From 40f3848867784b0fbb185fc7b935fd8175375f80 Mon Sep 17 00:00:00 2001
From: "Zhao, Yujie" <yujie.zhao@merck.com>
Date: Fri, 24 Jan 2025 18:21:17 -0500
Subject: [PATCH] change parallel simulation set up

---
 DESCRIPTION                           |  4 +---
 vignettes/sim_fixed_design_custom.Rmd | 12 +++---------
 vignettes/sim_gs_design_custom.Rmd    | 18 ++++--------------
 3 files changed, 8 insertions(+), 26 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 4372f7d4..410115a6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -60,9 +60,7 @@ Suggests:
     survMisc,
     survRM2,
     testthat,
-    tidyr,
-    doRNG,
-    tictoc
+    tidyr
 LinkingTo:
     Rcpp
 Roxygen: list(markdown = TRUE)
diff --git a/vignettes/sim_fixed_design_custom.Rmd b/vignettes/sim_fixed_design_custom.Rmd
index 45048bb6..a989d741 100644
--- a/vignettes/sim_fixed_design_custom.Rmd
+++ b/vignettes/sim_fixed_design_custom.Rmd
@@ -15,8 +15,6 @@ library(dplyr)
 library(tibble)
 library(gt)
 library(doFuture)
-library(doRNG)
-library(tictoc)
 
 set.seed(2025)
 ```
@@ -279,17 +277,14 @@ one_sim <- function(sim_id = 1,
 
 After that, we will execute `one_sim()` multiple times using parallel computation. The following lines of code takes 4 works to run 100 simulations. 
 ```{r}
-tic()
-registerDoFuture()
-registerDoRNG()
-plan("multisession", workers = 4)
 set.seed(2025)
 
 ans <- foreach(
   sim_id = seq_len(n_sim),
   .combine = "rbind",
-  .errorhandling = "stop"
-  ) %dorng% {
+  .errorhandling = "stop",
+  .options.future = list(seed = TRUE)
+  ) %dofuture% {
     ans_new <- one_sim(
       sim_id = sim_id, 
       # arguments from Step 1: design characteristic
@@ -315,7 +310,6 @@ ans <- foreach(
   }
 
 plan("sequential")
-toc()
 ```
 
 The output from the parallel computation resembles the output of `sim_fix_n()` described in the vignette [Simulate Fixed Designs with Ease via sim_fixed_n](https://merck.github.io/simtrial/articles/sim_fixed_design_simple.html). Each row in the output corresponds to the simulation results for each testing method per each repeation. 
diff --git a/vignettes/sim_gs_design_custom.Rmd b/vignettes/sim_gs_design_custom.Rmd
index 14425be6..4fca0e5f 100644
--- a/vignettes/sim_gs_design_custom.Rmd
+++ b/vignettes/sim_gs_design_custom.Rmd
@@ -15,8 +15,6 @@ library(dplyr)
 library(tibble)
 library(gt)
 library(doFuture)
-library(doRNG)
-library(tictoc)
 
 set.seed(2025)
 ```
@@ -35,7 +33,7 @@ If users are interested in more complex scenarios such as the ones listed below,
 The parameters setup to scratch a group sequential simulation is very similar to Step 1 of the vignette [Simulate Group Sequential Designs with Ease via sim_gs_n](https://merck.github.io/simtrial/articles/sim_gs_design_simple.html). To shorten the length of this vignette, we will use the same design characteristics and cutting method.
 
 ```{r}
-n_sim <- 1e4
+n_sim <- 1e2
 stratum <- data.frame(stratum = "All", p = 1)
 block <- rep(c("experimental", "control"), 2)
 enroll_rate <- data.frame(stratum = "All", rate = 1, duration = 12)
@@ -128,17 +126,12 @@ one_sim <- function(sim_id = 1,
 Then we run `one_sim()` `r n_sim` times via parallel computation. 
 
 ```{r}
-tic()
-registerDoFuture()
-registerDoRNG()
-plan("multisession", workers = 4)
-set.seed(2025)
-
 ans <- foreach(
   sim_id = seq_len(n_sim),
   .combine = "rbind",
-  .errorhandling = "stop"
-  ) %dorng% {
+  .errorhandling = "stop",
+  .options.future = list(seed = TRUE)
+  ) %dofuture% {
     ans_new <- one_sim(
       sim_id = sim_id, 
       # arguments from Step 1: design characteristic
@@ -159,9 +152,6 @@ ans <- foreach(
                               
     ans_new
   }
-
-plan("sequential")
-toc()
 ```
 
 The output of `sim_gs_n` is a data frame with one row per simulation per analysis per testing method.