Documentation cleanup to cleanup issues from check_win_devel().

DESCRIPTION

@@ -4,8 +4,7 @@ Version: 0.1.0
 Authors@R: c(
     person("Andrew", "Redd", , "andrew.redd@hsc.utah.edu", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-6149-2438")),
-    person("Yujing", "Gao", , "ygao39@ncsu.edu", role = c("aut"),
-           comment = c(ORCID = "0000-0002-6149-2438")),
+    person("Yujing", "Gao", , "ygao39@ncsu.edu", role = c("aut")),
     person("Shu", "Yang", , "syang24@ncsu.edu", role = c("aut")),
     person("Bonnie", "Smith", , "bsmit179@jhmi.edu", role = c("aut")),
     person("Agatha", "Mallett", , "agatha@geometrian.com", role = c("ctb", "ctr")),

R/RcppExports.R

@@ -37,35 +37,39 @@ pcoriaccel_NW <- function(Xb, Y, xb, y_seq, h, kernel = "K2_Biweight") {
 #' Otherwise, if the second argument is a vector, it is interpreted as a column vector.
 #' @param matrA first matrix
 #' @param matrB second matrix
-#' @return `matrA * matrB`
+#' @return The product of `matrA` and `matrB`.
 #' @keywords internal
+#' @noRd
 pcoriaccel_mmul <- function(matrA, matrB) {
     .Call(`_SensIAT_pcoriaccel_mmul`, matrA, matrB)
 }
 
 #' Inner product (dot product) of two vectors.
 #' @param vecA first vector
 #' @param vecB second vector
-#' @return `vecAᵀ * vecB = vecA • vecB`
+#' @return scalar product of `vecA` and `vecB`
 #' @keywords internal
+#' @noRd
 pcoriaccel_inner_prod <- function(vecA, vecB) {
     .Call(`_SensIAT_pcoriaccel_inner_prod`, vecA, vecB)
 }
 
 #' Outer sum of two vectors.
 #' @param vecA first vector
 #' @param vecB second vector
-#' @return `vecA ⊕ vecB`
+#' @return Matrix where each element of `vecA`(row) is added to the element of `vecB`(column).
 #' @keywords internal
+#' @noRd
 pcoriaccel_outer_sum <- function(vecA, vecB) {
     .Call(`_SensIAT_pcoriaccel_outer_sum`, vecA, vecB)
 }
 
 #' Outer product of two vectors.
 #' @param vecA first vector
 #' @param vecB second vector
-#' @return `vecA * vecBᵀ = vecA ⊗ vecB`
+#' @return matrix of the outer product of vectors `vecA` and `vecB`.
 #' @keywords internal
+#' @noRd
 pcoriaccel_outer_prod <- function(vecA, vecB) {
     .Call(`_SensIAT_pcoriaccel_outer_prod`, vecA, vecB)
 }
@@ -74,6 +78,7 @@ pcoriaccel_outer_prod <- function(vecA, vecB) {
 #' @param vec the vector
 #' @return `sort(unique(vec))`
 #' @keywords internal
+#' @noRd
 pcoriaccel_sorted_unique <- function(vec) {
     .Call(`_SensIAT_pcoriaccel_sorted_unique`, vec)
 }
@@ -129,7 +134,7 @@ pcoriaccel_integrate_simp <- function(integrand, lo, hi, tol = 1.490116e-08) {
     .Call(`_SensIAT_pcoriaccel_integrate_simp`, integrand, lo, hi, tol)
 }
 
-#' Compiled version of `evaluate_basis(⋯)` function
+#' Compiled version of `evaluate_basis()` function
 #'
 #' @param spline_basis   The spline basis, S4 class `orthogonalsplinebasis::SplineBasis`
 #' @param x              The point to evaluate

R/compute_influence_for_one_alpha_and_one_patient.R

@@ -81,12 +81,12 @@ function(
                 resolution = control$resolution,
                 variables = variables, ...
             ) |> as.vector()
-        } else if (control$integration.method == 'linear') {
-            compute_influence_term_2_linearly(
-                df_i, expected_value, base=base,
-                variables = variables, ...
-            ) |>
-                unlist() |> as.vector()
+        # } else if (control$integration.method == 'linear') {
+        #     compute_influence_term_2_linearly(
+        #         df_i, expected_value, base=base,
+        #         variables = variables, ...
+        #     ) |>
+        #         unlist() |> as.vector()
         } else if (control$integration.method == 'quadv') {
             compute_influence_term_2_quadv_sim(
                 df_i, expected_value, base=base,
@@ -96,15 +96,15 @@ function(
                 alpha = alpha,
                 ...
             )
-        } else if (control$integration.method == 'quadvcpp') {
-            compute_influence_term_2_quadv_cpp_sim(
-                df_i, expected_value, base=base,
-                outcome.model = outcome.model,
-                tol = control$tol,
-                variables = variables,
-                alpha = alpha,
-                ...
-            )
+        # } else if (control$integration.method == 'quadvcpp') {
+        #     compute_influence_term_2_quadv_cpp_sim(
+        #         df_i, expected_value, base=base,
+        #         outcome.model = outcome.model,
+        #         tol = control$tol,
+        #         variables = variables,
+        #         alpha = alpha,
+        #         ...
+        #     )
         } else rlang::abort('Unknown integration method')
     influence <- colSums(term1) + term2
     V_inverse <- solve(GramMatrix(base))