evaluate_eif_indirect_M1: Function to evaluate the canonical gradient of the indirect...
In benkeser/intermed:

Function to evaluate the canonical gradient of the indirect effect through M1 at a given set of estimated nuisance parameters and for each observation.

evaluate_eif_indirect_M1(
  Y,
  A,
  M1,
  M2,
  Qbar,
  Q_M,
  Qbarbar,
  gn,
  a,
  a_star,
  all_mediator_values,
  ...
)

`Y`	The outcome
`A`	The treatment
`M1`	First mediator
`M2`	Second mediator
`Qbar`	Outcome regression list
`Q_M`	Mediator distribution list
`Qbarbar`	A list; needs to have entries named M1_M2_a and M1_star_M2_star_a_star corresponding to, respectively, the outcome regression under A = a, marginalized with respect to joint mediator distribution given C and A = a, and the outcome regression under A = a_star, marginalized with respect to the joint mediator distribution given C and A = a_star.
`gn`	List of mediator values
`a`	Treatment value
`a_star`	Referent treatment value