objective_fn_numint: Compute Unlinked Monotone Regression objective function...
In UMR: Unmatched Monotone Regression

Compute Unlinked Monotone Regression objective function numerically

objective_fn_numint(
  mm,
  ww_m = NULL,
  yy,
  ww_y = NULL,
  Phi,
  subdivisions = 1000L
)

`mm`	Current (unsorted) estimate/iterate at which to compute gradient. (Length is <= than the number of X observations in the problem).
`ww_m`	Weights (nonnegative, sum to 1) corresponding to mm. Same length as mm.
`yy`	Y (response) observation vector (numeric vector). Alternatively, yy may be an ecdf, i.e. ecdf(yy) or getEcdf(yy, weights).
`ww_y`	Weights (nonnegative, sum to 1) corresponding to yy. Same length as yy. Default is just 1/length(yy) for each value. If yy is non-numeric i.e. yy is an ecdf() then ww_y is ignored.
`Phi`	This is the error (cumulative) distribution function, a function object (Balabdaoui, Doss, Durot (2020+). Function accepting vector or matrix arguments.
`subdivisions`	Passed argument to integrate().