chore: release v0.5.6

.Rbuildignore

@@ -10,6 +10,7 @@
 ^data-raw$
 doxyfile
 DoxygenLayout.xml
+^doxydoc$
 .covrignore
 ^CRAN-RELEASE$
 ^doc$
@@ -23,3 +24,4 @@ vignettes/benchmarks*
 ^vignettes/precompile.R
 ^CRAN-SUBMISSION$
 ^Makefile$
+^CODEOWNERS$

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: hesim
 Type: Package
 Title: Health Economic Simulation Modeling and Decision Analysis
-Version: 0.5.5
+Version: 0.5.6
 Authors@R: c(
     person("Devin", "Incerti", , "devin.incerti@gmail.com", role = c("aut", "cre")),
     person("Jeroen P.", "Jansen", role = "aut"),
@@ -56,4 +56,4 @@ Suggests:
     truncnorm
 VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3

NEWS.md

@@ -1,3 +1,6 @@
+## hesim 0.5.6
+Ensure compatibility with `ggplot v4.0.0`. 
+
 ## hesim 0.5.5
 Minor updates to the `.Rd` files and tests to fix problem identified with the CRAN package checks.
 

R/plot.R

@@ -293,13 +293,27 @@ autoplot.survival <- function(object, labels = NULL, ci = FALSE,
   # Add confidence intervals 
   if (ci) {
     if (ci_style == "line") {
-      p <-  p + ggplot2::geom_ribbon(ggplot2::aes(x = t, ymin = lower, ymax = upper,
-                                                  fill = .data[["curve"]]), 
-                                     alpha = 0, linetype = "dashed")
+      p <-  p + ggplot2::geom_ribbon(
+        ggplot2::aes(
+          x = t, 
+          ymin = lower, 
+          ymax = upper,
+          fill = .data[["curve"]]
+        ), 
+        alpha = 0, linetype = "dashed",
+        show.legend = FALSE
+        )
     } else if (ci_style == "ribbon") {
-      p <- p + ggplot2::geom_ribbon(ggplot2::aes(x = t, ymin = lower, ymax = upper,
-                                                 fill = .data[["curve"]]), 
-                                    alpha = geom_alpha)
+      p <- p + ggplot2::geom_ribbon(
+        ggplot2::aes(
+          x = t, 
+          ymin = lower, 
+          ymax = upper,
+          fill = .data[["curve"]]
+        ), 
+        alpha = geom_alpha,
+        show.legend = FALSE
+      )
     }
     p <- p + ggplot2::scale_fill_discrete("Curve")
   }
@@ -373,13 +387,28 @@ autoplot.stateprobs <- function(object, labels = NULL, ci = FALSE,
   # Add confidence intervals 
   if (ci) {
     if (ci_style == "line") {
-      p <-  p + ggplot2::geom_ribbon(ggplot2::aes(x = t, ymin = lower, ymax = upper,
-                                                  fill = .data[["strategy_id"]]), 
-                                     alpha = 0, linetype = "dashed")
+      p <-  p + ggplot2::geom_ribbon(
+        ggplot2::aes(
+          x = t, 
+          ymin = lower, 
+          ymax = upper, 
+          fill = .data[["strategy_id"]]
+        ), 
+        alpha = 0, 
+        linetype = "dashed",
+        show.legend = FALSE
+      )
     } else if (ci_style == "ribbon") {
-      p <- p + ggplot2::geom_ribbon(ggplot2::aes(x = t, ymin = lower, ymax = upper,
-                                            fill = .data[["strategy_id"]]), 
-                               alpha = geom_alpha)
+      p <- p + ggplot2::geom_ribbon(
+        ggplot2::aes(
+          x = t, 
+          ymin = lower, 
+          ymax = upper,
+          fill = .data[["strategy_id"]]
+        ), 
+        alpha = geom_alpha,
+        show.legend = FALSE
+      )
     }
     p <- p + ggplot2::scale_fill_discrete("Strategy")
   }

R/utils.R

@@ -381,6 +381,7 @@ get_n_samples.array <- function(x){
 #' Code to use the hesim package inline. Not directly called by the user.
 #' @param ... arguments
 #' @examples
+#'  \dontrun{
 #'   library(Rcpp)
 #'   sourceCpp(code="
 #'   // [[Rcpp::depends(hesim)]]
@@ -393,6 +394,8 @@ get_n_samples.array <- function(x){
 #'   }")
 #'   set.seed(12345)
 #'   test_inline_gengamma(1.0, 1.0, 1.0)
+#'   }
+#'   
 #' @keywords internal
 #' @rdname plugin
 inlineCxxPlugin <- function(...) {

cran-comments.md

@@ -1,14 +1,9 @@
 ## Release summary
-The purpose of this release is to fix problems identified by the 
-CRAN checks. 
+The purpose of this release is to fix errors caused by the latest release of
+ggplot2. This is a resubmission that:
 
-## Test environments
-* Ubuntu 20.04.6 (on GitHub actions), R-devel
-* Ubuntu 20.04.6 (on GitHub actions), R-release 4.4.1
-* Microsoft Windows Server 2022 10.0.20348 (on GitHub actions) R-release 4.4.1
-* win-builder (devel, release)
-* macOS builder
-* R-hub (atlas, gcc14, ubuntu-gcc12, linux (R-devel), macos (R-devel), macos-arm64 (R-devel), windows (R-devel))
+1. Updates a broken link for the data.table website.
+2. Ensures all examples complete in under 10 seconds during CRAN checks.
 
-## Win Builder R-devel results
+## R CMD check results
 0 errors | 0 warnings | 0 notes

tests/testthat/test-plot.R

@@ -55,10 +55,10 @@ test_that("autoplot.survival() allows confidence intervals", {
   # Confidence intervals as lines
   p <- autoplot(surv, labels = labs, ci = TRUE)
 
-  expect_equal(get_labs(p)$fill, "Curve")
+  expect_equal(get_labs(p)$colour, "Curve")
   # Confidence intervals as ribbon
   p <- autoplot(surv, labels = labs, ci = TRUE, ci_style = "ribbon")
-  expect_equal(get_labs(p)$fill, "Curve")
+  expect_equal(get_labs(p)$colour, "Curve")
 })
 
 test_that("autoplot.survival() works with patient weights", {
@@ -98,10 +98,10 @@ test_that("autoplot.stateprobs() allows confidence intervals", {
   # Confidence intervals as lines
   p <- autoplot(stprobs, labels = labs, ci = TRUE)
 
-  expect_equal(get_labs(p)$fill, "Strategy")
+  expect_equal(get_labs(p)$colour, "Strategy")
   # Confidence intervals as ribbon
   p <- autoplot(stprobs, labels = labs, ci = TRUE, ci_style = "ribbon")
-  expect_equal(get_labs(p)$fill, "Strategy")
+  expect_equal(get_labs(p)$colour, "Strategy")
 
 })
 

vignettes/intro.Rmd

@@ -229,7 +229,7 @@ n_samples <- 1000
 ```
 
 ### Disease model
-The disease model is constructed as a function of the fitted multi-state model (using the stored regression coefficients) and input data. The input data must be an object of class `expanded_hesim_data`, which is a [`data.table`](https://rdatatable.gitlab.io/data.table/) containing the covariates for the statistical model. In our multi-state model, each row is a unique treatment strategy, patient, and health-state transition.
+The disease model is constructed as a function of the fitted multi-state model (using the stored regression coefficients) and input data. The input data must be an object of class `expanded_hesim_data`, which is a [`data.table`](https://github.com/Rdatatable/data.table) containing the covariates for the statistical model. In our multi-state model, each row is a unique treatment strategy, patient, and health-state transition.
 
 An `expanded_hesim_data` object can be created by expanding an object of class `hesim_data` using `expand.hesim_data()`.
 

vignettes/markov-inhomogeneous-cohort.Rmd

@@ -331,11 +331,11 @@ set_labels(stateprob_summary, labels = labs,
            new_names = c("strategy_name", "state_name"))
 ggplot(stateprob_summary, aes(x = t, y = count_mean)) +
   geom_line(aes(col = strategy_name)) +
-  geom_ribbon(aes(x = t, ymin = count_lower, ymax = count_upper,
-                  fill = strategy_name), alpha = .3) +
   facet_wrap(~state_name, scale = "free_y") +
   xlab("Year") + ylab("Count") +
-  scale_fill_discrete("Strategy") + scale_color_discrete("Strategy")
+  scale_colour_discrete(name = "Strategy") +
+  geom_ribbon(aes(x = t, ymin = count_lower, ymax = count_upper,
+                  fill = strategy_name), alpha = .3, show.legend = FALSE)
 ```
 
 ## Costs and QALYs