Merge pull request #600 from Merck/599-cran-release-v117

CRAN release v1.1.7

Author: LittleBeannie

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: gsDesign2
 Title: Group Sequential Design with Non-Constant Effect
-Version: 1.1.6.1
+Version: 1.1.7
 Authors@R: c(
     person("Keaven", "Anderson", email = "[email protected]", role = c("aut")),
     person("Yujie", "Zhao", email = "[email protected]", role = c("aut", "cre")),

NEWS.md

@@ -1,8 +1,19 @@
 # gsDesign2 1.1.7
 
-## User interface improvements
+## Documentation
+
+- The vignette discussing the futility boundary design is finalized (#583, thanks to @LittleBeannie)
+- An example calculating expected events for multiple strata is added (#597, thanks to @LittleBeannie)
+- The documentation of common parameters is unified by `@inheritparams` (#598, thanks to @LittleBeannie)
+- A contributing guide with overview of package architecture is added (#596, thanks to @jdblischak)
+
+## Programming improvements
 
+- The S3 classes are re-factored (#584, thanks to @jdblischak)
+- The input parameters are not displayed when printing the objects  (#586, thanks to @jdblischak)
 - `text_summary()` supports design objects with spending functions specified as character strings (#587, thanks to @yihui).
+- The attribute assignment `uninteger_is_from` is fixed in `to_integer()` (#591, thanks to @jdblischak)
+- `text_summary()` is updated to support fixed designs from `gs_design_ahr()` (#592, thanks to @jdblischak)
 
 # gsDesign2 1.1.6
 

R/expected_event.R

@@ -109,13 +109,15 @@
 #' )
 #'
 #' # Single time period example, multiple strata
-#' enroll_rate <- define_enroll_rate(stratum = rep(c("Biomarker-positive", "Biomarker-negative"), each = 4),
-#'                                   duration = c(2, 2, 2, 6, 2, 2, 2, 6),
-#'                                   rate = c(1:4, 1:4))
-#' failure_rate <- define_fail_rate(stratum = rep(c("Biomarker-positive", "Biomarker-negative"), each = 2),
-#'                                  duration = c(3, 100, 3, 100),
-#'                                  fail_rate = log(2) / c(8, 12, 8, 10),
-#'                                  dropout_rate = 0.001)
+#' enroll_rate <- define_enroll_rate(
+#'   stratum = rep(c("Biomarker-positive", "Biomarker-negative"), each = 4),
+#'   duration = c(2, 2, 2, 6, 2, 2, 2, 6),
+#'   rate = c(1:4, 1:4))
+#' failure_rate <- define_fail_rate(
+#'   stratum = rep(c("Biomarker-positive", "Biomarker-negative"), each = 2),
+#'   duration = c(3, 100, 3, 100),
+#'   fail_rate = log(2) / c(8, 12, 8, 10),
+#'   dropout_rate = 0.001)
 #' # Number of expected events by stratum
 #' sapply(c("Biomarker-positive", "Biomarker-negative"),
 #'        function(ss){