EL_confint: Empirical likelihood pointwise confidence bands for...
In hoangtt1989/NOIS: Non-parametric Outlier Identification and Smoothing

Returns EL confidence bands.

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EL_confint(NOIS_fit, conf_level = 0.05, fit_type = "NOIS",
  bias_correct = T, parallel = F, calib_type = "F", left = 0,
  right = 20, maxit = 50)

`NOIS_fit`	A `NOIS_fit`.
`conf_level`	The significance level.
`fit_type`	The type of fit to use for confidence bands. Valid types are `c('NOIS', 'regular')`, where regular is the non-robust fit..
`bias_correct`	A logical indicating usage of bias correction.
`parallel`	A logical indicating parallel computation. A backend must be registered first.
`calib_type`	The distribution for calibrating Wilks' theorem. Valid types are `c('F', 'chisq')`.
`left`	The left summand for the root finding procedure.
`right`	The right summand for the root finding procedure.
`maxit`	The maximum number of iterations for each root finding procedure.

A list with the following components.

`up_predicted`	The upper band.
`low_predicted`	The lower band.
`time`	Elapsed time.
`up_iter`	Number of iterations for the upper band.
`low_iter`	Number of iterations for the lower band.