Defines an update procedure (submodel+loss function)

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

Constructor

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) "scaled_var" corresponds to sqrt(Var(D)/n)/logn (the default) while (2) "sample_size" corresponds to 1/n.
fluctuation_type: Whether to include the auxiliary covariate for the fluctuation model as a covariate or to treat it as a weight. Note that the option "weighted" is incompatible with a multi-epsilon submodel (one_dimensional = FALSE).
use_best: If TRUE, the final updated likelihood is set to the likelihood that minimizes the ED instead of the likelihood at the last update step.
verbose: If TRUE, diagnostic output is generated about the updating procedure.