danforthcenter · joshqsumner · Aug 31, 2026 · Aug 27, 2026 · Aug 27, 2026 · Aug 27, 2026
diff --git a/.gitignore b/.gitignore
@@ -16,3 +16,4 @@ tools/lib
 tools/*.html
 \#\*R:scripts\*\#
 demon.org
+tools/coverage-test-failures.zip
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
 Version: 1.4.1
-Date: 2026-06-23 12:55:28 UTC
-SHA: 2f488b3aa26db9b410db06b9992defa0d3dabdcb
+Date: 2026-06-23 14:20:55 UTC
+SHA: a6dd636783dd7e4c660715e64382cf5834562c60
diff --git a/R/brmSS.R b/R/brmSS.R
@@ -20,7 +20,8 @@
 #'  and T_5(mu, 3) priors for changepoint parameters.
 #'   This is done because the values are strictly positive and the lognormal distribution
 #'   is easily interpreted. The changepoint priors are T distributions for symmetry, 5 DF
-#'   having been chosen for heavy but not unmanageable tails.
+#'   having been chosen for heavy but not unmanageable tails. Constant priors can be specified
+#'   by providing the value as a string (ie, "3" for a constant 3 value for that variable).
 #'   If this argument is not provided then priors are not
 #'   returned and a different set of priors will need to be made for the model using
 #'   \code{brms::set_prior}. This works similarly to the \code{params} argument

diff --git a/R/brmSSHelpers.R b/R/brmSSHelpers.R
@@ -82,7 +82,7 @@
     default_prior <- .explicitDefaultPrior(formula, df, family)
     default_interaction_prior <- default_prior[grepl(":", default_prior$coef), ]
     tenth_of_priors <- lapply(priors, function(x) {
-      return(mean(x) / 10)
+      return(mean(as.numeric(x)) / 10)
     })
     for (nlp in unique(default_interaction_prior$nlpar)) {
       sd <- ifelse(nlp %in% names(tenth_of_priors), tenth_of_priors[[nlp]], 3)
@@ -191,6 +191,10 @@
 .stanStringHelper <- function(priors, pars, USEGROUP) {
   if (!is.null(pars)) {
     priorStanStrings <- lapply(pars, function(par) {
+      if (methods::is(priors[[par]][1], "character")) {
+        par_string <- paste0("constant(", priors[[par]], ")") # constant prior
+        return(par_string)
+      }
       if (!grepl("changePoint|I$", par)) {
         par_string <- paste0("lognormal(log(", priors[[par]], "), 0.25)") # growth parameters are LN
       } else {

diff --git a/R/conjugate.R b/R/conjugate.R
@@ -369,7 +369,7 @@ conjugate <- function(s1 = NULL, s2 = NULL,
     sample <- samplesList[[i]]
     prior <- priors[[i]]
     #* `Check sample class`
-    if (is.matrix(sample) | is.data.frame(sample)) {
+    if (is.matrix(sample) || is.data.frame(sample)) {
       vec_suffix <- "mv"
       sample <- .mvSampleFormatting(sample)
     } else if (is.vector(sample)) {
@@ -531,7 +531,7 @@ conjugate <- function(s1 = NULL, s2 = NULL,
     sample <- samplesList[[i]]
     prior <- priors[[i]]
     #* `Check sample class`
-    if (is.matrix(sample) | is.data.frame(sample)) {
+    if (is.matrix(sample) || is.data.frame(sample)) {
       vec <- FALSE
     } else if (is.vector(sample)) {
       vec <- TRUE

diff --git a/R/growthSS.R b/R/growthSS.R
@@ -93,6 +93,8 @@
 #' If this is not provided then starting values are picked with \code{stats::selfStart}.
 #'  When type = "brms" these should be provided and are treated as the means of
 #'   lognormal priors for all growth model parameters and T_5(mu, 3) priors for changepoint parameters.
+#'   Constant priors can be specified by providing the value as a string
+#'   (ie, "3" for a constant 3 value for that variable).
 #'   This is done because the values are strictly positive and the lognormal distribution
 #'   is easily interpreted. The changepoint priors are T distributions for symmetry, 5 DF
 #'   having been chosen for heavy but not unmanageable tails.

diff --git a/R/growthSim.R b/R/growthSim.R
@@ -268,13 +268,13 @@
 #'
 
 growthSim <- function(
-    model = c(
-      "logistic", "logistic4", "logistic5", "gompertz", "double logistic", "double gompertz",
-      "monomolecular", "exponential", "linear", "power law", "frechet", "weibull", "gumbel",
-      "logarithmic", "bragg", "lorentz", "beta"
-    ),
-    n = 20, t = 25, params = list(), D = 0,
-    returnParams = FALSE) {
+  model = c(
+    "logistic", "logistic4", "logistic5", "gompertz", "double logistic", "double gompertz",
+    "monomolecular", "exponential", "linear", "power law", "frechet", "weibull", "gumbel",
+    "logarithmic", "bragg", "lorentz", "beta"
+  ),
+  n = 20, t = 25, params = list(), D = 0,
+  returnParams = FALSE) {
   if (grepl("count:", model)) {
     COUNT <- TRUE
     model <- trimws(gsub("count:", "", model))
@@ -388,7 +388,7 @@ growthSim <- function(
     names(iterNoise) <- sub(paste0(iterModelFindParams, u, "|Point."), "", names(iterNoise))
 
     iter_data <- do.call(rbind, lapply(1:n, function(i) {
-      if (is.null(nextChangepoints) | u == length(component_models)) {
+      if (is.null(nextChangepoints) || u == length(component_models)) {
         iterChangepointsRand <- rep(t, length(iterParams[[1]]))
       } else {
         iterChangepointsRand <- lapply(nextChangepoints, function(fc) {

diff --git a/R/mv_ag.R b/R/mv_ag.R
@@ -142,7 +142,7 @@ mv_ag <- function(df, group, mvCols = "frequencies", n_per_group = 1, outRows =
     }
     rownames(mv) <- seq_len(nrow(mv))
     nms <- sample(rownames(mv), nrow(mv), replace = FALSE)
-    if (nrow(mv) > 1 & iter_n > 1) {
+    if (nrow(mv) > 1 && iter_n > 1) {
       index <- cut(seq_len(nrow(mv)), iter_n)
       nms_split <- split(nms, index)
     } else {
@@ -199,7 +199,7 @@ mv_ag <- function(df, group, mvCols = "frequencies", n_per_group = 1, outRows =
       return(id_d)
     }))
     #* separate IDS into groups
-    if (length(IDS) > 1 & iter_n > 1) {
+    if (length(IDS) > 1 && iter_n > 1) {
       index <- cut(seq_along(IDS), iter_n)
       ids_split <- split(IDS, index)
     } else {

diff --git a/R/readpcv3.R b/R/readpcv3.R
@@ -72,15 +72,12 @@
 #' @export
 
 read.pcv.3 <- function(
-    file = NULL,
-    snapshotFile = NULL,
-    designFile = NULL,
-    metaCol = "meta",
-    metaForm = "vis_view_angle_zoom_horizontal_gain_exposure_v_new_n_rep",
-    joinSnapshot = "id",
-    conversions = NULL,
-    mode = "long",
-    ...) {
+  file = NULL, snapshotFile = NULL, designFile = NULL,
+  metaCol = "meta",
+  metaForm = "vis_view_angle_zoom_horizontal_gain_exposure_v_new_n_rep",
+  joinSnapshot = "id", conversions = NULL,
+  mode = "long", ...) {
+
   phenos <- read.pcv(filepath = file, mode = mode, ...)
 
   #* `parse metadata`

diff --git a/R/stat_brms_model.R b/R/stat_brms_model.R
@@ -101,7 +101,11 @@ statBrmsMod <- ggplot2::ggproto("StatBrm", Stat,
       }
       old <- old[, !(names(old) %in% names(new)), drop = FALSE]
       non_constant <- vapply(old, vctrs::vec_unique_count, integer(1)) > 1L
-      non_constant_columns <<- c(non_constant_columns, names(old)[non_constant])
+      assign(
+        "non_constant_columns",
+        c(non_constant_columns, names(old)[non_constant]),
+        parent.frame()
+      )
       vc <- vctrs:::vec_cbind(
         new,
         old[rep(1, nrow(new)), , drop = FALSE]
@@ -214,7 +218,11 @@ statBrmsStaticMod <- ggplot2::ggproto("StatStaticBrm", Stat,
       }
       old <- old[, !(names(old) %in% names(new)), drop = FALSE]
       non_constant <- vapply(old, vctrs::vec_unique_count, integer(1)) > 1L
-      non_constant_columns <<- c(non_constant_columns, names(old)[non_constant])
+      assign(
+        "non_constant_columns",
+        c(non_constant_columns, names(old)[non_constant]),
+        parent.frame()
+      )
       vc <- vctrs:::vec_cbind(
         new,
         old[rep(1, nrow(new)), , drop = FALSE]

diff --git a/R/stat_nlme_model.R b/R/stat_nlme_model.R
@@ -103,7 +103,11 @@ statNlmeMod <- ggplot2::ggproto("StatNlme", Stat,
       }
       old <- old[, !(names(old) %in% names(new)), drop = FALSE]
       non_constant <- vapply(old, vctrs::vec_unique_count, integer(1)) > 1L
-      non_constant_columns <<- c(non_constant_columns, names(old)[non_constant])
+      assign(
+        "non_constant_columns",
+        c(non_constant_columns, names(old)[non_constant]),
+        parent.frame()
+      )
       vc <- vctrs:::vec_cbind(
         new,
         old[rep(1, nrow(new)), , drop = FALSE]

diff --git a/R/stat_nls_model.R b/R/stat_nls_model.R
@@ -64,7 +64,11 @@ statNlsMod <- ggplot2::ggproto("StatNls", Stat,
       }
       old <- old[, !(names(old) %in% names(new)), drop = FALSE]
       non_constant <- vapply(old, vctrs::vec_unique_count, integer(1)) > 1L
-      non_constant_columns <<- c(non_constant_columns, names(old)[non_constant])
+      assign(
+        "non_constant_columns",
+        c(non_constant_columns, names(old)[non_constant]),
+        parent.frame()
+      )
       vc <- vctrs:::vec_cbind(
         new,
         old[rep(1, nrow(new)), , drop = FALSE]

diff --git a/man/growthSS.Rd b/man/growthSS.Rd
diff --git a/tests/testthat/test-brmsModels.R b/tests/testthat/test-brmsModels.R
@@ -15,6 +15,11 @@ test_that("Logistic brms model pipeline", {
     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 = "gam",
+    list("A" = 130, "B" = 10, "C" = "3"), # "3" as a string for a constant prior
+    df = simdf, type = "brms"
+  )
   ss <- growthSS(
     model = "logistic", form = y ~ time | id / group, sigma = "gam",
     list("A" = 130, "B" = 10, "C" = 3),