diff --git a/DESCRIPTION b/DESCRIPTION
index 9a2c9657..b77bd240 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -33,7 +33,6 @@ Imports:
     dplyr,
     withr,
     tidyr,
-    circular,
     stats,
     matrixStats,
     methods
diff --git a/NAMESPACE b/NAMESPACE
index ebc3ec4f..1c978211 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -30,8 +30,6 @@ export(dmixture2p)
 export(dmixture3p)
 export(dsdm)
 export(fit_model)
-export(gen_3p_data)
-export(gen_imm_data)
 export(get_model_prior)
 export(k2sd)
 export(mixture2p)
diff --git a/R/bmm_model_mixture2p.R b/R/bmm_model_mixture2p.R
index 6d1d48b0..9ce600fd 100644
--- a/R/bmm_model_mixture2p.R
+++ b/R/bmm_model_mixture2p.R
@@ -32,7 +32,7 @@
 #' @examples
 #' \dontrun{
 #' # generate artificial data
-#' dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+#' dat <- data.frame(y = rmixture2p(n=2000))
 #'
 #' # define formula
 #' ff <- brms::bf(y ~ 1,
diff --git a/R/bmm_model_mixture3p.R b/R/bmm_model_mixture3p.R
index 677e1acd..63fe7bf0 100644
--- a/R/bmm_model_mixture3p.R
+++ b/R/bmm_model_mixture3p.R
@@ -44,7 +44,12 @@
 #' @examples
 #' \dontrun{
 #' # generate artificial data from the Bays et al (2009) 3-parameter mixture model
-#' dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+#' dat <- data.frame(
+#'   y = rmixture3p(n=2000, mu = c(0,1,-1.5,2)),
+#'   nt1_loc = 1,
+#'   nt2_loc = -1.5,
+#'   nt3_loc = 2
+#' )
 #'
 #' # define formula
 #' ff <- brms::bf(y ~ 1,
@@ -53,14 +58,14 @@
 #'               thetant ~ 1)
 #'
 #' # specify the 3-parameter model
-#' model <- mixture3p(non_targets = paste0('nt',1:3,'_loc'), setsize=4)
+#' model <- mixture3p(non_targets = paste0('nt',1:3,'_loc'), setsize = 4)
 #'
 #' # fit the model
 #' fit <- fit_model(formula = ff,
 #'                  data = dat,
 #'                  model = model,
 #'                  parallel=T,
-#'                  iter=500,
+#'                  iter = 500,
 #'                  backend='cmdstanr')
 #' }
 mixture3p <- .model_mixture3p
diff --git a/R/fit_model.R b/R/fit_model.R
index 56a91be1..7fd471fc 100644
--- a/R/fit_model.R
+++ b/R/fit_model.R
@@ -47,18 +47,17 @@
 #' @examples
 #' \dontrun{
 #' # generate artificial data from the Bays et al (2009) 3-parameter mixture model
-#' dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+#' dat <- rsdm(n=2000)
 #'
 #' # define formula
 #' ff <- brms::bf(y ~ 1,
-#'               kappa ~ 1,
-#'               thetat ~ 1,
-#'               thetant ~ 1)
+#'                c ~ 1,
+#'                kappa ~ 1)
 #'
 #' # fit the model
 #' fit <- fit_model(formula = ff,
 #'                  data = dat,
-#'                  model = mixture3p(non_targets = paste0('nt',1:3,'_loc'), setsize=4),
+#'                  model = sdmSimple(),
 #'                  parallel=T,
 #'                  iter=500,
 #'                  backend='cmdstanr')
diff --git a/R/get_model_prior.R b/R/get_model_prior.R
index 80bc05c6..fa5ecfe2 100644
--- a/R/get_model_prior.R
+++ b/R/get_model_prior.R
@@ -34,22 +34,18 @@
 #'
 #' @examples
 #' \dontrun{
-#' # generate artificial data from the Bays et al (2009) 3-parameter mixture model
-#' dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+#' # generate artificial data from the Signal Discrimination Model
+#' dat <- rsdm(n=2000)
 #'
 #' # define formula
 #' ff <- brms::bf(y ~ 1,
-#'                kappa ~ 1,
-#'                thetat ~ 1,
-#'                thetant ~ 1)
-#'
-#' # simulate data
-#' dat <- gen_3p_data(N = 200)
+#'                c ~ 1,
+#'                kappa ~ 1)
 #'
 #' # fit the model
 #' get_model_prior(formula = ff,
 #'                 data = dat,
-#'                 model = mixture3p(non_targets = paste0('nt',1,'_loc'), setsize = 2)
+#'                 model = sdmSimple()
 #' )
 #' }
 #'
diff --git a/R/random_data_generation.R b/R/random_data_generation.R
deleted file mode 100644
index 8e5c1e7d..00000000
--- a/R/random_data_generation.R
+++ /dev/null
@@ -1,203 +0,0 @@
-#' @title Generate artificial data from the Bays et al (2009) 3-parameter
-#'   mixture model
-#' @description Given a set of parameters, and desired setsize, it generates
-#'   responses in a continuous reproduction task, with corresponding proportions
-#'   coming from memory, non-target location errors, or guessing.
-#'
-#' @param N Numeric. Number of samples
-#' @param pmem Numeric. Probability of response coming from memory
-#' @param pnt Numeric. Probability of response coming from a non-target item
-#' @param kappa Numeric. Precision of the von mises distribution
-#' @param setsize Numeric. Number of presented items
-#' @param relative_resp Logical. if TRUE, returns response error relative to the
-#'   target and all non-target item locations are coded relative to the target.
-#'   If FALSE, returns actual response and the location of all items is absolute
-#' @keywords simulation
-#'
-#' @return A data.frame object. y is the response (if relative_resp==F) or
-#'   response error (if relative_resp==T), t_loc is the value of the target
-#'   (only if relative_resp==F), nt1_loc to nti_loc, are values of the
-#'   non-targets, where i=setsize-1.
-#'
-#' @export
-#'
-#' @examples
-#' # example code
-#' nsub = 30
-#' df_3p_parms <- data.frame(
-#'   theta_pmem = rnorm(nsub, mean = 2, sd = 1),
-#'   theta_pnt = rnorm(nsub, mean = 1, sd = 0.5),
-#'   kappa = pmax(0,rnorm(nsub, mean = 5, sd = 1)),
-#'   pmem = numeric(nsub),
-#'   pnt = numeric(nsub),
-#'   pguess = numeric(nsub)
-#' )
-#'
-#' # transform continous mixture weights into probabilities using the softmax
-#' df_3p_parms[,c("pmem","pnt","pguess")] <- apply(df_3p_parms[,c("theta_pmem","theta_pnt")],
-#'                                                 1,softmax)
-#'
-gen_3p_data <- function(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=2, relative_resp=T) {
-  t_loc <- NULL
-  resample <- function(x, ...) x[sample.int(length(x), ...)]
-
-  # set parameters
-  pguess = 1-pmem-pnt   # probability of guessing
-
-  # uniformly distributed  locations on the circle
-  locations <- matrix(0, nrow = N, ncol = setsize)
-  for (i in 1:setsize) {
-    locations[,i] <- brms::rvon_mises(N, 0, 0)
-  }
-
-  idx_t <- 1:(round(pmem*N))                                   # which responses are target based
-  target_resp <- brms::rvon_mises(round(pmem*N),               # vector of target reponses
-                                  locations[idx_t,1],
-                                  kappa)
-  if (pnt > 0) {
-    Npnt <- round(pnt*N)
-    idx_nt <- (round(pmem*N)+1):(round(pmem*N)+Npnt)           # which responses are non-target
-    idx_loc <- resample(2:setsize, Npnt, replace=T)              # which lure generated the response
-    idx_select <- cbind(idx_nt, idx_loc)
-    nontarget_resp <- brms::rvon_mises(round(pnt*N),           # vector of nontarget responses
-                                       locations[idx_select],
-                                       kappa)
-  } else {
-    nontarget_resp <- c()
-  }
-
-  if (pguess > 0) {
-    guess_resp <- brms::rvon_mises(N-round(pmem*N)-round(pnt*N), 0, 0) # vector of guessing responses
-  } else {
-    guess_resp <- c()
-  }
-
-  # put the data together in a data.frame
-  dat <- data.frame(y = c(target_resp, nontarget_resp, guess_resp))
-  dat <- do.call(cbind, list(dat, as.data.frame(locations)))
-  names(dat) <- c('y','t_loc', paste0('nt',1:(setsize-1),'_loc'))
-
-
-  # if a relative response is requested
-  # recode responses such that the response is the error relative to the target
-  # also recode nt_loc, such that it is relative to the target as well
-  if (relative_resp) {
-    dat <- bmm::wrap(dat-dat$t_loc)
-    dat <- dplyr::select(dat, -t_loc)
-  }
-  return(dat)
-}
-
-#' @title Generate artificial data from the Interference measurement model
-#' @description Given a set of parameters, and desired setsize, this function generates
-#' data following the assumptions of the interference measurement models
-#'
-#' @param parms Matrix/DataFrame. Matrix or Data frame of IMM parameters,
-#'    must contain at least one row of values for:
-#'      c = context activation,
-#'      a = general activation,
-#'      s = spatial similarity gradient,
-#'      n = background noise,
-#'      kappa = precision of memory representations
-#' @param ntrials Numeric. number of responses to simulate for each subject
-#' @param setsize Numeric. Number of items in memory set
-#' @keywords simulation
-#'
-#' @return A data.frame object. resp is the response, respErr is the relative
-#'    response to the target, Item1 to ItemN is the absolute location of the
-#'    N items from -pi to pi for the number of items N specified by the setsize
-#'    variable, D1 to DN is the spatial distance of the N items for the N items
-#'    specified by the setsize variable
-#'
-#' @export
-gen_imm_data <- function(parms,
-                         ntrials = 200, setsize=6){
-  # pre-allocate & collect variables for the data simulation
-  nsubj <- nrow(parms) # number of subject
-
-  # pre-allocate activation matrix
-  acts <- matrix(0, 1, setsize + 1)
-
-  # switch off warnings -> rvonmises function throws a lot of information when
-  # generating random responses
-  options(warn = -1)
-
-  # pre-allocate data frame for collecting all simulated data
-  simData <- data.frame(
-    ID = integer(), trial = integer(), setsize = integer(),
-    resp = numeric(), respErr = numeric(),
-    c = numeric(), a = numeric(), s = numeric(), n = numeric(), kappa = numeric()
-  )
-  names <- colnames(simData)
-
-  # pre-allocate item location & spatial distance variable
-  for (s in 1:(2*setsize)) {
-    simData[,ncol(simData) + 1] <- numeric()
-  }
-
-  # update column names
-  newNames <- c(names,paste0("Item",1:setsize,"_abs"),paste0("spaD",1:setsize))
-  colnames(simData) <- newNames
-
-  # Loop through all subject
-  for (idx in 1:nsubj) {
-    x <- NULL
-    dev <- NaN
-
-    memset <- matrix(0, ntrials, setsize)
-    colnames(memset) <- paste0("Item",1:setsize,"_abs")
-
-    D <- matrix(0, ntrials, setsize)
-    colnames(D) <- paste0("spaD",1:setsize)
-
-    # Loop through all trials
-    for (trial in 1:ntrials) {
-      # generate random memory set & distances for each trial
-      memset[trial,1:setsize] <- bmm::wrap(circular::rvonmises(setsize, 0, 0,  control.circular = list(units = "radians")))  # draw items from uniform
-      D[trial,2:setsize] <- stats::runif(setsize - 1, 0.5, pi)   # draw distances of non-targets from uniform range 0.5 to pi
-
-      # compute activation for all items + random guessing
-      acts[1:setsize] <- parms[idx,"a"] + exp(-parms[idx,"s"]*D[trial,]) * parms[idx,"c"]
-      acts[setsize + 1] <- parms[idx,"n"]
-
-      # convert activations into probabilities
-      P <- exp(acts)/sum(exp(acts))
-
-      # randomly select from which distribution the response will come from
-      cumP <- cumsum(P)
-      draw <- stats::runif(1,0,1)
-      aa <- draw < cumP
-      choice <- min(which(aa == TRUE))
-
-      # draw response dependent on choice
-      if (choice <= setsize) x[trial] <- bmm::wrap(circular::rvonmises(1, memset[trial,choice], parms[idx,"kappa"]))  # response from a memory distribution
-      if (choice > setsize) x[trial] <- bmm::wrap(circular::rvonmises(1, 0, 0))               # response from noise distribution
-
-      # compute response error as the deviation of the response from the target item
-      dev[trial] = bmm::wrap(x[trial] - memset[trial,1])
-    }
-
-    # collect subject data
-    subData <- data.frame(ID = idx, setsize = setsize, trial = 1:ntrials, resp = x, respErr = dev,
-                          c = parms[idx,"c"], a = parms[idx,"a"], n = parms[idx, "n"],
-                          s = parms[idx, "s"], kappa = parms[idx,"kappa"])
-
-    # bind all subject data together
-    subData <- cbind(subData,memset,D)
-
-    # bind subject data to full data set
-    simData <- rbind(simData,subData)
-  }
-
-  # switch warnings back on
-  options(warn = 0)
-
-  # add relative locations to the simulated data
-  locData <- simData[,grepl("Item",colnames(simData))]
-  relLocData <- bmm::wrap(locData - locData$Item1_abs)
-  colnames(relLocData) <- paste0("Item",1:setsize,"_rel")
-  simData <- cbind(simData,relLocData)
-
-  # return the simulated data
-  return(simData)
-}
diff --git a/README.Rmd b/README.Rmd
index 93c84bf7..3fabd483 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -58,12 +58,17 @@ responses can come from three different sources - noisy representation of the
 target, confusion with noisy representation of non-target items, or guessing
 based on a uniform distribution. To estimate these parameters for a dataset, we
 can use the `fit_model()` function. First, let's generate a dataset with known
-parameters. We can use the function `gen_3p_data()`
+parameters. We can use the function `rmixture3p()`
 
 ```{r example, message=FALSE, warning=FALSE}
 library(bmm)
 library(tidyverse)
-dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+dat <- data.frame(
+  y = rmixture3p(n = 2000, mu = c(0,1,-1.5,2)),
+  nt1_loc = 1,
+  nt2_loc = -1.5,
+  nt3_loc = 2
+)
 head(dat)
 ```
 
@@ -82,19 +87,12 @@ tails:
 hist(dat$y, breaks = 60, xlab = "Response error relative to target")
 ```
 Another key property of the data is that some error responses are not random,
-but that they are due to confusion of the target with one of the lures. We can
-visualize this by centering the response error relative to each of the possible
-non-target locations. We do this with the helper function
-`calc_error_relative_to_nontargets()`:
-
-```{r}
-dat %>% 
-  calc_error_relative_to_nontargets('y', paste0('nt',1:3,'_loc')) %>% 
-  ggplot(aes(y_nt)) +
-  geom_histogram()
-```
+but that they are due to confusion of the target with one of the lures. This is already 
+visible by the additional peaks in the histogram. Typically these peaks are not immediately
+visible as the non-target locations vary from trial to trial.
 
 Ok, so now let's fit the three-parameter model. We only need to do two things:
+
 - Specify the model formula
 - Call fit_model()
 
@@ -105,9 +103,9 @@ proportion for correct responses and mixing proportion for non-target swaps.
 
 ```{r}
 ff <- brms::bf(y ~ 1,
-         kappa ~ 1,
-         thetat ~ 1,
-         thetant ~ 1)
+               kappa ~ 1,
+               thetat ~ 1,
+               thetant ~ 1)
 ```
 
 Then specify the model and give it information about the required arguments. In
@@ -134,4 +132,3 @@ fit <- fit_model(formula = ff,
                  iter=500,
                  backend='cmdstanr')
 ```
-
diff --git a/README.md b/README.md
index 4005f931..27df3897 100644
--- a/README.md
+++ b/README.md
@@ -78,15 +78,20 @@ let’s generate a dataset with known parameters. We can use the function
 ``` r
 library(bmm)
 library(tidyverse)
-dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+dat <- data.frame(
+  y = rmixture3p(n = 2000, mu = c(0,1,-1.5,2)),
+  nt1_loc = 1,
+  nt2_loc = -1.5,
+  nt3_loc = 2
+)
 head(dat)
-#>             y    nt1_loc     nt2_loc    nt3_loc
-#> 1  0.51775824  1.0020511  0.76339413 -1.2897954
-#> 2  0.03960327 -0.6206519  1.09208784 -0.8679937
-#> 3 -0.48213423  1.1447270  2.81885045 -1.4363374
-#> 4 -0.34510101  2.7482095 -0.06612305  2.0340003
-#> 5  0.09348744 -1.3952246  2.17738363  0.7796131
-#> 6  0.39001975 -0.8049496  2.07371921 -1.1887811
+#>             y nt1_loc nt2_loc nt3_loc
+#> 1 -0.21935776       1    -1.5       2
+#> 2 -0.09542148       1    -1.5       2
+#> 3 -0.46152746       1    -1.5       2
+#> 4  2.20892562       1    -1.5       2
+#> 5 -0.52996268       1    -1.5       2
+#> 6 -0.47483031       1    -1.5       2
 ```
 
 We have a dataset of 2000 observations of response error, of which 60%
@@ -108,22 +113,15 @@ hist(dat$y, breaks = 60, xlab = "Response error relative to target")
 <img src="man/figures/README-unnamed-chunk-4-1.png" width="100%" />
 Another key property of the data is that some error responses are not
 random, but that they are due to confusion of the target with one of the
-lures. We can visualize this by centering the response error relative to
-each of the possible non-target locations. We do this with the helper
-function `calc_error_relative_to_nontargets()`:
-
-``` r
-dat %>% 
-  calc_error_relative_to_nontargets('y', paste0('nt',1:3,'_loc')) %>% 
-  ggplot(aes(y_nt)) +
-  geom_histogram()
-#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
-```
-
-<img src="man/figures/README-unnamed-chunk-5-1.png" width="100%" />
+lures. This is already visible by the additional peaks in the histogram.
+Typically these peaks are not immediately visible as the non-target
+locations vary from trial to trial.
 
 Ok, so now let’s fit the three-parameter model. We only need to do two
-things: - Specify the model formula - Call fit_model()
+things:
+
+- Specify the model formula
+- Call fit_model()
 
 In this example the parameters don’t vary over conditions, so we have no
 predictors. `y` is the name of the response error variable, whereas
@@ -133,9 +131,9 @@ for non-target swaps.
 
 ``` r
 ff <- brms::bf(y ~ 1,
-         kappa ~ 1,
-         thetat ~ 1,
-         thetant ~ 1)
+               kappa ~ 1,
+               thetat ~ 1,
+               thetant ~ 1)
 ```
 
 Then specify the model and give it information about the required
diff --git a/man/figures/README-unnamed-chunk-4-1.png b/man/figures/README-unnamed-chunk-4-1.png
index 688ac204..72dd9d83 100644
Binary files a/man/figures/README-unnamed-chunk-4-1.png and b/man/figures/README-unnamed-chunk-4-1.png differ
diff --git a/man/fit_model.Rd b/man/fit_model.Rd
index 2fb8adba..56b0a9c7 100644
--- a/man/fit_model.Rd
+++ b/man/fit_model.Rd
@@ -73,18 +73,17 @@ Type \code{help(package=bmm)} for a full list of available help topics.
 \examples{
 \dontrun{
 # generate artificial data from the Bays et al (2009) 3-parameter mixture model
-dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+dat <- rsdm(n=2000)
 
 # define formula
 ff <- brms::bf(y ~ 1,
-              kappa ~ 1,
-              thetat ~ 1,
-              thetant ~ 1)
+               c ~ 1,
+               kappa ~ 1)
 
 # fit the model
 fit <- fit_model(formula = ff,
                  data = dat,
-                 model = mixture3p(non_targets = paste0('nt',1:3,'_loc'), setsize=4),
+                 model = sdmSimple(),
                  parallel=T,
                  iter=500,
                  backend='cmdstanr')
diff --git a/man/gen_3p_data.Rd b/man/gen_3p_data.Rd
deleted file mode 100644
index abd45b41..00000000
--- a/man/gen_3p_data.Rd
+++ /dev/null
@@ -1,60 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/random_data_generation.R
-\name{gen_3p_data}
-\alias{gen_3p_data}
-\title{Generate artificial data from the Bays et al (2009) 3-parameter
-mixture model}
-\usage{
-gen_3p_data(
-  N = 2000,
-  pmem = 0.6,
-  pnt = 0.3,
-  kappa = 10,
-  setsize = 2,
-  relative_resp = T
-)
-}
-\arguments{
-\item{N}{Numeric. Number of samples}
-
-\item{pmem}{Numeric. Probability of response coming from memory}
-
-\item{pnt}{Numeric. Probability of response coming from a non-target item}
-
-\item{kappa}{Numeric. Precision of the von mises distribution}
-
-\item{setsize}{Numeric. Number of presented items}
-
-\item{relative_resp}{Logical. if TRUE, returns response error relative to the
-target and all non-target item locations are coded relative to the target.
-If FALSE, returns actual response and the location of all items is absolute}
-}
-\value{
-A data.frame object. y is the response (if relative_resp==F) or
-response error (if relative_resp==T), t_loc is the value of the target
-(only if relative_resp==F), nt1_loc to nti_loc, are values of the
-non-targets, where i=setsize-1.
-}
-\description{
-Given a set of parameters, and desired setsize, it generates
-responses in a continuous reproduction task, with corresponding proportions
-coming from memory, non-target location errors, or guessing.
-}
-\examples{
-# example code
-nsub = 30
-df_3p_parms <- data.frame(
-  theta_pmem = rnorm(nsub, mean = 2, sd = 1),
-  theta_pnt = rnorm(nsub, mean = 1, sd = 0.5),
-  kappa = pmax(0,rnorm(nsub, mean = 5, sd = 1)),
-  pmem = numeric(nsub),
-  pnt = numeric(nsub),
-  pguess = numeric(nsub)
-)
-
-# transform continous mixture weights into probabilities using the softmax
-df_3p_parms[,c("pmem","pnt","pguess")] <- apply(df_3p_parms[,c("theta_pmem","theta_pnt")],
-                                                1,softmax)
-
-}
-\keyword{simulation}
diff --git a/man/gen_imm_data.Rd b/man/gen_imm_data.Rd
deleted file mode 100644
index c9ffe007..00000000
--- a/man/gen_imm_data.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/random_data_generation.R
-\name{gen_imm_data}
-\alias{gen_imm_data}
-\title{Generate artificial data from the Interference measurement model}
-\usage{
-gen_imm_data(parms, ntrials = 200, setsize = 6)
-}
-\arguments{
-\item{parms}{Matrix/DataFrame. Matrix or Data frame of IMM parameters,
-must contain at least one row of values for:
-c = context activation,
-a = general activation,
-s = spatial similarity gradient,
-n = background noise,
-kappa = precision of memory representations}
-
-\item{ntrials}{Numeric. number of responses to simulate for each subject}
-
-\item{setsize}{Numeric. Number of items in memory set}
-}
-\value{
-A data.frame object. resp is the response, respErr is the relative
-response to the target, Item1 to ItemN is the absolute location of the
-N items from -pi to pi for the number of items N specified by the setsize
-variable, D1 to DN is the spatial distance of the N items for the N items
-specified by the setsize variable
-}
-\description{
-Given a set of parameters, and desired setsize, this function generates
-data following the assumptions of the interference measurement models
-}
-\keyword{simulation}
diff --git a/man/get_model_prior.Rd b/man/get_model_prior.Rd
index db90c056..de52152b 100644
--- a/man/get_model_prior.Rd
+++ b/man/get_model_prior.Rd
@@ -54,22 +54,18 @@ Type \code{help(package=bmm)} for a full list of available help topics.
 }
 \examples{
 \dontrun{
-# generate artificial data from the Bays et al (2009) 3-parameter mixture model
-dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+# generate artificial data from the Signal Discrimination Model
+dat <- rsdm(n=2000)
 
 # define formula
 ff <- brms::bf(y ~ 1,
-               kappa ~ 1,
-               thetat ~ 1,
-               thetant ~ 1)
-
-# simulate data
-dat <- gen_3p_data(N = 200)
+               c ~ 1,
+               kappa ~ 1)
 
 # fit the model
 get_model_prior(formula = ff,
                 data = dat,
-                model = mixture3p(non_targets = paste0('nt',1,'_loc'), setsize = 2)
+                model = sdmSimple()
 )
 }
 
diff --git a/man/mixture2p.Rd b/man/mixture2p.Rd
index 0c3edf53..0ac5b177 100644
--- a/man/mixture2p.Rd
+++ b/man/mixture2p.Rd
@@ -38,7 +38,7 @@ Two-parameter mixture model by Zhang and Luck (2008).
 \examples{
 \dontrun{
 # generate artificial data
-dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+dat <- data.frame(y = rmixture2p(n=2000))
 
 # define formula
 ff <- brms::bf(y ~ 1,
diff --git a/man/mixture3p.Rd b/man/mixture3p.Rd
index 696ac5c1..5155c5d2 100644
--- a/man/mixture3p.Rd
+++ b/man/mixture3p.Rd
@@ -48,7 +48,12 @@ Three-parameter mixture model by Bays et al (2009).
 \examples{
 \dontrun{
 # generate artificial data from the Bays et al (2009) 3-parameter mixture model
-dat <- gen_3p_data(N=2000, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
+dat <- data.frame(
+  y = rmixture3p(n=2000, mu = c(0,1,-1.5,2)),
+  nt1_loc = 1,
+  nt2_loc = -1.5,
+  nt3_loc = 2
+)
 
 # define formula
 ff <- brms::bf(y ~ 1,
@@ -57,14 +62,14 @@ ff <- brms::bf(y ~ 1,
               thetant ~ 1)
 
 # specify the 3-parameter model
-model <- mixture3p(non_targets = paste0('nt',1:3,'_loc'), setsize=4)
+model <- mixture3p(non_targets = paste0('nt',1:3,'_loc'), setsize = 4)
 
 # fit the model
 fit <- fit_model(formula = ff,
                  data = dat,
                  model = model,
                  parallel=T,
-                 iter=500,
+                 iter = 500,
                  backend='cmdstanr')
 }
 }
diff --git a/tests/testthat/test-fit_model.R b/tests/testthat/test-fit_model.R
index 5fe6767a..f80a26f3 100644
--- a/tests/testthat/test-fit_model.R
+++ b/tests/testthat/test-fit_model.R
@@ -1,7 +1,12 @@
 test_that('Available mock models run without errors',{
   skip_on_cran()
-  dat <- gen_imm_data(parms = data.frame(c=2,a=0.5,n=0,s=2,kappa=5),
-                          ntrial = 2, setsize = 5)
+  dat <- data.frame(
+    respErr = rIMM(n = 5),
+    Item2_rel = 2,
+    Item3_rel = -1.5,
+    spaD2 = 0.5,
+    spaD3 = 2
+  )
 
   # two-parameter model mock fit
   f <- brms::bf(respErr ~ 1, kappa ~ 1, thetat ~ 1)
@@ -10,10 +15,10 @@ test_that('Available mock models run without errors',{
   expect_type(mock_fit$fit_args, "list")
   expect_equal(names(mock_fit$fit_args[1:4]), c("formula", "data", "family", "prior"))
 
-
   # three-parameter model mock fit
   f <- brms::bf(respErr ~ 1, kappa ~ 1, thetat ~ 1, thetant ~ 1)
-  mock_fit <- fit_model(f, dat, mixture3p(setsize=5, non_targets = paste0('Item',2:5,'_rel')),
+  mock_fit <- fit_model(f, dat, mixture3p(setsize = 3,
+                                          non_targets = paste0('Item',2:3,'_rel')),
                         backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
@@ -21,7 +26,8 @@ test_that('Available mock models run without errors',{
 
   # IMMabc model mock fit
   f <- brms::bf(respErr ~ 1, kappa ~ 1, c ~ 1, a ~ 1)
-  mock_fit <- fit_model(f, dat, IMMabc(setsize=5, non_targets = paste0('Item',2:5,'_rel')),
+  mock_fit <- fit_model(f, dat, IMMabc(setsize =3,
+                                       non_targets = paste0('Item',2:3,'_rel')),
                         backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
@@ -29,7 +35,7 @@ test_that('Available mock models run without errors',{
 
   # IMMbsc model mock fit
   f <- brms::bf(respErr ~ 1, kappa ~ 1, c ~ 1, s ~ 1)
-  mock_fit <- fit_model(f, dat, IMMbsc(setsize=5, non_targets = paste0('Item',2:5,'_rel'), spaPos=paste0('spaD',2:5)),
+  mock_fit <- fit_model(f, dat, IMMbsc(setsize=3, non_targets = paste0('Item',2:3,'_rel'), spaPos=paste0('spaD',2:3)),
                         backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
@@ -37,7 +43,7 @@ test_that('Available mock models run without errors',{
 
   # IMMbsc model mock fit
   f <- brms::bf(respErr ~ 1, kappa ~ 1, c ~ 1, a ~ 1, s ~ 1)
-  mock_fit <- fit_model(f, dat, IMMfull(setsize=5, non_targets = paste0('Item',2:5,'_rel'), spaPos=paste0('spaD',2:5)), backend="mock", mock_fit=1, rename=FALSE)
+  mock_fit <- fit_model(f, dat, IMMfull(setsize=3, non_targets = paste0('Item',2:3,'_rel'), spaPos=paste0('spaD',2:3)), backend="mock", mock_fit=1, rename=FALSE)
   expect_equal(mock_fit$fit, 1)
   expect_type(mock_fit$fit_args, "list")
   expect_equal(names(mock_fit$fit_args[1:4]), c("formula", "data", "family", "prior"))
@@ -45,8 +51,13 @@ test_that('Available mock models run without errors',{
 
 test_that('Available models produce expected errors', {
   skip_on_cran()
-  dat <- gen_imm_data(parms = data.frame(c=2,a=0.5,n=0,s=2,kappa=5),
-                      ntrial = 100, setsize = 5)
+  dat <- data.frame(
+    respErr = rIMM(n = 5),
+    Item2_rel = 2,
+    Item3_rel = -1.5,
+    spaD2 = 0.5,
+    spaD3 = 2
+  )
 
   # Missing data
   okmodels <- supported_models(print_call=FALSE)
@@ -73,4 +84,3 @@ test_that('Available models produce expected errors', {
   }
 })
 
-
diff --git a/tests/testthat/test-get_model_prior.R b/tests/testthat/test-get_model_prior.R
index 9b6e446e..45d35a1c 100644
--- a/tests/testthat/test-get_model_prior.R
+++ b/tests/testthat/test-get_model_prior.R
@@ -1,7 +1,4 @@
 test_that("get_model_prior() returns a brmsprior object", {
-  # generate artificial data from the Bays et al (2009) 3-parameter mixture model
-  dat <- gen_3p_data(N=2, pmem=0.6, pnt=0.3, kappa=10, setsize=4, relative_resp=T)
-
   # define formula
   ff <- brms::bf(y ~ 1,
                  kappa ~ 1,
@@ -9,7 +6,9 @@ test_that("get_model_prior() returns a brmsprior object", {
                  thetant ~ 1)
 
   # simulate data
-  dat <- gen_3p_data(N = 200)
+  dat <- data.frame(y = rmixture3p(n = 200),
+                    nt1_loc = 2,
+                    nt2_loc = -1.5)
 
   # fit the model
   prior <- get_model_prior(formula = ff,