Cran

.Rbuildignore

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

.gitignore

@@ -15,3 +15,4 @@ docs
 tools/lib
 tools/*.html
 \#\*R:scripts\*\#
+*.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: 2026-06-15 15:59:18 UTC
+SHA: dbea2a109738231b67030e826fee70e27ca53408

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")),
@@ -21,8 +21,7 @@ License: GPL-2
 Encoding: UTF-8
 LazyData: true
 LazyDataCompression: xz
-Additional_repositories: https://mc-stan.org/r-packages/
-RoxygenNote: 7.3.2
+Additional_repositories: https://stan-dev.r-universe.dev
 Imports: 
     FactoMineR,
     rlang,
@@ -64,3 +63,5 @@ VignetteBuilder: knitr
 Config/testthat/edition: 3
 URL: https://github.com/danforthcenter/pcvr, https://plantcv.org/, https://danforthcenter.github.io/pcvr/
 BugReports: https://github.com/danforthcenter/pcvr/issues
+Config/roxygen2/version: 8.0.0
+RoxygenNote: 7.3.2

NEWS.md

@@ -1,3 +1,7 @@
+# pcvr 1.4.1
+
+Minor edits in tutorials/articles/examples for CRAN and accessibility.
+
 # pcvr 1.4.0
 
 Added `stat_growthss` to make ggplot layers of models from `growthSS`.

R/barg.R

@@ -103,25 +103,6 @@
 #' @seealso \link{plotPrior} for visual prior predictive checks.
 #' @examples
 #' \donttest{
-#' 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",
-#'   df = simdf, start = list(
-#'     "A" = 130, "B" = 12, "C" = 3,
-#'     "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))
 #'
 #' x <- conjugate(
 #'   s1 = rnorm(10, 10, 1), s2 = rnorm(10, 13, 1.5), method = "t",

R/brmPlot.R

@@ -26,17 +26,7 @@
 #' @importFrom stats as.formula
 #' @examples
 #' \donttest{
-#' 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 = "spline",
-#'   list("A" = 130, "B" = 10, "C" = 3),
-#'   df = simdf, type = "brms"
-#' )
-#' fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
+#' data(fit)
 #' growthPlot(fit = fit, form = y ~ time | group, groups = "a", df = ss$df)
 #' }
 #'

R/brmSurvPlot.R

@@ -24,21 +24,12 @@
 #'   n = 20, t = 50,
 #'   params = list("A" = c(1, 1), "B" = c(0.15, 0.1))
 #' )
-#' ss1 <- growthSS(
+#' ss <- growthSS(
 #'   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)
-#'
-#' # 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)
+#' data(surv)
+#' brmSurvPlot(surv, form = ss$pcvrForm, df = ss$df)
 #' }
 #'
 #' @return Returns a ggplot showing a brms model's credible

R/brmViolin.R

@@ -21,18 +21,7 @@
 #' @examples
 #' \donttest{
 #' set.seed(123)
-#' simdf <- growthSim(
-#'   "logistic",
-#'   n = 20, t = 25,
-#'   params = list("A" = c(200, 180, 190, 160), "B" = c(13, 11, 10, 10), "C" = c(3, 3, 3.25, 3.5))
-#' )
-#' ss <- growthSS(
-#'   model = "logistic", form = y ~ time | id / group, sigma = "int",
-#'   list("A" = 130, "B" = 10, "C" = 3),
-#'   df = simdf, type = "brms"
-#' )
-#'
-#' fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
+#' data(fit)
 #' 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

R/combineDraws.R

@@ -24,61 +24,12 @@
 #' @examples
 #' # note that this example will fit several models using Stan and may run slowly.
 #' \donttest{
-#' simdf <- growthSim("logistic",
-#'   n = 20, t = 25,
-#'   params = list(
-#'     "A" = c(200, 160, 220, 200, 140, 300),
-#'     "B" = c(13, 11, 10, 9, 16, 12),
-#'     "C" = c(3, 3.5, 3.2, 2.8, 3.3, 2.5)
-#'   )
-#' )
-#' ss_ab <- growthSS(
-#'   model = "logistic", form = y ~ time | id / group,
-#'   sigma = "logistic", df = simdf[simdf$group %in% c("a", "b"), ],
-#'   start = list(
-#'     "A" = 130, "B" = 12, "C" = 3,
-#'     "sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
-#'   ), type = "brms"
-#' )
-#'
-#' ss_cd <- growthSS(
-#'   model = "logistic", form = y ~ time | id / group,
-#'   sigma = "logistic", df = simdf[simdf$group %in% c("c", "d"), ],
-#'   start = list(
-#'     "A" = 130, "B" = 12, "C" = 3,
-#'     "sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
-#'   ), type = "brms"
-#' )
-#'
-#' ss_ef <- growthSS(
-#'   model = "logistic", form = y ~ time | id / group,
-#'   sigma = "logistic", df = simdf[simdf$group %in% c("e", "f"), ],
-#'   start = list(
-#'     "A" = 130, "B" = 12, "C" = 3,
-#'     "sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
-#'   ), type = "brms"
-#' )
-#' ss_ef2 <- growthSS(
-#'   model = "gompertz", form = y ~ time | id / group,
-#'   sigma = "logistic", df = simdf[simdf$group %in% c("e", "f"), ],
-#'   start = list(
-#'     "A" = 130, "B" = 12, "C" = 3,
-#'     "sigmaA" = 15, "sigmaB" = 10, "sigmaC" = 3
-#'   ), type = "brms"
-#' )
-#'
-#'
-#' 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)
+#' data(fit)
+#' fit_1 <- fit_2 <- fit
+#' x <- combineDraws(fit_1, fit_2, fit)
+#' draws_1 <- as.data.frame(fit_1)
+#' draws_1 <- draws_1[, grepl("^b_", colnames(draws_ef))]
+#' x2 <- combineDraws(fit_2, draws_1)
 #' }
 #'
 #' @return Returns a dataframe of posterior draws.

R/datasets.R

@@ -0,0 +1,26 @@
+#' Example model
+#'
+#' @description A logistic brms model of simulated growth data.
+#'
+#' @format A brmsfit object and a growthss object
+#' @examples
+#' \donttest{
+#' data(fit, package = "pcvr")
+#' summary(fit)
+#' }
+#'
+"fit"
+
+
+#' Example survival model
+#'
+#' @description An exponential survival brms model of simulated data.
+#'
+#' @format A brmsfit object and a growthss object
+#' @examples
+#' \donttest{
+#' data(surv, package = "pcvr")
+#' summary(surv)
+#' }
+#'
+"surv"

R/growthSS.R

@@ -336,15 +336,7 @@
 #' )
 #' 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)
-#' )
-#' }
-#'
+#' # the next step would typically be compiling/fitting the model with fitGrowth
 #'
 #' # formulas and priors will look different if there is only one group in the data
 #'

R/mvSS.R

@@ -34,12 +34,6 @@
 #'   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)
-#' }
 #'
 #' # when the model is longitudinal the same model is possible with growthSS
 #'
@@ -97,13 +91,6 @@
 #'   }
 #' }))
 #'
-#' \donttest{
-#' if (rlang::is_installed("mnormt")) {
-#'   m2 <- fitGrowth(ss_mv1, backend = "cmdstanr", iter = 1000, chains = 1, cores = 1)
-#'   growthPlot(m2, ss_mv1$pcvrForm, df = ss_mv1$df)
-#' }
-#' }
-#'
 #' @export
 
 mvSS <- function(model = "linear", form, sigma = NULL, df, start = NULL,

R/stat_growthSS.R

@@ -33,15 +33,7 @@
 #'
 #' @examples
 #' library(ggplot2)
-#' 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,
-#'   df = simdf, start = NULL, type = "nls"
-#' )
-#' fit <- fitGrowth(ss)
+#' data(fit)
 #' ggplot() +
 #'   stat_growthss(fit = fit, ss = ss)
 #'

cran-comments.md

@@ -1,42 +1,36 @@
-# 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>’
-
-  Possibly misspelled words in DESCRIPTION:
-    Kruschke (16:2, 17:2, 17:46)
-    Phenotyping (3:14)
-    phenotyping (12:44)
+❯ checking for future file timestamps ... NOTE
+  unable to verify current time
 
+## Notes
+
+Resubmitting due to cran testing error probably related to either not installing
+suggested packages or the change from `mc-stan.org` to `stan-dev.r--universe.dev`
+for some additional packages and for development features
+(ggplot2 geom support, more distributions, etc).
+Previously a NOTE related to the failure specified:
+
+```
+❯ checking package dependencies ... NOTE
+Package suggested but not available for checking: ‘cmdstanr’
 Suggests or Enhances not in mainstream repositories:
   cmdstanr
 Availability using Additional_repositories specification:
-  cmdstanr   yes   https://mc-stan.org/r-packages/
-❯ checking package namespace information ... OK
-❯ checking package dependencies ... NOTE
-Package suggested but not available for checking: ‘cmdstanr’
-
-## Notes
+  cmdstanr   yes   https://stan-dev.r-universe.dev
+```
 
-Resubmitting due to large changes in the development version.
+But the `cmdstanr` suggestion is available from the new additional repository specified in DESCRIPTION.
 
 No notes seem critical.
+
 Names (PlantCV, Kruschke) and words (phenotyping) in DESCRIPTION are not misspelled.
-The `cmdstanr` suggestion is available from the additional repository specified in DESCRIPTION.
 The R CMD check on MacOs via github actions yields a NOTE about installed package size as well.
 
-```
-* checking installed package size ... NOTE
-  installed size is  6.4Mb
-  sub-directories of 1Mb or more:
-    doc   4.1Mb
-    R     2.0Mb
-```
-
 Additionally there are some examples wrapped in `\donttest` that may take several minutes to run.
 
 ## Test environments