example1

Author: ehsanx

.Rbuildignore

@@ -4,3 +4,4 @@
 ^doc$
 ^Meta$
 ^readme\.html$
+^\.github$

.Rhistory

@@ -0,0 +1 @@
+*.html

.github/.gitignore

@@ -0,0 +1,51 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+
+name: R-CMD-check.yaml
+
+permissions: read-all
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: macos-latest,   r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::rcmdcheck
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'

.github/workflows/R-CMD-check.yaml

@@ -42,6 +42,54 @@
 #' @import stats
 #'
 #' @export
+#'
+#' @examples
+#' # Ensure required packages are loaded
+#' if (requireNamespace("survey", quietly = TRUE) &&
+#'     requireNamespace("NHANES", quietly = TRUE) &&
+#'     requireNamespace("dplyr", quietly = TRUE)) {
+#'
+#'   # 1. Prepare Data using NHANES
+#'   data(NHANESraw, package = "NHANES")
+#'   nhanes_adults_with_na <- NHANESraw %>%
+#'     dplyr::filter(Age >= 20) %>%
+#'     dplyr::mutate(
+#'       ObeseStatus = factor(ifelse(BMI >= 30, "Obese", "Not Obese"),
+#'                            levels = c("Not Obese", "Obese"))
+#'     )
+#'
+#'   adult_design_with_na <- survey::svydesign(
+#'     id = ~SDMVPSU,
+#'     strata = ~SDMVSTRA,
+#'     weights = ~WTMEC2YR,
+#'     nest = TRUE,
+#'     data = nhanes_adults_with_na
+#'   )
+#'
+#'   # 2. Basic Example: Create a simple Table 1
+#'   vars_to_summarize <- c("Age", "Gender", "Race1", "Education")
+#'   table1 <- svytable1(
+#'     design = adult_design_with_na,
+#'     strata_var = "ObeseStatus",
+#'     table_vars = vars_to_summarize
+#'   )
+#'   print(table1)
+#'
+#'   # 3. Advanced Example: Use reliability checks and get detailed metrics
+#'   results <- svytable1(
+#'     design = adult_design_with_na,
+#'     strata_var = "ObeseStatus",
+#'     table_vars = vars_to_summarize,
+#'     reliability_checks = TRUE,
+#'     return_metrics = TRUE
+#'   )
+#'
+#'   # View the table with unreliable estimates suppressed (*)
+#'   print(results$formatted_table)
+#'
+#'   # View the detailed report card of reliability checks
+#'   print(results$reliability_metrics)
+#' }
 svytable1 <- function(design, strata_var, table_vars,
                       mode = "mixed", commas = TRUE,
                       reliability_checks = FALSE, return_metrics = FALSE) {

R/svytable1.R

@@ -10,7 +10,7 @@ @misc{nhanes_reliability_estimates
   author = {Centers for Disease Control and Prevention},
   title = {NHANES Tutorials: Reliability of Estimates Module},
   organization = {National Center for Health Statistics},
-  year = {n.d.},
+  year = {2005},
   url = {https://wwwn.cdc.gov/nchs/nhanes/tutorials/reliabilityofestimates.aspx},
   note = {Accessed: October 12, 2025}
 }

man/svytable1.Rd

@@ -284,3 +284,6 @@ The output from `svyglmdiag()` provides a clear, term-by-term *report card* for
 ---
 
 Overall, `svyglmdiag()` complements traditional regression output by making it easier to identify which predictors are both statistically meaningful and statistically stable under complex survey design.
+
+
+## References