Remove deprecated functions; hard dependency on brms 2.21.0

.dev/bmm_default_priors.Rmd

@@ -89,7 +89,7 @@ All model parameters are `nlpar` so they get class `b` with coef `Intercept`
 ```{r}
 model <- mixture3p('dev_rad', nt_features = paste0('col_nt',1:7), setsize='set_size')
 formula <- bmf(kappa ~ 1, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -102,7 +102,7 @@ For kappa, which we include and intercept and a predictor, we get class `b` with
 
 ```{r}
 formula <- bmf(kappa ~ session, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -115,7 +115,7 @@ For kappa, which we include and intercept and a predictor, we get class `b` with
 
 ```{r}
 formula <- bmf(kappa ~ 0+session, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -126,7 +126,7 @@ terms(formula$kappa)
 
 ```{r}
 formula <- bmf(kappa ~ 0+session + ( 0+session|ID), thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -138,7 +138,7 @@ terms(formula$kappa)
 ```{r}
 
 formula <- bmf(kappa ~ session + cond, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -154,7 +154,7 @@ What we can do, is for each predictor, check how many levels appear.
 ```{r}
 
 formula <- bmf(kappa ~ 0 + session + cond, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -166,7 +166,7 @@ terms(formula$kappa)
 ```{r}
 
 formula <- bmf(kappa ~ 0 + session * cond, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -178,7 +178,7 @@ terms(formula$kappa)
 ```{r}
 
 formula <- bmf(kappa ~ 0 + session:cond + cond + session, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -190,7 +190,7 @@ terms(formula$kappa)
 ```{r}
 
 formula <- bmf(kappa ~ 0 + session:cond, thetat ~ 1, thetant ~ 1)
-get_model_prior(formula, dat, model)
+default_prior(formula, dat, model)
 ```
 
 ```{r}
@@ -235,11 +235,11 @@ A helper function to compare the priors with and without the default priors.
 
 ```{r}
 compare_priors <- function(formula,dat,model) {
-  p1 <- withr::with_options(list('bmm.default_priors'=FALSE), get_model_prior(formula, dat, model)) %>% 
+  p1 <- withr::with_options(list('bmm.default_priors'=FALSE), default_prior(formula, dat, model)) %>% 
     brms:::prepare_print_prior() %>% 
     rename(priorBRMS=prior) %>% 
     select(priorBRMS,class,coef,group,resp,dpar,nlpar)
-  p2 <- withr::with_options(list('bmm.default_priors'=TRUE), get_model_prior(formula, dat, model)) %>% 
+  p2 <- withr::with_options(list('bmm.default_priors'=TRUE), default_prior(formula, dat, model)) %>% 
     brms:::prepare_print_prior() %>% 
     rename(priorBMM=prior) %>% 
     select(priorBMM,class,coef,group,resp,dpar,nlpar)

.dev/visualizing_priors.R

@@ -2,7 +2,7 @@ data <- OberauerLin_2017
 data$session <- as.factor(data$session)
 formula <- bmf(c ~ 0 + set_size, kappa ~ session)
 model <- sdmSimple('dev_rad')
-get_model_prior(formula, data, model)
+default_prior(formula, data, model)
 
 
 x <- exp(rnorm(1000000,3,1.75))

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: bmm
 Title: Easy and Accesible Bayesian Measurement Models using 'brms'
-Version: 0.4.6.9000
+Version: 0.4.7.9000
 Authors@R: c(
     person("Vencislav", "Popov", , "[email protected]", role = c("aut", "cre", "cph")),
     person("Gidon", "Frischkorn", , "[email protected]", role = c("aut", "cph")),
@@ -53,5 +53,5 @@ Additional_repositories:
 VignetteBuilder: knitr
 Depends: 
     R (>= 3.6.0),
-    brms
+    brms (>= 2.21.0)
 LazyData: true

NAMESPACE

@@ -29,6 +29,7 @@ S3method(configure_prior,IMMfull)
 S3method(configure_prior,bmmmodel)
 S3method(configure_prior,default)
 S3method(configure_prior,mixture3p)
+S3method(default_prior,bmmformula)
 S3method(fit_info,brmsfit)
 S3method(fit_info,brmsfit_list)
 S3method(identical,bmmformula)
@@ -52,6 +53,7 @@ S3method(reset_env,brmsfamily)
 S3method(reset_env,brmsformula)
 S3method(reset_env,default)
 S3method(reset_env,formula)
+S3method(restructure,bmmfit)
 S3method(revert_postprocess_brm,default)
 S3method(revert_postprocess_brm,sdmSimple)
 S3method(rhs_vars,bmmformula)
@@ -82,11 +84,7 @@ export(dmixture3p)
 export(dsdm)
 export(fit_info)
 export(fit_model)
-export(get_model_prior)
-export(get_stancode)
-export(get_standata)
 export(k2sd)
-export(make_stancode_parblock)
 export(mixture2p)
 export(mixture3p)
 export(pIMM)
@@ -101,7 +99,6 @@ export(qmixture3p)
 export(qsdm)
 export(rIMM)
 export(rad2deg)
-export(restructure_bmm)
 export(revert_postprocess_brm)
 export(rmixture2p)
 export(rmixture3p)
@@ -113,6 +110,8 @@ export(supported_models)
 export(use_model_template)
 export(wrap)
 import(stats)
+importFrom(brms,default_prior)
+importFrom(brms,restructure)
 importFrom(brms,stancode)
 importFrom(brms,standata)
 importFrom(glue,glue)

NEWS.md

@@ -3,7 +3,7 @@
 ### New features
 * add a custom **summary()** method for bmm models (#144) 
 * add a global options **bmm.summary_backend** to control the backend used for the summary() method (choices are "bmm" and "brms")
-* deprecate **get_model_prior(), get_stancode() and get_standata()**. These functions will be removed in future versions of the package. Due to [recent changes](https://github.com/paul-buerkner/brms/pull/1604) in *brms* version 2.20.14, you can now use the *brms* functions `default_prior`, `stancode` and `standata` directly with *bmm* models (alternatively, their older aliases, "get_prior", "make_stancode", "make_standata").
+* BREAKING CHANGE: remove **get_model_prior(), get_stancode() and get_standata()**. Due to [recent changes](https://github.com/paul-buerkner/brms/pull/1604) in *brms* version 2.21.0, you can now use the *brms* functions `default_prior`, `stancode` and `standata` directly with *bmm* models.
 * function **restructure()** now allows to apply methods introduced in newer bmm versions to bmmfit objects created by older bmm versions
 * you can now specify any model parameter to be a constant by using an equal sign in the `bmmformula` (#142)
 * you can now choose to estimate parameters that are fixed to a constant by default for all models (#145)
@@ -13,9 +13,12 @@
 
 ### Bug fixes
 * fix a bug preventing the sort_data check from being executed (#72)
-* fix a bug with the summary() function not displaying implicit parameters (#152)
+* fix bugs with the summary() function not displaying implicit parameters (#152) and not working properly with some hierarhical designs (#173)
 * fix a bug in which the sort_data check occured in cases where it shouldn't (#158)
 
+### Other changes
+* `bmm` now requires the latest version of `brms` (>= 2.21.0). 
+
 # bmm 0.4.0
 
 ### New features

R/fit_model.R

@@ -22,7 +22,7 @@
 #' @param chains Numeric. Number of Markov chains (defaults to 4)
 #' @param prior One or more `brmsprior` objects created by [brms::set_prior()]
 #'   or related functions and combined using the c method or the + operator. See
-#'   also [get_model_prior()] for more help. Not necessary for the default model
+#'   also [default_prior()] for more help. Not necessary for the default model
 #'   fitting, but you can provide prior constraints to model parameters
 #' @param sort_data Logical. If TRUE, the data will be sorted by the predictor
 #'   variables for faster sampling. If FALSE, the data will not be sorted, but

R/helpers-data.R

@@ -221,50 +221,27 @@ rad2deg <- function(rad){
 #'   this function will return the combined stan data generated by `bmm` and
 #'   `brms`
 #'
-#' @details This function is deprecated. Please use `standata()` or
-#'   `make_standata()` (if using `brms` >= 2.20.14) instead. In `brms` >=
-#'   2.20.14, `make_standata()` became an alias for `standata()`, and
-#'   `standata()` is the recommended function to use.
-#'
 #' @inheritParams fit_model
 #' @param object A `bmmformula` object
-#' @param formula Deprecated. Use `object` instead.
-#' @param ... Further arguments passed to [brms::make_standata()]. See the
-#'   description of [brms::make_standata()] for more details
+#' @param ... Further arguments passed to [brms::standata()]. See the
+#'   description of [brms::standata()] for more details
 #'
 #' @returns A named list of objects containing the required data to fit a bmm
 #'   model with Stan.
 #'
-#'
-#' @seealso [supported_models()], [brms::make_standata()], [brms::standata()]
+#' @seealso [supported_models()], [brms::standata()]
 #'
 #' @export
 #'
 #' @keywords extract_info
-#' @importFrom brms standata
 #'
 #' @examples
 #' sdata1 <- standata(bmf(c ~ 1, kappa ~ 1),
 #'                    data = OberauerLin_2017,
 #'                    model = sdmSimple(resp_err = "dev_rad"))
 #' str(sdata1)
+#' @importFrom brms standata
 #' @export
-get_standata <- function(object, data, model, prior=NULL, formula = object, ...) {
-  if (utils::packageVersion('brms') >= "2.20.14") {
-    message("get_standata is deprecated. Please use standata() or make_standata() instead.")
-  } else {
-    message("get_standata is deprecated. Please use standata() instead.")
-  }
-  warnif(missing(object) && !missing(formula),
-         "The 'formula' argument is deprecated for consistency with brms (>= 2.20.14). \\
-         Please use 'object' instead.")
-
-  standata.bmmformula(object = formula, data = data,
-                      model = model, prior = prior, ...)
-}
-
-#' @export
-#' @rdname get_standata
 standata.bmmformula <- function(object, data, model, prior = NULL, ...) {
   # check model, formula and data, and transform data if necessary
   formula <- object
@@ -281,7 +258,9 @@ standata.bmmformula <- function(object, data, model, prior = NULL, ...) {
   # extract stan code
   dots <- list(...)
   fit_args <- combine_args(nlist(config_args, dots, prior))
-  brms::do_call(brms::make_standata, fit_args)
+  fit_args$object <- fit_args$formula
+  fit_args$formula <- NULL
+  brms::do_call(brms::standata, fit_args)
 }
 
 # check if the data is sorted by the predictors

R/helpers-model.R

@@ -608,48 +608,26 @@ use_model_template <- function(model_name,
 
 
 #' @title Generate Stan code for bmm models
-#' @description A wrapper around `brms::make_stancode()` for models specified
-#'   with `bmm`. Given the `model`, the `data` and the `formula` for the model,
+#' @description Given the `model`, the `data` and the `formula` for the model,
 #'   this function will return the combined stan code generated by `bmm` and
 #'   `brms`
 #'
 #' @inheritParams fit_model
 #' @param object A `bmmformula` object
-#' @param formula Deprecated. Use `object` instead.
-#' @param ... Further arguments passed to [brms::make_stancode()]. See the
-#'   description of [brms::make_stancode()] for more details
+#' @param ... Further arguments passed to [brms::stancode()]. See the
+#'   description of [brms::stancode()] for more details
 #'
-#' @details This function is deprecated. Please use `stancode()` or
-#'   `make_stancode()` (if using `brms` >= 2.20.14) instead. In `brms` >=
-#'   2.20.14, `make_stancode()` became an alias for `stancode()`, and
-#'   `stancode()` is the recommended function to use.
 #' @returns A character string containing the fully commented Stan code to fit a
 #'   bmm model.
 #'
-#' @seealso [supported_models()], [brms::make_stancode()]
-#' @export
+#' @seealso [supported_models()], [brms::stancode()]
 #' @keywords extract_info
-#' @importFrom brms stancode
 #' @examples
 #' scode1 <- stancode(bmf(c ~ 1, kappa ~ 1),
 #'                    data = OberauerLin_2017,
 #'                    model = sdmSimple(resp_err = "dev_rad"))
 #' cat(scode1)
-get_stancode <- function(object, data, model, prior=NULL, formula = object, ...) {
-  if (utils::packageVersion('brms') >= "2.20.14") {
-    message("get_stancode is deprecated. Please use stancode() or make_stancode() instead.")
-  } else {
-    message("get_stancode is deprecated. Please use stancode() instead.")
-  }
-  warnif(missing(object) && !missing(formula),
-         "The 'formula' argument is deprecated for consistency with brms (>= 2.20.14). \\
-         Please use 'object' instead.")
-
-  stancode.bmmformula(object = formula, data = data,
-                      model = model, prior = prior, ...)
-}
-
-#' @rdname get_stancode
+#' @importFrom brms stancode
 #' @export
 stancode.bmmformula <- function(object, data, model, prior = NULL, ...) {
   withr::local_options(bmm.sort_data = FALSE)
@@ -669,43 +647,7 @@ stancode.bmmformula <- function(object, data, model, prior = NULL, ...) {
   # extract stan code
   dots <- list(...)
   fit_args <- combine_args(nlist(config_args, dots, prior))
-  brms::do_call(brms::make_stancode, fit_args)
-}
-
-
-
-#' @title Get the parameter block from a generated Stan code for bmm models
-#' @description A wrapper around `get_stancode()` for models specified with
-#'   `bmm`. Given the `model`, the `data` and the `formula` for the model, this
-#'   function will return just the parameters block. Useful for figuring out
-#'   which paramters you can set initial values on
-#' @inheritParams fit_model
-#' @param ... Further arguments passed to [brms::make_stancode()]. See the
-#'   description of [brms::make_stancode()] for more details
-#'
-#' @keywords extract_info
-#'
-#' @returns A character string containing the parameter block of fully commented
-#'   Stan code to fit a bmm model.
-#'
-#'
-#' @seealso [supported_models()], [get_stancode()]
-#'
-#' @export
-make_stancode_parblock <- function(formula, data, model, prior=NULL, ...) {
-  stancode <- get_stancode(formula, data, model, prior, ...)
-  .extract_parblock(stancode)
-}
-
-
-#' @title Extract the parameter block from the Stan code
-#' @description Given the Stan code for a model, this function will extract the
-#'   parameter block from the Stan code
-#' @param stancode A character string containing the fully commented Stan code
-#' @noRd
-.extract_parblock <- function(stancode) {
-  parblock <- stringr::str_match(as.character(stancode),
-                                 "(?s)parameters \\{\\n(.*?)\\}\\ntransformed")[,2]
-  class(parblock) <- class(stancode)
-  parblock
+  fit_args$object <- fit_args$formula
+  fit_args$formula <- NULL
+  brms::do_call(brms::stancode, fit_args)
 }