fastIsoboles_iteration: Do one iteration of the grid refinement algorithm
In IntiQuan/populationIsoboles: Calculate isoboles efficiently

Do one iteration of the grid refinement algorithm

fastIsoboles_iteration(
  gridmin,
  gridmax,
  gridlen_i,
  path_j,
  grid_j,
  i,
  objfun,
  objvalue = 0.95
)

`gridmin, gridmax`	General grid parameters: Vectors with names x and y c(x = 0, y = 0)
`gridlen_i`	Grid lengths of iteration i: c(x = 40, y = 40)
`path_j, grid_j`	Values from iteration i-1. path_j: data.table with columns (xp,yp) with nodes of iteration j's approximation of the isobole path grid_j: data.table with columns (x,y,evaluatedCum, objvalueCum, evaluated1, ..., evaluatedj, objvalue1,..., objvaluej) The Cum columns contain all cumulated grid point evaluations of all iterations. evaluatedCum has a 1, if the objvalue has been calculated at that point, or a zero if this value has not been calculated The j columns contain the information about iteration j. evaluated2 has a 1 for all grid points which were evaluated in iteration 2.
`i`	Identifier for iteration i
`objfun`	Function(x,y) returning a vector of the same length with the corresponding objvalue values. Combined with objvalue, this could be called the objective function
`objvalue`	The value for which the contour lines are calculated. E.g. 0.95