For curve, surface and function fitting with an emphasis
 on splines, spatial data, geostatistics, and spatial statistics. The major
 methods
 include  Gaussian spatial process prediction (known as Kriging), cubic and thin plate splines, and compactly supported
 covariance functions for large data sets. The spline and spatial process
 methods are
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance function parameters by cross
 validation and also by  maximum likelihood. For spatial process prediction
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple format can be used for
 spatial prediction. As included are fast approximations for prediction
 and conditional simulation for larger data sets.
 There are also many useful functions for plotting
 and working with spatial data as images. This package also contains
 an implementation of sparse matrix methods for large spatial data
 sets based the  R sparse matrix package spam. Use
 help(fields) to get started and for an overview. All package graphics functions
 focus on  extending base R graphics and are easy to interpret and modify.
 The fields source
 code is deliberately commented and provides useful explanations of
 numerical details as a companion to the manual pages. The commented
 source code can be viewed by expanding the source code version of this package
 and looking in the R subdirectory. The reference for fields can be generated
 by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
 of this package was supported in part by the National Science Foundation  Grant
 1417857,  the National Center for Atmospheric Research, and Colorado School of Mines.
 See the Fields URL
 for a vignette on using this package and some background on spatial statistics.

Simulates plot data in multi-environment field trials with one or more traits. 
  Its core function generates plot errors that capture spatial trend, random error (noise), 
  and extraneous variation, which are combined at a user-defined ratio. 
  Phenotypes can be generated by combining the plot errors with simulated genetic values that capture 
  genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.