Cran2

.Rbuildignore

@@ -14,4 +14,5 @@
 ^pkgdown$
 ^vignettes/articles$
 ^DO_NOT_COMMIT*
+^demon.org
 ^\.#.*$

.gitignore

@@ -15,3 +15,4 @@ docs
 tools/lib
 tools/*.html
 \#\*R:scripts\*\#
+demon.org

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.3.1
-Date: 2025-08-18 20:38:57 UTC
-SHA: 03f0cc66b17667d39907be289ad0930cfb6ea1f6
+Version: 1.4.1
+Date: 2026-06-23 12:55:28 UTC
+SHA: 2f488b3aa26db9b410db06b9992defa0d3dabdcb

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: pcvr
 Type: Package
 Title: Plant Phenotyping and Bayesian Statistics
-Version: 1.4.0
+Version: 1.4.1
 Authors@R:
     c(person("Josh", "Sumner", email = "jsumner@danforthcenter.org",
              role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3399-9063")),
@@ -21,7 +21,7 @@ License: GPL-2
 Encoding: UTF-8
 LazyData: true
 LazyDataCompression: xz
-Additional_repositories: https://mc-stan.org/r-packages/
+Additional_repositories: https://stan-dev.r-universe.dev
 RoxygenNote: 7.3.2
 Imports: 
     FactoMineR,

R/barg.R

@@ -114,15 +114,16 @@
 #'     "sigmaA" = 20, "sigmaB" = 10, "sigmaC" = 2
 #'   ), type = "brms"
 #' )
-#' fit_test <- fitGrowth(ss,
-#'   iter = 600, cores = 1, chains = 1, backend = "cmdstanr",
-#'   sample_prior = "only" # only sampling from prior for speed
-#' )
-#' b <- barg(fit_test, ss)
-#' fit_2 <- fit_test
-#' fit_list <- list(fit_test, fit_2)
-#' x <- barg(fit_list, list(ss, ss))
-#'
+#' if (rlang::is_installed("cmdstanr")) {
+#'   fit_test <- fitGrowth(ss,
+#'     iter = 600, cores = 1, chains = 1, backend = "cmdstanr",
+#'     sample_prior = "only" # only sampling from prior for speed
+#'   )
+#'   b <- barg(fit_test, ss)
+#'   fit_2 <- fit_test
+#'   fit_list <- list(fit_test, fit_2)
+#'   x <- barg(fit_list, list(ss, ss))
+#' }
 #' x <- conjugate(
 #'   s1 = rnorm(10, 10, 1), s2 = rnorm(10, 13, 1.5), method = "t",
 #'   priors = list(

R/brmPlot.R

@@ -24,6 +24,7 @@
 #' @import ggplot2
 #' @import viridis
 #' @importFrom stats as.formula
+#' @importFrom rlang is_installed
 #' @examples
 #' \donttest{
 #' simdf <- growthSim(
@@ -36,8 +37,10 @@
 #'   list("A" = 130, "B" = 10, "C" = 3),
 #'   df = simdf, type = "brms"
 #' )
-#' fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
-#' growthPlot(fit = fit, form = y ~ time | group, groups = "a", df = ss$df)
+#' if (rlang::is_installed("cmdstanr")) {
+#'   fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
+#'   growthPlot(fit = fit, form = y ~ time | group, groups = "a", df = ss$df)
+#' }
 #' }
 #'
 #' @return Returns a ggplot showing a brms model's credible

R/brmSurvPlot.R

@@ -17,6 +17,7 @@
 #' @import ggplot2
 #' @import viridis
 #' @importFrom stats as.formula
+#' @importFrom rlang is_installed
 #' @examples
 #' \donttest{
 #' set.seed(123)
@@ -28,17 +29,20 @@
 #'   model = "survival weibull", form = y > 100 ~ time | id / group,
 #'   df = df, start = c(0, 5)
 #' )
-#' fit1 <- fitGrowth(ss1, iter = 600, cores = 2, chains = 2, backend = "cmdstanr")
-#' brmSurvPlot(fit1, form = ss1$pcvrForm, df = ss1$df)
-#'
+#' if (rlang::is_installed("cmdstanr")) {
+#'   fit1 <- fitGrowth(ss1, iter = 600, cores = 2, chains = 2, backend = "cmdstanr")
+#'   brmSurvPlot(fit1, form = ss1$pcvrForm, df = ss1$df)
+#' }
 #' # note that using the cumulative hazard to calculate survival is likely to underestimate
 #' # survival in these plots if events do not start immediately.
 #' ss2 <- growthSS(
 #'   model = "survival binomial", form = y > 100 ~ time | id / group,
 #'   df = df, start = c(-4, 3)
 #' )
-#' fit2 <- fitGrowth(ss2, iter = 600, cores = 2, chains = 2, backend = "cmdstanr")
-#' brmSurvPlot(fit2, form = ss2$pcvrForm, df = ss2$df)
+#' if (rlang::is_installed("cmdstanr")) {
+#'   fit2 <- fitGrowth(ss2, iter = 600, cores = 2, chains = 2, backend = "cmdstanr")
+#'   brmSurvPlot(fit2, form = ss2$pcvrForm, df = ss2$df)
+#' }
 #' }
 #'
 #' @return Returns a ggplot showing a brms model's credible

R/brmViolin.R

@@ -31,12 +31,13 @@
 #'   list("A" = 130, "B" = 10, "C" = 3),
 #'   df = simdf, type = "brms"
 #' )
-#'
-#' fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
-#' brmViolin(fit, ss, ".../A_groupd > 1.05") # all groups used
-#' brmViolin(fit, ss, "abs(1 - ((...)/(C_groupd - B_groupd))) > 0.05") # rather arbitrary
-#' brmViolin(fit, ss, "abs(1 - ((...)/(C_groupa - B_groupd))) > 0.05") # totally arbitrary
-#' brmViolin(fit, ss, "A_groupa/A_groupd > 1.05") # only these two groups
+#' if (rlang::is_installed("cmdstanr")) {
+#'   fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
+#'   brmViolin(fit, ss, ".../A_groupd > 1.05") # all groups used
+#'   brmViolin(fit, ss, "abs(1 - ((...)/(C_groupd - B_groupd))) > 0.05") # rather arbitrary
+#'   brmViolin(fit, ss, "abs(1 - ((...)/(C_groupa - B_groupd))) > 0.05") # totally arbitrary
+#'   brmViolin(fit, ss, "A_groupa/A_groupd > 1.05") # only these two groups
+#' }
 #' }
 #'
 #' @return Returns a ggplot showing a brms model's posterior distributions

R/combineDraws.R

@@ -14,6 +14,7 @@
 #' the objects, or if models have different formulations.
 #' @keywords brms
 #' @importFrom methods is
+#' @importFrom rlang is_installed
 #' @returns A data.frame of posterior draws, labeled to show which object they come from.
 #' @details
 #' If you fit models as part of a loop/apply function and end up with a list of models it may be
@@ -67,18 +68,19 @@
 #'   ), type = "brms"
 #' )
 #'
+#' if (rlang::is_installed("cmdstanr")) {
+#'   fit_ab <- fitGrowth(ss_ab, chains = 1, cores = 1, iter = 1000)
+#'   fit_ab2 <- fitGrowth(ss_ab, chains = 1, cores = 1, iter = 1200)
+#'   fit_cd <- fitGrowth(ss_cd, chains = 1, cores = 1, iter = 1000)
+#'   fit_ef <- fitGrowth(ss_ef, chains = 1, cores = 1, iter = 1000)
+#'   fit_ef2 <- fitGrowth(ss_ef2, chains = 1, cores = 1, iter = 1000)
 #'
-#' fit_ab <- fitGrowth(ss_ab, chains = 1, cores = 1, iter = 1000)
-#' fit_ab2 <- fitGrowth(ss_ab, chains = 1, cores = 1, iter = 1200)
-#' fit_cd <- fitGrowth(ss_cd, chains = 1, cores = 1, iter = 1000)
-#' fit_ef <- fitGrowth(ss_ef, chains = 1, cores = 1, iter = 1000)
-#' fit_ef2 <- fitGrowth(ss_ef2, chains = 1, cores = 1, iter = 1000)
-#'
-#' x <- combineDraws(fit_ab, fit_cd, fit_ef)
-#' draws_ef <- as.data.frame(fit_ef)
-#' draws_ef <- draws_ef[, grepl("^b_", colnames(draws_ef))]
-#' x2 <- combineDraws(fit_ab2, fit_cd, draws_ef)
-#' x3 <- combineDraws(fit_ab, fit_cd, fit_ef2)
+#'   x <- combineDraws(fit_ab, fit_cd, fit_ef)
+#'   draws_ef <- as.data.frame(fit_ef)
+#'   draws_ef <- draws_ef[, grepl("^b_", colnames(draws_ef))]
+#'   x2 <- combineDraws(fit_ab2, fit_cd, draws_ef)
+#'   x3 <- combineDraws(fit_ab, fit_cd, fit_ef2)
+#' }
 #' }
 #'
 #' @return Returns a dataframe of posterior draws.

R/growthSS.R

@@ -337,14 +337,6 @@
 #' lapply(ss, class)
 #' ss$initfun()
 #' # the next step would typically be compiling/fitting the model
-#' # here we use very few chains and very few iterations for speed, but more of both is better.
-#' \donttest{
-#' fit_test <- fitGrowth(ss,
-#'   iter = 500, cores = 1, chains = 1, backend = "cmdstanr",
-#'   control = list(adapt_delta = 0.999, max_treedepth = 20)
-#' )
-#' }
-#'
 #'
 #' # formulas and priors will look different if there is only one group in the data
 #'

R/mvSS.R

@@ -24,6 +24,7 @@
 #' optionally with a plot of simulated growth curves using draws from those priors.
 #' @seealso \link{fitGrowth} for fitting the model specified by this list.
 #' @examples
+#' \donttest{
 #' set.seed(123)
 #' mv_df <- mvSim(dists = list(rnorm = list(mean = 100, sd = 30)), wide = FALSE)
 #' mv_df$group <- rep(c("a", "b"), times = 900)
@@ -34,11 +35,11 @@
 #'   model = "linear", form = label | value ~ group, df = mv_df,
 #'   start = list("A" = 5), type = "brms", spectral_index = "none"
 #' )
-#' \donttest{
-#' mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
-#' growthPlot(mod1, ss1$pcvrForm, df = ss1$df)
-#' library(ggplot2)
-#' ggplot() + stat_brms_model(fit = mod1, ss = ss1)
+#' if (rlang::is_installed("cmdstanr")) {
+#'   mod1 <- fitGrowth(ss1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
+#'   growthPlot(mod1, ss1$pcvrForm, df = ss1$df)
+#'   library(ggplot2)
+#'   ggplot() + stat_brms_model(fit = mod1, ss = ss1)
 #' }
 #'
 #' # when the model is longitudinal the same model is possible with growthSS
@@ -97,8 +98,7 @@
 #'   }
 #' }))
 #'
-#' \donttest{
-#' if (rlang::is_installed("mnormt")) {
+#' if (rlang::is_installed(c("cmdstanr", "mnormt"))) {
 #'   m2 <- fitGrowth(ss_mv1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
 #'   growthPlot(m2, ss_mv1$pcvrForm, df = ss_mv1$df)
 #' }

cran-comments.md

@@ -1,8 +1,8 @@
-# pcvr 1.3.1
+# pcvr 1.4.1
 
 ## R CMD check results
 
-0 errors ✔ | 0 warnings ✔ | 2 notes ✖
+0 errors ✔ | 0 warnings ✔ | 1 notes ✖
 
 ❯ checking CRAN incoming feasibility ... [4s/6s] NOTE
   Maintainer: ‘Josh Sumner <jsumner@danforthcenter.org>’
@@ -15,14 +15,14 @@
 Suggests or Enhances not in mainstream repositories:
   cmdstanr
 Availability using Additional_repositories specification:
-  cmdstanr   yes   https://mc-stan.org/r-packages/
+  cmdstanr   yes   https://stan-dev.r-universe.dev
 ❯ checking package namespace information ... OK
 ❯ checking package dependencies ... NOTE
 Package suggested but not available for checking: ‘cmdstanr’
 
 ## Notes
 
-Resubmitting due to large changes in the development version.
+Resubmitting due to an error on cran when cmdstanr failed to install in testing.
 
 No notes seem critical.
 Names (PlantCV, Kruschke) and words (phenotyping) in DESCRIPTION are not misspelled.