tmle3_Update: Defines an update procedure (submodel+loss function)
In lurui0421/Super-Learning-: The Extensible TMLE Framework

Current Limitations: loss function and submodel are hard-coded (need to accept arguments for these)

1	tmle3_Update

An object of class R6ClassGenerator of length 24.

define_param(maxit, cvtmle, one_dimensional, constrain_step, delta_epsilon, verbose)

maxit: The maximum number of update iterations
cvtmle: If TRUE, use CV-likelihood values when calculating updates.
one_dimensional: If TRUE, collapse clever covariates into a one-dimensional clever covariate scaled by the mean of their EIFs.
constrain_step: If TRUE, step size is at most delta_epsilon (it can be smaller if a smaller step decreases the loss more).
delta_epsilon: The maximum step size allowed if constrain_step is TRUE.
convergence_type: The convergence criterion to use: (1) "se_logn" corresponds to sqrt(Var(D)/n)/logn (the default) while (2) "n_samp" corresponds to 1/n.
verbose: If TRUE, diagnostic output is generated about the updating procedure.