negationCheck: Check if custom weights are negations of each other
In ivmte: Instrumental Variables: Extrapolation by Marginal Treatment Effects

This function checks whether the user-declared weights for treated and control groups are in fact negations of each other. This is problematic for the GMM procedure when accounting for estimation error of the target weights.

negationCheck(
  data,
  target.knots0,
  target.knots1,
  target.weight0,
  target.weight1,
  N = 20
)

`data`	data set used for estimation. The comparisons are made only on values in the support of the data set.
`target.knots0`	user-defined set of functions defining the knots associated with splines weights for the control group. The arguments of the function should consist only of variable names in `data`. If the knot is constant across all observations, then the user can instead submit the value of the weight instead of a function.
`target.knots1`	user-defined set of functions defining the knots associated with splines weights for the treated group. The arguments of the function should be variable names in `data`. If the knot is constant across all observations, then the user can instead submit the value of the weight instead of a function.
`target.weight0`	user-defined weight function for the control group defining the target parameter. A list of functions can be submitted if the weighting function is in fact a spline. The arguments of the function should be variable names in `data`. If the weight is constant across all observations, then the user can instead submit the value of the weight instead of a function.
`target.weight1`	user-defined weight function for the treated group defining the target parameter. A list of functions can be submitted if the weighting function is in fact a spline. The arguments of the function should be variable names in `data`. If the weight is constant across all observations, then the user can instead submit the value of the weight instead of a function.
`N`	integer, default set to 20. This is the maxmimum number of points between treated and control groups to compare and determine whether or not the weights are indeed negations of one another. If the data set contains fewer than `N` unique values for a given set of variables, then all those unique values are used for the comparison.