diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 262a3bb5..9859588f 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -47,7 +47,7 @@ jobs:
         
       - name: Create and populate check.Renviron file
         run: |
-          echo R_CHECK_DONTTEST_EXAMPLES=false >> ~/check.Renviron
+          echo R_CHECK_DONTTEST_EXAMPLES=true >> ~/check.Renviron
           echo R_LIBS=/usr/lib/R/site-library >> ~/check.Renviron
         shell: bash
 
@@ -61,8 +61,8 @@ jobs:
           cmdstanr::check_cmdstan_toolchain(fix = TRUE)
           cmdstanr::install_cmdstan(cores = parallel::detectCores())
         shell: Rscript {0}
-        
+
       - uses: r-lib/actions/check-r-package@v2
         with:
-          upload-snapshots: true
-          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'
+          build_args: 'c("--no-manual", "--no-build-vignettes")'
+          args: 'c("--no-manual", "--ignore-vignettes")'
\ No newline at end of file
diff --git a/tests/testthat/test-brmsInterceptModels.R b/tests/testthat/test-brmsInterceptModels.R
index aa55a8df..6fa84e32 100644
--- a/tests/testthat/test-brmsInterceptModels.R
+++ b/tests/testthat/test-brmsInterceptModels.R
@@ -28,6 +28,7 @@ test_that("Logistic Decay brms Model with Intercept", {
   skip_if_not_installed("brms")
   skip_if_not_installed("cmdstanr")
   skip_on_cran()
+  skip_on_ci()
   options(cmdstanr_warn_inits = FALSE)
   set.seed(123)
   logistic_df <- growthSim(
@@ -52,6 +53,7 @@ test_that("Gompertz brms model pipeline", {
   skip_if_not_installed("brms")
   skip_if_not_installed("cmdstanr")
   skip_on_cran()
+  skip_on_ci()
   options(cmdstanr_warn_inits = FALSE)
   set.seed(123)
   simdf <- growthSim(
@@ -79,6 +81,7 @@ test_that("intercept in submodel works", {
   skip_if_not_installed("brms")
   skip_if_not_installed("cmdstanr")
   skip_on_cran()
+  skip_on_ci()
   options(cmdstanr_warn_inits = FALSE)
   set.seed(123)
   simdf <- growthSim("gompertz",
@@ -105,6 +108,7 @@ test_that("intercepts work in a changepoint model", {
   skip_if_not_installed("brms")
   skip_if_not_installed("cmdstanr")
   skip_on_cran()
+  skip_on_ci()
   options(cmdstanr_warn_inits = FALSE)
   set.seed(123)
   simdf <- growthSim("linear + linear",
diff --git a/tests/testthat/test-brmsModels.R b/tests/testthat/test-brmsModels.R
index cf529bc6..32f1f581 100644
--- a/tests/testthat/test-brmsModels.R
+++ b/tests/testthat/test-brmsModels.R
@@ -203,7 +203,8 @@ test_that("Hierarchical Model Works", {
     df = simdf, type = "brms",
     hierarchy = list("A" = "int_linear")
   )
-  fit <- fitGrowth(ss, iter = 600, cores = 1, chains = 1, backend = "cmdstanr")
+  fit <- fitGrowth(ss, iter = 200, cores = 1, chains = 1, backend = "cmdstanr",
+                   refresh = 0, silent = 2)
   expect_s3_class(fit, "brmsfit")
   p <- growthPlot(fit, ss$pcvrForm, df = ss$df)
   expect_s3_class(p, "ggplot")
@@ -214,6 +215,7 @@ test_that("Hierarchical Model Works", {
 test_that("Changepoint model can be specified", {
   skip_if_not_installed("brms")
   skip_if_not_installed("cmdstanr")
+  skip_on_cran()
   set.seed(123)
   noise <- do.call(rbind, lapply(1:30, function(i) {
     chngpt <- c(20, 21)
@@ -281,7 +283,8 @@ test_that("weibull survival", {
   prior <- c(0, 5)
   ss <- growthSS(model = model, form = form, df = df, start = prior)
   expect_equal(ss$prior$coef, c("groupa", "groupb"))
-  fit <- fitGrowth(ss, iter = 600, cores = 1, chains = 1, backend = "cmdstanr")
+  fit <- fitGrowth(ss, iter = 200, cores = 1, chains = 1, backend = "cmdstanr",
+                   refresh = 0, silent = 2)
   expect_s3_class(fit, "brmsfit")
   plot <- growthPlot(fit, form = ss$pcvrForm, df = ss$df)
   expect_s3_class(plot, "ggplot")
@@ -304,7 +307,8 @@ test_that("binomial survival", {
   )
   prior <- c(0, 5)
   ss <- growthSS(model = model, form = form, df = df, start = prior)
-  fit <- fitGrowth(ss, iter = 600, cores = 1, chains = 1, backend = "cmdstanr")
+  fit <- fitGrowth(ss, iter = 200, cores = 1, chains = 1, backend = "cmdstanr",
+                   refresh = 0, silent = 2)
   expect_s3_class(fit, "brmsfit")
   plot <- growthPlot(fit, form = ss$pcvrForm, df = ss$df)
   expect_s3_class(plot, "ggplot")
@@ -312,8 +316,8 @@ test_that("binomial survival", {
 
 test_that(".brmSurvSS options all work", {
   set.seed(123)
-  df <- growthSim("logistic",
-    n = 20, t = 25,
+  df <- growthSim(
+    "logistic", n = 20, t = 25,
     params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
   )
   surv <- .survModelParser("survival weibull")
@@ -358,7 +362,6 @@ test_that(".brmSurvSS options all work", {
     )
   )
   expect_equal(names(ss4), c("df", "family", "formula", "prior", "initfun", "pcvrForm"))
-
   surv <- .survModelParser("survival binomial")
   ss <- suppressMessages(
     .brmsSurvSS(
@@ -402,958 +405,1004 @@ test_that(".brmSurvSS options all work", {
 #* ***** `Not Run on the remote` *****
 #* ***********************************
 
-if (file.exists("/home/josh/Desktop/")) {
-  # only run locally, don't test for each R-CMD Check
-  test_that("Gompertz brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("gompertz",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(0.25, 0.25))
-    )
+test_that("Gompertz brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("gompertz",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(0.25, 0.25))
+  )
 
-    ss <- growthSS(
-      model = "gompertz", form = y ~ time | id / group, sigma = "int",
-      list("A" = 130, "B" = 10, "C" = 1),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A", "B", "C"))
+  ss <- growthSS(
+    model = "gompertz", form = y ~ time | id / group, sigma = "int",
+    list("A" = 130, "B" = 10, "C" = 1),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B", "C"))
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/gompertz_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("4 Parameter Logistic brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logistic4",
-      n = 20, t = 25,
-      params = list(
-        "A" = c(200, 160),
-        "B" = c(13, 11),
-        "C" = c(3, 2.5),
-        "D" = c(5, 20)
-      )
-    )
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/gompertz_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    ss <- growthSS(
-      model = "logistic4", form = y ~ time | id / group, sigma = "int",
-      list("A" = 130, "B" = 10, "C" = 1, "D" = 2),
-      df = simdf, type = "brms"
+test_that("4 Parameter Logistic brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logistic4",
+    n = 20, t = 25,
+    params = list(
+      "A" = c(200, 160),
+      "B" = c(13, 11),
+      "C" = c(3, 2.5),
+      "D" = c(5, 20)
     )
-    expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "D"))
+  )
+  ss <- growthSS(
+    model = "logistic4", form = y ~ time | id / group, sigma = "int",
+    list("A" = 130, "B" = 10, "C" = 1, "D" = 2),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "D"))
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/4PLogistic_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("5 Parameter Logistic brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logistic5",
-      n = 20, t = 25,
-      params = list(
-        "A" = c(200, 160),
-        "B" = c(13, 11),
-        "C" = c(2.75, 3.25),
-        "D" = c(5, 20),
-        "E" = c(1, 1.5)
-      )
-    )
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/4PLogistic_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    ss <- growthSS(
-      model = "logistic5", form = y ~ time | id / group, sigma = "int",
-      list("A" = 130, "B" = 10, "C" = 1, "D" = 2, "E" = 0.75),
-      df = simdf, type = "brms"
+test_that("5 Parameter Logistic brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logistic5",
+    n = 20, t = 25,
+    params = list(
+      "A" = c(200, 160),
+      "B" = c(13, 11),
+      "C" = c(2.75, 3.25),
+      "D" = c(5, 20),
+      "E" = c(1, 1.5)
     )
-    expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "D", "E"))
+  )
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  ss <- growthSS(
+    model = "logistic5", form = y ~ time | id / group, sigma = "int",
+    list("A" = 130, "B" = 10, "C" = 1, "D" = 2, "E" = 0.75),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "D", "E"))
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/5PLogistic_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Monomolecular brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("monomolecular",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(0.01, 0.08))
-    )
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    ss <- growthSS(
-      model = "monomolecular", form = y ~ time | id / group, sigma = "int",
-      list("A" = 130, "B" = 1),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A", "B"))
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/5PLogistic_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+test_that("Monomolecular brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("monomolecular",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(0.01, 0.08))
+  )
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/monomolecular_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Exponential brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("exponential",
-      n = 20, t = 25,
-      params = list("A" = c(15, 12), "B" = c(0.1, 0.085))
-    )
+  ss <- growthSS(
+    model = "monomolecular", form = y ~ time | id / group, sigma = "int",
+    list("A" = 130, "B" = 1),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B"))
 
-    ss <- growthSS(
-      model = "exponential", form = y ~ time | id / group, sigma = "int",
-      list("A" = 10, "B" = 1),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A", "B"))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/monomolecular_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/exponential_fitGrowth.png", plot,
-      width = 10,
-      height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Power law brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("power law",
-      n = 20, t = 25,
-      params = list("A" = c(15, 12), "B" = c(0.75, 0.8))
-    )
+test_that("Exponential brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("exponential",
+    n = 20, t = 25,
+    params = list("A" = c(15, 12), "B" = c(0.1, 0.085))
+  )
 
-    ss <- growthSS(
-      model = "power law", form = y ~ time | id / group, sigma = "linear",
-      list("A" = 10, "B" = 1),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A", "B"))
+  ss <- growthSS(
+    model = "exponential", form = y ~ time | id / group, sigma = "int",
+    list("A" = 10, "B" = 1),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B"))
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/powerlaw_fitGrowth.png", plot,
-      width = 10,
-      height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("linear brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("linear",
-      n = 20, t = 25,
-      params = list("A" = c(15, 12))
-    )
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/exponential_fitGrowth.png", plot,
+    width = 10,
+    height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    ss <- growthSS(
-      model = "linear", form = y ~ time | id / group, sigma = "int",
-      list("A" = 5),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A"))
+test_that("Power law brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("power law",
+    n = 20, t = 25,
+    params = list("A" = c(15, 12), "B" = c(0.75, 0.8))
+  )
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  ss <- growthSS(
+    model = "power law", form = y ~ time | id / group, sigma = "linear",
+    list("A" = 10, "B" = 1),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B"))
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/linear_fitGrowth.png", plot,
-      width = 10,
-      height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("logarithmic brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logarithmic",
-      n = 20, t = 25,
-      params = list("A" = c(15, 12))
-    )
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    ss <- growthSS(
-      model = "logarithmic", form = y ~ time | id / group, sigma = "int",
-      list("A" = 5),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A"))
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/powerlaw_fitGrowth.png", plot,
+    width = 10,
+    height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+test_that("linear brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("linear",
+    n = 20, t = 25,
+    params = list("A" = c(15, 12))
+  )
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/logarithmic_fitGrowth.png", plot,
-      width = 10,
-      height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("linear sub model with prior brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("linear",
-      n = 20, t = 25,
-      params = list("A" = c(15, 12))
-    )
+  ss <- growthSS(
+    model = "linear", form = y ~ time | id / group, sigma = "int",
+    list("A" = 5),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A"))
 
-    model <- "linear"
-    form <- y ~ time | id / group
-    sigma <- "linear"
-    priors <- list("A" = 5, "sigmaA" = 2)
-    df <- simdf
-    type <- "brms"
-
-    ss <- growthSS(
-      model = "linear", form = y ~ time | id / group, sigma = "linear",
-      list("A" = 5, "sigmaA" = 2),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A", "sigmaA"))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/linear_fitGrowth.png", plot,
+    width = 10,
+    height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/linear_fitGrowth.png", plot,
-      width = 10,
-      height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("GAM brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logistic",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
-    )
+test_that("logarithmic brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logarithmic",
+    n = 20, t = 25,
+    params = list("A" = c(15, 12))
+  )
 
-    ss <- growthSS(
-      model = "gam", form = y ~ time | id / group, sigma = "int",
-      df = simdf, type = "brms"
-    )
+  ss <- growthSS(
+    model = "logarithmic", form = y ~ time | id / group, sigma = "int",
+    list("A" = 5),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A"))
 
-    fit <- suppressWarnings(fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    ))
-    expect_s3_class(fit, "brmsfit")
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/gam_fitGrowth.png", plot,
-      width = 10,
-      height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/logarithmic_fitGrowth.png", plot,
+    width = 10,
+    height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
+test_that("linear sub model with prior brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("linear",
+    n = 20, t = 25,
+    params = list("A" = c(15, 12))
+  )
 
+  model <- "linear"
+  form <- y ~ time | id / group
+  sigma <- "linear"
+  priors <- list("A" = 5, "sigmaA" = 2)
+  df <- simdf
+  type <- "brms"
 
-  test_that("linear+linear brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("linear + linear",
-      n = 20, t = 25,
-      params = list("linear1A" = c(15), "changePoint1" = c(8), "linear2A" = c(3))
-    )
+  ss <- growthSS(
+    model = "linear", form = y ~ time | id / group, sigma = "linear",
+    list("A" = 5, "sigmaA" = 2),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "sigmaA"))
 
-    ss <- growthSS(
-      model = "linear + linear", form = y ~ time, sigma = "spline",
-      list("linear1A" = 10, "changePoint1" = 5, "linear2A" = 2),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "", "linear1A", "changePoint1", "linear2A"))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/linear_fitGrowth.png", plot,
+    width = 10,
+    height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusLinear_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("linear+logistic brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("linear + logistic",
-      n = 20, t = 25,
-      params = list(
-        "linear1A" = c(15, 12), "changePoint1" = c(8, 6),
-        "logistic2A" = c(100, 150), "logistic2B" = c(10, 8), "logistic2C" = c(3, 2.5)
-      )
-    )
+test_that("GAM brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logistic",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
 
-    ss <- growthSS(
-      model = "linear + logistic", form = y ~ time | id / group, sigma = "spline",
-      list(
-        "linear1A" = 10, "changePoint1" = 5,
-        "logistic2A" = 100, "logistic2B" = 10, "logistic2C" = 3
-      ),
-      df = simdf, type = "brms"
-    )
+  ss <- growthSS(
+    model = "gam", form = y ~ time | id / group, sigma = "int",
+    df = simdf, type = "brms"
+  )
 
-    expect_equal(ss$prior$nlpar, c(
-      "", "", "linear1A", "changePoint1",
-      "logistic2A", "logistic2B", "logistic2C"
-    ))
+  fit <- suppressWarnings(fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  ))
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/gam_fitGrowth.png", plot,
+    width = 10,
+    height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusLogistic_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
 
 
-  test_that("linear+gam brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("linear + logistic",
-      n = 20, t = 25, # using logistic data, but modeling as a gam
-      params = list(
-        "linear1A" = c(15, 12), "changePoint1" = c(8, 6),
-        "logistic2A" = c(100, 150), "logistic2B" = c(10, 8), "logistic2C" = c(3, 2.5)
-      )
-    )
+test_that("linear+linear brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("linear + linear",
+    n = 20, t = 25,
+    params = list("linear1A" = c(15), "changePoint1" = c(8), "linear2A" = c(3))
+  )
 
-    ss <- growthSS(
-      model = "linear + gam", form = y ~ time | id / group, sigma = "homo",
-      list("linear1A" = 10, "changePoint1" = 5),
-      df = simdf, type = "brms"
-    )
+  ss <- growthSS(
+    model = "linear + linear", form = y ~ time, sigma = "spline",
+    list("linear1A" = 10, "changePoint1" = 5, "linear2A" = 2),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "", "linear1A", "changePoint1", "linear2A"))
 
-    expect_equal(ss$prior$nlpar, c("", "linear1A", "changePoint1"))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusLinear_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusGAM_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
+test_that("linear+logistic brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("linear + logistic",
+    n = 20, t = 25,
+    params = list(
+      "linear1A" = c(15, 12), "changePoint1" = c(8, 6),
+      "logistic2A" = c(100, 150), "logistic2B" = c(10, 8), "logistic2C" = c(3, 2.5)
     )
-    expect_s3_class(plot, "ggplot")
-  })
+  )
 
+  ss <- growthSS(
+    model = "linear + logistic", form = y ~ time | id / group, sigma = "spline",
+    list(
+      "linear1A" = 10, "changePoint1" = 5,
+      "logistic2A" = 100, "logistic2B" = 10, "logistic2C" = 3
+    ),
+    df = simdf, type = "brms"
+  )
 
-  test_that("linear + linear + linear brms model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("linear + linear + linear",
-      n = 25, t = 50,
-      params = list(
-        "linear1A" = c(10, 12), "changePoint1" = c(8, 6), "linear2A" = c(1, 2),
-        "changePoint2" = c(25, 30), "linear3A" = c(20, 24)
-      )
-    )
+  expect_equal(ss$prior$nlpar, c(
+    "", "", "linear1A", "changePoint1",
+    "logistic2A", "logistic2B", "logistic2C"
+  ))
 
-    ss <- growthSS(
-      model = "linear + linear + linear", form = y ~ time | id / group, sigma = "spline",
-      list("linear1A" = 10, "changePoint1" = 5, "linear2A" = 2, "changePoint2" = 15, "linear3A" = 5),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c(
-      "", "", "linear1A", "changePoint1",
-      "linear2A", "changePoint2", "linear3A"
-    ))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusLogistic_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusLinearPlusLinear_fitGrowth.png",
-      plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Logistic brms logistic sub model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logistic",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
-    )
 
-    ss <- growthSS(
-      model = "logistic", form = y ~ time | id / group, sigma = "logistic",
-      list("A" = 130, "B" = 10, "C" = 3, "sigmaA" = 20, "sigmaB" = 10, "sigmaC" = 2),
-      df = simdf, type = "brms"
+test_that("linear+gam brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("linear + logistic",
+    n = 20, t = 25, # using logistic data, but modeling as a gam
+    params = list(
+      "linear1A" = c(15, 12), "changePoint1" = c(8, 6),
+      "logistic2A" = c(100, 150), "logistic2B" = c(10, 8), "logistic2C" = c(3, 2.5)
     )
-    expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "sigmaA", "sigmaB", "sigmaC"))
+  )
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    ) # that's fast
-    expect_s3_class(fit, "brmsfit")
+  ss <- growthSS(
+    model = "linear + gam", form = y ~ time | id / group, sigma = "homo",
+    list("linear1A" = 10, "changePoint1" = 5),
+    df = simdf, type = "brms"
+  )
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_logisticSubModel.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Logistic brms gompertz sub model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logistic",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
-    )
+  expect_equal(ss$prior$nlpar, c("", "linear1A", "changePoint1"))
 
-    ss <- growthSS(
-      model = "logistic", form = y ~ time | id / group, sigma = "gompertz",
-      list("A" = 130, "B" = 10, "C" = 3, "sigmaA" = 20, "sigmaB" = 10, "sigmaC" = 2),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "sigmaA", "sigmaB", "sigmaC"))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusGAM_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_gompSubModel.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Logistic brms monomolecular sub model pipeline", {
-    set.seed(123)
-    simdf <- growthSim("logistic",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
-    )
 
-    ss <- growthSS(
-      model = "logistic", form = y ~ time | id / group, sigma = "monomolecular",
-      list("A" = 130, "B" = 10, "C" = 3, "sigmaA" = 5, "sigmaB" = 0.5),
-      df = simdf, type = "brms"
+test_that("linear + linear + linear brms model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("linear + linear + linear",
+    n = 25, t = 50,
+    params = list(
+      "linear1A" = c(10, 12), "changePoint1" = c(8, 6), "linear2A" = c(1, 2),
+      "changePoint2" = c(25, 30), "linear3A" = c(20, 24)
     )
-    expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "sigmaA", "sigmaB"))
+  )
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  ss <- growthSS(
+    model = "linear + linear + linear", form = y ~ time | id / group, sigma = "spline",
+    list("linear1A" = 10, "changePoint1" = 5, "linear2A" = 2, "changePoint2" = 15, "linear3A" = 5),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c(
+    "", "", "linear1A", "changePoint1",
+    "linear2A", "changePoint2", "linear3A"
+  ))
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_monoSubModel.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-
-  test_that("int+int homoskedastic model pipeline", {
-    set.seed(123)
-
-    noise <- do.call(rbind, lapply(1:30, function(i) {
-      chngpt <- rnorm(2, 18, 2)
-      noise_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[1], group = "a",
-          y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[2], group = "b",
-          y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
-        )
-      )
-      return(noise_i)
-    }))
-    noise2 <- do.call(rbind, lapply(1:30, function(i) {
-      start1 <- max(noise[noise$id == paste0("id_", i) & noise$group == "a", "time"])
-      start2 <- max(noise[noise$id == paste0("id_", i) & noise$group == "b", "time"])
-
-      noise2_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = start1:40, group = "a",
-          y = c(runif(length(start1:40), 15, 50))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = start2:40, group = "b",
-          y = c(runif(length(start2:40), 15, 50))
-        )
-      )
-      return(noise2_i)
-    }))
-    simdf <- rbind(noise, noise2)
-
-    ss <- growthSS(
-      model = "int + int", form = y ~ time | id / group, sigma = "int",
-      list("int1" = 10, "changePoint1" = 10, "int2" = 20),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "int1", "changePoint1", "int2"))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/linearPlusLinearPlusLinear_fitGrowth.png",
+    plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusInt_fitGrowth.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("int+int fixed changepoint homoskedastic model pipeline", {
-    set.seed(123)
-
-    noise <- do.call(rbind, lapply(1:30, function(i) {
-      chngpt <- c(20, 21)
-      noise_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[1], group = "a",
-          y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[2], group = "b",
-          y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
-        )
-      )
-      return(noise_i)
-    }))
-    noise2 <- do.call(rbind, lapply(1:30, function(i) {
-      start1 <- max(noise[noise$id == paste0("id_", i) & noise$group == "a", "time"])
-      start2 <- max(noise[noise$id == paste0("id_", i) & noise$group == "b", "time"])
-
-      noise2_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = start1:40, group = "a",
-          y = c(runif(length(start1:40), 15, 50))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = start2:40, group = "b",
-          y = c(runif(length(start2:40), 15, 50))
-        )
-      )
-      return(noise2_i)
-    }))
-    simdf <- rbind(noise, noise2)
-
-    ss <- growthSS(
-      model = "int + int", form = y ~ time | id / group, sigma = "int",
-      list("int1" = 10, "fixedChangePoint1" = 20, "int2" = 20),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c("", "int1", "int2"))
+test_that("Logistic brms logistic sub model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logistic",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  ss <- growthSS(
+    model = "logistic", form = y ~ time | id / group, sigma = "logistic",
+    list("A" = 130, "B" = 10, "C" = 3, "sigmaA" = 20, "sigmaB" = 10, "sigmaC" = 2),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "sigmaA", "sigmaB", "sigmaC"))
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusInt_fixedChngpt_fitGrowth.png",
-      plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  ) # that's fast
+  expect_s3_class(fit, "brmsfit")
 
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_logisticSubModel.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
+test_that("Logistic brms gompertz sub model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logistic",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
 
-  test_that("int+int thresholded homoskedasticity model pipeline", {
-    set.seed(123)
+  ss <- growthSS(
+    model = "logistic", form = y ~ time | id / group, sigma = "gompertz",
+    list("A" = 130, "B" = 10, "C" = 3, "sigmaA" = 20, "sigmaB" = 10, "sigmaC" = 2),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "sigmaA", "sigmaB", "sigmaC"))
 
-    noise <- do.call(rbind, lapply(1:30, function(i) {
-      chngpt <- rnorm(2, 18, 2)
-      noise_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[1], group = "a",
-          y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[2], group = "b",
-          y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
-        )
-      )
-      return(noise_i)
-    }))
-    noise2 <- do.call(rbind, lapply(1:30, function(i) {
-      start1 <- max(noise[noise$id == paste0("id_", i) & noise$group == "a", "time"])
-      start2 <- max(noise[noise$id == paste0("id_", i) & noise$group == "b", "time"])
-
-      noise2_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = start1:40, group = "a",
-          y = c(runif(length(start1:40), 15, 50))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = start2:40, group = "b",
-          y = c(runif(length(start2:40), 15, 50))
-        )
-      )
-      return(noise2_i)
-    }))
-    simdf <- rbind(noise, noise2)
-
-    ss <- growthSS(
-      model = "int + int", form = y ~ time | id / group, sigma = "int + int",
-      list(
-        "int1" = 10, "changePoint1" = 10, "int2" = 20, "sigmaint1" = 10,
-        "sigmachangePoint1" = 10, "sigmaint2" = 10
-      ),
-      df = simdf, type = "brms"
-    )
-    expect_equal(ss$prior$nlpar, c(
-      "", "int1", "changePoint1", "int2", "sigmaint1",
-      "sigmachangePoint1", "sigmaint2"
-    ))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_gompSubModel.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusInt_heteroskedastic_fitGrowth.png",
-      plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
+test_that("Logistic brms monomolecular sub model pipeline", {
+  skip_on_ci()
+  skip_on_cran()
+  set.seed(123)
+  simdf <- growthSim("logistic",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
+
+  ss <- growthSS(
+    model = "logistic", form = y ~ time | id / group, sigma = "monomolecular",
+    list("A" = 130, "B" = 10, "C" = 3, "sigmaA" = 5, "sigmaB" = 0.5),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "A", "B", "C", "sigmaA", "sigmaB"))
 
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_monoSubModel.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
 
-  test_that("int + linear model and submodel pipeline", {
-    set.seed(123)
-    noise <- do.call(rbind, lapply(1:30, function(i) {
-      chngpt <- rnorm(2, 18, 2)
-      noise_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[1], group = "a",
-          y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[2], group = "b",
-          y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
-        )
+test_that("int+int homoskedastic model pipeline", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  noise <- do.call(rbind, lapply(1:30, function(i) {
+    chngpt <- rnorm(2, 18, 2)
+    noise_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[1], group = "a",
+        y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
+      ),
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[2], group = "b",
+        y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
       )
-      return(noise_i)
-    }))
-    signal <- growthSim("linear",
-      n = 30, t = 20,
-      params = list("A" = c(3, 5))
     )
-    signal <- do.call(rbind, lapply(unique(paste0(signal$id, signal$group)), function(int) {
-      noisesub <- noise[paste0(noise$id, noise$group) == int, ]
-      signalSub <- signal[paste0(signal$id, signal$group) == int, ]
-      y_end <- noisesub[noisesub$time == max(noisesub$time), "y"]
-      signalSub$time <- signalSub$time + max(noisesub$time)
-      signalSub$y <- y_end + signalSub$y
-      return(signalSub)
-    }))
-    simdf <- rbind(noise, signal)
-    ss <- growthSS(
-      model = "int + linear", form = y ~ time | id / group, sigma = "int + linear",
-      list(
-        "int1" = 10, "changePoint1" = 10, "linear2A" = 20, "sigmaint1" = 10,
-        "sigmachangePoint1" = 10, "sigmalinear2A" = 10
+    return(noise_i)
+  }))
+  noise2 <- do.call(rbind, lapply(1:30, function(i) {
+    start1 <- max(noise[noise$id == paste0("id_", i) & noise$group == "a", "time"])
+    start2 <- max(noise[noise$id == paste0("id_", i) & noise$group == "b", "time"])
+
+    noise2_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = start1:40, group = "a",
+        y = c(runif(length(start1:40), 15, 50))
       ),
-      df = simdf, type = "brms"
+      data.frame(
+        id = paste0("id_", i), time = start2:40, group = "b",
+        y = c(runif(length(start2:40), 15, 50))
+      )
     )
-    expect_equal(ss$prior$nlpar, c(
-      "", "int1", "changePoint1", "linear2A", "sigmaint1",
-      "sigmachangePoint1", "sigmalinear2A"
-    ))
+    return(noise2_i)
+  }))
+  simdf <- rbind(noise, noise2)
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  ss <- growthSS(
+    model = "int + int", form = y ~ time | id / group, sigma = "int",
+    list("int1" = 10, "changePoint1" = 10, "int2" = 20),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "int1", "changePoint1", "int2"))
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df, timeRange = 1:40)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusLinear_heteroskedIntPlusLinear_fitGrowth.png",
-      plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-
-  test_that("int + Logistic brms int+spline sub model pipeline", {
-    set.seed(123)
-    noise <- do.call(rbind, lapply(1:30, function(i) {
-      chngpt <- rnorm(2, 18, 2)
-      noise_i <- rbind(
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[1], group = "a",
-          y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
-        ),
-        data.frame(
-          id = paste0("id_", i), time = 1:chngpt[2], group = "b",
-          y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
-        )
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
+
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusInt_fitGrowth.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
+
+test_that("int+int fixed changepoint homoskedastic model pipeline", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  noise <- do.call(rbind, lapply(1:30, function(i) {
+    chngpt <- c(20, 21)
+    noise_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[1], group = "a",
+        y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
+      ),
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[2], group = "b",
+        y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
       )
-      return(noise_i)
-    }))
-    signal <- growthSim("logistic",
-      n = 20, t = 30,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
     )
-    signal <- do.call(rbind, lapply(unique(paste0(signal$id, signal$group)), function(int) {
-      noisesub <- noise[paste0(noise$id, noise$group) == int, ]
-      signalSub <- signal[paste0(signal$id, signal$group) == int, ]
-      y_end <- noisesub[noisesub$time == max(noisesub$time), "y"]
-      signalSub$time <- signalSub$time + max(noisesub$time)
-      signalSub$y <- y_end + signalSub$y
-      return(signalSub)
-    }))
-    simdf <- rbind(noise, signal)
-    simdf <- simdf[simdf$time < 45, ]
-
-    ss <- growthSS(
-      model = "int+logistic", form = y ~ time | id / group, sigma = "int + spline",
-      list(
-        "int1" = 5, "changePoint1" = 10, "logistic2A" = 130, "logistic2B" = 10, "logistic2C" = 3,
-        "sigmaint1" = 5, "sigmachangePoint1" = 15
+    return(noise_i)
+  }))
+  noise2 <- do.call(rbind, lapply(1:30, function(i) {
+    start1 <- max(noise[noise$id == paste0("id_", i) & noise$group == "a", "time"])
+    start2 <- max(noise[noise$id == paste0("id_", i) & noise$group == "b", "time"])
+
+    noise2_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = start1:40, group = "a",
+        y = c(runif(length(start1:40), 15, 50))
       ),
-      df = simdf, type = "brms"
+      data.frame(
+        id = paste0("id_", i), time = start2:40, group = "b",
+        y = c(runif(length(start2:40), 15, 50))
+      )
     )
+    return(noise2_i)
+  }))
+  simdf <- rbind(noise, noise2)
 
+  ss <- growthSS(
+    model = "int + int", form = y ~ time | id / group, sigma = "int",
+    list("int1" = 10, "fixedChangePoint1" = 20, "int2" = 20),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c("", "int1", "int2"))
 
-    expect_equal(ss$prior$nlpar, c(
-      "", "int1", "changePoint1", "logistic2A", "logistic2B", "logistic2C",
-      "sigmaint1", "sigmachangePoint1"
-    ))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusInt_fixedChngpt_fitGrowth.png",
+    plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/intPluslogistic_intPlusGAMSubModel.png",
-      plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
 
 
-  test_that("fixed and estimated changepoints can be mixed in growth formula", {
-    simdf1 <- growthSim(
-      model = "logistic", n = 20, t = 20,
-      params = list("A" = c(180, 160), "B" = c(9, 11), "C" = c(3, 3.5))
+test_that("int+int thresholded homoskedasticity model pipeline", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  noise <- do.call(rbind, lapply(1:30, function(i) {
+    chngpt <- rnorm(2, 18, 2)
+    noise_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[1], group = "a",
+        y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
+      ),
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[2], group = "b",
+        y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
+      )
     )
+    return(noise_i)
+  }))
+  noise2 <- do.call(rbind, lapply(1:30, function(i) {
+    start1 <- max(noise[noise$id == paste0("id_", i) & noise$group == "a", "time"])
+    start2 <- max(noise[noise$id == paste0("id_", i) & noise$group == "b", "time"])
 
-    simdf2 <- growthSim(
-      model = "linear + linear", n = 20, t = 20,
-      params = list("linear1A" = c(6, 8), "changePoint1" = c(7, 9), "linear2A" = c(15, 20))
+    noise2_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = start1:40, group = "a",
+        y = c(runif(length(start1:40), 15, 50))
+      ),
+      data.frame(
+        id = paste0("id_", i), time = start2:40, group = "b",
+        y = c(runif(length(start2:40), 15, 50))
+      )
     )
+    return(noise2_i)
+  }))
+  simdf <- rbind(noise, noise2)
 
-    simdf2_adj <- do.call(rbind, lapply(unique(paste0(simdf2$id, simdf2$group)), function(int) {
-      p1 <- simdf1[paste0(simdf1$id, simdf1$group) == int, ]
-      p2 <- simdf2[paste0(simdf2$id, simdf2$group) == int, ]
-      y_end_p1 <- p1[p1$time == max(p1$time), "y"]
-      p2$time <- p2$time + max(p1$time)
-      p2$y <- y_end_p1 + p2$y
-      return(p2)
-    }))
-    simdf <- rbind(simdf1, simdf2_adj)
-
-    ss <- growthSS(
-      model = "logistic+linear+linear", form = y ~ time | id / group,
-      sigma = "logistic+linear", df = simdf,
-      start = list(
-        "logistic1A" = 130, "logistic1B" = 10, "logistic1C" = 3.5,
-        "fixedChangePoint1" = 20, "linear2A" = 5, "changePoint2" = 28, "linear3A" = 20,
-        "sigmalogistic1A" = 10, "sigmalogistic1B" = 12, "sigmalogistic1C" = 20,
-        "sigmafixedChangePoint1" = 20, "sigmalinear2A" = 3
-      ), type = "brms"
-    )
+  ss <- growthSS(
+    model = "int + int", form = y ~ time | id / group, sigma = "int + int",
+    list(
+      "int1" = 10, "changePoint1" = 10, "int2" = 20, "sigmaint1" = 10,
+      "sigmachangePoint1" = 10, "sigmaint2" = 10
+    ),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c(
+    "", "int1", "changePoint1", "int2", "sigmaint1",
+    "sigmachangePoint1", "sigmaint2"
+  ))
 
-    expect_equal(ss$prior$nlpar, c(
-      "", "logistic1A", "logistic1B", "logistic1C", "linear2A", "changePoint2",
-      "linear3A", "sigmalogistic1A", "sigmalogistic1B", "sigmalogistic1C",
-      "sigmalinear2A"
-    ))
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
 
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusInt_heteroskedastic_fitGrowth.png",
+    plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
 
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/threePart_fixedAndEstimatedChangepoint.png",
-      plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("logistic decay as a segment", {
-    simdf <- growthSim(
-      model = "logistic + logistic decay", n = 20, t = 45,
-      params = list(
-        "logistic1A" = c(120, 140), "logistic1B" = c(12, 10), "logistic1C" = c(3, 3.5),
-        "changePoint1" = c(20, 23),
-        "logistic2A" = c(90, 100), "logistic2B" = c(11, 13), "logistic2C" = c(3, 3.5)
+
+
+
+test_that("int + linear model and submodel pipeline", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  noise <- do.call(rbind, lapply(1:30, function(i) {
+    chngpt <- rnorm(2, 18, 2)
+    noise_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[1], group = "a",
+        y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
+      ),
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[2], group = "b",
+        y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
       )
     )
-    ss <- growthSS(
-      model = "logistic + logistic decay", form = y ~ time | id / group, sigma = "spline",
-      list(
-        "logistic1A" = 100, "logistic1B" = 10, "logistic1C" = 3, "changePoint1" = 20,
-        "logistic2A" = 100, "logistic2B" = 10, "logistic2C" = 3
+    return(noise_i)
+  }))
+  signal <- growthSim("linear",
+    n = 30, t = 20,
+    params = list("A" = c(3, 5))
+  )
+  signal <- do.call(rbind, lapply(unique(paste0(signal$id, signal$group)), function(int) {
+    noisesub <- noise[paste0(noise$id, noise$group) == int, ]
+    signalSub <- signal[paste0(signal$id, signal$group) == int, ]
+    y_end <- noisesub[noisesub$time == max(noisesub$time), "y"]
+    signalSub$time <- signalSub$time + max(noisesub$time)
+    signalSub$y <- y_end + signalSub$y
+    return(signalSub)
+  }))
+  simdf <- rbind(noise, signal)
+  ss <- growthSS(
+    model = "int + linear", form = y ~ time | id / group, sigma = "int + linear",
+    list(
+      "int1" = 10, "changePoint1" = 10, "linear2A" = 20, "sigmaint1" = 10,
+      "sigmachangePoint1" = 10, "sigmalinear2A" = 10
+    ),
+    df = simdf, type = "brms"
+  )
+  expect_equal(ss$prior$nlpar, c(
+    "", "int1", "changePoint1", "linear2A", "sigmaint1",
+    "sigmachangePoint1", "sigmalinear2A"
+  ))
+
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
+
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df, timeRange = 1:40)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/intPlusLinear_heteroskedIntPlusLinear_fitGrowth.png",
+    plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
+
+
+test_that("int + Logistic brms int+spline sub model pipeline", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  noise <- do.call(rbind, lapply(1:30, function(i) {
+    chngpt <- rnorm(2, 18, 2)
+    noise_i <- rbind(
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[1], group = "a",
+        y = c(runif(chngpt[1] - 1, 0, 20), rnorm(1, 5, 1))
       ),
-      df = simdf, type = "brms"
-    )
-    fit <- fitGrowth(ss,
-      backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
-      refresh = 0, silent = 2
-    )
-    plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
-    ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_plus_logisticDecay.png", plot,
-      width = 10, height = 6, dpi = 300, bg = "#ffffff"
-    )
-    expect_s3_class(plot, "ggplot")
-  })
-
-  test_that("Test flexsurv model", {
-    set.seed(123)
-    model <- "survival gompertz"
-    form <- y > 100 ~ time | id / group
-    df <- growthSim("logistic",
-      n = 20, t = 25,
-      params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
-    )
-    ss <- growthSS(model = model, form = form, df = df, type = "flexsurv")
-    library(flexsurv)
-    fit <- fitGrowth(ss)
-    p <- growthPlot(fit, form = ss$pcvrForm, df = ss$df)
-    expect_s3_class(p, "ggplot")
-  })
-
-  test_that("Logistic Poisson Model", {
-    set.seed(123)
-    form <- y ~ time | id / group
-    df <- growthSim("count: logistic",
-      n = 20, t = 25,
-      params = list("A" = c(10, 12), "B" = c(13, 11), "C" = c(3, 3.5))
-    )
-    ss <- growthSS(
-      model = "poisson: logistic", form = y ~ time | id / group, sigma = NULL,
-      df = df, start = list("A" = 8, "B" = 10, "C" = 3)
-    )
-    lapply(ss, head)
-    fit <- fitGrowth(ss,
-      iter = 600, cores = 1, chains = 1, backend = "cmdstanr",
-      refresh = 0, silent = 2
-    )
-    expect_s3_class(fit, "brmsfit")
-    p <- growthPlot(fit, ss$pcvrForm, df = ss$df)
-    expect_s3_class(p, "ggplot")
-  })
-
-  test_that("Beta DRC Model", {
-    set.seed(123)
-    form <- y ~ time | id / group
-    df <- growthSim(
-      "beta",
-      n = 20, t = 50,
-      params = list(
-        "A" = c(10, 10),
-        "B" = c(1.25, 1.3),
-        "C" = c(20, 22),
-        "D" = c(5, 5),
-        "E" = c(30, 32)
+      data.frame(
+        id = paste0("id_", i), time = 1:chngpt[2], group = "b",
+        y = c(runif(chngpt[2] - 1, 0, 20), rnorm(1, 5, 1))
       )
     )
-    #* consider using ss with nls to get ideas for parameters
-    ss <- growthSS(
-      model = "beta", form = y ~ time | id / group, sigma = NULL,
-      df = df, start = list("A" = 10, "B" = 1, "C" = 15, "D" = 3, "E" = 25)
+    return(noise_i)
+  }))
+  signal <- growthSim("logistic",
+    n = 20, t = 30,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
+  signal <- do.call(rbind, lapply(unique(paste0(signal$id, signal$group)), function(int) {
+    noisesub <- noise[paste0(noise$id, noise$group) == int, ]
+    signalSub <- signal[paste0(signal$id, signal$group) == int, ]
+    y_end <- noisesub[noisesub$time == max(noisesub$time), "y"]
+    signalSub$time <- signalSub$time + max(noisesub$time)
+    signalSub$y <- y_end + signalSub$y
+    return(signalSub)
+  }))
+  simdf <- rbind(noise, signal)
+  simdf <- simdf[simdf$time < 45, ]
+
+  ss <- growthSS(
+    model = "int+logistic", form = y ~ time | id / group, sigma = "int + spline",
+    list(
+      "int1" = 5, "changePoint1" = 10, "logistic2A" = 130, "logistic2B" = 10, "logistic2C" = 3,
+      "sigmaint1" = 5, "sigmachangePoint1" = 15
+    ),
+    df = simdf, type = "brms"
+  )
+
+
+  expect_equal(ss$prior$nlpar, c(
+    "", "int1", "changePoint1", "logistic2A", "logistic2B", "logistic2C",
+    "sigmaint1", "sigmachangePoint1"
+  ))
+
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
+
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/intPluslogistic_intPlusGAMSubModel.png",
+    plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
+
+
+test_that("fixed and estimated changepoints can be mixed in growth formula", {
+  skip_on_cran()
+  skip_on_ci()
+  simdf1 <- growthSim(
+    model = "logistic", n = 20, t = 20,
+    params = list("A" = c(180, 160), "B" = c(9, 11), "C" = c(3, 3.5))
+  )
+
+  simdf2 <- growthSim(
+    model = "linear + linear", n = 20, t = 20,
+    params = list("linear1A" = c(6, 8), "changePoint1" = c(7, 9), "linear2A" = c(15, 20))
+  )
+
+  simdf2_adj <- do.call(rbind, lapply(unique(paste0(simdf2$id, simdf2$group)), function(int) {
+    p1 <- simdf1[paste0(simdf1$id, simdf1$group) == int, ]
+    p2 <- simdf2[paste0(simdf2$id, simdf2$group) == int, ]
+    y_end_p1 <- p1[p1$time == max(p1$time), "y"]
+    p2$time <- p2$time + max(p1$time)
+    p2$y <- y_end_p1 + p2$y
+    return(p2)
+  }))
+  simdf <- rbind(simdf1, simdf2_adj)
+
+  ss <- growthSS(
+    model = "logistic+linear+linear", form = y ~ time | id / group,
+    sigma = "logistic+linear", df = simdf,
+    start = list(
+      "logistic1A" = 130, "logistic1B" = 10, "logistic1C" = 3.5,
+      "fixedChangePoint1" = 20, "linear2A" = 5, "changePoint2" = 28, "linear3A" = 20,
+      "sigmalogistic1A" = 10, "sigmalogistic1B" = 12, "sigmalogistic1C" = 20,
+      "sigmafixedChangePoint1" = 20, "sigmalinear2A" = 3
+    ), type = "brms"
+  )
+
+  expect_equal(ss$prior$nlpar, c(
+    "", "logistic1A", "logistic1B", "logistic1C", "linear2A", "changePoint2",
+    "linear3A", "sigmalogistic1A", "sigmalogistic1B", "sigmalogistic1C",
+    "sigmalinear2A"
+  ))
+
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
+
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/threePart_fixedAndEstimatedChangepoint.png",
+    plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
+
+test_that("logistic decay as a segment", {
+  skip_on_ci()
+  skip_on_cran()
+  simdf <- growthSim(
+    model = "logistic + logistic decay", n = 20, t = 45,
+    params = list(
+      "logistic1A" = c(120, 140), "logistic1B" = c(12, 10), "logistic1C" = c(3, 3.5),
+      "changePoint1" = c(20, 23),
+      "logistic2A" = c(90, 100), "logistic2B" = c(11, 13), "logistic2C" = c(3, 3.5)
     )
-    expect_s3_class(ss, "pcvrss")
-    tryCatch(
-      {
-        fit <- fitGrowth(ss,
-          iter = 600, cores = 1, chains = 1, backend = "cmdstanr",
-          refresh = 0, silent = 2
-        )
-        expect_s3_class(fit, "brmsfit")
-        p <- growthPlot(fit, ss$pcvrForm, df = ss$df)
-        expect_s3_class(p, "ggplot")
-      },
-      error = function(err) {}
+  )
+  ss <- growthSS(
+    model = "logistic + logistic decay", form = y ~ time | id / group, sigma = "spline",
+    list(
+      "logistic1A" = 100, "logistic1B" = 10, "logistic1C" = 3, "changePoint1" = 20,
+      "logistic2A" = 100, "logistic2B" = 10, "logistic2C" = 3
+    ),
+    df = simdf, type = "brms"
+  )
+  fit <- fitGrowth(ss,
+    backend = "cmdstanr", iter = 500, chains = 1, cores = 1,
+    refresh = 0, silent = 2
+  )
+  plot <- growthPlot(fit = fit, form = ss$pcvrForm, df = ss$df)
+  ggsave("~/scripts/fahlgren_lab/labMeetings/logistic_plus_logisticDecay.png", plot,
+    width = 10, height = 6, dpi = 300, bg = "#ffffff"
+  )
+  expect_s3_class(plot, "ggplot")
+})
+
+test_that("Test flexsurv model", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  model <- "survival gompertz"
+  form <- y > 100 ~ time | id / group
+  df <- growthSim("logistic",
+    n = 20, t = 25,
+    params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
+  ss <- growthSS(model = model, form = form, df = df, type = "flexsurv")
+  library(flexsurv)
+  fit <- fitGrowth(ss)
+  p <- growthPlot(fit, form = ss$pcvrForm, df = ss$df)
+  expect_s3_class(p, "ggplot")
+})
+
+test_that("Logistic Poisson Model", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  form <- y ~ time | id / group
+  df <- growthSim("count: logistic",
+    n = 5, t = 10,
+    params = list("A" = c(10, 12), "B" = c(13, 11), "C" = c(3, 3.5))
+  )
+  ss <- growthSS(
+    model = "poisson: logistic", form = y ~ time | id / group, sigma = NULL,
+    df = df, start = list("A" = 8, "B" = 10, "C" = 3)
+  )
+  fit <- fitGrowth(ss,
+    iter = 200, cores = 1, chains = 1, backend = "cmdstanr",
+    refresh = 0, silent = 2
+  )
+  expect_s3_class(fit, "brmsfit")
+  p <- growthPlot(fit, ss$pcvrForm, df = ss$df)
+  expect_s3_class(p, "ggplot")
+})
+
+test_that("Beta DRC Model", {
+  set.seed(123)
+  skip_on_ci()
+  skip_on_cran()
+  form <- y ~ time | id / group
+  df <- growthSim(
+    "beta",
+    n = 20, t = 50,
+    params = list(
+      "A" = c(10, 10),
+      "B" = c(1.25, 1.3),
+      "C" = c(20, 22),
+      "D" = c(5, 5),
+      "E" = c(30, 32)
     )
-  })
-}
+  )
+  #* consider using ss with nls to get ideas for parameters
+  ss <- growthSS(
+    model = "beta", form = y ~ time | id / group, sigma = NULL,
+    df = df, start = list("A" = 10, "B" = 1, "C" = 15, "D" = 3, "E" = 25)
+  )
+  expect_s3_class(ss, "pcvrss")
+  tryCatch(
+    {
+      fit <- fitGrowth(ss,
+        iter = 200, cores = 1, chains = 1, backend = "cmdstanr",
+        refresh = 0, silent = 2
+      )
+      expect_s3_class(fit, "brmsfit")
+      p <- growthPlot(fit, ss$pcvrForm, df = ss$df)
+      expect_s3_class(p, "ggplot")
+    },
+    error = function(err) {}
+  )
+})
diff --git a/tests/testthat/test-long_mv_workflow.R b/tests/testthat/test-long_mv_workflow.R
index b921f447..b63822a2 100644
--- a/tests/testthat/test-long_mv_workflow.R
+++ b/tests/testthat/test-long_mv_workflow.R
@@ -7,13 +7,7 @@ test_that("reading mv github data as long works", {
     "main/pcv4-multi-value-traits.csv"
   ), mode = "long")
 
-  expect_equal(dim(mv), c(513720, 21))
-  expect_equal(colnames(mv), c(
-    "camera", "imgtype", "zoom", "exposure", "gain", "frame", "rotation",
-    "lifter", "timestamp", "id", "barcode", "treatment", "velocity",
-    "cartag", "measurementlabel", "other", "image", "sample", "trait",
-    "value", "label"
-  ))
+  expect_equal(dim(mv), c(513720, 6))
 
   mv$genotype <- substr(mv$barcode, 3, 5)
   mv$genotype <- ifelse(mv$genotype == "002", "B73",
@@ -27,14 +21,14 @@ test_that("reading mv github data as long works", {
   )
   # test pcv.time
   mv <- pcv.time(mv, timeCol = "timestamp", group = "barcode", plot = FALSE)
-  expect_equal(dim(mv), c(513720, 24))
-  expect_equal(colnames(mv)[24], "DAS")
+  expect_equal(dim(mv), c(513720, 9))
+  expect_equal(colnames(mv)[9], "DAS")
 
   # test pcv.outliers
 
   mvNoOutliers <- suppressWarnings(pcv.outliers(
     df = mv, phenotype = "hue_frequencies", naTo0 = FALSE, plot = TRUE,
-    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode", "rotation")
+    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode")
   ))
 
   pct_removed <- nrow(mvNoOutliers$data) / nrow(mv)
@@ -43,11 +37,11 @@ test_that("reading mv github data as long works", {
 
   mvNoOutliers <- suppressWarnings(pcv.outliers(
     df = mv, phenotype = "hue_frequencies", naTo0 = FALSE, plot = FALSE, outlierMethod = "mahalanobis",
-    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode", "rotation")
+    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode")
   ))
 
-  pct_removed <- nrow(mvNoOutliers) / nrow(mv)
-  expect_equal(pct_removed, 0.945, tolerance = 0.015)
+  pct_kept <- nrow(mvNoOutliers) / nrow(mv)
+  expect_true(pct_kept > 0.95)
 
   #* test joyplot
   joyplot <- pcv.joyplot(mv[mv$DAS == 18, ],
diff --git a/tests/testthat/test-mvSSModels.R b/tests/testthat/test-mvSSModels.R
index fbedd138..df79c409 100644
--- a/tests/testthat/test-mvSSModels.R
+++ b/tests/testthat/test-mvSSModels.R
@@ -19,8 +19,9 @@ test_that("Test spectral index helpers", {
 #* `Non-Longitudinal Multi-Value Trait Models`
 
 set.seed(123)
-mv_df <- mvSim(dists = list(rnorm = list(mean = 100, sd = 30)), wide = FALSE)
-mv_df$group <- rep(c("a", "b"), times = 900)
+mv_df <- mvSim(dists = list(rnorm = list(mean = 100, sd = 30)), wide = FALSE,
+               n_samples = 3, counts = 50)
+mv_df$group <- rep(c("a", "b"), times = 270)
 mv_df <- mv_df[mv_df$value > 0, ]
 mv_df$label <- as.numeric(gsub("sim_", "", mv_df$variable))
 
@@ -35,7 +36,8 @@ test_that("Test brms mv trait non-longitudinal model skew model", {
     start = list("A" = 5), type = "brms", spectral_index = "ci_rededge"
   )
   expect_equal(ss1$family, "skew_normal")
-  mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
+  mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 200, chains = 1, cores = 1,
+                    refresh = 0, silent = 2)
   expect_s3_class(mod1, "brmsfit")
   p <- growthPlot(mod1, ss1$pcvrForm, df = ss1$df)
   expect_s3_class(p, "ggplot")
@@ -50,7 +52,8 @@ test_that("Test brms mv trait non-longitudinal model", {
     start = list("A" = 5), type = "brms", spectral_index = "none"
   )
   expect_equal(ss1$family, "student")
-  mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
+  mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 200, chains = 1, cores = 1,
+                    refresh = 0, silent = 2)
   expect_s3_class(mod1, "brmsfit")
   p <- growthPlot(mod1, ss1$pcvrForm, df = ss1$df)
   expect_s3_class(p, "ggplot")
@@ -74,6 +77,12 @@ test_that("Test nls mv trait non-longitudinal model", {
 
 test_that("Test nlrq mv trait non-longitudinal model", {
   skip_on_cran()
+  set.seed(123)
+  mv_df <- mvSim(dists = list(rnorm = list(mean = 100, sd = 30)), wide = FALSE,
+                 n_samples = 10, counts = 1000) # this test needs larger data
+  mv_df$group <- rep(c("a", "b"), times = 900)
+  mv_df <- mv_df[mv_df$value > 0, ]
+  mv_df$label <- as.numeric(gsub("sim_", "", mv_df$variable))
   ss1 <- mvSS(
     model = "linear", form = label | value ~ group, df = mv_df, tau = 0.5,
     start = list("A" = 5), type = "nlrq", spectral_index = "none"
@@ -98,25 +107,21 @@ test_that("Test nlrq mv trait non-longitudinal model", {
 m1 <- mvSim(
   dists = list(
     rnorm = list(mean = 100, sd = 30),
-    rnorm = list(mean = 110, sd = 25),
-    rnorm = list(mean = 120, sd = 20),
-    rnorm = list(mean = 135, sd = 15)
+    rnorm = list(mean = 110, sd = 25)
   ),
-  wide = FALSE, n = 6
+  wide = FALSE, n = 3, counts = 100
 )
-m1$time <- rep(1:4, times = 6 * 180)
+m1$time <- rep(1:2, times = 3 * 180)
 m2 <- mvSim(
   dists = list(
     rnorm = list(mean = 85, sd = 25),
-    rnorm = list(mean = 95, sd = 20),
-    rnorm = list(mean = 105, sd = 15),
-    rnorm = list(mean = 110, sd = 15)
+    rnorm = list(mean = 95, sd = 20)
   ),
-  wide = FALSE, n = 6
+  wide = FALSE, n = 3, counts = 100
 )
-m2$time <- rep(1:4, times = 6 * 180)
+m2$time <- rep(1:2, times = 3 * 180)
 mv_df2 <- rbind(m1, m2)
-mv_df2$group <- rep(c("a", "b"), each = 4320)
+mv_df2$group <- rep(c("a", "b"), each = 1080)
 mv_df2 <- mv_df2[mv_df2$value > 0, ]
 mv_df2$label <- as.numeric(gsub("sim_", "", mv_df2$variable))
 
@@ -128,7 +133,8 @@ test_that("Test brms mv trait longitudinal model", {
     model = "linear", form = label | value ~ time | group, df = mv_df2,
     start = list("A" = 50), type = "brms", spectral_index = "none"
   )
-  fit <- fitGrowth(ss_mv1, backend = "cmdstanr", iter = 600, chains = 1, cores = 1)
+  fit <- fitGrowth(ss_mv1, backend = "cmdstanr", iter = 100, chains = 1, cores = 1,
+                   refresh = 0, silent = 2)
   expect_s3_class(fit, "brmsfit")
   p <- growthPlot(fit, ss_mv1$pcvrForm, df = ss_mv1$df)
   expect_s3_class(p, "ggplot")
diff --git a/tests/testthat/test-survSS_helpers.R b/tests/testthat/test-survSS_helpers.R
index ac0d1822..82eed860 100644
--- a/tests/testthat/test-survSS_helpers.R
+++ b/tests/testthat/test-survSS_helpers.R
@@ -60,3 +60,6 @@ test_that(".flexSurvSS returns errors and is covered", {
   expect_equal(names(ss2$formula), c("f1", "f2"))
   expect_true(identical(ss2$df, df2))
 })
+if (!interactive()) {
+  dev.off()
+}
diff --git a/tests/testthat/test-sv-conjugate.R b/tests/testthat/test-sv-conjugate.R
index a40cc791..be127b45 100644
--- a/tests/testthat/test-sv-conjugate.R
+++ b/tests/testthat/test-sv-conjugate.R
@@ -441,3 +441,7 @@ test_that("bivariate conjugate lognormal works", {
   p <- plot(out2)
   expect_s3_class(p, "ggplot")
 })
+
+if (!interactive()) {
+  dev.off()
+}
diff --git a/tests/testthat/test-wide_mv_workflow.R b/tests/testthat/test-wide_mv_workflow.R
index 9cdd2b4c..1c5214a9 100644
--- a/tests/testthat/test-wide_mv_workflow.R
+++ b/tests/testthat/test-wide_mv_workflow.R
@@ -7,7 +7,7 @@ test_that("reading mv github data as long works", {
     "https://media.githubusercontent.com/media/joshqsumner/pcvrTestData/",
     "main/pcv4-multi-value-traits.csv"
   ), mode = "wide", reader = "fread")
-  expect_equal(dim(mv), c(2854, 198))
+  expect_equal(dim(mv), c(2854, 183))
 
   mv$genotype <- substr(mv$barcode, 3, 5)
   mv$genotype <- ifelse(mv$genotype == "002", "B73",
@@ -21,8 +21,8 @@ test_that("reading mv github data as long works", {
   )
   # test pcv.time
   mv <- pcv.time(mv, timeCol = "timestamp", group = "barcode", plot = FALSE)
-  expect_equal(dim(mv), c(2854, 201))
-  expect_equal(colnames(mv)[201], "DAS")
+  expect_equal(dim(mv), c(2854, 186))
+  expect_equal(colnames(mv)[186], "DAS")
 
   # test pcv.outliers
 
@@ -38,7 +38,7 @@ test_that("reading mv github data as long works", {
 
   mvNoOutliers <- suppressWarnings(pcv.outliers(
     df = mv, phenotype = phenotypes, naTo0 = FALSE, plot = TRUE, outlierMethod = "mahalanobis",
-    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode", "rotation")
+    group = c("DAS", "genotype", "fertilizer"), cutoff = 3, plotgroup = c("barcode")
   ))
 
   pct_removed <- nrow(mvNoOutliers$data) / nrow(mv)
@@ -65,7 +65,7 @@ test_that("reading mv github data as long works", {
       df = mv,
       group = c("DAS", "genotype", "fertilizer"),
       n_per_group = 2,
-      labelCol = "camera" # if only some of the id columns are there then error should be thrown
+      labelCol = "genotype" # if only some of the id columns are there then error should be thrown
     )
   )
 
@@ -79,7 +79,6 @@ test_that("reading mv github data as long works", {
   )
   expect_s3_class(emd$plot, "ggplot")
   expect_equal(dim(emd$data), c(110, 7))
-  expect_equal(sum(emd$data$emd), 5684.034, tolerance = 0.01)
 
   #* test network
   net <- pcv.net(emd$data, filter = 0.05)
@@ -87,3 +86,7 @@ test_that("reading mv github data as long works", {
   expect_equal(dim(net$nodes), c(10, 12))
   expect_equal(dim(net$edges), c(30, 11))
 })
+
+if (!interactive()) {
+  dev.off()
+}
diff --git a/tests/testthat/test-wide_sv_workflow.R b/tests/testthat/test-wide_sv_workflow.R
index e55930fc..51d2606e 100644
--- a/tests/testthat/test-wide_sv_workflow.R
+++ b/tests/testthat/test-wide_sv_workflow.R
@@ -77,14 +77,9 @@ test_that("reading sv github data as wide works", {
     model = "gompertz", form = area_pixels ~ DAS | barcode / group, sigma = "spline", df = sv,
     start = list("A" = 130, "B" = 10, "C" = 0.5)
   )
-  expect_type(ss, "list")
-
-  expect_s3_class(ss[["formula"]], "brmsformula")
-  expect_s3_class(ss[["prior"]], "brmsprior")
-  expect_type(ss[["initfun"]], "closure")
-  expect_s3_class(ss[["df"]], "data.frame")
-  expect_type(ss[["family"]], "character")
-  expect_s3_class(ss[["pcvrForm"]], "formula")
-
-  #* pending
+  expect_s3_class(ss, "pcvrss")
 })
+
+if (!interactive()) {
+  dev.off()
+}
diff --git a/tools/examples_timing.R b/tools/examples_timing.R
index ad44ed59..2759db7a 100644
--- a/tools/examples_timing.R
+++ b/tools/examples_timing.R
@@ -1,4 +1,3 @@
-wd <- getwd()
 library(ggplot2)
 setwd("~/pcvr/R")
 devtools::load_all("~/pcvr")
@@ -9,10 +8,12 @@ tdf <- do.call(rbind, lapply(dir("~/pcvr/man", pattern = ".[Rr]d$", full.names =
   })
   data.frame(time = x[["elapsed"]], fun = gsub(".*/", "", doc))
 }))
-setwd(wd)
-tdf
-if (!interactive) {pdf(NULL)}
-ggplot(tdf, aes(x = 1, y = time, fill = fun)) +
+setwd("~/pcvr/tools/")
+write.csv(tdf, file = "examples_timing.csv", row.names = FALSE)
+
+p1 <- ggplot(tdf, aes(x = 1, y = time, fill = fun)) +
   geom_col(position = "stack") +
   scale_fill_viridis_d()+
   theme_light()
+p1
+ggsave("examples_timing.png", plot = p1, width = 12, height = 6, dpi = 300, bg = "#ffffff")
diff --git a/tools/examples_timing.png b/tools/examples_timing.png
new file mode 100644
index 00000000..a4f6fb1a
Binary files /dev/null and b/tools/examples_timing.png differ
diff --git a/tools/tests_timing.R b/tools/tests_timing.R
index 10adceac..d8617f48 100644
--- a/tools/tests_timing.R
+++ b/tools/tests_timing.R
@@ -12,9 +12,51 @@ tdf <- do.call(rbind,
                  })
                  data.frame(time = x[["elapsed"]], Test = gsub(".*/", "", file))
                }))
-tdf
-if (!interactive()) {pdf(NULL)}
-ggplot(tdf, aes(x = 1, y = time, fill = Test)) +
+write.csv(tdf, file = "~/pcvr/tools/tests_timing.csv", row.names = FALSE)
+
+p1 <- ggplot(tdf, aes(x = 1, y = time, fill = Test)) +
   geom_col(position = "stack") +
   scale_fill_viridis_d()+
   theme_light()
+ggsave("~/pcvr/tools/tests_timing.png", plot = p1, width = 7, height = 6, dpi = 300, bg = "#ffffff")
+
+
+# now with CI and CRAN
+
+Sys.setenv("CI" = "true")
+sv <- NULL
+tdf <- do.call(rbind,
+               lapply(dir("~/pcvr/tests/testthat", pattern = ".[Rr]$", full.names = TRUE),
+                      function(file) {
+                        message(paste0("Running ", gsub(".*/", "", file)))
+                        x <- system.time({
+                          source(file)
+                        })
+                        data.frame(time = x[["elapsed"]], Test = gsub(".*/", "", file))
+                      }))
+write.csv(tdf, file = "~/pcvr/tools/tests_timing_ci.csv", row.names = FALSE)
+
+p2 <- ggplot(tdf, aes(x = 1, y = time, fill = Test)) +
+  geom_col(position = "stack") +
+  scale_fill_viridis_d()+
+  theme_light()
+ggsave("~/pcvr/tools/tests_timing_ci.png", plot = p2, width = 7, height = 6, dpi = 300, bg = "#ffffff")
+
+Sys.setenv("NOT_CRAN" = "false")
+sv <- NULL
+tdf <- do.call(rbind,
+               lapply(dir("~/pcvr/tests/testthat", pattern = ".[Rr]$", full.names = TRUE),
+                      function(file) {
+                        message(paste0("Running ", gsub(".*/", "", file)))
+                        x <- system.time({
+                          source(file)
+                        })
+                        data.frame(time = x[["elapsed"]], Test = gsub(".*/", "", file))
+                      }))
+write.csv(tdf, file = "~/pcvr/tools/tests_timing_cran.csv", row.names = FALSE)
+
+p3 <- ggplot(tdf, aes(x = 1, y = time, fill = Test)) +
+  geom_col(position = "stack") +
+  scale_fill_viridis_d()+
+  theme_light()
+ggsave("~/pcvr/tools/tests_timing_cran.png", plot = p3, width = 7, height = 6, dpi = 300, bg = "#ffffff")
diff --git a/tools/tests_timing.png b/tools/tests_timing.png
new file mode 100644
index 00000000..141bcaa9
Binary files /dev/null and b/tools/tests_timing.png differ
diff --git a/tools/tests_timing_ci.png b/tools/tests_timing_ci.png
new file mode 100644
index 00000000..76a2e775
Binary files /dev/null and b/tools/tests_timing_ci.png differ
diff --git a/tools/tests_timing_cran.png b/tools/tests_timing_cran.png
new file mode 100644
index 00000000..c2207c8f
Binary files /dev/null and b/tools/tests_timing_cran.png differ