fix links to vignettes

R/bmm_model_sdmSimple.R

@@ -49,7 +49,7 @@
 
 #' @title `r .model_sdmSimple()$name`
 #' @name SDM
-#' @details see `vignette("sdm-simple")` for a detailed description of the model
+#' @details see `vignette("bmm_sdm_simple")` for a detailed description of the model
 #'   and how to use it.
 #'   `r model_info(.model_sdmSimple())`
 #' @param resp_err The name of the variable in the dataset containing the

R/distributions.R

@@ -16,7 +16,7 @@
 #' @param kappa Vector of precision values
 #' @param log Logical; if `TRUE`, values are returned on the log scale.
 #' @param parametrization Character; either `"bessel"` or `"sqrtexp"`
-#'   (default). See `vignette("sdm-simple")` for details on the
+#'   (default). See `vignette("bmm_sdm_simple")` for details on the
 #'   parameterization.
 #' @param log.p Logical; if `TRUE`, probabilities are returned on the log
 #'   scale.
@@ -36,7 +36,7 @@
 #'
 #' @details **Parametrization**
 #'
-#' See `vignette("sdm-simple")` for details on the parameterization.
+#' See `vignette("bmm_sdm_simple")` for details on the parameterization.
 #' Oberauer (2023) introduced the SDM with the bessel parametrization. The
 #' sqrtexp parametrization is the default in the `bmm` package for
 #' numerical stability and efficiency. The two parametrizations are related by

R/helpers-parameters.R

@@ -43,7 +43,7 @@ k2sd <- function(K) {
 #'   `c_sqrtexp2bessel` converts from the sqrtexp parametrization to the
 #'   bessel parametrization.
 #' @keywords transform
-#' @details See `vignette("sdm-simple")` for details on the
+#' @details See `vignette("bmm_sdm_simple")` for details on the
 #'   parameterization. The sqrtexp parametrization is the default in the
 #'   `bmm` package.
 #' @export

README.Rmd

@@ -179,7 +179,7 @@ data <- OberauerLin_2017
 For this quick example, we will show how to setup fitting the Interference
 Measurement Model to this data. If you want a detailed description of this model
 and and in depth explanation of the parameters estimated in the model, please
-have a look at `vignette("IMM")`.
+have a look at `vignette("bmm_imm")`.
 
 ``` r
 model_formula <- bmmformula(c ~ 0 + set_size,

README.md

@@ -196,7 +196,7 @@ data <- OberauerLin_2017
 For this quick example, we will show how to setup fitting the
 Interference Measurement Model to this data. If you want a detailed
 description of this model and and in depth explanation of the parameters
-estimated in the model, please have a look at `vignette("IMM")`.
+estimated in the model, please have a look at `vignette("bmm_imm")`.
 
 ``` r
 model_formula <- bmmformula(c ~ 0 + set_size,

vignettes/articles/bmm_vwm_crt.Rmd

@@ -51,19 +51,19 @@ The `bmm` package implements several measurement models for analyzing continuous
 
 #### The two-parameter mixture model [@Zhang_Luck_2008] {.unnumbered}
 
-  - see `?mixture2p` and `vignette("mixture_models")`
+  - see `?mixture2p` and `vignette("bmm_mixture_models")`
 
 #### The three-parameter mixture model [@Bays_Catalao_Husain_2009] {.unnumbered}
 
-  - see `?mixture3p` and `vignette("mixture_models")`
+  - see `?mixture3p` and `vignette("bmm_mixture_models")`
 
 #### The Interference Measurement Model [@Oberauer_Lin_2017]  {.unnumbered}
 
-  - see `?IMM` and `vignette("IMM")`
+  - see `?IMM` and `vignette("bmm_imm")`
 
 #### The Signal Discrimination Model (SDM) by [@Oberauer_2023]  {.unnumbered}
 
-  - see `?sdmSimple` and `vignette("sdm-simple")`
+  - see `?sdmSimple` and `vignette("bmm_sdm_simple")`
 
 
 # References

vignettes/bmm_bmmformula.Rmd

@@ -155,7 +155,7 @@ my_formula <- bmf(
 bmmfit <- bmm:::bmmfit_mixture2p_vignette
 ```
 
-Let's assume you have fit a `mixture2p` model to a data set `my_data` as in the `vignette("mixture_models")`. For this, you had to specify a `bmmformula` and model object:
+Let's assume you have fit a `mixture2p` model to a data set `my_data` as in the `vignette("bmm_mixture_models")`. For this, you had to specify a `bmmformula` and model object:
 
 ```r
 user_formula <- bmf(