diff --git a/R/weightit2optweight.R b/R/weightit2optweight.R
index 1af04bf..ba43051 100644
--- a/R/weightit2optweight.R
+++ b/R/weightit2optweight.R
@@ -1,6 +1,6 @@
 #' Optimization-Based Weighting
 #' @name method_optweight
-#' @aliases method_optweight method_sbw
+#' @aliases method_optweight
 #' @usage NULL
 #'
 #' @description
diff --git a/man/method_optweight.Rd b/man/method_optweight.Rd
index 1e6120d..a3b01fb 100644
--- a/man/method_optweight.Rd
+++ b/man/method_optweight.Rd
@@ -2,7 +2,6 @@
 % Please edit documentation in R/weightit2optweight.R
 \name{method_optweight}
 \alias{method_optweight}
-\alias{method_sbw}
 \title{Optimization-Based Weighting}
 \description{
 This page explains the details of estimating optimization-based weights (also known as stable balancing weights) by setting \code{method = "optweight"} in the call to \code{\link[=weightit]{weightit()}} or \code{\link[=weightitMSM]{weightitMSM()}}. This method can be used with binary, multi-category, and continuous treatments.
diff --git a/vignettes/estimating-effects.Rmd b/vignettes/estimating-effects.Rmd
index 2d990e4..722168e 100644
--- a/vignettes/estimating-effects.Rmd
+++ b/vignettes/estimating-effects.Rmd
@@ -292,7 +292,7 @@ avg_comparisons(fit,
                 transform = "exp")
 ```
 
-The output displays the marginal RR, its Z-value, the p-value for the Z-test of the log RR against 0, and its confidence interval. (Note that even though the `Contrast` label still suggests the log RR, the RR is actually displayed.) To view the log RR and its standard error, omit the `transform` argument.
+The output displays the marginal RR, its Z-value, the p-value for the Z-test of the log RR against 0, and its confidence interval. (Note that even though the `Comparison` label still suggests the log RR, the RR is actually displayed.) To view the log RR and its standard error, omit the `transform` argument.
 
 For the marginal OR, the only thing that needs to change is that `comparison` should be set to `"lnoravg"`.
 
@@ -450,7 +450,7 @@ p <- avg_predictions(fit,
 
 Although one can examine the expected potential outcomes, it is often more useful to see them plotted. We can generate a plot of the ADRF and its pointwise confidence band using `ggplot2`[^4]:
 
-[^4]: You can also use `plot_predictions()`, though after requesting the predictions in the prior step it is quicker to use `ggplot()`.
+[^4]: You can also use `marginaleffects::plot_predictions()`, though after requesting the predictions in the prior step it is quicker to use `ggplot()`.
 
 ```{r, eval = me_ok, fig.height=3.5, fig.width=7}
 library("ggplot2")
@@ -458,7 +458,7 @@ ggplot(p, aes(x = Ac)) +
   geom_line(aes(y = estimate)) +
   geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
               alpha = .3) +
-  labs(x = "Ac", y = "E[Y|A]") +
+  labs(x = "Ac", y = "E[Y|Ac]") +
   theme_bw()
 ```
 
@@ -483,7 +483,7 @@ ggplot(s, aes(x = Ac)) +
   geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
               alpha = .3) +
   geom_hline(yintercept = 0, linetype = "dashed") +
-  labs(x = "Ac", y = "dE[Y|A]/dA") +
+  labs(x = "Ac", y = "dE[Y|Ac]/dAc") +
   theme_bw()
 ```