diff --git a/DESCRIPTION b/DESCRIPTION
index 0cf0c8ea7..5272d06fd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: ergm
-Version: 4.7-7420
+Version: 4.7-7421
 Date: 2024-10-07
 Title: Fit, Simulate and Diagnose Exponential-Family Models for Networks
 Authors@R: c(
diff --git a/R/mcmc.diagnostics.ergm.R b/R/mcmc.diagnostics.ergm.R
index 526f15612..73c5ff839 100644
--- a/R/mcmc.diagnostics.ergm.R
+++ b/R/mcmc.diagnostics.ergm.R
@@ -85,20 +85,19 @@ mcmc.diagnostics.default <- function(object, ...) {
 #'   observed network statistics. For that functionality, please use
 #'   the GOF command: \code{gof(object, GOF=~model)}.
 #'
-#'   In fact, an ergm output \code{object} contains the matrix of
-#'   statistics from the MCMC run as component \code{$sample}.  This
-#'   matrix is actually an object of class \code{mcmc} and can be used
-#'   directly in the \code{coda} package to assess MCMC
-#'   convergence. \emph{Hence all MCMC diagnostic methods available in
-#'   \code{coda} are available directly.} See the examples and
-#'   \url{https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/coda-readme/}.
+#'   In fact, an [ergm()] output object contains the sample of
+#'   statistics from the last MCMC run as element `$sample`. If
+#'   missing data MLE is fit, the corresponding element is named
+#'   `$sample.obs`. These are objects of [`mcmc`] and can be used
+#'   directly in the \CRANpkg{coda} package to assess MCMC
+#'   convergence.
 #'
 #'   More information can be found by looking at the documentation of
 #'   [ergm()].
 #'
-#' @param center Logical: If TRUE, center the samples on the observed
+#' @param center Logical: If `TRUE`, center the samples on the observed
 #'   statistics.
-#' @param esteq Logical: If TRUE, for statistics corresponding to
+#' @param esteq Logical: If `TRUE`, for statistics corresponding to
 #'   curved ERGM terms, summarize the curved statistics by their
 #'   negated estimating function values (evaluated at the MLE of any curved
 #'   parameters) (i.e., \eqn{\eta'_{I}(\hat{\theta})\cdot (g_{I}(Y)-g_{I}(y))}
diff --git a/man/mcmc.diagnostics.Rd b/man/mcmc.diagnostics.Rd
index 69fa0b235..f5cb109b5 100644
--- a/man/mcmc.diagnostics.Rd
+++ b/man/mcmc.diagnostics.Rd
@@ -23,10 +23,10 @@ mcmc.diagnostics(object, ...)
 
 \item{\dots}{Additional arguments, to be passed to plotting functions.}
 
-\item{center}{Logical: If TRUE, center the samples on the observed
+\item{center}{Logical: If \code{TRUE}, center the samples on the observed
 statistics.}
 
-\item{esteq}{Logical: If TRUE, for statistics corresponding to
+\item{esteq}{Logical: If \code{TRUE}, for statistics corresponding to
 curved ERGM terms, summarize the curved statistics by their
 negated estimating function values (evaluated at the MLE of any curved
 parameters) (i.e., \eqn{\eta'_{I}(\hat{\theta})\cdot (g_{I}(Y)-g_{I}(y))}
@@ -79,13 +79,12 @@ to ensure that the mean statistics from the model match the
 observed network statistics. For that functionality, please use
 the GOF command: \code{gof(object, GOF=~model)}.
 
-In fact, an ergm output \code{object} contains the matrix of
-statistics from the MCMC run as component \code{$sample}.  This
-matrix is actually an object of class \code{mcmc} and can be used
-directly in the \code{coda} package to assess MCMC
-convergence. \emph{Hence all MCMC diagnostic methods available in
-\code{coda} are available directly.} See the examples and
-\url{https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/coda-readme/}.
+In fact, an \code{\link[=ergm]{ergm()}} output object contains the sample of
+statistics from the last MCMC run as element \verb{$sample}. If
+missing data MLE is fit, the corresponding element is named
+\verb{$sample.obs}. These are objects of \code{\link[coda:mcmc]{mcmc}} and can be used
+directly in the \CRANpkg{coda} package to assess MCMC
+convergence.
 
 More information can be found by looking at the documentation of
 \code{\link[=ergm]{ergm()}}.