diff --git a/R/algorithm-dec.R b/R/algorithm-dec.R
index a40e410d..338b453b 100644
--- a/R/algorithm-dec.R
+++ b/R/algorithm-dec.R
@@ -19,8 +19,8 @@
 #'
 #' # Reach a pulse density of 1 on the overall dataset
 #' thinned1 = decimate_points(las, random(1))
-#' plot(grid_density(las))
-#' plot(grid_density(thinned1))
+#' plot(rasterize_density(las))
+#' plot(rasterize_density(thinned1))
 #' @name sample_random
 random = function(density, use_pulse = FALSE)
 {
@@ -91,7 +91,7 @@ random = function(density, use_pulse = FALSE)
 #'
 #' # Select points randomly to reach an homogeneous density of 1
 #' thinned <- decimate_points(las, homogenize(1,5))
-#' plot(grid_density(thinned, 10))
+#' plot(rasterize_density(thinned, 10))
 #' @name sample_homogenize
 homogenize = function(density, res = 5, use_pulse = FALSE)
 {
diff --git a/R/algorithm-dtm.R b/R/algorithm-dtm.R
index 821dce88..7465d7e4 100644
--- a/R/algorithm-dtm.R
+++ b/R/algorithm-dtm.R
@@ -99,13 +99,13 @@ knnidw = function(k = 10, p = 2, rmax = 50)
 #'
 #' This function is made to be used in \link{rasterize_terrain} or \link{normalize_height}. It
 #' implements an algorithm for spatial interpolation. Spatial interpolation is based on universal
-#' kriging using the \link[gstat:krige]{krige} function from \code{gstat}. This method combines the
+#' kriging using the `krige()` function from \code{gstat}. This method combines the
 #' KNN approach with the kriging approach. For each point of interest it kriges the terrain using
 #' the k-nearest neighbour ground points. This method is more difficult to manipulate but it is also
 #' the most advanced method for interpolating spatial data.
 #'
 #' @param k numeric. Number of k-nearest neighbours. Default 10.
-#' @param model A variogram model computed with \link[gstat:vgm]{vgm}. If NULL it performs an ordinary
+#' @param model A variogram model computed with `vgm()` from package `gstat`. If NULL it performs an ordinary
 #' or weighted least squares prediction.
 #'
 #' @export
@@ -125,6 +125,7 @@ knnidw = function(k = 10, p = 2, rmax = 50)
 #' plot_dtm3d(dtm)
 #' }
 #' @name dtm_kriging
+#' @md
 kriging = function(model = gstat::vgm(.59, "Sph", 874), k = 10L)
 {
   assert_package_is_installed("gstat")
diff --git a/R/plot.R b/R/plot.R
index 525acf5e..51199c76 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -2,7 +2,7 @@
 #'
 #' Plot displays a 3D interactive windows based on rgl for \link{LAS} objects\cr\cr
 #' Plot displays an interactive view for \link[=LAScatalog-class]{LAScatalog} objects with pan and
-#' zoom capabilities based on \link[mapview:mapview-package]{mapview}. If the coordinate reference
+#' zoom capabilities based on `mapview()` from package `mapview`. If the coordinate reference
 #' system (CRS) of the \code{LAScatalog} is non empty, the plot can be displayed on top of base maps
 #' (satellite data, elevation, street, and so on).\cr\cr
 #' Plot displays a \link[=LASheader-class]{LASheader} object exactly like it displays a LAScatalog
@@ -46,7 +46,7 @@
 #' @param overlaps logical. Highlight the overlaps between files.
 #'
 #' @param ... Will be passed to \link[rgl:3dobjects]{points3d} (LAS) or \link[graphics:plot.default]{plot}
-#' if \code{mapview = FALSE} or to \link[mapview:mapView]{mapview} if \code{mapview = TRUE} (LAScatalog).
+#' if \code{mapview = FALSE} or to `mapview()` if \code{mapview = TRUE} (LAScatalog).
 #'
 #' @examples
 #' \dontrun{
diff --git a/R/utils_threads.R b/R/utils_threads.R
index 1edb85f3..3fed22f9 100644
--- a/R/utils_threads.R
+++ b/R/utils_threads.R
@@ -86,7 +86,7 @@ get_lidr_threads = function()
 #' @noRd
 try_to_get_num_future_cores = function()
 {
-  if (!"future" %in% rownames(installed.packages()))
+  if (!"future" %in% rownames(utils::installed.packages()))
     return(1L)
 
   # nocov start
diff --git a/man/dtm_kriging.Rd b/man/dtm_kriging.Rd
index a2a2ec1d..10ba64a4 100644
--- a/man/dtm_kriging.Rd
+++ b/man/dtm_kriging.Rd
@@ -8,7 +8,7 @@
 kriging(model = gstat::vgm(0.59, "Sph", 874), k = 10L)
 }
 \arguments{
-\item{model}{A variogram model computed with \link[gstat:vgm]{vgm}. If NULL it performs an ordinary
+\item{model}{A variogram model computed with \code{vgm()} from package \code{gstat}. If NULL it performs an ordinary
 or weighted least squares prediction.}
 
 \item{k}{numeric. Number of k-nearest neighbours. Default 10.}
@@ -16,7 +16,7 @@ or weighted least squares prediction.}
 \description{
 This function is made to be used in \link{rasterize_terrain} or \link{normalize_height}. It
 implements an algorithm for spatial interpolation. Spatial interpolation is based on universal
-kriging using the \link[gstat:krige]{krige} function from \code{gstat}. This method combines the
+kriging using the \code{krige()} function from \code{gstat}. This method combines the
 KNN approach with the kriging approach. For each point of interest it kriges the terrain using
 the k-nearest neighbour ground points. This method is more difficult to manipulate but it is also
 the most advanced method for interpolating spatial data.
diff --git a/man/plot.Rd b/man/plot.Rd
index 4a602166..faf8fadb 100644
--- a/man/plot.Rd
+++ b/man/plot.Rd
@@ -50,7 +50,7 @@ pastel.colors(n)
 \item{y}{Unused (inherited from R base)}
 
 \item{...}{Will be passed to \link[rgl:3dobjects]{points3d} (LAS) or \link[graphics:plot.default]{plot}
-if \code{mapview = FALSE} or to \link[mapview:mapView]{mapview} if \code{mapview = TRUE} (LAScatalog).}
+if \code{mapview = FALSE} or to `mapview()` if \code{mapview = TRUE} (LAScatalog).}
 
 \item{color}{characters. The attribute used to color the point cloud. Default is Z coordinates. RGB
 is an allowed string even if it refers to three attributes simultaneously.}
@@ -104,7 +104,7 @@ Since v4.0.4 `mapview = TRUE` is also possible with LAS objects.}
 \description{
 Plot displays a 3D interactive windows based on rgl for \link{LAS} objects\cr\cr
 Plot displays an interactive view for \link[=LAScatalog-class]{LAScatalog} objects with pan and
-zoom capabilities based on \link[mapview:mapview-package]{mapview}. If the coordinate reference
+zoom capabilities based on `mapview()` from package `mapview`. If the coordinate reference
 system (CRS) of the \code{LAScatalog} is non empty, the plot can be displayed on top of base maps
 (satellite data, elevation, street, and so on).\cr\cr
 Plot displays a \link[=LASheader-class]{LASheader} object exactly like it displays a LAScatalog
diff --git a/man/sample_homogenize.Rd b/man/sample_homogenize.Rd
index 8b5d6f24..b12aa7c5 100644
--- a/man/sample_homogenize.Rd
+++ b/man/sample_homogenize.Rd
@@ -32,7 +32,7 @@ las = readLAS(LASfile, select = "xyz")
 
 # Select points randomly to reach an homogeneous density of 1
 thinned <- decimate_points(las, homogenize(1,5))
-plot(grid_density(thinned, 10))
+plot(rasterize_density(thinned, 10))
 }
 \seealso{
 Other point cloud decimation algorithms: 
diff --git a/man/sample_random.Rd b/man/sample_random.Rd
index e6ef5a01..c346b691 100644
--- a/man/sample_random.Rd
+++ b/man/sample_random.Rd
@@ -24,8 +24,8 @@ las = readLAS(LASfile, select = "xyz")
 
 # Reach a pulse density of 1 on the overall dataset
 thinned1 = decimate_points(las, random(1))
-plot(grid_density(las))
-plot(grid_density(thinned1))
+plot(rasterize_density(las))
+plot(rasterize_density(thinned1))
 }
 \seealso{
 Other point cloud decimation algorithms: 
diff --git a/tests/testthat/test-classify_ground.R b/tests/testthat/test-classify_ground.R
index b6decbcf..53da475b 100644
--- a/tests/testthat/test-classify_ground.R
+++ b/tests/testthat/test-classify_ground.R
@@ -11,11 +11,10 @@ opt_progress(ctg) <- FALSE
 ws = seq(3,21, 5)
 th = seq(0.1, 2, length.out = length(ws))
 
-mypmf = pmf(ws, th)
-mycsf = csf(TRUE, 1, 1, time_step = 1)
-
 test_that("classify_ground pmf works with LAS", {
 
+  mypmf = pmf(ws, th)
+
   las <- classify_ground(las, mypmf)
 
   n = names(las)
@@ -30,6 +29,8 @@ test_that("classify_ground pmf works with LAS", {
 test_that("classify_ground pmf works with LAScatalog", {
   opt_chunk_buffer(ctg) <- 30
 
+  mypmf = pmf(ws, th)
+
   expect_error(classify_ground(ctg, mypmf), "output file")
 
   opt_output_files(ctg) <- paste0(tempdir(), "/file_{XLEFT}_{YBOTTOM}")
@@ -44,6 +45,8 @@ test_that("classify_ground csf works with LAS", {
 
   skip_if_not_installed("RCSF")
 
+  mycsf = csf(TRUE, 1, 1, time_step = 1)
+
   las <- classify_ground(las, mycsf)
 
   n = names(las)
@@ -58,6 +61,8 @@ test_that("classify_ground csf works with LAScatalog", {
   skip_if_not_installed("RCSF")
   skip_on_cran()
 
+  mycsf = csf(TRUE, 1, 1, time_step = 1)
+
   opt_output_files(ctg) <- paste0(tempdir(), "/file_{XLEFT}_{YBOTTOM}_ground")
   opt_chunk_buffer(ctg) <- 30
 
@@ -102,7 +107,7 @@ test_that("makeZhangParam works", {
 })
 
 test_that("classify_ground does not erase former classification (but new ground points)", {
-
+  mypmf = pmf(ws, th)
   las <- topography
   las <- filter_poi(las, X < mean(X), Y < mean(Y))
   las$Classification[las$Classification == LASGROUND] <- LASUNCLASSIFIED
diff --git a/tests/testthat/test-print.R b/tests/testthat/test-print.R
index 4d1cfddf..1dfdfa47 100644
--- a/tests/testthat/test-print.R
+++ b/tests/testthat/test-print.R
@@ -66,7 +66,7 @@ test_that("print works with lidR algorithms ", {
   k <- tin()
   j <- p2r(2)
   l <- random(2)
-  m <- csf()
+  m <- pmf(3,4)
   #o <- wing2015()
   p <- Roussel2020()