fit.varmer: Training eta parameter for the varmer function
In VARMER: Variational Merging

Training eta parameter for the varmer function evaluating a vector of etas using Cross-validation. The best eta is the one yielding the highest KGE metric.

fit.varmer(
  stations.sf,
  v,
  etas = c(10, 100, 500, 1000, 5000),
  idw_formula = Variable ~ 1,
  factor_agg = 2,
  drty.out = tempdir(),
  apply_varmer = T
)

`stations.sf`	data.frame with the observations metadata
`v`	grided image
`etas`	(optional) vector of eta values to evaluate in a CV exercise
`idw_formula`	formula for the idw interpolation
`factor_agg`	scalar which defines the aggregation factor to apply to the raster images in order to reduce computation requirements for solving varmer
`drty.out`	(optional) output folder for the CV metrics
`apply_varmer`	(optional) boolean which determines if a merging image is produced with the best eta