Fix braces in Rd

5 files changed, 10 insertions, 10 deletions

diff --git a/DESCRIPTION b/DESCRIPTION
index 6c9614f..1e52b57 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: unmarked
 Version: 1.4.0
-Date: 2024-01-05
+Date: 2024-01-08
 Type: Package
 Title: Models for Data from Unmarked Animals
 Authors@R: c(
diff --git a/man/MesoCarnivores.Rd b/man/MesoCarnivores.Rd
index c51e06f..8d274a4 100644
--- a/man/MesoCarnivores.Rd
+++ b/man/MesoCarnivores.Rd
@@ -18,12 +18,12 @@
     \item{\code{redfox}}{A 1437x3 occupancy matrix for red fox}
     \item{\code{sitecovs}}{A data frame containing covariates for the 1437 sites, with the following columns:
       \itemize{
-        \item{\code{Dist_5km}{Proportion of disturbed land in 5 km radius}}
-        \item{\code{HDens_5km}{Housing density in 5 km radius}}
-        \item{\code{Latitude}{Latitude / 100}}
-        \item{\code{Longitude}{Longitude / 100}}
-        \item{\code{People_site}{Number of photos of people at site / 1000}}
-        \item{\code{Trail}{1 if camera was on trail, 0 if not}}
+        \item{\code{Dist_5km}}{Proportion of disturbed land in 5 km radius}
+        \item{\code{HDens_5km}}{Housing density in 5 km radius}
+        \item{\code{Latitude}}{Latitude / 100}
+        \item{\code{Longitude}}{Longitude / 100}
+        \item{\code{People_site}}{Number of photos of people at site / 1000}
+        \item{\code{Trail}}{1 if camera was on trail, 0 if not}
       }
     }
   }
diff --git a/man/occuFP.Rd b/man/occuFP.Rd
index 64feff2..daadb4e 100644
--- a/man/occuFP.Rd
+++ b/man/occuFP.Rd
@@ -45,7 +45,7 @@ are specified to belong to 1 of the 3 data types and all or a subset of the data
 
 For type 1 data, the detection process is assumed to fit the assumptions of the standard MacKenzie model
 where false negative probabilities are estimated but false positive detections are assumed not to occur. If all of your
-data is of this type you should use code{occu} to analyze data. The detection parameter p, which is modeled using the
+data is of this type you should use \code{occu} to analyze data. The detection parameter p, which is modeled using the
 detformula is the only observation parameter for these data.
 
 For type 2 data, both false negative and false positive detection probabilities are estimated. If all data is of this
diff --git a/man/unmarked-package.Rd b/man/unmarked-package.Rd
index 3bf8db0..c0595a3 100644
--- a/man/unmarked-package.Rd
+++ b/man/unmarked-package.Rd
@@ -260,6 +260,6 @@ Sillett, S. and Chandler, R.B. and Royle, J.A. and Kery, M. and
 
 
 \docType{package}
-\author{Ian Fiske, Richard Chandler, Andy Royle, Marc K\'{e}ry, David
+\author{Ian Fiske, Richard Chandler, Andy Royle, Marc Kery, David
   Miller, and Rebecca Hutchinson}
 \keyword{package}
diff --git a/tests/testthat/test_distsampOpen.R b/tests/testthat/test_distsampOpen.R
index bd25e8b..2797b80 100644
--- a/tests/testthat/test_distsampOpen.R
+++ b/tests/testthat/test_distsampOpen.R
@@ -325,7 +325,7 @@ test_that("distsampOpen dynamics models work",{
 
   fm <- distsampOpen(~1, ~1, ~1, data = umf, K=25, keyfun="unif",
                      dynamics="autoreg")
-  expect_equivalent(coef(fm), c(1.518686, -0.018026, -5.628779), tol=1e-5)
+  expect_equivalent(coef(fm), c(1.518686, -0.018026, -5.628779), tol=1e-4)
 
   #Sketchy estimates
   #Maybe just because data were simulated using a different process?