raster, sp, caret, ggplot2, grDevices, spatialEco, rgeos
knitr, rmarkdown, kableExtra, imager, RStoolbox,
randomForest, rgdal

raster, sp, caret, ggplot2, grDevices, spatialEco, rgeos,
stringdist, concaveman
knitr, rmarkdown, kableExtra, imager, randomForest, rgdal,
RStoolbox

raster, sp, caret, ggplot2, grDevices, geosphere, vegan,
rgeos, stringdist, concaveman
knitr, rmarkdown, kableExtra, imager, randomForest, rgdal,
RStoolbox, foreign, maptools

FIELDS is a collection of programs for curve and function
fitting with an emphasis on spatial data. The major methods implemented
include cubic and thin plate splines, universal Kriging and Kriging for
large data sets. The main feature is that any covariance function
implemented in R can be used for spatial prediction.

Fields is a collection of programs for curve and function
fitting with an emphasis on spatial data and spatial statistics. The
major methods implemented include cubic and thin plate splines, universal
Kriging and Kriging for large data sets. One main feature is that any
covariance function implemented in R can be used for spatial prediction.

Fields is a collection of programs for curve and function
fitting with an emphasis on spatial data and spatial statistics. The
major methods implemented include cubic and thin plate splines, universal
Kriging and Kriging for large data sets. One main feature is any
covariance function implemented in R can be used for spatial prediction.

Fields is a collection of programs for curve and function
fitting with an emphasis on spatial data and spatial statistics. The
major methods implemented include cubic, robust, and thin plate splines,
universal Kriging and Kriging for large data sets. One main feature is any
covariance function implemented in R can be used for spatial prediction.
There are also some useful functions for plotting and working with
spatial data as images. This package also contains implementation of a
smooth wavelet basis suitable for spatial models.

Fields is a collection of programs for curve and function
fitting with an emphasis on spatial data and spatial statistics. The
major methods implemented include cubic, robust, and thin plate splines,
universal Kriging and Kriging for large data sets. One main feature is any
covariance function implemented in R can be used for spatial prediction.
There are also some useful functions for plotting and working with
spatial  data as images.