diff --git a/R/smoothness.R b/R/smoothness.R
index b45202b5f..28c2f4a0e 100644
--- a/R/smoothness.R
+++ b/R/smoothness.R
@@ -1,4 +1,10 @@
-#' Quantify the smoothness of a vector
+#' Series smoothness
+#'
+#' Functions to quantify the smoothness of a vector, which can be used in some cases
+#' as an index of "linearity". A smooth series is one that does not have abrupt changes in
+#' its values. The smoothness of a series can be measured in different ways, such
+#' as the standard deviation of the standardized differences or the lag-one
+#' autocorrelation.
 #'
 #' @param x Numeric vector (similar to a time series).
 #' @param method Can be `"diff"` (the standard deviation of the standardized
@@ -12,6 +18,13 @@
 #' plot(x)
 #' smoothness(x, method = "cor")
 #' smoothness(x, method = "diff")
+#'
+#' # A boootstrapped value can also be computed
+#' smoothness(x, iterations = 100)
+#'
+#' # When perfectly linear, the "smoothness" is 1
+#' smoothness(1:10)
+#'
 #' @return Value of smoothness.
 #' @references https://stats.stackexchange.com/questions/24607/how-to-measure-smoothness-of-a-time-series-in-r
 #'
diff --git a/man/smoothness.Rd b/man/smoothness.Rd
index f2e1d2662..5b93db2b5 100644
--- a/man/smoothness.Rd
+++ b/man/smoothness.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/smoothness.R
 \name{smoothness}
 \alias{smoothness}
-\title{Quantify the smoothness of a vector}
+\title{Series smoothness}
 \usage{
 smoothness(x, method = "cor", lag = 1, iterations = NULL, ...)
 }
@@ -24,13 +24,24 @@ errors. If \code{NULL} (default), parametric standard errors are computed.}
 Value of smoothness.
 }
 \description{
-Quantify the smoothness of a vector
+Functions to quantify the smoothness of a vector, which can be used in some cases
+as an index of "linearity". A smooth series is one that does not have abrupt changes in
+its values. The smoothness of a series can be measured in different ways, such
+as the standard deviation of the standardized differences or the lag-one
+autocorrelation.
 }
 \examples{
 x <- (-10:10)^3 + rnorm(21, 0, 100)
 plot(x)
 smoothness(x, method = "cor")
 smoothness(x, method = "diff")
+
+# A boootstrapped value can also be computed
+smoothness(x, iterations = 100)
+
+# When perfectly linear, the "smoothness" is 1
+smoothness(1:10)
+
 }
 \references{
 https://stats.stackexchange.com/questions/24607/how-to-measure-smoothness-of-a-time-series-in-r