Import all functions once in R/gsDesign2-package.R, and remove the pkg:: qualifiers

NAMESPACE

@@ -87,7 +87,6 @@ importFrom(stats,qnorm)
 importFrom(stats,setNames)
 importFrom(stats,stepfun)
 importFrom(stats,uniroot)
-importFrom(survival,Surv)
 importFrom(tibble,tibble)
 importFrom(utils,tail)
 useDynLib(gsDesign2, .registration = TRUE)

R/ahr.R

@@ -72,8 +72,6 @@
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @importFrom data.table setDF setDT
-#'
 #' @export
 #'
 #' @examples

R/ahr_blinded.R

@@ -60,9 +60,6 @@
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @importFrom tibble tibble
-#' @importFrom survival Surv
-#'
 #' @export
 #'
 #' @examples

R/as_gt.R

@@ -258,10 +258,10 @@ as_gt.gs_design <- function(
   )
 
   x <- parts$x %>%
-    dplyr::group_by(Analysis) %>%
+    group_by(Analysis) %>%
     gt::gt() %>%
     gt::tab_spanner(
-      columns = dplyr::all_of(colname_spannersub),
+      columns = all_of(colname_spannersub),
       label = colname_spanner
     ) %>%
     gt::tab_header(title = parts$title, subtitle = parts$subtitle)
@@ -409,7 +409,7 @@ gsd_parts <- function(
   )
 
   list(
-    x = dplyr::arrange(x2, Analysis),
+    x = arrange(x2, Analysis),
     title = title, subtitle = subtitle,
     footnote = footnote %||% gsd_footnote(method, columns),
     alpha = max(filter(x, Bound == bound[1])[[alpha_column]])

R/check_arg.R

@@ -255,7 +255,7 @@ check_enroll_rate_fail_rate <- function(enroll_rate, fail_rate) {
   if ("stratum" %in% colnames(enroll_rate) && "stratum" %in% colnames(fail_rate)) {
     strata_enroll <- unique(enroll_rate$stratum)
     strata_fail <- unique(fail_rate$stratum)
-    strata_common <- dplyr::intersect(strata_enroll, strata_fail)
+    strata_common <- intersect(strata_enroll, strata_fail)
 
     if (sum(strata_common %in% strata_enroll) != length(strata_enroll)) {
       stop("The `Strata` column in the input argument `enroll_rate` and `fail_rate` must be the same!")

R/define.R

@@ -54,7 +54,7 @@ define_enroll_rate <- function(
     duration,
     rate,
     stratum = "All") {
-  df <- tibble::tibble(
+  df <- tibble(
     stratum = stratum,
     duration = duration,
     rate = rate
@@ -117,7 +117,7 @@ define_fail_rate <- function(
     dropout_rate,
     hr = 1,
     stratum = "All") {
-  df <- tibble::tibble(
+  df <- tibble(
     stratum = stratum,
     duration = duration,
     fail_rate = fail_rate,

R/expected_accural.R

@@ -44,10 +44,6 @@
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @importFrom dplyr filter lead
-#' @importFrom tibble tibble
-#' @importFrom stats stepfun
-#'
 #' @export
 #'
 #' @examples

R/expected_time.R

@@ -45,8 +45,6 @@
 #'    }
 #'  }
 #'
-#' @importFrom stats uniroot
-#'
 #' @export
 #'
 #' @examples

R/fixed_design_ahr.R

@@ -39,8 +39,6 @@
 #'   and `1 - alpha` otherwise).
 #' @param study_duration Study duration.
 #'
-#' @importFrom dplyr filter
-#'
 #' @returns A list of design characteristic summary.
 #'
 #' @export
@@ -121,7 +119,7 @@ fixed_design_ahr <- function(
       analysis_time = study_duration
     )
   }
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "ahr",
     n = d$analysis$n,
     event = d$analysis$event,

R/fixed_design_fh.R

@@ -24,8 +24,6 @@
 #' @param rho test parameter in Fleming-Harrington method.
 #' @param gamma test parameter in Fleming-Harrington method.
 #'
-#' @importFrom dplyr filter
-#'
 #' @export
 #'
 #' @rdname fixed_design
@@ -120,7 +118,7 @@ fixed_design_fh <- function(
       analysis_time = study_duration
     )
   }
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "fh",
     n = d$analysis$n,
     event = d$analysis$event,

R/fixed_design_lf.R

@@ -173,7 +173,7 @@ fixed_design_lf <- function(
       rr[, 1]
     })
   )
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "lf",
     n = d$n,
     event = d$d,

R/fixed_design_maxcombo.R

@@ -118,7 +118,7 @@ fixed_design_maxcombo <- function(
     )
   }
   # get the output of MaxCombo
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "maxcombo",
     n = d$analysis$n,
     event = d$analysis$event,

R/fixed_design_mb.R

@@ -118,7 +118,7 @@ fixed_design_mb <- function(
     )
   }
   # get the output of MB
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "mb",
     n = d$analysis$n,
     event = d$analysis$event,

R/fixed_design_milestone.R

@@ -106,7 +106,7 @@ fixed_design_milestone <- function(
     )
   }
   # get the output of MaxCombo
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "milestone",
     n = d$analysis$n,
     event = d$analysis$event,

R/fixed_design_rd.R

@@ -76,26 +76,26 @@ fixed_design_rd <- function(
   # Generate design ----
   if (is.null(power)) {
     d <- gs_power_rd(
-      p_c = tibble::tibble(stratum = "All", rate = p_c),
-      p_e = tibble::tibble(stratum = "All", rate = p_e),
+      p_c = tibble(stratum = "All", rate = p_c),
+      p_e = tibble(stratum = "All", rate = p_e),
       ratio = ratio,
       upper = gs_b, upar = qnorm(1 - alpha),
       lower = gs_b, lpar = -Inf,
-      n = tibble::tibble(stratum = "All", n = n, analysis = 1),
+      n = tibble(stratum = "All", n = n, analysis = 1),
       rd0 = rd0, weight = "unstratified"
     )
   } else {
     d <- gs_design_rd(
-      p_c = tibble::tibble(stratum = "All", rate = p_c),
-      p_e = tibble::tibble(stratum = "All", rate = p_e),
+      p_c = tibble(stratum = "All", rate = p_c),
+      p_e = tibble(stratum = "All", rate = p_e),
       alpha = alpha, beta = 1 - power, ratio = ratio,
       upper = gs_b, upar = qnorm(1 - alpha),
       lower = gs_b, lpar = -Inf,
       rd0 = rd0, weight = "unstratified"
     )
   }
   # get the output of MaxCombo
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "rd",
     n = d$analysis$n,
     bound = (d$bound %>% filter(bound == "upper"))$z,

R/fixed_design_rmst.R

@@ -105,7 +105,7 @@ fixed_design_rmst <- function(
     )
   }
   # get the output
-  ans <- tibble::tibble(
+  ans <- tibble(
     design = "rmst",
     n = d$analysis$n,
     event = d$analysis$event,

R/gsDesign2-package.R

@@ -16,8 +16,16 @@
 #  You should have received a copy of the GNU General Public License
 #  along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-#' @keywords internal
-#' @useDynLib gsDesign2, .registration = TRUE
-#' @importFrom stats setNames
+#' @importFrom data.table ":=" as.data.table copy first last rbindlist setDF
+#'   setDT setcolorder setorderv
+#' @importFrom dplyr "%>%" all_of arrange desc filter full_join group_by lead
+#'   left_join mutate one_of rename row_number select summarize ungroup
+#' @importFrom gsDesign gsDesign sfLDOF
+#' @importFrom mvtnorm GenzBretz
+#' @importFrom stats pnorm qnorm setNames stepfun uniroot
+#' @importFrom tibble tibble
+#' @importFrom utils tail
 #' @importFrom Rcpp sourceCpp
+#' @useDynLib gsDesign2, .registration = TRUE
+#' @keywords internal
 "_PACKAGE"

R/gs_design_ahr.R

@@ -89,10 +89,6 @@
 #' @details
 #' To be added.
 #'
-#' @importFrom dplyr all_of mutate full_join select arrange desc
-#' @importFrom gsDesign gsDesign sfLDOF
-#' @importFrom stats qnorm
-#'
 #' @export
 #'
 #' @examples

R/gs_design_combo.R

@@ -27,8 +27,6 @@
 #'
 #' @return A list with input parameters, enrollment rate, analysis, and bound.
 #'
-#' @importFrom mvtnorm GenzBretz
-#'
 #' @export
 #'
 #' @examples
@@ -267,12 +265,12 @@ gs_design_combo <- function(
 
 
   out <- db %>%
-    dplyr::select(
+    select(
       analysis, bound, time, n, event, z,
       probability, probability_null
     ) %>%
-    dplyr::rename(probability0 = probability_null) %>%
-    dplyr::mutate(`nominal p` = pnorm(z * (-1)))
+    rename(probability0 = probability_null) %>%
+    mutate(`nominal p` = pnorm(z * (-1)))
 
 
   # get bounds to output

R/gs_design_npe.R

@@ -124,9 +124,6 @@
 #'
 #' @author Keaven Anderson \email{keaven_anderson@@merck.com}
 #'
-#' @importFrom tibble tibble
-#' @importFrom stats qnorm uniroot
-#'
 #' @export
 #'
 #' @examples
@@ -469,7 +466,7 @@ gs_design_npe <- function(
       full_join(
         ans_h0 %>%
           select(analysis, bound, probability) %>%
-          dplyr::rename(probability0 = probability)
+          rename(probability0 = probability)
       )
   )
 

R/gs_design_rd.R

@@ -62,10 +62,6 @@
 #' @details
 #' To be added.
 #'
-#' @importFrom gsDesign gsDesign sfLDOF
-#' @importFrom stats qnorm
-#' @importFrom dplyr mutate select arrange desc
-#'
 #' @export
 #'
 #' @examples
@@ -170,7 +166,7 @@ gs_design_rd <- function(p_c = tibble::tibble(stratum = "All", rate = .2),
   x_fix <- gs_info_rd(
     p_c = p_c,
     p_e = p_e,
-    n = tibble::tibble(
+    n = tibble(
       analysis = 1,
       stratum = p_c$stratum,
       n = if (is.null(stratum_prev)) {
@@ -188,7 +184,7 @@ gs_design_rd <- function(p_c = tibble::tibble(stratum = "All", rate = .2),
   x_gs <- gs_info_rd(
     p_c = p_c,
     p_e = p_e,
-    n = tibble::tibble(
+    n = tibble(
       analysis = rep(1:k, n_strata),
       stratum = rep(p_c$stratum, each = k),
       n = if (is.null(stratum_prev)) {

R/gs_design_wlr.R

@@ -35,8 +35,6 @@
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @importFrom dplyr all_of
-#'
 #' @export
 #' @return A list with input parameters, enrollment rate, analysis, and bound.
 #'

R/gs_info_rd.R

@@ -32,8 +32,6 @@
 #'   theta0 (standardized treatment effect under null hypothesis),
 #'   and statistical information.
 #'
-#' @importFrom dplyr group_by left_join mutate select summarize ungroup
-#'
 #' @export
 #'
 #' @examples
@@ -183,13 +181,13 @@ gs_info_rd <- function(
   suppressMessages(
     tbl <- n %>%
       left_join(p_c) %>%
-      dplyr::rename(p_c = rate) %>%
+      rename(p_c = rate) %>%
       left_join(p_e) %>%
-      dplyr::rename(p_e = rate) %>%
+      rename(p_e = rate) %>%
       left_join(if ("data.frame" %in% class(rd0)) {
         rd0
       } else {
-        tibble::tibble(analysis = 1:n_analysis, rd0 = rd0)
+        tibble(analysis = 1:n_analysis, rd0 = rd0)
       }) %>%
       mutate(
         n_e = n / (1 + ratio),
@@ -222,7 +220,7 @@ gs_info_rd <- function(
       tbl <- tbl %>%
         left_join(
           tbl %>%
-            dplyr::group_by(analysis) %>%
+            group_by(analysis) %>%
             summarize(sum_ss = sum(n_c * n_e / (n_c + n_e)))
         ) %>%
         mutate(weight_per_k_per_s = n_c * n_e / (n_c + n_e) / sum_ss) %>%
@@ -233,7 +231,7 @@ gs_info_rd <- function(
       tbl <- tbl %>%
         left_join(
           tbl %>%
-            dplyr::group_by(analysis) %>%
+            group_by(analysis) %>%
             summarize(sum_inv_var_per_s = sum(1 / sigma2_H1_per_k_per_s))
         ) %>%
         mutate(weight_per_k_per_s = 1 / sigma2_H1_per_k_per_s / sum_inv_var_per_s) %>%
@@ -243,7 +241,7 @@ gs_info_rd <- function(
 
   # Pool the strata together ----
   ans <- tbl %>%
-    dplyr::group_by(analysis) %>%
+    group_by(analysis) %>%
     summarize(
       n = sum(n),
       rd = sum((p_c - p_e) * d * weight_per_k_per_s),
@@ -261,7 +259,7 @@ gs_info_rd <- function(
       info1 = 1 / sigma2_H1,
       info0 = 1 / sigma2_H0
     ) %>%
-    dplyr::ungroup() %>%
+    ungroup() %>%
     select(analysis, n, rd, rd0, theta1, theta0, info1, info0)
 
   return(ans)

R/gs_info_wlr.R

@@ -49,8 +49,6 @@
 #' The [expected_time()] function is used to get events and average HR at
 #' targeted `analysis_time`.
 #'
-#' @importFrom utils tail
-#'
 #' @export
 #'
 #' @examples