Nothing

```
#' Obtain coefficients
#'
#' This function returns coefficients from a model fit by [owl()].
#'
#' If `exact == FALSE` and `sigma` is not in `object`,
#' then the returned coefficients will be approximated by linear interpolation.
#' If coefficients from another type of penalty sequence
#' (with a different `lambda`) are required, however,
#' please use [owl()] to refit the model.
#'
#' @param object an object of class `'Owl'`.
#' @param ... arguments that are passed on to [stats::update()] (and therefore
#' also to [owl()]) if `exact = TRUE` and the given penalty
#' is not in `object`
#' @inheritParams predict.Owl
#'
#' @return Coefficients from the model.
#'
#' @export
#' @examples
#' fit <- owl(mtcars$mpg, mtcars$vs, n_sigma = 1)
#' coef(fit)
coef.Owl <- function(object,
sigma = NULL,
exact = FALSE,
simplify = TRUE,
...) {
beta <- object$coefficients
n_penalties <- dim(beta)[3]
penalty <- object$sigma
value <- sigma
if (is.null(value)) {
n_penalties <- length(penalty)
} else if (all(value %in% penalty)) {
n_penalties <- length(value)
beta <- beta[, , penalty %in% value, drop = FALSE]
} else if (exact) {
object <- stats::update(object, sigma = sigma, ...)
beta <- object$coefficients
} else {
stopifnot(value >= 0)
interpolation_list <- interpolatePenalty(penalty, value)
beta <- interpolateCoefficients(beta, interpolation_list)
n_penalties <- length(value)
}
if (simplify)
beta <- drop(beta)
beta
}
```

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