_model,mixture2p)
 S3method(configure_model,mixture3p)
 S3method(configure_model,sdmSimple)
+S3method(fit_info,brmsfit)
+S3method(fit_info,brmsfit_list)
 S3method(model_info,bmmmodel)
 S3method(postprocess_brm,bmmmodel)
 S3method(postprocess_brm,default)
 S3method(postprocess_brm,sdmSimple)
 S3method(postprocess_brm,vwm)
+S3method(rhs_vars,bmmformula)
+S3method(rhs_vars,formula)
+S3method(update,bmmfit)
 export("%>%")
 export(IMMabc)
 export(IMMbsc)
 export(IMMfull)
+export(bmf)
+export(bmf2bf)
+export(bmmformula)
 export(c_bessel2sqrtexp)
 export(c_sqrtexp2bessel)
 export(calc_error_relative_to_nontargets)
@@ -29,6 +41,7 @@ export(deg2rad)
 export(dmixture2p)
 export(dmixture3p)
 export(dsdm)
+export(fit_info)
 export(fit_model)
 export(get_model_prior)
 export(get_stancode)

NEWS.md

@@ -1,3 +1,26 @@
+# bmm 0.3.0
+
+### New features
+
+* BREAKING CHANGE: The `fit_model` function now requires a `bmmformula` to be passed. The  syntax of the `bmmformula` or its short form `bmf` is equal to specifying a `brmsformula`. However, as of this version the `bmmformula` only specifies how parameters of a `bmmmodel` change across experimental conditions or continuous predictors. The response variables that the model is fit to now have to be specified when the model is defined using `model = bmmmodel()`. (#79)
+* BREAKING CHANGE: The `non_target` and `spaPos` variables for the `mixture3p` and `IMM` models were relabled to `nt_features` and `nt_distances` for consistency. This is also to communicate that distance is not limited to spatial distance but distances on any feature dimensions of the retrieval cues. Currently, still only a single generalization gradient for the cue features is possible. 
+* This release includes reference fits for all implemented models to ensure that future changes to the package do not compromise the included models and change the results that their implementations produce.
+* The `check_formula` methods have been adapted to match the new `bmmformula` syntax. It now evaluates if formulas have been specified using the `bmmformula` function, if formulas for all parameters of a `bmmmodel` have been specified and warns the user that only a fixed intercept will be estimated if no formula for one of the parameters was provided. Additionally, `check_formula` throws an error should formulas be provided that do not match a parameter of the called `bmmmodel` unless they are part of a non-linear transformation.
+* You can now specify formulas for internally fixed parameters such as `mu` in all visual working memory models. This allows you to predict if there is response bias in the data. If a formula is not provided for `mu`, the model will assume that the mean of the response distribution is fixed to zero.
+* there is now an option `bmm.silent` that allows to suppress messages
+* the baseline activation `b` was removed from the `IMM` models, as this is internally fixed 
+to zero for scaling and as of now cannot be predicted by independent variables because the model would be unidentifiable.
+* the arguments used to fit the bmm model are now accessible in the `bmmfit` object via the `fit$bmm_fit_args` list.
+* add class('bmmfit') to the object returned from fit_model() allowing for more flexible postprocessing of the underlying `brmsfit` object. The object is now of class('bmmfit', 'brmsfit')
+
+### Bug Fixes
+* an error with the treatment of distances in the `IMMfull` and the `IMMbsc` has been corrected. This versions ensures that only positive distances can be passed to any of the two models.
+* removed a warning regarding the scaling of the distances in the `IMMfull` and the `IMMbsc` that was specific only for circular distances.
+
+### Documentation
+* All vignettes have been update to the new `bmmformula` syntax.
+
+
 # bmm 0.2.2
 
 ### Bug Fixes