diff --git a/.lintr b/.lintr
index c2b2094..df85707 100644
--- a/.lintr
+++ b/.lintr
@@ -1,5 +1,6 @@
-linters: with_defaults(
+linters: linters_with_defaults(
   line_length_linter = line_length_linter(120),
   cyclocomp_linter = NULL,
-  object_usage_linter = NULL
+  object_usage_linter = NULL,
+  commented_code_linter = NULL
   )
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index df8cac2..051bf3a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,7 +9,6 @@ repos:
         args: [--style_pkg=styler, --style_fun=tidyverse_style]
       - id: roxygenize
         additional_dependencies:
-          - plumber
           - shiny
       # codemeta must be above use-tidy-description when both are used
       # -   id: codemeta-description-updated
diff --git a/DESCRIPTION b/DESCRIPTION
index 46a6d7c..c7b9623 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -6,6 +6,8 @@ Version: 0.0.0.9004
 Date: 2022-11-09
 Authors@R: c(
     person("Daniel", "Sabanés Bové", , "daniel.sabanes_bove@roche.com", role = c("aut", "cre")),
+    person("Joe", "Zhu", , "joe.zhu@roche.com", role = c("aut")),
+    person("Emily", "de la Rua", , "emily.de_la_rua@contractors.roche.com", role = "aut"),
     person("F. Hoffmann-La Roche AG", role = c("cph", "fnd"))
   )
 Description: Create tables and graphs for GEE model fits.
@@ -16,8 +18,15 @@ Depends:
     R (>= 4.0),
     tern (>= 0.7.9)
 Imports:
+    checkmate,
+    dplyr,
+    geeasy,
+    geepack,
+    nlme,
+    rtables,
+    stats,
     formatters,
-    rtables
+    emmeans (>= 1.4.5)
 Suggests:
     knitr,
     rmarkdown,
diff --git a/NAMESPACE b/NAMESPACE
index 5402a28..01df84a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,3 +1,23 @@
 # Generated by roxygen2: do not edit by hand
 
-export(dummy_function)
+S3method(QIC,tern_gee)
+S3method(VarCorr,tern_gee)
+S3method(as.rtable,tern_gee)
+S3method(lsmeans,tern_gee_logistic)
+export(a_lsmeans_logistic)
+export(as.rtable)
+export(fit_gee)
+export(lsmeans)
+export(s_lsmeans_logistic)
+export(summarize_gee_logistic)
+export(vars_gee)
+import(checkmate)
+import(rtables)
+importFrom(emmeans,emmeans)
+importFrom(geepack,QIC)
+importFrom(nlme,VarCorr)
+importFrom(rtables,add_colcounts)
+importFrom(stats,acf)
+importFrom(stats,as.formula)
+importFrom(tern,as.rtable)
+importFrom(tern,f_conf_level)
diff --git a/NEWS.md b/NEWS.md
index 72f5fd0..eba5d7f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,7 @@
 # tern.gee 0.0.0.9004
-### New features
 
-* New package
+### Miscellaneous
+
+* Initialize the package.
+* Update pkgdown site and README, add unit tests.
+* Add examples for `fit_gee`, `lsmeans`, `s_lsmeans_logistic`, `summarize_gee_logistic`.
diff --git a/R/data.R b/R/data.R
new file mode 100644
index 0000000..da037e4
--- /dev/null
+++ b/R/data.R
@@ -0,0 +1,15 @@
+#' Example dataset for `tern.gee` package.
+#'
+#' Measurements of FEV1 (forced expired volume in one second) is a measure of
+#' how quickly the lungs can be emptied.
+#' Low levels of FEV1 may indicate chronic obstructive pulmonary disease (COPD).
+#' @format A `tibble` with 800 rows and 7 variables:
+#'
+#'   - `USUBJID`: price, in US dollars.
+#'   - `AVISIT`: visit number.
+#'   - `ARMCD`: treatment, `TRT` or `PBO`.
+#'   - `RACE`: 3-category race.
+#'   - `SEX`: sex.
+#'   - `FEV1_BL`: FEV1 at baseline (%).
+#'   - `FEV1`: FEV1 at study visits.
+"fev_data"
diff --git a/R/fit_gee.R b/R/fit_gee.R
new file mode 100644
index 0000000..567b819
--- /dev/null
+++ b/R/fit_gee.R
@@ -0,0 +1,178 @@
+#' Set Variables to Use in GEE Model
+#'
+#' @param response (`character`)\cr name of response variable.
+#' @param covariates (`character`)\cr vector of names of variables to use as covariates.
+#' @param id (`character`)\cr name of variable to use to identify unique IDs.
+#' @param arm (`character`)\cr name of arm variable.
+#' @param visit (`character`)\cr name of visit variable.
+#'
+#' @return A list of variables that can be used as the `vars` argument in [fit_gee()].
+#' @export
+#'
+#' @examples
+#' vars_gee()
+#'
+#' vars_gee(
+#'   response = "CHG",
+#'   covariates = c("SEX", "RACE"),
+#'   id = "SUBJID",
+#'   arm = "ARMCD",
+#'   visit = "AVISITN"
+#' )
+vars_gee <- function(response = "AVAL",
+                     covariates = c(),
+                     id = "USUBJID",
+                     arm = "ARM",
+                     visit = "AVISIT") {
+  list(
+    response = response,
+    covariates = covariates,
+    id = id,
+    arm = arm,
+    visit = visit
+  )
+}
+
+#' @keywords internal
+build_formula <- function(vars) {
+  assert_list(vars)
+  arm_part <- if (is.null(vars$arm)) NULL else vars$arm
+  rhs_formula <- paste(
+    c(arm_part, vars$covariates),
+    collapse = " + "
+  )
+  stats::as.formula(paste(
+    vars$response,
+    "~",
+    rhs_formula
+  ))
+}
+
+#' @keywords internal
+build_family <- function(regression) {
+  assert_string(regression)
+
+  result_object <- switch(regression,
+    logistic = stats::binomial(link = "logit"),
+    stop(paste("regression type", regression, "not supported"))
+  )
+
+  result_class <- paste0("tern_gee_", regression)
+
+  list(
+    object = result_object,
+    class = result_class,
+    control = geeasy::geelm.control(scale.fix = TRUE)
+  )
+}
+
+#' @keywords internal
+build_cor_details <- function(cor_str, vars, data) {
+  assert_string(cor_str)
+  assert_list(vars)
+  assert_data_frame(data)
+
+  result_str <- switch(cor_str,
+    "unstructured" = "unstructured",
+    "toeplitz" = "m-dependent",
+    "compound symmetry" = "exchangeable",
+    "auto-regressive" = "ar1",
+    stop(paste("correlation structure", cor_str, "not available"))
+  )
+
+  result_mv <- switch(cor_str,
+    "unstructured" = 1,
+    "toeplitz" = nlevels(data[[vars$visit]]) - 1,
+    "compound symmetry" = 1,
+    "auto-regressive" = 1
+  )
+
+  list(
+    str = result_str,
+    mv = result_mv
+  )
+}
+
+order_data <- function(data, vars) {
+  assert_data_frame(data)
+  assert_list(vars)
+
+  if (is.character(data[[vars$visit]])) {
+    message(paste("visit variable", vars$visit, "will be coerced to factor for ordering"))
+    message("order is:")
+    data[[vars$visit]] <- factor(data[[vars$visit]])
+    cat(toString(levels(data[[vars$visit]])))
+  }
+  if (is.factor(data[[vars$id]]) || is.character(data[[vars$id]])) {
+    data[[vars$id]] <- as.integer(as.factor(data[[vars$id]]))
+  }
+  assert_numeric(data[[vars$id]])
+
+  right_order <- order(data[[vars$id]], data[[vars$visit]])
+  data[right_order, ]
+}
+
+#' Fit a GEE Model
+#'
+#' @param vars (`list`)\cr see [vars_gee()].
+#' @param data (`data.frame`)\cr input data.
+#' @param regression (`string`)\cr choice of regression model.
+#' @param cor_struct (`string`)\cr assumed correlation structure.
+#'
+#' @details The correlation structure can be:
+#' * `unstructured`: No constraints are placed on the correlations.
+#' * `toeplitz`: Assumes a banded correlation structure, i.e. the correlation
+#'    between two time points depends on the distance between the time indices.
+#' * `compound symmetry`: Constant correlation between all time points.
+#' * `auto-regressive`: Auto-regressive order 1 correlation matrix.
+#'
+#' @return Object of class `tern_gee` as well as specific to the kind of regression
+#'   which was used.
+#' @export
+#'
+#' @examples
+#' df <- fev_data
+#' df$AVAL <- as.integer(fev_data$FEV1 > 30)
+#'
+#' fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)
+#'
+#' fit_gee(vars = vars_gee(arm = "ARMCD"), data = df, cor_struct = "compound symmetry")
+fit_gee <- function(vars = vars_gee(),
+                    data,
+                    regression = c("logistic"),
+                    cor_struct = c("unstructured", "toeplitz", "compound symmetry", "auto-regressive")) {
+  formula <- build_formula(vars)
+
+  regression <- match.arg(regression)
+  family <- build_family(regression)
+
+  data <- order_data(data, vars)
+  data[[".id"]] <- data[[vars$id]]
+  data[[".waves"]] <- as.integer(data[[vars$visit]])
+
+  cor_struct <- match.arg(cor_struct)
+  cor_details <- build_cor_details(cor_struct, vars, data)
+
+  fit <- geeasy::geelm(
+    formula = formula,
+    id = .id,
+    waves = .waves,
+    data = data,
+    family = family$object,
+    corstr = cor_details$str,
+    Mv = cor_details$mv,
+    control = family$control
+  )
+
+  fit$qic <- geepack::QIC(fit)
+  fit$visit_levels <- levels(data[[vars$visit]])
+  fit$vars <- vars
+  fit$data <- data
+  assert_factor(data[[vars$arm]])
+  fit$ref_level <- levels(data[[vars$arm]])[1L]
+
+  structure(
+    fit,
+    class = c(family$class, "tern_gee", class(fit))
+  )
+}
diff --git a/R/gee_methods.R b/R/gee_methods.R
new file mode 100644
index 0000000..6fb1b5d
--- /dev/null
+++ b/R/gee_methods.R
@@ -0,0 +1,53 @@
+#' Methods for GEE Models
+#'
+#' Additional methods which can simplify working with the GEE result object.
+#' @name gee_methods
+NULL
+
+#' @rdname gee_methods
+#' @importFrom nlme VarCorr
+#'
+#' @param x (`tern_gee`)\cr result of [fit_gee()].
+#' @inheritParams nlme::VarCorr
+#'
+#' @exportS3Method
+VarCorr.tern_gee <- function(x, sigma = 1, ...) { # nolint
+  dim_mat <- length(x$visit_levels)
+  tmp <- id_mat <- diag(dim_mat)
+  corest <- x$geese$alpha
+
+  # Start with lower-triangular matrix part.
+  lower_mat <- switch(x$corstr,
+    unstructured = , # Since this is the same as exchangeable, we can do this.
+    exchangeable = {
+      tmp[lower.tri(tmp)] <- corest
+      tmp
+    },
+    ar1 = {
+      row_col_diff <- row(tmp) - col(tmp)
+      tmp[lower.tri(tmp)] <- corest^(row_col_diff[lower.tri(row_col_diff)])
+      tmp
+    },
+    `m-dependent` = {
+      row_col_diff <- row(tmp) - col(tmp)
+      tmp[lower.tri(tmp)] <- corest[row_col_diff[lower.tri(row_col_diff)]]
+      tmp
+    }
+  )
+
+  # Construct the full symmetric matrix.
+  mat <- lower_mat + t(lower_mat) - id_mat
+  rownames(mat) <- colnames(mat) <- x$visit_levels
+  mat
+}
+
+#' @rdname gee_methods
+#' @importFrom geepack QIC
+#'
+#' @param object (`tern_gee`)\cr result of [fit_gee()].
+#' @inheritParams geepack::QIC
+#'
+#' @exportS3Method
+QIC.tern_gee <- function(object, ...) { # nolint
+  object$qic
+}
diff --git a/R/hello.R b/R/hello.R
deleted file mode 100644
index 2d24a14..0000000
--- a/R/hello.R
+++ /dev/null
@@ -1,7 +0,0 @@
-#' A dummy function
-#' @export
-dummy_function <- function() {
-  rtables::basic_table()
-  formatters::divider_height()
-  as.rtable(data.frame(x = 1:10))
-}
diff --git a/R/lsmeans.R b/R/lsmeans.R
new file mode 100644
index 0000000..0422efc
--- /dev/null
+++ b/R/lsmeans.R
@@ -0,0 +1,75 @@
+#' Extract Least Square Means from a GEE Model
+#'
+#' @param object (`tern_gee`)\cr result of [fit_gee()].
+#' @param conf_level (`proportion`)\cr confidence level
+#' @param weights (`string`)\cr type of weights to be used for the least square means,
+#'   see [emmeans::emmeans()] for details.
+#' @param ... additional arguments for methods
+#'
+#' @return A `data.frame` with least-square means and contrasts. Additional
+#'   classes allow to dispatch downstream methods correctly, too.
+#' @export
+#'
+#' @examples
+#' df <- fev_data
+#' df$AVAL <- rbinom(n = nrow(df), size = 1, prob = 0.5)
+#' fit <- fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)
+#'
+#' lsmeans(fit)
+#'
+#' lsmeans(fit, conf_level = 0.90, weights = "equal")
+lsmeans <- function(object,
+                    conf_level = 0.95,
+                    weights = "proportional",
+                    ...) {
+  UseMethod("lsmeans", object)
+}
+
+#' @rdname lsmeans
+#' @exportS3Method
+lsmeans.tern_gee_logistic <- function(object,
+                                      conf_level = 0.95,
+                                      weights = "proportional",
+                                      ...) {
+  specs <- as.formula(paste("~", object$vars$arm))
+  prop_emm <- emmeans::emmeans(
+    object = object,
+    specs = specs,
+    weights = weights,
+    type = "response",
+    data = object$data
+  )
+
+  prop_df <- cbind(
+    as.data.frame(prop_emm)[, c(object$vars$arm, "prob", "SE", "asymp.LCL", "asymp.UCL")],
+    n = as.list(prop_emm)$extras[, ".wgt."]
+  )
+  names(prop_df) <- c(object$vars$arm, "prop_est", "prop_est_se", "prop_lower_cl", "prop_upper_cl", "n")
+  ref_level <- levels(object$data[[object$vars$arm]])[1L]
+
+  or_emm <- stats::confint(
+    graphics::pairs(prop_emm, reverse = TRUE),
+    level = conf_level
+  )
+  or_df <- as.data.frame(or_emm)
+  or_df$comparator <- gsub(pattern = ".+ / (.+)", replacement = "\\1", x = or_df$contrast)
+  or_df[[object$vars$arm]] <- gsub(pattern = "(.+) / .+", replacement = "\\1", x = or_df$contrast)
+  or_df <- or_df[or_df$comparator == ref_level, ]
+  or_df <- rbind(NA, or_df)
+  or_df[1L, object$vars$arm] <- ref_level
+  or_df <- or_df[, c(object$vars$arm, "odds.ratio", "asymp.LCL", "asymp.UCL")]
+  or_df <- cbind(or_df, log(or_df[, -1L]), conf_level)
+  names(or_df) <- c(
+    object$vars$arm,
+    "or_est", "or_lower_cl", "or_upper_cl",
+    "log_or_est", "log_or_lower_cl", "log_or_upper_cl",
+    "conf_level"
+  )
+
+  result <- merge(prop_df, or_df, by = object$vars$arm)
+
+  structure(
+    result,
+    class = c("lsmeans_logistic", class(result))
+  )
+}
diff --git a/R/package.R b/R/package.R
new file mode 100644
index 0000000..d3f34ea
--- /dev/null
+++ b/R/package.R
@@ -0,0 +1,13 @@
+#' `tern.gee` Package
+#'
+#' Create tables and graphs for GEE model fits.
+#'
+"_PACKAGE"
+
+#' @import checkmate
+#' @import rtables
+#' @importFrom rtables add_colcounts
+#' @importFrom stats acf as.formula
+#' @importFrom tern f_conf_level
+#' @importFrom emmeans emmeans
+NULL
diff --git a/R/tabulate_gee.R b/R/tabulate_gee.R
new file mode 100644
index 0000000..fab8903
--- /dev/null
+++ b/R/tabulate_gee.R
@@ -0,0 +1,176 @@
+#' Tabulation of a GEE Model
+#'
+#' Functions to produce tables from a fitted GEE produced with [fit_gee()].
+#'
+#' @name tabulate_gee
+NULL
+
+#' @importFrom tern as.rtable
+#' @export
+tern::as.rtable
+
+#' @exportS3Method
+#' @describeIn tabulate_gee Extracts the coefficient table or covariance matrix estimate from a `tern_gee` object.
+#' @inheritParams tern::as.rtable
+#' @param type (`character`)\cr type of table to extract from `tern_gee` object.
+as.rtable.tern_gee <- function(x, # nolint
+                               type = c("coef", "cov"),
+                               ...) {
+  type <- match.arg(type)
+  switch(type,
+    coef = h_gee_coef(x, ...),
+    cov = h_gee_cov(x, ...)
+  )
+}
+
+#' @keywords internal
+h_gee_coef <- function(x, format = "xx.xxxx", conf_level = 0.95, ...) {
+  fixed_table <- as.data.frame(stats::coef(summary(x)))
+  assert_number(conf_level, lower = 0.001, upper = 0.999)
+
+  fixed_table[["Std. Error"]] <- fixed_table[["Robust S.E."]]
+  fixed_table[["z value"]] <- fixed_table[["Robust z"]]
+  fixed_table[["Pr(>|z|)"]] <- 2 * stats::pnorm(abs(fixed_table[["z value"]]), lower.tail = FALSE)
+  q <- stats::qnorm((1 + conf_level) / 2)
+  ci_string <- tern::f_conf_level(conf_level)
+  lower_string <- paste("Lower", ci_string)
+  upper_string <- paste("Upper", ci_string)
+  fixed_table[[lower_string]] <- fixed_table$Estimate - q * fixed_table[["Std. Error"]]
+  fixed_table[[paste("Upper", ci_string)]] <- fixed_table$Estimate + q * fixed_table[["Std. Error"]]
+
+  est_se_ci_table <- as.rtable(
+    fixed_table[, c("Estimate", "Std. Error", lower_string, upper_string)],
+    format = format
+  )
+  z_table <- as.rtable(fixed_table[, c("z value"), drop = FALSE], format = format)
+  pvalue_table <- as.rtable(fixed_table[, "Pr(>|z|)", drop = FALSE], format = "x.xxxx | (<0.0001)")
+
+  cbind_rtables(est_se_ci_table, z_table, pvalue_table)
+}
+
+#' @keywords internal
+h_gee_cov <- function(x, format = "xx.xxxx") {
+  cov_estimate <- VarCorr(x)
+  as.rtable(as.data.frame(cov_estimate), format = format)
+}
+
+# lsmeans_logistic ----
+
+#' @describeIn tabulate_gee Statistics function which extracts estimates from a
+#'   [lsmeans()] data frame based on a logistic GEE model.
+#'
+#' @param df (`data.frame`)\cr data set resulting from [lsmeans()].
+#' @param .in_ref_col (`logical`)\cr `TRUE` when working with the reference level, `FALSE` otherwise.
+#'
+#' @export
+#'
+#' @examples
+#' library(dplyr)
+#'
+#' df <- fev_data %>%
+#'   mutate(AVAL = as.integer(fev_data$FEV1 > 30))
+#' df_counts <- df %>%
+#'   select(USUBJID, ARMCD) %>%
+#'   unique()
+#'
+#' lsmeans_df <- lsmeans(fit_gee(vars = vars_gee(arm = "ARMCD"), data = df))
+#'
+#' s_lsmeans_logistic(lsmeans_df[1, ], .in_ref_col = TRUE)
+#'
+#' s_lsmeans_logistic(lsmeans_df[2, ], .in_ref_col = FALSE)
+s_lsmeans_logistic <- function(df, .in_ref_col) {
+  if_not_ref <- function(x) `if`(.in_ref_col, character(), x)
+  list(
+    n = df$n,
+    adj_prop_se = c(df$prop_est, df$prop_est_se), # to be confirmed
+    adj_prop_ci = formatters::with_label(c(df$prop_lower_cl, df$prop_upper_cl), f_conf_level(df$conf_level)),
+    odds_ratio_est = if_not_ref(df$or_est),
+    odds_ratio_ci = formatters::with_label(
+      if_not_ref(c(df$or_lower_cl, df$or_upper_cl)),
+      f_conf_level(df$conf_level)
+    ),
+    log_odds_ratio_est = if_not_ref(df$log_or_est),
+    log_odds_ratio_ci = formatters::with_label(
+      if_not_ref(c(df$log_or_lower_cl, df$log_or_upper_cl)),
+      f_conf_level(df$conf_level)
+    )
+  )
+}
+
+## a_lsmeans_logistic ----
+
+#' @describeIn tabulate_gee Formatted Analysis function which can be further customized by calling
+#'   [rtables::make_afun()] on it. It is used as `afun` in [rtables::analyze()].
+#'
+#' @export
+a_lsmeans_logistic <- make_afun(
+  s_lsmeans_logistic,
+  .labels = c(
+    adj_prop_se = "Adjusted Mean Proportion (SE)",
+    odds_ratio_est = "Odds Ratio",
+    log_odds_ratio_est = "Log Odds Ratio"
+  ),
+  .formats = c(
+    n = "xx.",
+    adj_prop_se = sprintf_format("%.2f (%.2f)"),
+    adj_prop_ci = "(xx.xx, xx.xx)",
+    odds_ratio_est = "xx.xx",
+    odds_ratio_ci = "(xx.xx, xx.xx)",
+    log_odds_ratio_est = "xx.xx",
+    log_odds_ratio_ci = "(xx.xx, xx.xx)"
+  ),
+  .indent_mods = c(
+    adj_prop_ci = 1L,
+    odds_ratio_ci = 1L,
+    log_odds_ratio_ci = 1L
+  ),
+  .null_ref_cells = FALSE
+)
+
+# Note: In production it would be nice to allow an S3 dispatch according to the
+# class of the lsmeans input, however for now in the prototype we keep it simple.
+# see later then to tern::summarize_variables for how to do that.
+
+#' @describeIn tabulate_gee Analyze function for tabulating least-squares means estimates
+#'   from logistic GEE least square mean results.
+#'
+#' @param lyt (`layout`)\cr input layout where analyses will be added to.
+#' @param table_names (`character`)\cr this can be customized in case that the same `vars`
+#'   are analyzed multiple times, to avoid warnings from `rtables`.
+#' @param .stats (`character`)\cr statistics to select for the table.
+#' @param .formats (named `character` or `list`)\cr formats for the statistics.
+#' @param .indent_mods (named `integer`)\cr indent modifiers for the labels.
+#' @param .labels (named `character`)\cr labels for the statistics (without indent).
+#'
+#' @export
+#'
+#' @examples
+#' basic_table() %>%
+#'   split_cols_by("ARMCD") %>%
+#'   add_colcounts() %>%
+#'   summarize_gee_logistic(
+#'     .in_ref_col = FALSE
+#'   ) %>%
+#'   build_table(lsmeans_df, alt_counts_df = df_counts)
+summarize_gee_logistic <- function(lyt,
+                                   ...,
+                                   table_names = "lsmeans_logistic_summary",
+                                   .stats = NULL,
+                                   .formats = NULL,
+                                   .indent_mods = NULL,
+                                   .labels = NULL) {
+  afun <- make_afun(
+    a_lsmeans_logistic,
+    .stats = .stats,
+    .formats = .formats,
+    .indent_mods = .indent_mods,
+    .labels = .labels
+  )
+  analyze(
+    lyt = lyt,
+    vars = "n",
+    afun = afun,
+    table_names = table_names,
+    extra_args = list(...)
+  )
+}
diff --git a/README.md b/README.md
index 400acd6..cf58c38 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,90 @@
 
 <!-- start badges -->
 [![Code Coverage](https://raw.githubusercontent.com/insightsengineering/tern.gee/_xml_coverage_reports/data/main/badge.svg)](https://raw.githubusercontent.com/insightsengineering/tern.gee/_xml_coverage_reports/data/main/coverage.xml)
+[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
 <!-- end badges -->
 
-Create TLGs using Generalized Estimating Equations (GEE)
+## Overview
+
+`tern.gee` provides an interface for generalized estimating equations (GEE) within the
+[`tern`](https://insightsengineering.github.io/tern) framework
+to produce commonly used tables (using [`rtables`](https://roche.github.io/rtables)) and graphs.
+It builds on the R-package `geepack` for the actual GEE calculations.
+
+## When to use this package
+
+If you would like to use the [`tern`](https://insightsengineering.github.io/tern) framework for
+tabulation and graphs, then this package is ideal for your GEE fits.
+However if you use another reporting framework then it will be better to directly use
+`geepack` and perform the tabulation and plots differently.
+
+## Main Features
+
+* Fitting of GEE models to continuous longitudinal data collected over several time points
+  (called visits) and optionally treatment arms.
+* Tabulation of least square means per visit and treatment arm.
+* Tabulation of the covariance matrix estimate.
+
+## Installation
+
+### GitHub
+
+It is recommended that you [create and use a Github PAT](https://docs.github.com/en/github/authenticating-to-github/keeping-your-account-and-data-secure/creating-a-personal-access-token) to install the latest version of this package. Once you have the PAT, run the following:
+
+```r
+Sys.setenv(GITHUB_PAT = "your_access_token_here")
+if (!require("remotes")) install.packages("remotes")
+remotes::install_github("insightsengineering/tern.gee@*release")
+```
+
+### `NEST` distribution
+
+A stable release of all `NEST` packages is also available [here](https://github.com/insightsengineering/depository#readme).
+
+## Getting started
+
+You can get started by trying out the example:
+
+```r
+library(dplyr)
+library(tern.gee)
+
+fev_data$FEV1_BINARY <- as.integer(fev_data$FEV1 > 30)
+fev_counts <- fev_data %>%
+  select(USUBJID, ARMCD) %>%
+  unique()
+
+gee_fit <- fit_gee(
+  vars = list(
+    response = "FEV1_BINARY",
+    covariates = c("RACE", "SEX"),
+    arm = "ARMCD",
+    id = "USUBJID",
+    visit = "AVISIT"
+  ),
+  data = fev_data
+)
+
+lsmeans_df <- lsmeans(gee_fit, data = fev_data)
+
+basic_table() %>%
+  split_cols_by("ARMCD") %>%
+  add_colcounts() %>%
+  summarize_gee_logistic(
+    .in_ref_col = FALSE
+  ) %>%
+  build_table(lsmeans_df, alt_counts_df = fev_counts)
+```
+
+This specifies a GEE with the `FEV1_BINARY` outcome and the `RACE` and `SEX` covariates for subjects identified by `USUBJID` and treatment arm `ARMCD` observed over time points identified by `AVISIT` in the `fev_data` data set. By default, logistic regression is used and an unstructured covariance matrix is assumed. The least square means assume equal weights for factor combinations.
+
+## Stargazers
+
+### Current
+
+[![Stargazers repo roster for @insightsengineering/tern.gee](https://reporoster.com/stars/insightsengineering/tern.gee)](https://github.com/insightsengineering/tern.gee/stargazers)
+[![Forkers repo roster for @insightsengineering/tern.gee](https://reporoster.com/forks/insightsengineering/tern.gee)](https://github.com/insightsengineering/tern.gee/network/members)
+
+### Over time
+
+[![Stargazers over time](https://starchart.cc/insightsengineering/tern.gee.svg)](https://starchart.cc/insightsengineering/tern.gee)
diff --git a/_pkgdown.yml b/_pkgdown.yml
index ce33f8f..465b9b9 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,3 +1,4 @@
+---
 url: https://insightsengineering.github.io/tern.gee
 
 template:
@@ -7,3 +8,26 @@ navbar:
   right:
     - icon: fa-github
       href: https://github.com/insightsengineering/tern.gee
+
+reference:
+  - title: Overview Pages
+    contents:
+      - tern.gee-package
+
+  - title: Helper Methods
+    contents:
+      - gee_methods
+
+  - title: Model Specific Functions
+    desc: These functions help with fitting or extracting results from
+          specific models.
+    contents:
+      - as.rtable.tern_gee
+      - fit_gee
+      - lsmeans
+      - tabulate_gee
+      - vars_gee
+
+  - title: Example Data
+    contents:
+      - fev_data
diff --git a/data/fev_data.rda b/data/fev_data.rda
new file mode 100644
index 0000000..585676a
Binary files /dev/null and b/data/fev_data.rda differ
diff --git a/design/compare_sas.R b/design/compare_sas.R
new file mode 100644
index 0000000..ee6e3c5
--- /dev/null
+++ b/design/compare_sas.R
@@ -0,0 +1,32 @@
+library(r2stream)
+
+test_data <- fev_data
+test_vars <- fev_vars
+
+model <- fit_gee(
+  vars = test_vars,
+  data = test_data,
+  regression = "logistic",
+  cor_struct = "unstructured"
+)
+
+sascode <- list(
+  "test" = "
+      proc genmod data=ana.dat descending;
+      class RACE(ref = 'Asian') USUBJID ARMCD(ref = 'PBO') AVISIT;
+      model FEV1_BIN = ARMCD RACE / dist=bin link=logit ;
+      repeated subject=USUBJID / within=AVISIT type=UN CORRW;
+      run;
+  "
+)
+
+sas_data <- test_data
+sas_data$FEV1_BIN <- sas_data$FEV1_BINARY
+sas_data$FEV1_BINARY <- NULL
+
+result <- r2stream::bee_sas(dat = list("dat" = sas_data), sascode = sascode)
+result$test$sas_log
+writeLines(result$test$sas_out, con = "sas_log.txt")
+
+QIC(model)
+VarCorr(model)
diff --git a/design/reprex.R b/design/reprex.R
new file mode 100644
index 0000000..0350dd1
--- /dev/null
+++ b/design/reprex.R
@@ -0,0 +1,383 @@
+data <- structure(list(FEV1_BIN = c(
+  NA, 1L, NA, 0L, NA, 1L, 1L, 1L,
+  NA, 1L, NA, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L, 1L, NA, NA, 1L, NA,
+  1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA, NA, 1L, NA,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L, 1L, NA, NA, 1L, NA,
+  NA, NA, 1L, 1L, NA, 1L, NA, 1L, 0L, NA, NA, 1L, 1L, 1L, NA, 1L,
+  1L, 1L, 1L, 1L, NA, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  0L, 1L, 1L, NA, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  0L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L,
+  0L, NA, 1L, NA, 1L, NA, NA, 1L, 0L, 1L, NA, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, NA, 1L, NA, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA,
+  1L, NA, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, NA, NA, 1L,
+  0L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L, 1L, 1L, 1L, 0L, 1L, 1L, NA,
+  0L, 1L, 1L, NA, NA, 1L, 1L, NA, 1L, 1L, 1L, NA, 0L, 1L, 1L, NA,
+  0L, 1L, NA, 1L, NA, 1L, NA, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA,
+  1L, 1L, 1L, NA, 0L, NA, NA, 1L, NA, 1L, 1L, 1L, NA, 1L, 1L, NA,
+  1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA, 0L, NA, NA,
+  NA, 1L, 1L, 1L, 1L, 0L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 0L, 1L, NA, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L, 1L, 1L, NA, 1L, 1L,
+  NA, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 0L, NA, 1L, 1L, 1L,
+  NA, NA, NA, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, NA, 1L, 1L,
+  1L, 1L, 1L, 1L, NA, NA, NA, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L,
+  0L, NA, 1L, NA, 0L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 0L, NA, NA, NA,
+  NA, NA, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L, 1L, 1L, 1L, NA, 1L, 1L,
+  NA, 1L, 1L, NA, 1L, 1L, NA, 1L, 1L, 1L, NA, 1L, 0L, 0L, 1L, 1L,
+  NA, 1L, NA, NA, 1L, 1L, 1L, NA, NA, 1L, 1L, NA, 1L, 1L, 1L, 1L,
+  1L, 0L, 1L, NA, 1L, NA, 1L, 1L, NA, 1L, 1L, NA, 0L, 1L, 1L, 1L,
+  NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, NA, 1L, 1L, NA,
+  NA, NA, NA, 1L, 1L, NA, 1L, NA, 1L, 1L, 1L, NA, 0L, 1L, 1L, 1L,
+  1L, 1L, 1L, NA, 1L, NA, 1L, 1L, 0L, NA, NA, NA, 1L, 1L, 1L, NA,
+  1L, 1L, NA, NA, 1L, NA, NA, NA, NA, NA, NA, 1L, NA, NA, 1L, NA,
+  1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L,
+  1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, 1L, 1L, NA,
+  0L, 1L, 1L, 0L, NA, 1L, 1L, 1L, NA, 1L, 1L, 1L, NA, 1L, 1L, 1L,
+  1L, 1L, 1L, 0L, 1L, NA, NA, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, NA,
+  NA, NA, 1L, 1L, 0L, 1L, NA, 1L, NA, 1L, NA, 1L, NA, NA, NA, NA,
+  0L, 1L, NA, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L,
+  1L, 1L, 1L, NA, NA, 1L, 1L, NA, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L,
+  1L, 1L, NA, 1L, NA, 1L, NA, 1L, 0L, 1L, 1L, 1L, 1L, 1L, NA, 1L,
+  1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, NA, 1L, 1L, 1L,
+  0L, 1L, NA, NA, NA, 1L, 1L, 1L, NA, 1L, NA, NA, 0L, NA, NA, 1L,
+  NA, 1L, NA, 1L, NA, 1L, 1L, 1L, 1L, NA, NA, NA, 0L, NA, NA, NA,
+  1L, 1L, NA, 1L, 0L, 1L, 1L, NA, 0L, 1L, NA, NA, NA, 1L, 1L, 1L,
+  NA, 0L, NA, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, NA, 1L, 1L, 1L, NA,
+  0L, 1L, NA, 1L, 1L, 1L, NA, 1L, NA, NA, 1L, NA, 1L, NA, NA, 1L,
+  1L, NA, 1L, 1L, 0L, 1L, 1L, NA, 1L, NA, 1L, 1L, NA, NA, 1L, 1L,
+  1L, 1L, NA, NA, NA, 1L, NA, 1L, NA, 1L, 1L, 1L, 1L, 1L, NA, 1L,
+  1L, NA, NA, NA, 1L, NA, 1L, NA, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L,
+  1L, NA, NA, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 0L, 1L, 1L, 1L,
+  NA, 1L, 1L, NA, 0L, 1L, NA, 1L, NA, NA, NA, 1L, 1L, 1L, NA, 1L,
+  NA, NA, NA, NA, 1L, 1L, NA, 1L
+), ARMCD = structure(c(
+  2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
+  1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L
+), levels = c(
+  "PBO",
+  "TRT"
+), class = "factor"), RACE = structure(c(
+  2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
+  3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
+  3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
+  3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
+  3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
+  1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
+  3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L,
+  3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
+  2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
+  2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
+  2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L
+), levels = c(
+  "Asian",
+  "Black or African American", "White"
+), class = "factor"), id = c(
+  1L,
+  1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L,
+  5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L,
+  9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L,
+  12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L, 15L,
+  15L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L,
+  19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L,
+  22L, 22L, 22L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L,
+  25L, 25L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L,
+  28L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L,
+  32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L,
+  35L, 35L, 35L, 36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, 38L, 38L,
+  38L, 38L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, 40L, 41L, 41L, 41L,
+  41L, 42L, 42L, 42L, 42L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L,
+  45L, 45L, 45L, 45L, 46L, 46L, 46L, 46L, 47L, 47L, 47L, 47L, 48L,
+  48L, 48L, 48L, 49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L, 51L, 51L,
+  51L, 51L, 52L, 52L, 52L, 52L, 53L, 53L, 53L, 53L, 54L, 54L, 54L,
+  54L, 55L, 55L, 55L, 55L, 56L, 56L, 56L, 56L, 57L, 57L, 57L, 57L,
+  58L, 58L, 58L, 58L, 59L, 59L, 59L, 59L, 60L, 60L, 60L, 60L, 61L,
+  61L, 61L, 61L, 62L, 62L, 62L, 62L, 63L, 63L, 63L, 63L, 64L, 64L,
+  64L, 64L, 65L, 65L, 65L, 65L, 66L, 66L, 66L, 66L, 67L, 67L, 67L,
+  67L, 68L, 68L, 68L, 68L, 69L, 69L, 69L, 69L, 70L, 70L, 70L, 70L,
+  71L, 71L, 71L, 71L, 72L, 72L, 72L, 72L, 73L, 73L, 73L, 73L, 74L,
+  74L, 74L, 74L, 75L, 75L, 75L, 75L, 76L, 76L, 76L, 76L, 77L, 77L,
+  77L, 77L, 78L, 78L, 78L, 78L, 79L, 79L, 79L, 79L, 80L, 80L, 80L,
+  80L, 81L, 81L, 81L, 81L, 82L, 82L, 82L, 82L, 83L, 83L, 83L, 83L,
+  84L, 84L, 84L, 84L, 85L, 85L, 85L, 85L, 86L, 86L, 86L, 86L, 87L,
+  87L, 87L, 87L, 88L, 88L, 88L, 88L, 89L, 89L, 89L, 89L, 90L, 90L,
+  90L, 90L, 91L, 91L, 91L, 91L, 92L, 92L, 92L, 92L, 93L, 93L, 93L,
+  93L, 94L, 94L, 94L, 94L, 95L, 95L, 95L, 95L, 96L, 96L, 96L, 96L,
+  97L, 97L, 97L, 97L, 98L, 98L, 98L, 98L, 99L, 99L, 99L, 99L, 100L,
+  100L, 100L, 100L, 101L, 101L, 101L, 101L, 102L, 102L, 102L, 102L,
+  103L, 103L, 103L, 103L, 104L, 104L, 104L, 104L, 105L, 105L, 105L,
+  105L, 106L, 106L, 106L, 106L, 107L, 107L, 107L, 107L, 108L, 108L,
+  108L, 108L, 109L, 109L, 109L, 109L, 110L, 110L, 110L, 110L, 111L,
+  111L, 111L, 111L, 112L, 112L, 112L, 112L, 113L, 113L, 113L, 113L,
+  114L, 114L, 114L, 114L, 115L, 115L, 115L, 115L, 116L, 116L, 116L,
+  116L, 117L, 117L, 117L, 117L, 118L, 118L, 118L, 118L, 119L, 119L,
+  119L, 119L, 120L, 120L, 120L, 120L, 121L, 121L, 121L, 121L, 122L,
+  122L, 122L, 122L, 123L, 123L, 123L, 123L, 124L, 124L, 124L, 124L,
+  125L, 125L, 125L, 125L, 126L, 126L, 126L, 126L, 127L, 127L, 127L,
+  127L, 128L, 128L, 128L, 128L, 129L, 129L, 129L, 129L, 130L, 130L,
+  130L, 130L, 131L, 131L, 131L, 131L, 132L, 132L, 132L, 132L, 133L,
+  133L, 133L, 133L, 134L, 134L, 134L, 134L, 135L, 135L, 135L, 135L,
+  136L, 136L, 136L, 136L, 137L, 137L, 137L, 137L, 138L, 138L, 138L,
+  138L, 139L, 139L, 139L, 139L, 140L, 140L, 140L, 140L, 141L, 141L,
+  141L, 141L, 142L, 142L, 142L, 142L, 143L, 143L, 143L, 143L, 144L,
+  144L, 144L, 144L, 145L, 145L, 145L, 145L, 146L, 146L, 146L, 146L,
+  147L, 147L, 147L, 147L, 148L, 148L, 148L, 148L, 149L, 149L, 149L,
+  149L, 150L, 150L, 150L, 150L, 151L, 151L, 151L, 151L, 152L, 152L,
+  152L, 152L, 153L, 153L, 153L, 153L, 154L, 154L, 154L, 154L, 155L,
+  155L, 155L, 155L, 156L, 156L, 156L, 156L, 157L, 157L, 157L, 157L,
+  158L, 158L, 158L, 158L, 159L, 159L, 159L, 159L, 160L, 160L, 160L,
+  160L, 161L, 161L, 161L, 161L, 162L, 162L, 162L, 162L, 163L, 163L,
+  163L, 163L, 164L, 164L, 164L, 164L, 165L, 165L, 165L, 165L, 166L,
+  166L, 166L, 166L, 167L, 167L, 167L, 167L, 168L, 168L, 168L, 168L,
+  169L, 169L, 169L, 169L, 170L, 170L, 170L, 170L, 171L, 171L, 171L,
+  171L, 172L, 172L, 172L, 172L, 173L, 173L, 173L, 173L, 174L, 174L,
+  174L, 174L, 175L, 175L, 175L, 175L, 176L, 176L, 176L, 176L, 177L,
+  177L, 177L, 177L, 178L, 178L, 178L, 178L, 179L, 179L, 179L, 179L,
+  180L, 180L, 180L, 180L, 181L, 181L, 181L, 181L, 182L, 182L, 182L,
+  182L, 183L, 183L, 183L, 183L, 184L, 184L, 184L, 184L, 185L, 185L,
+  185L, 185L, 186L, 186L, 186L, 186L, 187L, 187L, 187L, 187L, 188L,
+  188L, 188L, 188L, 189L, 189L, 189L, 189L, 190L, 190L, 190L, 190L,
+  191L, 191L, 191L, 191L, 192L, 192L, 192L, 192L, 193L, 193L, 193L,
+  193L, 194L, 194L, 194L, 194L, 195L, 195L, 195L, 195L, 196L, 196L,
+  196L, 196L, 197L, 197L, 197L, 197L, 198L, 198L, 198L, 198L, 199L,
+  199L, 199L, 199L, 200L, 200L, 200L, 200L
+), waves = c(
+  1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
+  3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L
+)), row.names = c(
+  NA,
+  -800L
+), class = c("tbl_df", "tbl", "data.frame"))
+
+head(data)
+
+# geeasy ----
+library(geeasy)
+model <- geeasy::geelm(
+  formula = FEV1_BIN ~ ARMCD + RACE,
+  id = id,
+  waves = waves,
+  data = data,
+  family = binomial(),
+  corstr = "unstructured",
+  control = geeasy::geelm.control(scale.fix = TRUE)
+)
+
+# proc genmod data=ana.dat descending;
+# class RACE(ref = 'Asian') USUBJID ARMCD(ref = 'PBO') AVISIT;
+# model FEV1_BIN = ARMCD RACE / dist=bin link=logit ;
+# repeated subject=USUBJID / within=AVISIT type=UN CORRW;
+# run;
+
+# Working Correlation Matrix
+#
+#            Col1         Col2         Col3         Col4
+#
+# Row1       1.0000      -0.0868      -0.1859      -0.1480
+# Row2      -0.0868       1.0000       0.0788       0.0077
+# Row3      -0.1859       0.0788       1.0000       0.0698
+# Row4      -0.1480       0.0077       0.0698       1.0000
+
+# We obtain different correlation parameter estimates:
+model$geese$alpha
+# [1] -0.056458907 -0.125737003  0.067815234 -0.083599271 -0.057773977  0.009216794
+
+# GEE Fit Criteria
+#
+# QIC          303.9454
+# QICu         303.6369
+
+# We obtain different QIC values:
+QIC(model)
+#        QIC        QICu   Quasi Lik         CIC      params        QICC
+# 302.767472  303.226407 -147.613203    3.770533    4.000000  303.950268
+
+# Analysis Of GEE Parameter Estimates
+# Empirical Standard Error Estimates
+#
+# Standard   95% Confidence
+# Parameter                           Estimate    Error       Limits            Z Pr > |Z|
+#
+# Intercept                             1.7193   0.2775   1.1755   2.2632    6.20   <.0001
+# ARMCD     TRT                         0.6781   0.3299   0.0315   1.3247    2.06   0.0398
+# ARMCD     PBO                         0.0000   0.0000   0.0000   0.0000     .      .
+# RACE      Black or African American   0.1492   0.3246  -0.4869   0.7853    0.46   0.6457
+# RACE      White                       1.3111   0.5543   0.2246   2.3975    2.37   0.0180
+# RACE      Asian                       0.0000   0.0000   0.0000   0.0000     .      .
+
+# We obtain different estimates:
+coef(model)
+# (Intercept)     ARMCDTRT   RACEBlack or African American     RACEWhite
+# 1.7582943      0.6784657                       0.1705619     1.3864354
+
+# And this fails, not sure why?
+summary(model)
+# seems the wts vector is too long (original size of the data instead of na omitted)
+# Passing in na.omit(data) above works.
+
+# SAS ----
+sascode <- list(
+  "test" = "
+      proc genmod data=ana.dat descending;
+      class RACE(ref = 'Asian') id ARMCD(ref = 'PBO') waves;
+      model FEV1_BIN = ARMCD RACE / dist=bin link=logit ;
+      repeated subject=id / within=waves type=UN CORRW;
+      run;
+  "
+)
+
+sas_data <- data
+result <- r2stream::bee_sas(dat = list("dat" = sas_data), sascode = sascode)
+result$test$sas_log
+writeLines(result$test$sas_out, con = "sas_log.txt")
+
+# geeM ----
+library(geeM)
+complete_data <- na.omit(data)
+geem_model <- geeM::geem(
+  formula = FEV1_BIN ~ ARMCD + RACE,
+  id = id,
+  waves = complete_data$waves,
+  data = complete_data,
+  family = binomial(),
+  corstr = "unstructured",
+  scale.fix = TRUE
+)
+# takes very long...
diff --git a/design/sas_log.txt b/design/sas_log.txt
new file mode 100644
index 0000000..7be97b3
--- /dev/null
+++ b/design/sas_log.txt
@@ -0,0 +1,105 @@
+                                                           The SAS System                   Friday, October 21, 2022 11:09:00 AM   1
+
+                                                        The GENMOD Procedure
+
+                                                         Model Information
+
+                                                   Data Set               ANA.DAT
+                                                   Distribution          Binomial
+                                                   Link Function            Logit
+                                                   Dependent Variable    FEV1_BIN
+
+
+                                              Number of Observations Read         800
+                                              Number of Observations Used         537
+                                              Number of Events                    492
+                                              Number of Trials                    537
+                                              Missing Values                      263
+
+
+                                                      Class Level Information
+ 
+                              Class      Levels    Values
+
+                              RACE            3    Black or African American White Asian             
+                              ID            197    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
+                                                   21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
+                                                   38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 55
+                                                   56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
+                                                   73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88   
+                                                   ...                                               
+                              ARMCD           2    TRT PBO                                           
+                              WAVES           4    1 2 3 4                                           
+
+
+                                                          Response Profile
+ 
+                                                  Ordered                    Total
+                                                    Value    FEV1_BIN    Frequency
+
+                                                        1    1                 492
+                                                        2    0                  45
+
+PROC GENMOD is modeling the probability that FEV1_BIN='1'.
+
+
+                                                       Parameter Information
+ 
+                                  Parameter       Effect       RACE                         ARMCD
+
+                                  Prm1            Intercept                                      
+                                  Prm2            ARMCD                                     TRT  
+                                  Prm3            ARMCD                                     PBO  
+                                  Prm4            RACE         Black or African American         
+                                  Prm5            RACE         White                             
+                                  Prm6            RACE         Asian                             
+
+
+                            Algorithm converged.                                                       
+                                                           The SAS System                   Friday, October 21, 2022 11:09:00 AM   2
+
+                                                        The GENMOD Procedure
+
+                                                       GEE Model Information
+
+                                          Correlation Structure               Unstructured
+                                          Within-Subject Effect           WAVES (4 levels)
+                                          Subject Effect                   ID (200 levels)
+                                          Number of Clusters                           200
+                                          Clusters With Missing Values                 161
+                                          Correlation Matrix Dimension                   4
+                                          Maximum Cluster Size                           4
+                                          Minimum Cluster Size                           0
+
+
+                            Algorithm converged.                                                       
+
+
+                                                     Working Correlation Matrix
+ 
+                                                   Col1         Col2         Col3         Col4
+
+                                      Row1       1.0000      -0.0868      -0.1859      -0.1480
+                                      Row2      -0.0868       1.0000       0.0788       0.0077
+                                      Row3      -0.1859       0.0788       1.0000       0.0698
+                                      Row4      -0.1480       0.0077       0.0698       1.0000
+
+
+                                                         GEE Fit Criteria
+
+                                                       QIC          303.9454
+                                                       QICu         303.6369
+
+
+                                                Analysis Of GEE Parameter Estimates
+                                                 Empirical Standard Error Estimates
+ 
+                                                                   Standard   95% Confidence
+                      Parameter                           Estimate    Error       Limits            Z Pr > |Z|
+
+                      Intercept                             1.7193   0.2775   1.1755   2.2632    6.20   <.0001
+                      ARMCD     TRT                         0.6781   0.3299   0.0315   1.3247    2.06   0.0398
+                      ARMCD     PBO                         0.0000   0.0000   0.0000   0.0000     .      .    
+                      RACE      Black or African American   0.1492   0.3246  -0.4869   0.7853    0.46   0.6457
+                      RACE      White                       1.3111   0.5543   0.2246   2.3975    2.37   0.0180
+                      RACE      Asian                       0.0000   0.0000   0.0000   0.0000     .      .    
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 4408fdb..af7f2f9 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -1 +1,14 @@
-TLGs
+Bové
+COPD
+FEV
+Forkers
+Github
+Hoffmann
+README
+Rua
+Sabanés
+WIP
+de
+funder
+pkgdown
+repo
diff --git a/inst/plumber/hello/plumber.R b/inst/plumber/hello/plumber.R
deleted file mode 100644
index e98ebdb..0000000
--- a/inst/plumber/hello/plumber.R
+++ /dev/null
@@ -1,4 +0,0 @@
-#* Personal greeting as a Plumber API
-#* @param name Your name (character string; e.g. "john doe").
-#* @get /echo
-tern.gee::hello
diff --git a/man/dummy_function.Rd b/man/dummy_function.Rd
deleted file mode 100644
index 1c11bf2..0000000
--- a/man/dummy_function.Rd
+++ /dev/null
@@ -1,11 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/hello.R
-\name{dummy_function}
-\alias{dummy_function}
-\title{A dummy function}
-\usage{
-dummy_function()
-}
-\description{
-A dummy function
-}
diff --git a/man/fev_data.Rd b/man/fev_data.Rd
new file mode 100644
index 0000000..6747c07
--- /dev/null
+++ b/man/fev_data.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{fev_data}
+\alias{fev_data}
+\title{Example dataset for \code{tern.gee} package.}
+\format{
+A \code{tibble} with 800 rows and 7 variables:
+\itemize{
+\item \code{USUBJID}: price, in US dollars.
+\item \code{AVISIT}: visit number.
+\item \code{ARMCD}: treatment, \code{TRT} or \code{PBO}.
+\item \code{RACE}: 3-category race.
+\item \code{SEX}: sex.
+\item \code{FEV1_BL}: FEV1 at baseline (\%).
+\item \code{FEV1}: FEV1 at study visits.
+}
+}
+\usage{
+fev_data
+}
+\description{
+Measurements of FEV1 (forced expired volume in one second) is a measure of
+how quickly the lungs can be emptied.
+Low levels of FEV1 may indicate chronic obstructive pulmonary disease (COPD).
+}
+\keyword{datasets}
diff --git a/man/fit_gee.Rd b/man/fit_gee.Rd
new file mode 100644
index 0000000..1afd664
--- /dev/null
+++ b/man/fit_gee.Rd
@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/fit_gee.R
+\name{fit_gee}
+\alias{fit_gee}
+\title{Fit a GEE Model}
+\usage{
+fit_gee(
+  vars = vars_gee(),
+  data,
+  regression = c("logistic"),
+  cor_struct = c("unstructured", "toeplitz", "compound symmetry", "auto-regressive")
+)
+}
+\arguments{
+\item{vars}{(\code{list})\cr see \code{\link[=vars_gee]{vars_gee()}}.}
+
+\item{data}{(\code{data.frame})\cr input data.}
+
+\item{regression}{(\code{string})\cr choice of regression model.}
+
+\item{cor_struct}{(\code{string})\cr assumed correlation structure.}
+}
+\value{
+Object of class \code{tern_gee} as well as specific to the kind of regression
+which was used.
+}
+\description{
+Fit a GEE Model
+}
+\details{
+The correlation structure can be:
+\itemize{
+\item \code{unstructured}: No constraints are placed on the correlations.
+\item \code{toeplitz}: Assumes a banded correlation structure, i.e. the correlation
+between two time points depends on the distance between the time indices.
+\item \verb{compound symmetry}: Constant correlation between all time points.
+\item \code{auto-regressive}: Auto-regressive order 1 correlation matrix.
+}
+}
+\examples{
+df <- fev_data
+df$AVAL <- as.integer(fev_data$FEV1 > 30)
+
+fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)
+
+fit_gee(vars = vars_gee(arm = "ARMCD"), data = df, cor_struct = "compound symmetry")
+}
diff --git a/man/gee_methods.Rd b/man/gee_methods.Rd
new file mode 100644
index 0000000..88fb1fb
--- /dev/null
+++ b/man/gee_methods.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gee_methods.R
+\name{gee_methods}
+\alias{gee_methods}
+\alias{VarCorr.tern_gee}
+\alias{QIC.tern_gee}
+\title{Methods for GEE Models}
+\usage{
+\method{VarCorr}{tern_gee}(x, sigma = 1, ...)
+
+\method{QIC}{tern_gee}(object, ...)
+}
+\arguments{
+\item{x}{(\code{tern_gee})\cr result of \code{\link[=fit_gee]{fit_gee()}}.}
+
+\item{sigma}{an optional numeric value used as a multiplier for the
+    standard deviations.  The default is \code{x$sigma} or \code{1}
+    depending on \code{\link{class}(x)}.}
+
+\item{...}{further optional arguments passed to other methods (none
+    for the methods documented here).}
+
+\item{object}{(\code{tern_gee})\cr result of \code{\link[=fit_gee]{fit_gee()}}.}
+}
+\description{
+Additional methods which can simplify working with the GEE result object.
+}
diff --git a/man/lsmeans.Rd b/man/lsmeans.Rd
new file mode 100644
index 0000000..210a3df
--- /dev/null
+++ b/man/lsmeans.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/lsmeans.R
+\name{lsmeans}
+\alias{lsmeans}
+\alias{lsmeans.tern_gee_logistic}
+\title{Extract Least Square Means from a GEE Model}
+\usage{
+lsmeans(object, conf_level = 0.95, weights = "proportional", ...)
+
+\method{lsmeans}{tern_gee_logistic}(object, conf_level = 0.95, weights = "proportional", ...)
+}
+\arguments{
+\item{object}{(\code{tern_gee})\cr result of \code{\link[=fit_gee]{fit_gee()}}.}
+
+\item{conf_level}{(\code{proportion})\cr confidence level}
+
+\item{weights}{(\code{string})\cr type of weights to be used for the least square means,
+see \code{\link[emmeans:emmeans]{emmeans::emmeans()}} for details.}
+
+\item{...}{additional arguments for methods}
+}
+\value{
+A \code{data.frame} with least-square means and contrasts. Additional
+classes allow to dispatch downstream methods correctly, too.
+}
+\description{
+Extract Least Square Means from a GEE Model
+}
+\examples{
+df <- fev_data
+df$AVAL <- rbinom(n = nrow(df), size = 1, prob = 0.5)
+fit <- fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)
+
+lsmeans(fit)
+
+lsmeans(fit, conf_level = 0.90, weights = "equal")
+}
diff --git a/man/reexports.Rd b/man/reexports.Rd
new file mode 100644
index 0000000..e4880f2
--- /dev/null
+++ b/man/reexports.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tabulate_gee.R
+\docType{import}
+\name{reexports}
+\alias{reexports}
+\alias{as.rtable}
+\title{Objects exported from other packages}
+\keyword{internal}
+\description{
+These objects are imported from other packages. Follow the links
+below to see their documentation.
+
+\describe{
+  \item{tern}{\code{\link[tern]{as.rtable}}}
+}}
+
diff --git a/man/tabulate_gee.Rd b/man/tabulate_gee.Rd
new file mode 100644
index 0000000..e1b6725
--- /dev/null
+++ b/man/tabulate_gee.Rd
@@ -0,0 +1,89 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tabulate_gee.R
+\name{tabulate_gee}
+\alias{tabulate_gee}
+\alias{as.rtable.tern_gee}
+\alias{s_lsmeans_logistic}
+\alias{a_lsmeans_logistic}
+\alias{summarize_gee_logistic}
+\title{Tabulation of a GEE Model}
+\usage{
+\method{as.rtable}{tern_gee}(x, type = c("coef", "cov"), ...)
+
+s_lsmeans_logistic(df, .in_ref_col)
+
+a_lsmeans_logistic(df, .in_ref_col)
+
+summarize_gee_logistic(
+  lyt,
+  ...,
+  table_names = "lsmeans_logistic_summary",
+  .stats = NULL,
+  .formats = NULL,
+  .indent_mods = NULL,
+  .labels = NULL
+)
+}
+\arguments{
+\item{x}{the object which should be converted to an \code{rtable}.}
+
+\item{type}{(\code{character})\cr type of table to extract from \code{tern_gee} object.}
+
+\item{...}{additional arguments for methods.}
+
+\item{df}{(\code{data.frame})\cr data set resulting from \code{\link[=lsmeans]{lsmeans()}}.}
+
+\item{.in_ref_col}{(\code{logical})\cr \code{TRUE} when working with the reference level, \code{FALSE} otherwise.}
+
+\item{lyt}{(\code{layout})\cr input layout where analyses will be added to.}
+
+\item{table_names}{(\code{character})\cr this can be customized in case that the same \code{vars}
+are analyzed multiple times, to avoid warnings from \code{rtables}.}
+
+\item{.stats}{(\code{character})\cr statistics to select for the table.}
+
+\item{.formats}{(named \code{character} or \code{list})\cr formats for the statistics.}
+
+\item{.indent_mods}{(named \code{integer})\cr indent modifiers for the labels.}
+
+\item{.labels}{(named \code{character})\cr labels for the statistics (without indent).}
+}
+\description{
+Functions to produce tables from a fitted GEE produced with \code{\link[=fit_gee]{fit_gee()}}.
+}
+\section{Functions}{
+\itemize{
+\item \code{as.rtable(tern_gee)}: Extracts the coefficient table or covariance matrix estimate from a \code{tern_gee} object.
+
+\item \code{s_lsmeans_logistic()}: Statistics function which extracts estimates from a
+\code{\link[=lsmeans]{lsmeans()}} data frame based on a logistic GEE model.
+
+\item \code{a_lsmeans_logistic()}: Formatted Analysis function which can be further customized by calling
+\code{\link[rtables:make_afun]{rtables::make_afun()}} on it. It is used as \code{afun} in \code{\link[rtables:analyze]{rtables::analyze()}}.
+
+\item \code{summarize_gee_logistic()}: Analyze function for tabulating least-squares means estimates
+from logistic GEE least square mean results.
+
+}}
+\examples{
+library(dplyr)
+
+df <- fev_data \%>\%
+  mutate(AVAL = as.integer(fev_data$FEV1 > 30))
+df_counts <- df \%>\%
+  select(USUBJID, ARMCD) \%>\%
+  unique()
+
+lsmeans_df <- lsmeans(fit_gee(vars = vars_gee(arm = "ARMCD"), data = df))
+
+s_lsmeans_logistic(lsmeans_df[1, ], .in_ref_col = TRUE)
+
+s_lsmeans_logistic(lsmeans_df[2, ], .in_ref_col = FALSE)
+basic_table() \%>\%
+  split_cols_by("ARMCD") \%>\%
+  add_colcounts() \%>\%
+  summarize_gee_logistic(
+    .in_ref_col = FALSE
+  ) \%>\%
+  build_table(lsmeans_df, alt_counts_df = df_counts)
+}
diff --git a/man/tern.gee-package.Rd b/man/tern.gee-package.Rd
new file mode 100644
index 0000000..eaa70f8
--- /dev/null
+++ b/man/tern.gee-package.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/package.R
+\docType{package}
+\name{tern.gee-package}
+\alias{tern.gee}
+\alias{tern.gee-package}
+\title{\code{tern.gee} Package}
+\description{
+Create tables and graphs for GEE model fits.
+}
+\seealso{
+Useful links:
+\itemize{
+  \item \url{https://github.com/insightsengineering/tern.gee/}
+  \item Report bugs at \url{https://github.com/insightsengineering/tern.gee/issues}
+}
+
+}
+\author{
+\strong{Maintainer}: Daniel Sabanés Bové \email{daniel.sabanes_bove@roche.com}
+
+Authors:
+\itemize{
+  \item Joe Zhu \email{joe.zhu@roche.com}
+  \item Emily de la Rua \email{emily.de_la_rua@contractors.roche.com}
+}
+
+Other contributors:
+\itemize{
+  \item F. Hoffmann-La Roche AG [copyright holder, funder]
+}
+
+}
diff --git a/man/vars_gee.Rd b/man/vars_gee.Rd
new file mode 100644
index 0000000..14ff783
--- /dev/null
+++ b/man/vars_gee.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/fit_gee.R
+\name{vars_gee}
+\alias{vars_gee}
+\title{Set Variables to Use in GEE Model}
+\usage{
+vars_gee(
+  response = "AVAL",
+  covariates = c(),
+  id = "USUBJID",
+  arm = "ARM",
+  visit = "AVISIT"
+)
+}
+\arguments{
+\item{response}{(\code{character})\cr name of response variable.}
+
+\item{covariates}{(\code{character})\cr vector of names of variables to use as covariates.}
+
+\item{id}{(\code{character})\cr name of variable to use to identify unique IDs.}
+
+\item{arm}{(\code{character})\cr name of arm variable.}
+
+\item{visit}{(\code{character})\cr name of visit variable.}
+}
+\value{
+A list of variables that can be used as the \code{vars} argument in \code{\link[=fit_gee]{fit_gee()}}.
+}
+\description{
+Set Variables to Use in GEE Model
+}
+\examples{
+vars_gee()
+
+vars_gee(
+  response = "CHG",
+  covariates = c("SEX", "RACE"),
+  id = "SUBJID",
+  arm = "ARMCD",
+  visit = "AVISITN"
+)
+}
diff --git a/staged_dependencies.yaml b/staged_dependencies.yaml
index fb79fca..7d88465 100644
--- a/staged_dependencies.yaml
+++ b/staged_dependencies.yaml
@@ -14,3 +14,6 @@ upstream_repos:
     repo: insightsengineering/tern
     host: https://github.com
 downstream_repos:
+  insightsengineering/teal.modules.clinical:
+    repo: insightsengineering/teal.modules.clinical
+    host: https://github.com
diff --git a/tern.gee.Rproj b/tern.gee.Rproj
index 85d0bd5..ded5673 100644
--- a/tern.gee.Rproj
+++ b/tern.gee.Rproj
@@ -1,7 +1,7 @@
 Version: 1.0
 
-RestoreWorkspace: Default
-SaveWorkspace: Default
+RestoreWorkspace: No
+SaveWorkspace: No
 AlwaysSaveHistory: Default
 
 EnableCodeIndexing: Yes
@@ -14,7 +14,6 @@ LaTeX: pdfLaTeX
 
 AutoAppendNewline: Yes
 StripTrailingWhitespace: Yes
-LineEndingConversion: Posix
 
 BuildType: Package
 PackageInstallArgs: --no-multiarch --with-keep.source
diff --git a/tests/testthat.R b/tests/testthat.R
index a13b4c1..ff07f92 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -1,9 +1,3 @@
 pkg_name <- "tern.gee"
-if (requireNamespace("testthat", quietly = TRUE)) {
-  library(testthat)
-  reporter <- MultiReporter$new(list(
-    CheckReporter$new()
-  ))
-  test_results <- test_check(pkg_name, reporter = reporter)
-  saveRDS(test_results, "unit_testing_results.rds")
-}
+library(pkg_name, character.only = TRUE)
+testthat::test_check(pkg_name)
diff --git a/tests/testthat/helper-setup.R b/tests/testthat/helper-setup.R
new file mode 100644
index 0000000..1639fac
--- /dev/null
+++ b/tests/testthat/helper-setup.R
@@ -0,0 +1,10 @@
+# example ----
+
+fev_data$FEV1_BINARY <- as.integer(fev_data$FEV1 > 30)
+fev_vars <- vars_gee(
+  response = "FEV1_BINARY",
+  covariates = "RACE",
+  arm = "ARMCD",
+  id = "USUBJID",
+  visit = "AVISIT"
+)
diff --git a/tests/testthat/test-dummy.R b/tests/testthat/test-dummy.R
deleted file mode 100644
index d6bbc7a..0000000
--- a/tests/testthat/test-dummy.R
+++ /dev/null
@@ -1,6 +0,0 @@
-test_that("dummy", {
-  # Compare
-  result <- "Hello, Tim"
-  expected <- "Hello, Tim"
-  expect_identical(result, expected)
-})
diff --git a/tests/testthat/test-fit_gee.R b/tests/testthat/test-fit_gee.R
new file mode 100644
index 0000000..970887c
--- /dev/null
+++ b/tests/testthat/test-fit_gee.R
@@ -0,0 +1,83 @@
+# build_formula ----
+
+test_that("build_formula builds the correct formula", {
+  vars <- list(response = "AVAL", id = "USUBJID", arm = "ARMCD", visit = "AVISIT", covariates = c("RACE", "SEX"))
+  result <- as.character(build_formula(vars))
+  expected <- as.character(AVAL ~ ARMCD + RACE + SEX)
+  expect_identical(result, expected)
+})
+
+test_that("build_formula works without covariates", {
+  result <- expect_silent(build_formula(vars_gee()))
+  expected <- AVAL ~ ARM
+  expect_equal(result, expected, ignore_attr = TRUE)
+})
+
+# build_family ----
+
+test_that("build_family returns the correct class", {
+  result <- build_family("logistic")
+  expected <- "tern_gee_logistic"
+  expect_identical(result$class, expected)
+  expect_class(result$object, "family")
+})
+
+test_that("build_family gives error message if incorrect regression type is given", {
+  expect_error(build_family("BLAHHH"), "regression type BLAHHH not supported")
+})
+
+# build_cor_details ----
+
+test_that("build_cor_details returns the correct correlation details", {
+  result <- as.character(build_cor_details("unstructured", fev_vars, fev_data))
+  expected <- c("unstructured", "1")
+  expect_identical(result, expected)
+})
+
+test_that("build_cor_details gives error message if incorrect correlation structure is given", {
+  expect_error(build_cor_details("BLAHHH", fev_vars, fev_data), "correlation structure BLAHHH not available")
+})
+
+# fit_gee ----
+
+## logistic ----
+
+test_that("fit_gee works as expected for unstructured correlation structure", {
+  result <- expect_silent(fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "unstructured"
+  ))
+  expect_class(result, c("tern_gee_logistic", "tern_gee", "geelm"))
+})
+
+test_that("fit_gee works as expected for AR1 correlation structure", {
+  result <- expect_silent(fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "auto-regressive"
+  ))
+  expect_class(result, c("tern_gee_logistic", "tern_gee", "geelm"))
+})
+
+test_that("fit_gee works as expected for compound symmetry correlation structure", {
+  result <- expect_silent(fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "compound symmetry"
+  ))
+  expect_class(result, c("tern_gee_logistic", "tern_gee", "geelm"))
+})
+
+test_that("fit_gee works as expected for Toeplitz correlation structure", {
+  result <- expect_silent(fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "toeplitz"
+  ))
+  expect_class(result, c("tern_gee_logistic", "tern_gee", "geelm"))
+})
diff --git a/tests/testthat/test-gee_methods.R b/tests/testthat/test-gee_methods.R
new file mode 100644
index 0000000..780d6b5
--- /dev/null
+++ b/tests/testthat/test-gee_methods.R
@@ -0,0 +1,59 @@
+model <- fit_gee(
+  vars = fev_vars,
+  data = fev_data,
+  regression = "logistic",
+  cor_struct = "unstructured"
+)
+
+test_that("vcov works as expected", {
+  result <- expect_silent(vcov(model))
+  expect_matrix(result, nrows = 4, ncols = 4)
+})
+
+test_that("VarCorr works as expected for unstructured correlation structure", {
+  model <- fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "unstructured"
+  )
+
+  result <- expect_silent(VarCorr(model))
+  expect_true(all(eigen(result)$values > 0))
+})
+
+test_that("VarCorr works as expected for compound symmetry correlation structure", {
+  model <- fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "compound symmetry"
+  )
+
+  result <- expect_silent(VarCorr(model))
+  expect_true(all(eigen(result)$values > 0))
+})
+
+test_that("VarCorr works as expected for AR1 correlation structure", {
+  model <- fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "auto-regressive"
+  )
+
+  result <- expect_silent(VarCorr(model))
+  expect_true(all(eigen(result)$values > 0))
+})
+
+test_that("VarCorr works as expected for Toeplitz correlation structure", {
+  model <- fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "toeplitz"
+  )
+
+  result <- expect_silent(VarCorr(model))
+  expect_true(all(eigen(result)$values > 0))
+})
diff --git a/tests/testthat/test-lsmeans.R b/tests/testthat/test-lsmeans.R
new file mode 100644
index 0000000..dd8661a
--- /dev/null
+++ b/tests/testthat/test-lsmeans.R
@@ -0,0 +1,18 @@
+test_that("lsmeans works for logistic model", {
+  model <- fit_gee(
+    vars = fev_vars,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "unstructured"
+  )
+
+  result <- lsmeans(model)
+  expect_data_frame(result)
+  expect_class(result, "lsmeans_logistic")
+  expect_named(result, c(
+    "ARMCD", "prop_est", "prop_est_se", "prop_lower_cl", "prop_upper_cl", "n",
+    "or_est", "or_lower_cl", "or_upper_cl", "log_or_est", "log_or_lower_cl", "log_or_upper_cl",
+    "conf_level"
+  ))
+  expect_identical(nrow(result), 2L)
+})
diff --git a/tests/testthat/test-tabulate_gee.R b/tests/testthat/test-tabulate_gee.R
new file mode 100644
index 0000000..1996e06
--- /dev/null
+++ b/tests/testthat/test-tabulate_gee.R
@@ -0,0 +1,128 @@
+dat_adsl <- fev_data %>%
+  dplyr::select(USUBJID, ARMCD) %>%
+  dplyr::distinct()
+
+model <- fit_gee(
+  vars = fev_vars,
+  data = fev_data,
+  regression = "logistic",
+  cor_struct = "unstructured"
+)
+
+# to do: finish test once h_gee_coef is working correctly
+test_that("h_gee_coef works as expected", {
+  # result <- expect_silent(h_gee_coef(model))
+  # result2 <- as.rtable(model, type = "coef", format = "xx.xxxx")
+  # expect_identical(result, result2)
+  #
+  # result_matrix <- to_string_matrix(result)
+  # expected_matrix <- structure(
+  #   c(
+  #   ),
+  #   dim = c()
+  # )
+  # expect_identical(result_matrix, expected_matrix)
+})
+
+test_that("h_gee_cov works as expected", {
+  result <- expect_silent(h_gee_cov(model))
+  result2 <- as.rtable(model, type = "cov", format = "xx.xxxx")
+  expect_identical(result, result2)
+
+  result_matrix <- to_string_matrix(result)
+  expected_matrix <- structure(
+    c(
+      "", "VIS1", "VIS2", "VIS3", "VIS4",
+      "VIS1", "1.0000", "-0.0565", "-0.1257", "0.0678",
+      "VIS2", "-0.0565", "1.0000", "-0.0836", "-0.0578",
+      "VIS3", "-0.1257", "-0.0836", "1.0000", "0.0092",
+      "VIS4", "0.0678", "-0.0578", "0.0092", "1.0000"
+    ),
+    dim = c(5L, 5L)
+  )
+  expect_identical(result_matrix, expected_matrix)
+})
+
+test_that("s_lsmeans_logistic works as expected when not in reference column", {
+  result <- s_lsmeans_logistic(lsmeans(model)[2, ], FALSE)
+  expected <- list(
+    n = 380L,
+    adj_prop_se = c(0.94694895, 0.01347376),
+    adj_prop_ci = formatters::with_label(c(0.9134350, 0.9679432), label = "95% CI"),
+    odds_ratio_est = 1.970852,
+    odds_ratio_ci = formatters::with_label(c(1.031652, 3.765083), label = "95% CI"),
+    log_odds_ratio_est = 0.6784657,
+    log_odds_ratio_ci = formatters::with_label(c(0.03116157, 1.32576989), label = "95% CI")
+  )
+  expect_equal(result, expected, tolerance = 1e-2)
+})
+
+test_that("s_mmrm_lsmeans works as expected when in reference column", {
+  result <- s_lsmeans_logistic(lsmeans(model)[1, ], TRUE)
+  expected <- list(
+    n = 420L,
+    adj_prop_se = c(0.9005656, 0.0198212),
+    adj_prop_ci = formatters::with_label(c(0.8544189, 0.9332277), label = "95% CI"),
+    odds_ratio_est = character(0),
+    odds_ratio_ci = formatters::with_label(character(0), label = "95% CI"),
+    log_odds_ratio_est = character(0),
+    log_odds_ratio_ci = formatters::with_label(character(0), label = "95% CI")
+  )
+  expect_equal(result, expected, tolerance = 1e-2)
+})
+
+test_that("summarize_gee_logistic works as expected with covariates in the model", {
+  result <- basic_table() %>%
+    split_cols_by("ARMCD", ref_group = model$ref_level) %>%
+    add_colcounts() %>%
+    summarize_gee_logistic() %>%
+    build_table(
+      df = lsmeans(model),
+      alt_counts_df = dat_adsl
+    )
+
+  result_matrix <- to_string_matrix(result)
+  expected_matrix <- structure(
+    c(
+      "", "", "n", "Adjusted Mean Proportion (SE)", "95% CI", "Odds Ratio", "95% CI", "Log Odds Ratio", "95% CI",
+      "PBO", "(N=105)", "420", "0.90 (0.02)", "(0.85, 0.93)", "", "", "", "",
+      "TRT", "(N=95)", "380", "0.95 (0.01)", "(0.91, 0.97)", "1.97", "(1.03, 3.77)", "0.68", "(0.03, 1.33)"
+    ),
+    dim = c(9L, 3L)
+  )
+  expect_identical(result_matrix, expected_matrix)
+})
+
+test_that("summarize_gee_logistic works as expected with no covariates in the model", {
+  fev_vars_nocov <- vars_gee(
+    response = "FEV1_BINARY",
+    arm = "ARMCD",
+    id = "USUBJID",
+    visit = "AVISIT"
+  )
+  model <- fit_gee(
+    vars = fev_vars_nocov,
+    data = fev_data,
+    regression = "logistic",
+    cor_struct = "unstructured"
+  )
+  result <- basic_table() %>%
+    split_cols_by("ARMCD", ref_group = model$ref_level) %>%
+    add_colcounts() %>%
+    summarize_gee_logistic() %>%
+    build_table(
+      df = lsmeans(model),
+      alt_counts_df = dat_adsl
+    )
+
+  result_matrix <- to_string_matrix(result)
+  expected_matrix <- structure(
+    c(
+      "", "", "n", "Adjusted Mean Proportion (SE)", "95% CI", "Odds Ratio", "95% CI", "Log Odds Ratio", "95% CI",
+      "PBO", "(N=105)", "420", "0.88 (0.02)", "(0.84, 0.92)", "", "", "", "",
+      "TRT", "(N=95)", "380", "0.94 (0.01)", "(0.91, 0.97)", "2.18", "(1.16, 4.10)", "0.78", "(0.15, 1.41)"
+    ),
+    dim = c(9L, 3L)
+  )
+  expect_identical(result_matrix, expected_matrix)
+})
diff --git a/vignettes/hello.Rmd b/vignettes/hello.Rmd
deleted file mode 100644
index 5ec24d9..0000000
--- a/vignettes/hello.Rmd
+++ /dev/null
@@ -1,12 +0,0 @@
-
----
-title: "Hello"
-date: "`r Sys.Date()`"
----
-
-Hello World!
-
-```{r}
-library(tern.gee)
-hello("tern.gee!")
-```
diff --git a/vignettes/introduction.Rmd b/vignettes/introduction.Rmd
new file mode 100644
index 0000000..4f53612
--- /dev/null
+++ b/vignettes/introduction.Rmd
@@ -0,0 +1,23 @@
+---
+title: "Introduction to `tern.gee`"
+date: "`r Sys.Date()`"
+output:
+    rmarkdown::html_document:
+        theme: "spacelab"
+        highlight: "kate"
+        toc: true
+        toc_float: true
+vignette: >
+    %\VignetteIndexEntry{Introduction}
+    %\VignetteEngine{knitr::rmarkdown}
+    %\VignetteEncoding{UTF-8}
+editor_options:
+    markdown: 
+        wrap: 72
+---
+
+Placeholder for introduction vignette.
+
+```{r}
+library(tern.gee)
+```