Removes pipe

DESCRIPTION

@@ -32,7 +32,7 @@ Description: Provides a robust approach to land use mapping using multi-dimensio
     while also requiring minimal training sets. The package includes tools for training the 1-NN-TWDTW model, 
     visualizing temporal patterns, producing land use maps, and visualizing the results.
 License: GPL (>= 3)
-URL: https://github.com/vwmaus/dtwSat/, http://www.victor-maus.com/dtwSat/
+URL: https://github.com/vwmaus/dtwSat/
 BugReports: https://github.com/vwmaus/dtwSat/issues/
 Maintainer: Victor Maus <vwmaus1@gmail.com>
 VignetteBuilder: 

R/train.R

@@ -29,25 +29,26 @@
 #' \dontrun{
 #'
 #' # Read training samples
-#' samples <-
-#'   system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat") |>
-#'   st_read(quiet = TRUE)
+#' samples_path <- 
+#'   system.file("mato_grosso_brazil/samples.gpkg", package = "dtwSat")
+#' 
+#' samples <- st_read(samples_path, quiet = TRUE)
 #'
 #' # Get satellite image time sereis files
-#' tif_files <- system.file("mato_grosso_brazil", package = "dtwSat") |>
-#'   dir(pattern = "\\.tif$", full.names = TRUE)
+#' tif_path <- system.file("mato_grosso_brazil", package = "dtwSat")
+#' tif_files <- dir(tif_path, pattern = "\\.tif$", full.names = TRUE)
 #'
 #' # Get acquisition dates
-#' acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files)) |>
-#'   as.Date(format = "%Y%m%d")
+#' acquisition_date <- regmatches(tif_files, regexpr("[0-9]{8}", tif_files))
+#' acquisition_date <- as.Date(acquisition_date, format = "%Y%m%d")
 #'
 #' # Create a 3D datacube
 #' dc <- read_stars(tif_files,
 #'                  proxy = FALSE,
 #'                  along = list(time = acquisition_date),
-#'                  RasterIO = list(bands = 1:6)) |>
-#'       st_set_dimensions(3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR")) |>
-#'       split(c("band")) 
+#'                  RasterIO = list(bands = 1:6))
+#' dc <- st_set_dimensions(dc, 3, c("EVI", "NDVI", "RED", "BLUE", "NIR", "MIR"))
+#' dc <- split(dc, c("band"))
 #'
 #' # Create a knn1-twdtw model
 #' m <- twdtw_knn1(x = dc,
@@ -56,9 +57,9 @@
 #'                 time_scale = 'day',
 #'                 time_weight = c(steepness = 0.1, midpoint = 50),
 #'                 formula = band ~ s(time))
-#' 
+#'
 #' print(m)
-#' 
+#'
 #' # Visualize model patterns
 #' plot(m)
 #'

inst/REFERENCES.bib

@@ -27,7 +27,6 @@ @misc{Didan:2015
   author = {Kamel Didan},
   title =  {{MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global
              250m SIN Grid V006 [Data set], NASA EOSDIS LP DAAC}},
-  year =   {2015},
-  doi =    {10.5067/MODIS/MOD13Q1.006}
+  year =   {2015}
 }