grouping.classify.once: clusters firms based on their cross-sectional wage...
In chaudhary-amit/acblm: Two step estimation of three sided heterogeniety

clusters firms based on their cross-sectional wage distributions

grouping.classify.once(
  measures,
  k = 10,
  nstart = 1000,
  iter.max = 200,
  step = 20
)

`measures`	object created using grouping.getMeasures
`k`	number of groups
`nstart`	(default:1000) total number of starting values
`iter.max`	(default:100) max number of step for each repetition
`step`	step size in the repeating
`sdata`	cross sectional data, needs a column j (firm id) and w (log wage)
`Nw`	number of points to use for wage distribution