% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ReliaGrowR-package.R
\docType{package}
\name{ReliaGrowR-package}
\alias{ReliaGrowR}
\alias{ReliaGrowR-package}
\title{ReliaGrowR: Reliability Growth Analysis}
\description{
Modeling and plotting functions for Reliability Growth Analysis (RGA).
The package implements three families of models and provides a REST API
interface via \link[plumber:deprecated_r6]{plumber}.
}
\section{Reliability Growth Models}{


\strong{Crow-AMSAA (NHPP power-law)}

The core model fits a Non-Homogeneous Poisson Process (NHPP) with a
Weibull intensity function to cumulative failure data. Parameters are
estimated by least-squares log-log regression (default) or maximum
likelihood.  A growth rate > 0 indicates reliability improvement.

\strong{Piecewise NHPP}

Extends Crow-AMSAA by fitting separate NHPP segments separated by
change points.  Change points can be detected automatically via the
\link[segmented:segmented]{segmented} package or supplied by the user.

\strong{Duane}

Log-log regression of cumulative MTBF versus cumulative time, providing
a graphical and analytical representation of reliability growth.
}

\section{Main Functions}{
\tabular{ll}{
   Function \tab Description \cr
   \code{\link[=rga]{rga()}} \tab Fit Crow-AMSAA or Piecewise NHPP model \cr
   \code{\link[=predict_rga]{predict_rga()}} \tab Forecast cumulative failures from a fitted model \cr
   \code{\link[=duane]{duane()}} \tab Fit Duane model \cr
   \code{\link[=rdt]{rdt()}} \tab Reliability Demonstration Test plan calculator \cr
   \code{\link[=weibull_to_rga]{weibull_to_rga()}} \tab Convert Weibull data to RGA format \cr
   \code{\link[=sim_failures]{sim_failures()}} \tab Simulate failures from a conditional Weibull model \cr
   \code{\link[=qqplot.rga]{qqplot.rga()}} \tab Q-Q goodness-of-fit plot for an \code{rga} object \cr
   \code{\link[=ppplot.rga]{ppplot.rga()}} \tab P-P goodness-of-fit plot for an \code{rga} object \cr
   \code{\link[=grwr_api]{grwr_api()}} \tab Launch the plumber REST API \cr
}
}

\section{S3 Classes and Methods}{
\tabular{ll}{
   Class \tab Methods \cr
   \code{rga} \tab \code{\link[=print.rga]{print.rga()}}, \code{\link[=plot.rga]{plot.rga()}} \cr
   \code{rga_predict} \tab \code{\link[=print.rga_predict]{print.rga_predict()}}, \code{\link[=plot.rga_predict]{plot.rga_predict()}} \cr
   \code{duane} \tab \code{\link[=print.duane]{print.duane()}}, \code{\link[=plot.duane]{plot.duane()}} \cr
   \code{rdt} \tab \code{\link[=print.rdt]{print.rdt()}} \cr
}
}

\section{References}{

Crow, L. H. (1975). \emph{Reliability Analysis for Complex Repairable Systems.}
AMSAA Technical Report No. 138. US Army Materiel Systems Analysis Activity.

Duane, J. T. (1964). Learning curve approach to reliability monitoring.
\emph{IEEE Transactions on Aerospace}, 2(2), 563–566.
\doi{10.1109/TA.1964.4319640}

Guo, H., Mettas, A., Sarakakis, G., & Niu, P. (2010). Piecewise NHPP
models with maximum likelihood estimation for repairable systems. In
\emph{Proceedings of the 2010 Annual Reliability and Maintainability Symposium}
(pp. 1–6). IEEE. \doi{10.1109/RAMS.2010.5448029}

Muggeo, V. M. R. (2024). \emph{segmented: Regression Models with Break-Points /
Change-Points Estimation.} R package.
\url{https://cran.r-project.org/package=segmented}
}

\seealso{
Useful links:
\itemize{
  \item \url{https://paulgovan.github.io/ReliaGrowR/}
  \item \url{https://github.com/paulgovan/ReliaGrowR}
  \item Report bugs at \url{https://github.com/paulgovan/ReliaGrowR/issues}
}

}
\author{
\strong{Maintainer}: Paul Govan \email{paul.govan2@gmail.com} (\href{https://orcid.org/0000-0002-1821-8492}{ORCID}) [copyright holder]

}
\keyword{internal}
