Defines an update procedure (submodel+loss function) for survival data

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

Constructor

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) "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).

verbose

If TRUE, diagnostic output is generated about the updating procedure.