diff --git a/DESCRIPTION b/DESCRIPTION
index 64508da4..44861556 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: simtrial
 Type: Package
 Title: Clinical Trial Simulation
-Version: 0.3.0.1
+Version: 0.3.0.2
 Authors@R: c(
     person("Keaven", "Anderson", email = "keaven_anderson@merck.com", role = c("aut")),
     person("Yilong", "Zhang", email = "elong0527@gmail.com", role =  c("aut")),
@@ -48,7 +48,6 @@ Imports:
     stats,
     survival,
     tibble,
-    tidyr,
     utils
 Suggests:
     Matrix,
@@ -62,7 +61,8 @@ Suggests:
     rmarkdown,
     stringr,
     survMisc,
-    testthat
+    testthat,
+    tidyr
 LinkingTo:
     Rcpp
 Roxygen: list(markdown = TRUE)
diff --git a/NAMESPACE b/NAMESPACE
index c2070561..a121ed78 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -30,14 +30,10 @@ importFrom(data.table,setDF)
 importFrom(data.table,setDT)
 importFrom(data.table,setorderv)
 importFrom(doFuture,"%dofuture%")
-importFrom(dplyr,filter)
-importFrom(dplyr,full_join)
 importFrom(dplyr,group_by)
 importFrom(dplyr,mutate)
-importFrom(dplyr,right_join)
 importFrom(dplyr,select)
 importFrom(dplyr,starts_with)
-importFrom(dplyr,summarize)
 importFrom(future,plan)
 importFrom(magrittr,"%>%")
 importFrom(methods,is)
@@ -46,5 +42,4 @@ importFrom(mvtnorm,pmvnorm)
 importFrom(survival,Surv)
 importFrom(survival,is.Surv)
 importFrom(tibble,tibble)
-importFrom(tidyr,replace_na)
 useDynLib(simtrial, .registration = TRUE)
diff --git a/R/mb_weight.R b/R/mb_weight.R
index c74954a9..ee307faf 100644
--- a/R/mb_weight.R
+++ b/R/mb_weight.R
@@ -43,8 +43,8 @@
 #'   Set `delay = Inf`, `w_max = 2` to be consistent with recommendation of
 #'   Magirr (2021).
 #'
-#' @return A vector with weights for the Magirr-Burman weighted logrank test
-#'   for the data in `x`.
+#' @return A data frame. The column `mb_weight` contains the weights for the
+#'   Magirr-Burman weighted logrank test for the data in `x`.
 #'
 #' @details
 #' We define \eqn{t^*} to be the input variable `delay`.
@@ -65,10 +65,6 @@
 #' "Non‐proportional hazards in immuno‐oncology: Is an old perspective needed?"
 #' _Pharmaceutical Statistics_ 20 (3): 512--527.
 #'
-#' @importFrom dplyr group_by summarize filter right_join
-#'   mutate full_join select
-#' @importFrom tidyr replace_na
-#'
 #' @export
 #'
 #' @examples
@@ -141,26 +137,25 @@ mb_weight <- function(x, delay = 4, w_max = Inf) {
   }
 
   # Compute max weight by stratum
-  x2 <- x %>% group_by(stratum)
+  x2 <- as.data.table(x)
   # Make sure you don't lose any stratum!
-  tbl_all_stratum <- x2 %>% summarize()
+  tbl_all_stratum <- data.table(stratum = unique(x2$stratum))
 
-  ans <- x2 %>%
-    # Look only up to delay time
-    filter(tte <= delay) %>%
-    # Weight before delay specified as 1/S
-    summarize(max_weight = max(1 / s)) %>%
-    # Get back stratum with no records before delay ends
-    right_join(tbl_all_stratum, by = "stratum") %>%
-    # `max_weight` is 1 when there are no records before delay ends
-    mutate(max_weight = replace_na(max_weight, 1)) %>%
-    # Cut off weights at w_max
-    mutate(max_weight = pmin(w_max, max_weight)) %>%
-    # Now merge max_weight back to stratified dataset
-    full_join(x2, by = "stratum") %>%
-    # Weight is min of max_weight and 1/S which will increase up to delay
-    mutate(mb_weight = pmin(max_weight, 1 / s)) %>%
-    select(-max_weight)
+  # Look only up to delay time
+  ans <- x2[tte <= delay, ]
+  # Weight before delay specified as 1/S
+  ans <- ans[, .(max_weight = max(1 / s)), by = "stratum"]
+  # Get back stratum with no records before delay ends
+  ans <- ans[tbl_all_stratum, on = "stratum"]
+  # `max_weight` is 1 when there are no records before delay ends
+  ans[, max_weight := ifelse(is.na(max_weight), 1, max_weight)]
+  # Cut off weights at w_max
+  ans[, max_weight := pmin(w_max, max_weight)]
+  # Now merge max_weight back to stratified dataset
+  ans <- merge.data.table(ans, x2, by = "stratum", all = TRUE)
+  # Weight is min of max_weight and 1/S which will increase up to delay
+  ans[, mb_weight := pmin(max_weight, 1 / s)]
+  ans[, max_weight := NULL]
 
-  ans
+  setDF(ans)
 }
diff --git a/R/simfix2simPWSurv.R b/R/simfix2simPWSurv.R
index f6fe0300..81c36351 100644
--- a/R/simfix2simPWSurv.R
+++ b/R/simfix2simPWSurv.R
@@ -42,7 +42,6 @@
 #' @export
 #'
 #' @examples
-#' library(tidyr)
 #' library(dplyr)
 #' library(tibble)
 #'
diff --git a/data-raw/DATASET.R b/data-raw/DATASET.R
index 37a5b679..da826a7d 100644
--- a/data-raw/DATASET.R
+++ b/data-raw/DATASET.R
@@ -1,5 +1,5 @@
 ## code to prepare `DATASET` dataset goes here
-library(tidyr)
+library(tibble)
 set.seed(6671)
 ds <- simPWSurv(
   n = 200,
diff --git a/man/mb_weight.Rd b/man/mb_weight.Rd
index e97cfefe..c8a43dcb 100644
--- a/man/mb_weight.Rd
+++ b/man/mb_weight.Rd
@@ -17,8 +17,8 @@ Set \code{delay = Inf}, \code{w_max = 2} to be consistent with recommendation of
 Magirr (2021).}
 }
 \value{
-A vector with weights for the Magirr-Burman weighted logrank test
-for the data in \code{x}.
+A data frame. The column \code{mb_weight} contains the weights for the
+Magirr-Burman weighted logrank test for the data in \code{x}.
 }
 \description{
 Magirr and Burman (2019) proposed a weighted logrank test to have better
diff --git a/man/simfix2simpwsurv.Rd b/man/simfix2simpwsurv.Rd
index 698ae2d6..ccc810e4 100644
--- a/man/simfix2simpwsurv.Rd
+++ b/man/simfix2simpwsurv.Rd
@@ -31,7 +31,6 @@ to analyze or otherwise evaluate individual simulations in ways that
 \code{\link[=sim_fixed_n]{sim_fixed_n()}} does not.
 }
 \examples{
-library(tidyr)
 library(dplyr)
 library(tibble)
 
diff --git a/tests/testthat/generate-backwards-compatible-test-data.R b/tests/testthat/generate-backwards-compatible-test-data.R
index 8499e217..4b95221b 100644
--- a/tests/testthat/generate-backwards-compatible-test-data.R
+++ b/tests/testthat/generate-backwards-compatible-test-data.R
@@ -269,4 +269,35 @@ generate_test_data <- function(
     dropout_rate = dropout_rate
   )
   saveRDS(ex4, file.path(outdir, "sim_pw_surv_ex4.rds"))
+
+  # mb_weight() ----------------------------------------------------------------
+
+  # Use default enrollment and event rates at cut at 100 events
+  # For transparency, may be good to set either `delay` or `w_max` to `Inf`
+  set.seed(12345)
+  x <- sim_pw_surv(n = 200)
+  x <- cut_data_by_event(x, 125)
+  x <- counting_process(x, arm = "experimental")
+
+  # Example 1
+  # Compute Magirr-Burman weights with `delay = 6`
+  ZMB <- mb_weight(x, delay = 6, w_max = Inf)
+  S <- with(ZMB, sum(o_minus_e * mb_weight))
+  V <- with(ZMB, sum(var_o_minus_e * mb_weight^2))
+  z <- S / sqrt(V)
+
+  # Compute p-value of modestly weighted logrank of Magirr-Burman
+  ex1 <- pnorm(z)
+  saveRDS(ex1, file.path(outdir, "mb_weight_ex1.rds"))
+
+  # Example 2
+  # Now compute with maximum weight of 2 as recommended in Magirr, 2021
+  ZMB2 <- mb_weight(x, delay = Inf, w_max = 2)
+  S <- with(ZMB2, sum(o_minus_e * mb_weight))
+  V <- with(ZMB2, sum(var_o_minus_e * mb_weight^2))
+  z <- S / sqrt(V)
+
+  # Compute p-value of modestly weighted logrank of Magirr-Burman
+  ex2 <- pnorm(z)
+  saveRDS(ex2, file.path(outdir, "mb_weight_ex2.rds"))
 }
diff --git a/tests/testthat/test-backwards-compatibility.R b/tests/testthat/test-backwards-compatibility.R
index 4446dbee..8e437eef 100644
--- a/tests/testthat/test-backwards-compatibility.R
+++ b/tests/testthat/test-backwards-compatibility.R
@@ -279,3 +279,37 @@ test_that("sim_fixed_n()", {
   expected <- readRDS("fixtures/backwards-compatibility/sim_fixed_n_ex3.rds")
   expect_equal(observed, expected)
 })
+
+test_that("mb_weight()", {
+  # Use default enrollment and event rates at cut at 100 events
+  # For transparency, may be good to set either `delay` or `w_max` to `Inf`
+  set.seed(12345)
+  x <- sim_pw_surv(n = 200)
+  x <- cut_data_by_event(x, 125)
+  x <- counting_process(x, arm = "experimental")
+
+  # Example 1
+  # Compute Magirr-Burman weights with `delay = 6`
+  ZMB <- mb_weight(x, delay = 6, w_max = Inf)
+  S <- with(ZMB, sum(o_minus_e * mb_weight))
+  V <- with(ZMB, sum(var_o_minus_e * mb_weight^2))
+  z <- S / sqrt(V)
+
+  # Compute p-value of modestly weighted logrank of Magirr-Burman
+  observed <- pnorm(z)
+  expected <- readRDS("fixtures/backwards-compatibility/mb_weight_ex1.rds")
+  expect_equal(observed, expected)
+
+  # Example 2
+  # Now compute with maximum weight of 2 as recommended in Magirr, 2021
+  ZMB2 <- mb_weight(x, delay = Inf, w_max = 2)
+  S <- with(ZMB2, sum(o_minus_e * mb_weight))
+  V <- with(ZMB2, sum(var_o_minus_e * mb_weight^2))
+  z <- S / sqrt(V)
+
+  # Compute p-value of modestly weighted logrank of Magirr-Burman
+  observed <- pnorm(z)
+  expected <- readRDS("fixtures/backwards-compatibility/mb_weight_ex2.rds")
+  expect_equal(observed, expected)
+})
+
diff --git a/tests/testthat/test-double_programming_simPWSurv.R b/tests/testthat/test-double_programming_simPWSurv.R
index ebe11405..ef7fa4d6 100644
--- a/tests/testthat/test-double_programming_simPWSurv.R
+++ b/tests/testthat/test-double_programming_simPWSurv.R
@@ -31,8 +31,8 @@ x <- sim_pw_surv(
 )
 
 # prepare to test block
-block1 <- x %>% filter(stratum == "Low")
-block2 <- x %>% filter(stratum == "High")
+block1 <- x %>% dplyr::filter(stratum == "Low")
+block2 <- x %>% dplyr::filter(stratum == "High")
 bktest1 <- c()
 j <- 1
 for (i in seq(1, floor(nrow(block1) / 4))) {
diff --git a/tests/testthat/test-independent_test_cutData.R b/tests/testthat/test-independent_test_cutData.R
index 8c9f9b33..cafc8c6a 100644
--- a/tests/testthat/test-independent_test_cutData.R
+++ b/tests/testthat/test-independent_test_cutData.R
@@ -14,7 +14,7 @@ test_that("x is a time-to-event data set", {
 cut_date <- 10
 xcut <- cut_data_by_date(x, cut_date)
 test_that("only paitients recorded by cut_data_by_date are included", {
-  Npts <- dim(filter(x, enroll_time <= cut_date))[1]
+  Npts <- dim(dplyr::filter(x, enroll_time <= cut_date))[1]
   Nptscut <- length(xcut$tte)
   testthat::expect_equal(Npts, Nptscut)
 })
@@ -25,6 +25,6 @@ test_that("Time to event (tte) is cut off at the cut_date", {
 
 test_that("the event variable is calculated correctly", {
   Nevent <- sum(x$fail * (x$cte <= cut_date))
-  Neventcut <- dim(filter(xcut, event == 1))[1]
+  Neventcut <- dim(dplyr::filter(xcut, event == 1))[1]
   testthat::expect_equal(Nevent, Neventcut)
 })
diff --git a/tests/testthat/test-independent_test_getCutDateForCount.R b/tests/testthat/test-independent_test_getCutDateForCount.R
index 17e01ca6..5d0b4d6e 100644
--- a/tests/testthat/test-independent_test_getCutDateForCount.R
+++ b/tests/testthat/test-independent_test_getCutDateForCount.R
@@ -5,7 +5,7 @@ test_that("get_cut_date_by_event returns the correct cut date", {
 
   x <- y %>%
     dplyr::ungroup() %>%
-    filter(fail == 1) %>%
+    dplyr::filter(fail == 1) %>%
     dplyr::arrange(cte) %>%
     dplyr::slice(event)
   expect_equal(x$cte, ycutdate)
diff --git a/tests/testthat/test-independent_test_pvalue_maxcombo.R b/tests/testthat/test-independent_test_pvalue_maxcombo.R
index 81080c5b..398650a9 100644
--- a/tests/testthat/test-independent_test_pvalue_maxcombo.R
+++ b/tests/testthat/test-independent_test_pvalue_maxcombo.R
@@ -52,7 +52,7 @@ testthat::test_that("the p-values correspond to pvalue_maxcombo", {
   tst.rslt2 <- subset(tst.rsltt, grepl("FH", tst.rsltt$W))
   cov.tst.rslt11 <- tst.rslt2$Var
   d2$V <- cov.tst.rslt11
-  d1d2 <- full_join(d1, d2, by = c("X1", "X2"))
+  d1d2 <- dplyr::full_join(d1, d2, by = c("X1", "X2"))
   cov.tst.rslt1 <- d1d2$V
   cov.tst.1 <- matrix(NA, nrow = length(wt1), ncol = length(wt1))
   cov.tst.1[lower.tri(cov.tst.1, diag = FALSE)] <- cov.tst.rslt1
diff --git a/tests/testthat/test-independent_test_wlr.R b/tests/testthat/test-independent_test_wlr.R
index 11099116..50efadb2 100644
--- a/tests/testthat/test-independent_test_wlr.R
+++ b/tests/testthat/test-independent_test_wlr.R
@@ -78,7 +78,7 @@ testthat::test_that("wlr calculated correct correlation value when input a seque
   tst.rslt2 <- subset(tst.rsltt, grepl("FH", tst.rsltt$W))
   cov.tst.rslt11 <- tst.rslt2$Var
   d2$V <- cov.tst.rslt11
-  d1d2 <- full_join(d1, d2, by = c("X1", "X2"))
+  d1d2 <- dplyr::full_join(d1, d2, by = c("X1", "X2"))
   cov.tst.rslt1 <- d1d2$V
   cov.tst.1 <- matrix(NA, nrow = length(wt1), ncol = length(wt1))
   cov.tst.1[lower.tri(cov.tst.1, diag = FALSE)] <- cov.tst.rslt1
diff --git a/vignettes/modestWLRTVignette.Rmd b/vignettes/modestWLRTVignette.Rmd
index a6484a05..e614e4f2 100644
--- a/vignettes/modestWLRTVignette.Rmd
+++ b/vignettes/modestWLRTVignette.Rmd
@@ -81,7 +81,7 @@ Next, we consider the @Magirr2021 extension of the modestly weighted logrank tes
 $$w(t, \tau, w_{\max}) = \min\left(w_{\max},\left(\frac{1}{S(\min(t,\tau))}\right)\right).$$
 
 This requires generating weights and then computing the test.
-We begin with the default of `w_max=\Inf` which corresponds to the original @MagirrBurman test and set the time until maximum weight $\tau$ with `delay = 6`.
+We begin with the default of `w_max=Inf` which corresponds to the original @MagirrBurman test and set the time until maximum weight $\tau$ with `delay = 6`.
 
 ```{r}
 ZMB <- MBdelay %>%
@@ -122,7 +122,7 @@ pnorm(Z_modified_FH$z)
 ### Freidlin and Korn strong null hypothesis example
 
 The underlying survival is worse for the experimental group is uniformly worse for the experimental group until the very end of the study.
-This was presented by @FKNPH2019. For this case, we have a hazard ratio of 16 for 1/10 of 1 year (1.2 months), followed by a hazard ratio of
+This was presented by @FKNPH2019. For this case, we have a hazard ratio of 16 for 1/10 of 1 year (1.2 months), followed by a hazard ratio of 0.76 thereafter.
 
 First, we specify study duration, sample size and enrollment rates. The enrollment rate is assumed constant during the enrollment period until the targeted sample size is reached.
 For failure rates, we consider the delayed treatment effect example of @MagirrBurman.
@@ -144,7 +144,7 @@ fail_rate <- tibble::tibble(
 )
 ```
 
-Now we generate a single dataset with the above characteristics and cut data for analysis at 36 months post start of enrollment.
+Now we generate a single dataset with the above characteristics and cut data for analysis at 5 years post start of enrollment.
 Then we plot Kaplan-Meier curves for the resulting dataset (red curve for experimental treatment, black for control):
 
 ```{r, message=FALSE,warning=FALSE,fig.width=7.5, fig.height=4}