diff --git a/DESCRIPTION b/DESCRIPTION
index 1639eff..d0ab853 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -6,6 +6,7 @@ Depends:
     R (>= 3.6.0)
 Imports:
     stats,
+    rlang,
     foreach (>= 1.4.7),
     matrixStats (>= 0.63.0),
     CVXR (>= 0.99-6),
diff --git a/NAMESPACE b/NAMESPACE
index 3fa3e25..9d290bf 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 importFrom(dplyr, "%>%")
 importFrom(foreach, "%dopar%")
 importFrom(foreach, "%do%")
+importFrom(rlang, ".data")
 export(computeConditionalCS_DeltaRM)
 export(computeConditionalCS_DeltaRMB)
 export(computeConditionalCS_DeltaRMM)
diff --git a/R/flci.R b/R/flci.R
index ff22d33..ba49299 100644
--- a/R/flci.R
+++ b/R/flci.R
@@ -226,23 +226,23 @@
     hGrid <- base::seq(from = hMin, to = h0, length.out = numPoints)
     biasDF <- purrr::map_dfr(.x = hGrid,
                              .f = function(h){ .findWorstCaseBiasGivenH(h, sigma = sigma, numPrePeriods = numPrePeriods, numPostPeriods = numPostPeriods, l_vec = l_vec, returnDF = T) %>% dplyr::mutate(h = h) } )
-    biasDF <- biasDF %>% dplyr::rename(bias = value)
+    biasDF <- biasDF %>% dplyr::rename(bias = .data$value)
     biasDF <-
       dplyr::left_join(
         biasDF %>% dplyr::mutate(id = 1),
         data.frame(m = M, id = 1),
-        by = "id") %>% dplyr::select(-id)
+        by = "id") %>% dplyr::select(-.data$id)
   
     biasDF <- biasDF %>%
-      dplyr::rename(maxBias = bias) %>% dplyr::filter(maxBias < Inf)
+      dplyr::rename(maxBias = .data$bias) %>% dplyr::filter(.data$maxBias < Inf)
     biasDF <- biasDF %>%
-      dplyr::mutate(maxBias = maxBias * m) %>%
-      dplyr::mutate(CI.halflength = .qfoldednormal(p = 1-alpha, mu = maxBias/h) * h)
+      dplyr::mutate(maxBias = .data$maxBias * .data$m) %>%
+      dplyr::mutate(CI.halflength = .qfoldednormal(p = 1-alpha, mu = .data$maxBias/.data$h) * .data$h)
   
     optimalCIDF <- biasDF %>%
-      dplyr::group_by(m) %>%
-      dplyr::filter(status == "optimal" | status == "optimal_inaccurate") %>%
-      dplyr::filter(CI.halflength == min(CI.halflength))
+      dplyr::group_by(.data$m) %>%
+      dplyr::filter(.data$status == "optimal" | .data$status == "optimal_inaccurate") %>%
+      dplyr::filter(.data$CI.halflength == min(.data$CI.halflength))
   } else {
     optimalCIDF <- .findWorstCaseBiasGivenH(hstar, sigma, numPrePeriods, numPostPeriods, l_vec, T)
     optimalCIDF$m <- M
diff --git a/R/sensitivityresults.R b/R/sensitivityresults.R
index 62e8669..ff9ce59 100644
--- a/R/sensitivityresults.R
+++ b/R/sensitivityresults.R
@@ -21,6 +21,7 @@ createSensitivityResults <- function(betahat, sigma,
 
   .stopIfNotConformable(betahat, sigma, numPrePeriods, numPostPeriods, l_vec)
   .warnIfNotSymmPSD(sigma)
+  m <- NULL
 
   # If Mvec is null, construct default Mvec
   if (base::is.null(Mvec)) {
@@ -389,10 +390,10 @@ createSensitivityPlot <- function(robustResults, originalResults, rescaleFactor
   df <- df %>% dplyr::mutate_at( c("M", "ub", "lb"), ~ .x * rescaleFactor)
 
   # Filter out observations above maxM (after rescaling)
-  df <- df %>% dplyr::filter(M <= maxM)
+  df <- df %>% dplyr::filter(.data$M <= maxM)
 
-  p <- ggplot2::ggplot(data = df, ggplot2::aes(x=M)) +
-    ggplot2::geom_errorbar(ggplot2::aes(ymin = lb, ymax = ub, color = base::factor(method)),
+  p <- ggplot2::ggplot(data = df, ggplot2::aes(x=.data$M)) +
+    ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$lb, ymax = .data$ub, color = base::factor(.data$method)),
                            width = Mgap * rescaleFactor / 2) +
     ggplot2::scale_color_manual(values = base::c("red", '#01a2d9')) +
     ggplot2::theme(legend.title=ggplot2::element_blank(), legend.position="bottom") +
@@ -421,6 +422,7 @@ createSensitivityResults_relativeMagnitudes <- function(betahat, sigma,
 
   .stopIfNotConformable(betahat, sigma, numPrePeriods, numPostPeriods, l_vec)
   .warnIfNotSymmPSD(sigma)
+  m <- NULL
 
   # If Mbarvec is null, construct default Mbarvec to be 10 values on [0,2].
   if (base::is.null(Mbarvec)) {
@@ -694,10 +696,10 @@ createSensitivityPlot_relativeMagnitudes <- function(robustResults, originalResu
   df <- df %>% dplyr::mutate_at( base::c("Mbar", "ub", "lb"), ~ .x * rescaleFactor)
 
   # Filter out observations above maxM (after rescaling)
-  df <- df %>% dplyr::filter(Mbar <= maxMbar)
+  df <- df %>% dplyr::filter(.data$Mbar <= maxMbar)
 
-  p <- ggplot2::ggplot(data = df, ggplot2::aes(x=Mbar)) +
-    ggplot2::geom_errorbar(ggplot2::aes(ymin = lb, ymax = ub, color = base::factor(method)),
+  p <- ggplot2::ggplot(data = df, ggplot2::aes(x=.data$Mbar)) +
+    ggplot2::geom_errorbar(ggplot2::aes(ymin = .data$lb, ymax = .data$ub, color = base::factor(.data$method)),
                            width = Mbargap * rescaleFactor / 2) +
     ggplot2::scale_color_manual(values = base::c("red", '#01a2d9')) +
     ggplot2::theme(legend.title=ggplot2::element_blank(), legend.position="bottom") +
@@ -747,7 +749,7 @@ createEventStudyPlot <- function(betahat, stdErrors = NULL, sigma = NULL,
                                                    se   = base::c(stdErrors[1:numPrePeriods], NA, stdErrors[(numPrePeriods+1):(numPrePeriods+numPostPeriods)])),
                                     ggplot2::aes(x = t)) +
     ggplot2::geom_point(ggplot2::aes(y = beta), color = "red") +
-    ggplot2::geom_errorbar(ggplot2::aes(ymin = beta - stats::qnorm(1-alpha/2)*se, ymax = beta + stats::qnorm(1-alpha/2)*se), width = 0.5, colour = "#01a2d9") +
+    ggplot2::geom_errorbar(ggplot2::aes(ymin = beta - stats::qnorm(1-alpha/2)*.data$se, ymax = beta + stats::qnorm(1-alpha/2)*.data$se), width = 0.5, colour = "#01a2d9") +
     ggplot2::theme(legend.position = "none") + 
     ggplot2::labs(x = "Event time", y = "") +
     ggplot2::scale_x_continuous(breaks = base::seq(from = base::min(timeVec), to = base::max(timeVec), by = 1),