Move function to internal

NAMESPACE

@@ -14,7 +14,6 @@ export(expected_accrual)
 export(expected_event)
 export(expected_time)
 export(fixed_design)
-export(gridpts)
 export(gs_b)
 export(gs_create_arm)
 export(gs_design_ahr)
@@ -33,8 +32,6 @@ export(gs_power_rd)
 export(gs_power_wlr)
 export(gs_spending_bound)
 export(gs_spending_combo)
-export(h1)
-export(hupdate)
 export(ppwe)
 export(s2pwe)
 export(to_integer)

R/gridpts_h1_hupdate.R

@@ -51,7 +51,7 @@
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @export
+#' @noRd
 #'
 #' @examples
 #' # Approximate variance of standard normal (i.e., 1)
@@ -93,7 +93,7 @@ gridpts <- function(r = 18, mu = 0, a = -Inf, b = Inf) {
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @export
+#' @noRd
 #'
 #' @examples
 #' # Replicate variance of 1, mean of 35
@@ -137,7 +137,7 @@ h1 <- function(r = 18, theta = 0, info = 1, a = -Inf, b = Inf) {
 #' }
 #' \if{html}{The contents of this section are shown in PDF user manual only.}
 #'
-#' @export
+#' @noRd
 #'
 #' @examples
 #' # 2nd analysis with no interim bound and drift 0 should have mean 0, variance 1

R/gs_spending_bound.R

@@ -81,39 +81,6 @@
 #' @importFrom stats qnorm
 #'
 #' @export
-#'
-#' @examples
-#' info <- (1:3) * 10
-#' info_frac <- info / max(info)
-#' k <- length(info_frac)
-#'
-#' # 1st analysis
-#' a1 <- gs_spending_bound(
-#'   k = 1, efficacy = FALSE, theta = 0,
-#'   par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, timing = info_frac, param = NULL),
-#'   hgm1 = NULL
-#' )
-#'
-#' b1 <- gs_spending_bound(
-#'   k = 1, efficacy = TRUE, theta = 0,
-#'   par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, timing = info_frac, param = NULL),
-#'   hgm1 = NULL
-#' )
-#' cat("The (lower, upper) boundary at the 1st analysis is (", a1, ", ", b1, ").\n")
-#'
-#' # 2nd analysis
-#' a2 <- gs_spending_bound(
-#'   k = 2, efficacy = FALSE, theta = 0,
-#'   par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, timing = info_frac, param = NULL),
-#'   hgm1 = h1(r = 18, theta = 0, info = info[1], a = a1, b = b1)
-#' )
-#'
-#' b2 <- gs_spending_bound(
-#'   k = 2, efficacy = TRUE, theta = 0,
-#'   par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, timing = info_frac, param = NULL),
-#'   hgm1 = h1(r = 18, theta = 0, info = info[1], a = a1, b = b1)
-#' )
-#' cat("The upper boundary at the 2nd analysis is (", a2, ", ", b2, ").\n")
 gs_spending_bound <- function(k = 1,
                               par = list(
                                 sf = gsDesign::sfLDOF,

_pkgdown.yml

@@ -97,9 +97,6 @@ reference:
     - expected_accrual
     - ppwe
     - s2pwe
-    - gridpts
-    - h1
-    - hupdate
     - gs_create_arm
 
 articles:

vignettes/articles/story-npe-integration.Rmd

@@ -195,7 +195,7 @@ The columns in the resulting table are
 ```{r}
 # Set up grid over continuation region
 # Null hypothesis
-grid1_0 <- h1(theta = 0, info = info[1], a = a1, b = b1)
+grid1_0 <- gsDesign2:::h1(theta = 0, info = info[1], a = a1, b = b1)
 grid1_0 %>% head()
 ```
 The probability of not crossing a bound under the null hypothesis is computed as follows:
@@ -212,7 +212,7 @@ cat(
 We now set up numerical integration grid under the alternate hypothesis and the compute continuation probability.
 
 ```{r}
-grid1_1 <- h1(theta = theta[1], info = info[1], a = a1, b = b1)
+grid1_1 <- gsDesign2:::h1(theta = theta[1], info = info[1], a = a1, b = b1)
 prob_h1_continue <- sum(grid1_1$h)
 h1mean <- sqrt(info[1]) * theta[1]
 cat(
@@ -248,7 +248,7 @@ Under the null hypothesis, we need to update to the interval above `b2_0`.
 
 ```{r}
 # Upper rejection region grid under H0
-grid2_0 <- hupdate(theta = 0, info = info[2], a = b2_0, b = Inf, im1 = info[1], gm1 = grid1_0)
+grid2_0 <- gsDesign2:::hupdate(theta = 0, info = info[2], a = b2_0, b = Inf, im1 = info[1], gm1 = grid1_0)
 pupper_0 <- sum(grid2_0$h)
 cat(
   "Upper spending at analysis 2\n Target:", spend0, "\n Using initial bound approximation:",
@@ -272,7 +272,7 @@ cat(
   "Original bound approximation:", b2_0,
   "\nUpdated bound approximation:", b2_1
 )
-grid2_0 <- hupdate(theta = 0, info = info[2], a = b2_1, b = Inf, im1 = info[1], gm1 = grid1_0)
+grid2_0 <- gsDesign2:::hupdate(theta = 0, info = info[2], a = b2_1, b = Inf, im1 = info[1], gm1 = grid1_0)
 pupper_1 <- sum(grid2_0$h)
 cat(
   "\nOriginal boundary crossing probability:", pupper_0,
@@ -286,7 +286,7 @@ We now update the lower bound in an analogous fashion.
 
 ```{r}
 # Lower rejection region grid under H1
-grid2_1 <- hupdate(
+grid2_1 <- gsDesign2:::hupdate(
   theta = theta[2], info = info[2], a = -Inf, b = a2_0,
   thetam1 = theta[1], im1 = info[1], gm1 = grid1_1
 )
@@ -305,7 +305,7 @@ cat(
   "\nUpdated bound approximation:", a2_1
 )
 
-grid2_1 <- hupdate(
+grid2_1 <- gsDesign2:::hupdate(
   theta = theta[2], info = info[2], a = -Inf, b = a2_1,
   thetam1 = theta[1], im1 = info[1], gm1 = grid1_1
 )

vignettes/articles/usage-gs-spending-bound.Rmd

@@ -61,13 +61,13 @@ cat("The (lower, upper) boundary at the 1st analysis is (", a1, ", ", b1, ").\n"
 a2 <- gs_spending_bound(
   k = 2, efficacy = FALSE, theta = 0,
   par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, timing = info_frac, param = NULL),
-  hgm1 = h1(r = 18, theta = 0, info = info[1], a = a1, b = b1)
+  hgm1 = gsDesign2:::h1(r = 18, theta = 0, info = info[1], a = a1, b = b1)
 )
 
 b2 <- gs_spending_bound(
   k = 2, efficacy = TRUE, theta = 0,
   par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, timing = info_frac, param = NULL),
-  hgm1 = h1(r = 18, theta = 0, info = info[1], a = a1, b = b1)
+  hgm1 = gsDesign2:::h1(r = 18, theta = 0, info = info[1], a = a1, b = b1)
 )
 cat("The upper boundary at the 2nd analysis is (", a2, ", ", b2, ").\n")
 ```