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diff --git a/inst/dataset_generation/ds_mixed.R b/inst/dataset_generation/ds_mixed.R
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+# Synthetic data for hierarchical kinetic models
+# Refactored version of the code previously in tests/testthat/setup_script.R
+# The number of datasets was 3 for FOMC, and 10 for HS in that script, now it
+# is always 15 for consistency
+
+library(mkin)  # We use mkinmod and mkinpredict
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+n <- 15
+log_sd <- 0.3
+err_1 = list(const = 1, prop = 0.05)
+tc <- function(value) sigma_twocomp(value, err_1$const, err_1$prop)
+const <- function(value) 2
+
+set.seed(123456)
+SFO <- mkinmod(parent = mkinsub("SFO"))
+sfo_pop <- list(parent_0 = 100, k_parent = 0.03)
+sfo_parms <- as.matrix(data.frame(
+    k_parent = rlnorm(n, log(sfo_pop$k_parent), log_sd)))
+set.seed(123456)
+ds_sfo <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(SFO, sfo_parms[i, ],
+    c(parent = sfo_pop$parent_0), sampling_times)
+  add_err(ds_mean, tc, n = 1)[[1]]
+})
+attr(ds_sfo, "pop") <- sfo_pop
+attr(ds_sfo, "parms") <- sfo_parms
+
+set.seed(123456)
+FOMC <- mkinmod(parent = mkinsub("FOMC"))
+fomc_pop <- list(parent_0 = 100, alpha = 2, beta = 8)
+fomc_parms <- as.matrix(data.frame(
+    alpha = rlnorm(n, log(fomc_pop$alpha), 0.4),
+    beta = rlnorm(n, log(fomc_pop$beta), 0.2)))
+set.seed(123456)
+ds_fomc <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(FOMC, fomc_parms[i, ],
+    c(parent = fomc_pop$parent_0), sampling_times)
+  add_err(ds_mean, tc, n = 1)[[1]]
+})
+attr(ds_fomc, "pop") <- fomc_pop
+attr(ds_fomc, "parms") <- fomc_parms
+
+set.seed(123456)
+DFOP <- mkinmod(parent = mkinsub("DFOP"))
+dfop_pop <- list(parent_0 = 100, k1 = 0.06, k2 = 0.015, g = 0.4)
+dfop_parms <- as.matrix(data.frame(
+  k1 = rlnorm(n, log(dfop_pop$k1), log_sd),
+  k2 = rlnorm(n, log(dfop_pop$k2), log_sd),
+  g = plogis(rnorm(n, qlogis(dfop_pop$g), log_sd))))
+set.seed(123456)
+ds_dfop <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(DFOP, dfop_parms[i, ],
+    c(parent = dfop_pop$parent_0), sampling_times)
+  add_err(ds_mean, tc, n = 1)[[1]]
+})
+attr(ds_dfop, "pop") <- dfop_pop
+attr(ds_dfop, "parms") <- dfop_parms
+
+set.seed(123456)
+HS <- mkinmod(parent = mkinsub("HS"))
+hs_pop <- list(parent_0 = 100, k1 = 0.08, k2 = 0.01, tb = 15)
+hs_parms <- as.matrix(data.frame(
+  k1 = rlnorm(n, log(hs_pop$k1), log_sd),
+  k2 = rlnorm(n, log(hs_pop$k2), log_sd),
+  tb = rlnorm(n, log(hs_pop$tb), 0.1)))
+set.seed(123456)
+ds_hs <- lapply(1:n, function(i) {
+  ds_mean <- mkinpredict(HS, hs_parms[i, ],
+    c(parent = hs_pop$parent_0), sampling_times)
+  add_err(ds_mean, const, n = 1)[[1]]
+})
+attr(ds_hs, "pop") <- hs_pop
+attr(ds_hs, "parms") <- hs_parms
+
+set.seed(123456)
+DFOP_SFO <- mkinmod(
+  parent = mkinsub("DFOP", "m1"),
+  m1 = mkinsub("SFO"),
+  quiet = TRUE)
+dfop_sfo_pop <- list(parent_0 = 100,
+  k_m1 = 0.007, f_parent_to_m1 = 0.5,
+  k1 = 0.1, k2 = 0.02, g = 0.5)
+dfop_sfo_parms <- as.matrix(data.frame(
+  k1 = rlnorm(n, log(dfop_sfo_pop$k1), log_sd),
+  k2 = rlnorm(n, log(dfop_sfo_pop$k2), log_sd),
+  g = plogis(rnorm(n, qlogis(dfop_sfo_pop$g), log_sd)),
+  f_parent_to_m1 = plogis(rnorm(n,
+      qlogis(dfop_sfo_pop$f_parent_to_m1), log_sd)),
+  k_m1 = rlnorm(n, log(dfop_sfo_pop$k_m1), log_sd)))
+ds_dfop_sfo_mean <- lapply(1:n,
+  function(i) {
+    mkinpredict(DFOP_SFO, dfop_sfo_parms[i, ],
+      c(parent = dfop_sfo_pop$parent_0, m1 = 0), sampling_times)
+  }
+)
+set.seed(123456)
+ds_dfop_sfo <- lapply(ds_dfop_sfo_mean, function(ds) {
+  add_err(ds,
+    sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2),
+    n = 1, secondary = "m1")[[1]]
+})
+attr(ds_dfop_sfo, "pop") <- dfop_sfo_pop
+attr(ds_dfop_sfo, "parms") <- dfop_sfo_parms
+
+#save(ds_sfo, ds_fomc, ds_dfop, ds_hs, ds_dfop_sfo, file = "data/ds_mixed.rda", version = 2)