diff --git a/R/measure_diffusion.R b/R/measure_diffusion.R
index 8da7142d..3bb59484 100644
--- a/R/measure_diffusion.R
+++ b/R/measure_diffusion.R
@@ -1,6 +1,6 @@
 # net_diffusion ####
 
-#' Diffusion metrics for networks
+#' Measures of network diffusion
 #' @description
 #'   These functions allow measurement of various features of
 #'   a diffusion process:
@@ -17,6 +17,7 @@
 #'   to be protected through vaccination, isolation, or recovery for herd immunity to be reached.
 #'   - `network_hazard()` measures the hazard rate or instantaneous probability that
 #'   nodes will adopt/become infected at that time
+#'   
 #' @param diff_model A valid network diffusion model,
 #'   as created by `as_diffusion()` or `play_diffusion()`.
 #' @family measures
@@ -199,7 +200,7 @@ network_hazard <- function(diff_model){
 
 # node_diffusion ####
 
-#' Diffusion metrics for nodes
+#' Measures of nodes in a diffusion
 #' @description
 #'   These functions allow measurement of various features of
 #'   a diffusion process:
@@ -215,6 +216,7 @@ network_hazard <- function(diff_model){
 #'   a given mark
 #'   - `node_is_exposed()`: Marks the nodes that are susceptible,
 #'   i.e. are in the immediate neighbourhood of given mark vector
+#'   
 #' @inheritParams cohesion
 #' @inheritParams net_diffusion
 #' @family measures
diff --git a/R/model_regression.R b/R/model_regression.R
index a156df56..81753cc9 100644
--- a/R/model_regression.R
+++ b/R/model_regression.R
@@ -1,5 +1,6 @@
 #' Linear and logistic regression for network data
 #' 
+#' @description
 #' This function provides an implementation of
 #' the multiple regression quadratic assignment procedure (MRQAP)
 #' for both one-mode and two-mode network linear models.
diff --git a/R/model_tests.R b/R/model_tests.R
index 46cd5fea..39b054e7 100644
--- a/R/model_tests.R
+++ b/R/model_tests.R
@@ -1,7 +1,17 @@
 #' Conditional uniform graph and permutation tests
 #' 
-#' These functions conduct conditional uniform graph (CUG) 
-#' or permutation (QAP) tests of any graph-level statistic.
+#' @description
+#'   These functions conduct tests of any network-level statistic:
+#'   
+#'   - `test_random()` performs a conditional uniform graph (CUG) test
+#'   of a measure against a distribution of measures on random networks 
+#'   of the same dimensions.
+#'   - `test_permutation()` performs a quadratic assignment procedure (QAP) test 
+#'   of a measure against a distribution of measures on permutations 
+#'   of the original network.
+#'   - `test_gof()` performs a chi-squared test on the squared Mahalanobis distance 
+#'   between a diff_model and diff_models objects.
+#'   
 #' @name tests
 #' @inheritParams regression
 #' @family models
@@ -10,8 +20,7 @@
 #'   e.g. the name of the attribute.
 NULL
 
-#' @describeIn tests Returns test results for some measure on an object
-#'   against a distribution of measures on random networks of the same dimensions
+#' @rdname tests 
 #' @examples 
 #' marvel_friends <- to_unsigned(ison_marvel_relationships)
 #' marvel_friends <- to_giant(marvel_friends) %>% 
@@ -62,8 +71,7 @@ test_random <- function(.data, FUN, ...,
   class(out) <- "network_test"
   out
 }
-#' @describeIn tests Returns test results for some measure on an object
-#'   against a distribution of measures on permutations of the original network
+#' @rdname tests 
 #' @examples 
 #' (qaptest <- test_permutation(marvel_friends, 
 #'                 network_heterophily, attribute = "Attractive",
@@ -161,8 +169,7 @@ plot.network_test <- function(x, ...,
                         color="red", linewidth=1.2) + ggplot2::ylab("Density")
 }
 
-#' @describeIn tests Returns the squared Mahalanobis distance 
-#'   and chi-squared results for diff_model and diff_models objects
+#' @rdname tests 
 #' @param diff_model A diff_model object is returned by
 #'   `play_diffusion()` or `as_diffusion()` and contains
 #'   a single empirical or simulated diffusion.
diff --git a/man/net_diffusion.Rd b/man/net_diffusion.Rd
index 9c3677ce..2f130e62 100644
--- a/man/net_diffusion.Rd
+++ b/man/net_diffusion.Rd
@@ -7,7 +7,7 @@
 \alias{network_reproduction}
 \alias{network_immunity}
 \alias{network_hazard}
-\title{Diffusion metrics for networks}
+\title{Measures of network diffusion}
 \source{
 \code{{netdiffuseR}}
 }
diff --git a/man/node_diffusion.Rd b/man/node_diffusion.Rd
index 8bef67a5..2475f0af 100644
--- a/man/node_diffusion.Rd
+++ b/man/node_diffusion.Rd
@@ -7,7 +7,7 @@
 \alias{node_thresholds}
 \alias{node_infection_length}
 \alias{node_exposure}
-\title{Diffusion metrics for nodes}
+\title{Measures of nodes in a diffusion}
 \usage{
 node_adoption_time(diff_model)
 
diff --git a/man/tests.Rd b/man/tests.Rd
index 91fe2651..fd28f351 100644
--- a/man/tests.Rd
+++ b/man/tests.Rd
@@ -70,21 +70,18 @@ a single empirical or simulated diffusion.}
 \code{play_diffusions()} and contains a series of diffusion simulations.}
 }
 \description{
-These functions conduct conditional uniform graph (CUG)
-or permutation (QAP) tests of any graph-level statistic.
-}
-\section{Functions}{
+These functions conduct tests of any network-level statistic:
 \itemize{
-\item \code{test_random()}: Returns test results for some measure on an object
-against a distribution of measures on random networks of the same dimensions
-
-\item \code{test_permutation()}: Returns test results for some measure on an object
-against a distribution of measures on permutations of the original network
-
-\item \code{test_gof()}: Returns the squared Mahalanobis distance
-and chi-squared results for diff_model and diff_models objects
-
-}}
+\item \code{test_random()} performs a conditional uniform graph (CUG) test
+of a measure against a distribution of measures on random networks
+of the same dimensions.
+\item \code{test_permutation()} performs a quadratic assignment procedure (QAP) test
+of a measure against a distribution of measures on permutations
+of the original network.
+\item \code{test_gof()} performs a chi-squared test on the squared Mahalanobis distance
+between a diff_model and diff_models objects.
+}
+}
 \section{Mahalanobis distance}{
 
 \code{test_gof()} takes a single diff_model object,