diff --git a/R/IPD_stats.R b/R/IPD_stats.R
index 67fc7c2..ef7c9ef 100644
--- a/R/IPD_stats.R
+++ b/R/IPD_stats.R
@@ -147,8 +147,8 @@ IPD_stats.default <- function() {
 #' using bootstrapping
 #'
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @export
 #' 
@@ -174,8 +174,8 @@ IPD_stats.maic <- function(strategy,
 #' IPD_stats.stc
 #' 
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #' @export
 #' 
 IPD_stats.stc <- function(strategy,
@@ -196,8 +196,8 @@ IPD_stats.stc <- function(strategy,
 #' IPD_stats.gcomp_ml
 #'
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @return
 #' @export
@@ -220,8 +220,8 @@ IPD_stats.gcomp_ml <- function(strategy,
 #' IPD_stats.gcomp_stan
 #'
 #' @param strategy 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @return
 #' @export
@@ -230,7 +230,7 @@ IPD_stats.gcomp_stan <- function(strategy,
                                  ipd, ald) {
   
   ppv <- gcomp_stan(formula = strategy$formula,
-                    dat = ipd)
+                    ipd = ipd, ald = ald)
   
   # compute marginal log-odds ratio for A vs C for each MCMC sample
   # by transforming from probability to linear predictor scale
diff --git a/R/gcomp_stan.R b/R/gcomp_stan.R
index 2fa4123..51673b4 100644
--- a/R/gcomp_stan.R
+++ b/R/gcomp_stan.R
@@ -2,8 +2,8 @@
 #' G-computation using Stan
 #'
 #' @param formula 
-#' @param ipd 
-#' @param ald 
+#' @template args-ipd
+#' @template args-ald
 #'
 #' @return
 #' @export
@@ -15,9 +15,7 @@ gcomp_stan <- function(formula = as.formula("y ~ X3 + X4 + trt*X1 + trt*X2"),
   
   # remove treatment
   cov_names <- cov_names[cov_names != treat_names]
-  
-  n_covariates <- length(covariate_names)
-  
+  n_covariates <- length(cov_names)
   rho <- cor(ipd[, cov_names])
   
   # covariate simulation for BC trial using copula package
@@ -40,7 +38,6 @@ gcomp_stan <- function(formula = as.formula("y ~ X3 + X4 + trt*X1 + trt*X2"),
   # simulated BC pseudo-population of size 1000
   x_star <- as.data.frame(rMvdc(1000, mvd))
   
-  browser()
   colnames(x_star) <- cov_names
   
   # outcome logistic regression fitted to IPD using MCMC (Stan)
diff --git a/man-roxygen/args-ald.R b/man-roxygen/args-ald.R
new file mode 100644
index 0000000..7e99408
--- /dev/null
+++ b/man-roxygen/args-ald.R
@@ -0,0 +1 @@
+#' @param ald Aggregate-level data
\ No newline at end of file
diff --git a/man-roxygen/args-ipd.R b/man-roxygen/args-ipd.R
new file mode 100644
index 0000000..57a7bd5
--- /dev/null
+++ b/man-roxygen/args-ipd.R
@@ -0,0 +1 @@
+#' @param ipd Individual-level data
\ No newline at end of file
diff --git a/man/gcomp_stan.Rd b/man/gcomp_stan.Rd
index 9408fb9..0fd669f 100644
--- a/man/gcomp_stan.Rd
+++ b/man/gcomp_stan.Rd
@@ -2,15 +2,17 @@
 % Please edit documentation in R/gcomp_stan.R
 \name{gcomp_stan}
 \alias{gcomp_stan}
-\title{gcomp_stan}
+\title{G-computation using Stan}
 \usage{
-gcomp_stan(formula = as.formula("y ~ X3 + X4 + trt*X1 + trt*X2"), dat = AC.IPD)
+gcomp_stan(formula = as.formula("y ~ X3 + X4 + trt*X1 + trt*X2"), ipd, ald)
 }
 \arguments{
 \item{formula}{}
 
-\item{dat}{}
+\item{ipd}{Individual-level data}
+
+\item{ald}{Aggregate-level data}
 }
 \description{
-gcomp_stan
+G-computation using Stan
 }