% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidy_wblr.R
\name{tidy_wblr}
\alias{tidy_wblr}
\title{Extract Tidy Parameter Estimates from a wblr Object.}
\usage{
tidy_wblr(wblr_obj)
}
\arguments{
\item{wblr_obj}{A single object of class `'wblr'`, or a list of such objects.
Each object must have been fitted with [WeibullR::wblr.fit()].}
}
\value{
A named list with two elements:
  \describe{
    \item{`estimates`}{A `data.frame` with one row per `wblr` object and
      columns `dist`, `method_fit`, `param1_name`, `param1`, `param2_name`,
      `param2`, `param3_name`, `param3`, `gof_metric`, `gof_value`,
      `method_conf`, `n_failures`.}
    \item{`bounds`}{For a single object, a `data.frame` of confidence bound
      data (columns `Datum`, `unrel`, `Lower`, `Upper`), or `NULL` if no
      confidence bounds were computed. For a list of objects, a list of such
      data frames (one per object).}
  }
}
\description{
Returns a tidy data frame of distribution parameter estimates, goodness-of-fit
metrics, and (optionally) confidence bound data from one or more fitted `wblr`
objects. This makes it straightforward to use ReliaPlotR output in reproducible
research workflows — export to CSV, include in tables, or compare multiple fits.
}
\details{
Parameters are returned on their natural scales. For Weibull and
three-parameter Weibull (\code{weibull3p}) models, \code{param1} is the
shape parameter \eqn{\beta} and \code{param2} is the scale parameter
\eqn{\eta}; \code{param3} is the location parameter \eqn{\gamma} for
\code{weibull3p} (NA otherwise). For lognormal models, \code{param1} is
\eqn{\mu_{log}} and \code{param2} is \eqn{\sigma_{log}}.

Goodness-of-fit is reported as R\eqn{^2} (column value \code{"R2"}) for
rank-regression fits, or log-likelihood (\code{"loglikelihood"}) for MLE
fits.
}
\examples{
library(WeibullR)
failures <- c(30, 49, 82, 90, 96)
obj <- wblr.conf(wblr.fit(wblr(failures), method.fit = "mle"), method.conf = "lrb")
result <- tidy_wblr(obj)
result$estimates
result$bounds

# List of objects
obj2 <- wblr.conf(wblr.fit(wblr(c(20, 40, 60, 80, 100)), method.fit = "mle"),
                  method.conf = "lrb")
result2 <- tidy_wblr(list(obj, obj2))
result2$estimates
}
\references{
Meeker, W. Q., and Escobar, L. A. (1998). \emph{Statistical Methods for
Reliability Data}. Wiley.
}
\seealso{
[plotly_wblr()] for the corresponding interactive probability plot.
}
