Revise according to CoPilot's comments

Author: LittleBeannie

R/gs_power_npe.R

@@ -18,41 +18,41 @@
 
 #' @title Group sequential design computation with non-constant effect and information.
 #'
-#' @description The following 2 functions allows a non-constant treatment effect over time,
+#' @description The following two functions allow a non-constant treatment effect over time,
 #' but also can be applied for the usual homogeneous effect size designs.
-#' They requires treatment effect and statistical information at each analysis
+#' They require treatment effect and statistical information at each analysis
 #' as well as a method of deriving bounds, such as spending.
-#' Initial bound types supported are spending bounds, and fixed bounds.
-#' The above routine enables two things not available in the gsDesign package: 1)
+#' Initial bound types supported are spending bounds and fixed bounds.
+#' These routines enables two things not available in the gsDesign package: 1)
 #' non-constant effect, 2) more flexibility in boundary selection.
 #'
 #' \code{gs_power_npe()} derives group sequential bounds and boundary crossing probabilities
-#' for a design, given treatment effect and information at each analysis, as well as the
+#' for a design, given treatment effect and information at each analysis and the
 #' method of deriving bounds, such as spending.
 #'
 #' \code{gs_design_npe()} derives group sequential design size,
 #' bounds and boundary crossing probabilities based on proportionate
 #' information and effect size at analyses, as well as the
 #' method of deriving bounds, such as spending.
 #'
-#' The only differences in arguments of the 2 functions are the \code{alpha} and \code{beta}
-#' used in the \code{gs_design_npe()}.
+#' The only differences in arguments between the two functions are the \code{alpha} and \code{beta}
+#' parameters used in the \code{gs_design_npe()}.
 #'
 #' @param theta Natural parameter for group sequential design representing
 #'   expected cumulative drift at all analyses; used for power calculation.
 #'   It can be a scalar (constant treatment effect) or a vector (non-constant treatment effect).
-#'   Users are required to provide the value of `theta`.
+#'   The user must provide a value for `theta`.
 #' @param theta0 Natural parameter for null hypothesis.
 #'   It can be a scalar (constant treatment effect) or a vector (non-constant treatment effect).
-#'   Default is 0. If users provide values other than 0, it impacts upper bound computation.
+#'   The default is 0. If a value other than 0 is provided, it affects upper bound computation.
 #' @param theta1 Natural parameter for alternate hypothesis,
 #'   if needed for lower bound computation.
 #'   It can be a scalar (constant treatment effect) or a vector (non-constant treatment effect).
 #'   The default is the same as `theta`, which yields the usual beta-spending.
 #'   If set to 0, spending is 2-sided under the null hypothesis.
 #' @param info Statistical information at all analyses for input `theta`.
-#'   It is a vector of all positive numbers with increasing order.
-#'   Users are required to input it corresponding to `theta`.
+#'   It is a vector of positive numbers in increasing order.
+#'   The user must provide values corresponding to `theta`.
 #' @param info0 Statistical information under null hypothesis.
 #'   It is a vector of all positive numbers with increasing order.
 #'   Default is set to be the same as `info`.
@@ -96,8 +96,8 @@
 #'   - `theta`: same as the input.
 #'   - `theta1`: same as the input.
 #'   - `info`: statistical information at each analysis.
-#'      + If it is returned by `gs_power_npe`, the `info`, `info0`, `info1` is same as the input.
-#'      + If it is returned by `gs_design_npe`, the `info`, `info0`, `info1` is change by some constant scale
+#'      + If it is returned by `gs_power_npe`, the `info`, `info0`, `info1` are same as the input.
+#'      + If it is returned by `gs_design_npe`, the `info`, `info0`, `info1` are changed by a constant scale factor.
 #' factor to ensure the design has power `1 - beta`.
 #'   - `info0`: statistical information under the null at each analysis.
 #'   - `info1`: statistical information under the alternative at each analysis.