@@ -10,9 +10,9 @@
Loading
10 10
#' @param x [sp::SpatialPolygons()] or
11 11
#'   [sp::SpatialPolygonsDataFrame()] object.
12 12
#'
13 -
#' @param y [raster::RasterLayer-class],
14 -
#'   [raster::RasterStack-class], or
15 -
#'   [raster::RasterBrick-class] object.
13 +
#' @param y [raster::raster()],
14 +
#'   [raster::stack()], or
15 +
#'   [raster::brick()] object.
16 16
#'
17 17
#' @param ids `integer` vector of ids. Defaults to indices of layers in
18 18
#'   argument to `y`.
@@ -55,7 +55,7 @@
Loading
55 55
#'
56 56
#' This function simulates species distributions for RAP.
57 57
#'
58 -
#' @param x [raster::RasterLayer-class] or
58 +
#' @param x [raster::raster()] or
59 59
#'   [sp::SpatialPolygons()] object delineate the spatial extent to
60 60
#'   delineate study area.
61 61
#'
@@ -73,7 +73,7 @@
Loading
73 73
#'   [RandomFields::RFsimulate()] and converting to logistic values
74 74
#'   using [boot::inv.logit()].
75 75
#'
76 -
#' @return `RasterStack` with layers for each species.
76 +
#' @return [raster::stack()] with layers for each species.
77 77
#'
78 78
#' @seealso [RandomFields::RFsimulate()].
79 79
#'
@@ -113,7 +113,7 @@
Loading
113 113
#'
114 114
#' This function simulates attribute space data for RAP.
115 115
#'
116 -
#' @param x `RasterLayer` or [sp::SpatialPolygons()] object
116 +
#' @param x [raster::raster()] or [sp::SpatialPolygons()] object
117 117
#'   delineate the spatial extent to delineate study area.
118 118
#'
119 119
#' @param d `integer` number of dimensions. Defaults to 2.
@@ -129,7 +129,7 @@
Loading
129 129
#' @details Distributions are simulated by passing `model` to
130 130
#'   [RandomFields::RFsimulate()].
131 131
#'
132 -
#' @return `RasterStack` with layers for each dimension of the space.
132 +
#' @return [raster::stack()] with layers for each dimension of the space.
133 133
#'
134 134
#' @seealso [RandomFields::RFsimulate()].
135 135
#'

@@ -25,7 +25,7 @@
Loading
25 25
                                               args = list(x = x_coords[, 1],
26 26
                                                           y = x_coords[, 2])))
27 27
  }
28 -
  # return RasterStack
28 +
  # return RasterStack object
29 29
  return(raster::stack(ret))
30 30
}
31 31

@@ -91,8 +91,8 @@
Loading
91 91
92 92
#' Rasterize polygon data using GDAL
93 93
#'
94 -
#' This function converts a `SpatialPolygonsDataFrame` to a
95 -
#' `RasterLayer` using GDAL. It is expected to be faster than
94 +
#' This function converts a [sp::SpatialPolygonsDataFrame()] to a
95 +
#' [raster::raster()] using GDAL. It is expected to be faster than
96 96
#' [raster::rasterize()] for large datasets. However, it will be
97 97
#' significantly slower for small datasets because the data will need to be
98 98
#' written and read from disk.
@@ -106,7 +106,7 @@
Loading
106 106
#'   output raster. If not supplied, default behaviour is to burn polygon
107 107
#'   indices into the [raster::raster()].
108 108
#'
109 -
#' @return `RasterLayer` object.
109 +
#' @return [raster::raster()] object.
110 110
#'
111 111
#' @seealso [raster::rasterize()], [is.gdalInstalled()].
112 112
#'

@@ -23,7 +23,7 @@
Loading
23 23
    r[raster::Which(!is.na(r))] <- valMTX[, i]
24 24
    r
25 25
  }))
26 -
  # return RasterStack
26 +
  # return RasterStack object
27 27
  raster::stack(stk)
28 28
}
29 29
Files Coverage
R 71.53%
src 84.42%
Project Totals (41 files) 76.38%
Sunburst
The inner-most circle is the entire project, moving away from the center are folders then, finally, a single file. The size and color of each slice is representing the number of statements and the coverage, respectively.
Icicle
The top section represents the entire project. Proceeding with folders and finally individual files. The size and color of each slice is representing the number of statements and the coverage, respectively.
Grid
Each block represents a single file in the project. The size and color of each block is represented by the number of statements and the coverage, respectively.
Loading