sieveSurv: Nonparametrically estimate the survival function using...
In sarahlotspeich/sieveSurv: Nonparametric Survival Estimator with B-spline Sieves

Nonparametrically estimate the survival function using B-spline sieves

sieveSurv(
  time,
  event,
  covar = NULL,
  bspline,
  data,
  logTransform = FALSE,
  tol = 1e-04,
  max_iter = 1000,
  assume_last = FALSE
)

`time`	Column name for follow-up time
`event`	Column name for event indicators
`covar`	(Optional) column name(s) for additional fully-observed covariates. Default is `covar=NULL`, which estimates unconditional survival.
`bspline`	Column names for B-spline basis.
`data`	Dataframe or matrix containing (at least) named columns `time`, `event`, `covar`, and `bspline`.
`logTransform`	If `FALSE`, `time` is log transformed before density estimation. Default is `TRUE`.
`tol`	Tolerance to define convergence. Default is `tol=1E-4`.
`max_iter`	Maximum number of iterations allowed for the EM algorithm. Default is `max_iter = 1000`.
`assume_last`	Assume last observed `time` is an event (for use when the integration of the survival function is desired). Default is `FALSE`.

A list with the following five elements:

`surv_uncensor`	subset of uncensored rows of `data` with added column `surv` with survival estimate at their event times
`coeff`	a matrix of the B-spline coefficients at convergence
`od_loglik`	value of the observed-data log-likelihood for the B-spline coefficients at convergence
`conv`	indicator of convergence for the EM algorithn
`conv_msg`	description of nonconvergence if `conv = FALSE`