4.0.8 to CRAN

DESCRIPTION

 Package: lidaRtRee
 Type: Package
-Version: 4.0.7
+Version: 4.0.8
 Title: Forest Analysis with Airborne Laser Scanning (LiDAR) Data
-Date: 2024-09-13
+Date: 2025-01-22
 Authors@R: c(
     person("Jean-Matthieu", "Monnet", email = "jean-matthieu.monnet@inrae.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9948-9891")),
     person("Pascal", "Obstétar", email = "pascal.obstetar@onf.fr", role = c("ctb"), comment = c(ORCID = "0000-0002-2811-7548")))

NEWS.md

+# lidaRtRee 4.0.8
+
+## Change
+
+* number of threads used by package `lidR` set to 2 in examples.
+* projection information (epsg 2154) added to external data `las_chablais3.las`.
+
 # lidaRtRee 4.0.7
 
 ## New
@@ -8,7 +15,7 @@
 ## Change
 
 * `boxcox_tr()` and `boxcox_itr()` do not automatically replace `0` with `NA` anymore.
-* `RasterLayer` objects are not supported anymore as the `raster` package  package is now obsolete.
+* `RasterLayer` objects are not supported anymore as the `raster` package is now obsolete.
 * in `aba_build_model()`, when specified the minimum `threshold` parameter now replaces non finite values in predictions. 
 * added `...` argument in `aba_build_model()`: passed to `leaps::regsubsets()`
 

R/aba.R

@@ -54,7 +54,7 @@
 #' data(quatre_montagnes)
 #' # build ABA model for basal area, with all metrics as predictors
 #' model_aba <- aba_build_model(quatre_montagnes$G_m2_ha, quatre_montagnes[, 9:76],
-#'   transform = "boxcox", nmax = 3
+#'   transform = "boxcox", nmax = 2
 #' )
 #' # summary of regression model
 #' summary(model_aba$model)
@@ -624,9 +624,10 @@ aba_combine_strata <- function(model.list, plotsId = NULL) {
 #' @examples
 #' # load Quatre Montagnes dataset
 #' data(quatre_montagnes)
-#' # build ABA model for basal area, with all metrics as predictors
-#' model_aba <- aba_build_model(quatre_montagnes$G_m2_ha, quatre_montagnes[, 9:76],
-#'   transform = "boxcox", nmax = 3
+#' # build ABA model for basal area, with three metrics as predictors
+#' model_aba <- aba_build_model(quatre_montagnes$G_m2_ha,
+#'                              quatre_montagnes[, c("zpcum8", "ipcumzq70", "p_hmin")],
+#'   transform = "log", nmax = 2
 #' )
 #'
 #' # plot field values VS predictions in cross-validation

R/common.R

@@ -16,8 +16,6 @@
 aa_las_chablais3 <- function() {
   LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
   las_chablais3 <- lidR::readLAS(LASfile)
-  # set projection
-  lidR::projection(las_chablais3) <- 2154
   las_chablais3
 }
 
@@ -43,9 +41,9 @@ aa_las_chablais3 <- function() {
 #' # load LAS file
 #' LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
 #' las_chablais3 <- lidR::readLAS(LASfile)
-#' # set projection
-#' lidR::projection(las_chablais3) <- 2154
 #'
+#' # set number of threads
+#' lidR::set_lidr_threads(2)
 #' # create a digital surface model with first-return points, resolution 0.5 m
 #' dsm <- points2DSM(lidR::filter_first(las_chablais3), res = 0.5)
 #'
@@ -95,9 +93,9 @@ points2DSM <- function(.las, res = 1, xmin, xmax, ymin, ymax) {
 #' # load LAS file
 #' LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
 #' las_chablais3 <- lidR::readLAS(LASfile)
-#' # set projection
-#' lidR::projection(las_chablais3) <- 2154
-#'
+#' 
+#' # set number of threads
+#' lidR::set_lidr_threads(2)
 #' # create digital terrain model with points classified as ground
 #' dtm <- points2DTM(las_chablais3)
 #'

R/extdata.R

@@ -17,8 +17,6 @@
 #' @examples
 #' LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
 #' las_chablais3 <- lidR::readLAS(LASfile)
-#' # set projection information
-#' lidR::projection(las_chablais3) <- 2154
 #' las_chablais3
 #' @name las_chablais3
 NULL

R/metrics.R

@@ -20,9 +20,9 @@
 #' # load LAS file
 #' LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
 #' las_chablais3 <- lidR::readLAS(LASfile)
-#' # set projection
-#' lidR::projection(las_chablais3) <- 2154
-#'
+#' 
+#' # set number of threads
+#' lidR::set_lidr_threads(2)
 #' # extract four point clouds from LAS object
 #' llas <- list()
 #' llas[["A"]] <- lidR::clip_circle(las_chablais3, 974350, 6581680, 10)
@@ -109,7 +109,9 @@ clouds_metrics <- function(llasn,
 #' # load LAS file
 #' LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
 #' las_chablais3 <- lidR::readLAS(LASfile)
-#'
+#' 
+#' # set number of threads
+#' lidR::set_lidr_threads(2)
 #' # extract two point clouds from LAS object
 #' llas <- lidR::clip_circle(las_chablais3,
 #'                           c(974350, 974390),
@@ -235,6 +237,8 @@ std_tree_metrics <- function(x, area_ha = NA) {
 #' @return a data.frame with altitude, exposition (gr), slope (gr) and adjR2 of 
 #' plane fitting
 #' @examples
+#' # set number of threads
+#' lidR::set_lidr_threads(2)
 #' # sample points
 #' XYZ <- data.frame(x = runif(200, -10, 10), y = runif(200, -10, 10))
 #' XYZ$z <- 350 + 0.3 * XYZ$x + 0.1 * XYZ$y + rnorm(200, mean = 0, sd = 0.5)
@@ -348,6 +352,8 @@ terrain_points_metrics <- function(p, centre = NULL, r = NULL) {
 #' LASfile <- system.file("extdata", "las_chablais3.laz", package="lidaRtRee")
 #' las_chablais3 <- lidR::readLAS(LASfile)
 #'
+#' # set number of threads
+#' lidR::set_lidr_threads(2)
 #' # extract two point clouds from LAS object
 #' llas <- lidR::clip_circle(las_chablais3,
 #'                           c(974350, 974390),

R/tree_detection.R

@@ -1145,7 +1145,7 @@ cimg2Raster <- function(cimg, r = NULL) {
 #' filtered to avoid effect of holes on volume and surface computation
 #' @param NA_replace numeric. value to replace NA values with.
 #' @param maxpixels numeric. maximum number of pixels to be converted to cimg 
-#' (argument passed to \code{\link{as.cimg}}).
+#' (argument passed to \code{\link[imager]{as.cimg}}).
 #' @return A cimg object
 #' @examples
 #' data(chm_chablais3)

README.md

@@ -10,7 +10,7 @@
 
 # Install
 
-* `R` >= 4.2.3 recommended, package `lidR` >= 4.0.0 required.
+* `R` >= 4.2.3 recommended, package `lidR` >= 4.1.1 required.
 * Install from [CRAN](https://cran.r-project.org/package=lidaRtRee), by running in the `R` console: `install.packages("lidaRtRee")`.
 * Build development version from source with the `devtools` package: `devtools::install_git("https://forgemia.inra.fr/lidar/lidaRtRee")`.