Quantile regression approximated w/ huber loss followed by post-processing

rq.fit.two_pass(
  X,
  y,
  tau = 0.5,
  weights = NULL,
  control,
  lambda,
  intercept = NULL,
  init_beta = NULL,
  n_samples = min(c(ceiling(nrow(X)/10), 10000)),
  ...
)

Arguments

X: design matrix
y: outcome vector
tau: target quantile
weights: optional weight vector
control: ignored for now
lambda: ignored for now
intercept: optional integer indicating intercept column that identifies initial values
init_beta: initial guess at betas
n_samples: number of observations to use in "warmup" regression
...: other arguments, ignored for now