% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PRA-package.R
\docType{package}
\name{PRA-package}
\alias{PRA}
\alias{PRA-package}
\title{PRA: Project Risk Analysis}
\description{
Data analysis for Project Risk Management via the Second Moment Method,
Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis,
Earned Value Management, Learning Curves, Bayesian Methods, and more.
}
\section{Key modules}{

\describe{
\item{\strong{Monte Carlo Simulation} (\code{mcs()})}{
Propagates schedule or cost uncertainty through a triangular distribution
model using \code{mc2d}. Returns an S3 object with a \code{print} method.}
\item{\strong{Second Moment Method} (\code{smm()})}{
Analytical first- and second-moment propagation of uncertainty without
simulation. Returns an S3 object with a \code{print} method.}
\item{\strong{Earned Value Management} (\code{pv()}, \code{ev()}, \code{ac()}, \code{sv()}, \code{cv()},
\code{spi()}, \code{cpi()}, \code{eac()}, \code{etc()}, \code{tcpi()}, \code{vac()})}{
Full suite of EVM performance metrics and forecasting functions.}
\item{\strong{Contingency Analysis} (\code{contingency()})}{
Derives cost or schedule contingency from simulation output at a
user-specified confidence level.}
\item{\strong{Sensitivity Analysis} (\code{sensitivity()})}{
Ranks risk drivers by their contribution to overall project uncertainty.}
\item{\strong{Learning Curves} (\code{fit_sigmoidal()}, \code{predict_sigmoidal()},
\code{plot_sigmoidal()})}{
Fits Pearl, Gompertz, or Logistic sigmoidal growth models to
cumulative cost or progress data using nonlinear least squares
(\code{minpack.lm}).}
\item{\strong{Bayesian Risk Inference} (\code{risk_prob()}, \code{risk_post_prob()})}{
Beta-Binomial conjugate updating for risk event probabilities.}
\item{\strong{Correlation Matrices} (\code{cor_matrix()})}{
Constructs valid positive-definite correlation matrices for use in
multivariate simulations.}
\item{\strong{Design Structure Matrix} (\code{parent_dsm()}, \code{grandparent_dsm()})}{
Derives direct (parent) and indirect (grandparent) dependency structure
matrices from a binary adjacency matrix.}
}
}

\section{References}{

Damnjanovic, Ivan, and Kenneth Reinschmidt. Data analytics for engineering and
construction project risk management. No. 172534. Cham, Switzerland: Springer, 2020.
}

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

}
\author{
Paul Govan \email{paul.govan2@gmail.com}
(ORCID: \href{https://orcid.org/0000-0002-1821-8492}{0000-0002-1821-8492})
}
\keyword{internal}
