PRA

Data Analysis for Project Risk Managment via the Second Moment Method (SMM), Monte Carlo (MC) Simulation, Bayesian methods, Design Structure Matrices (DSMs), and more.

Installation

To install the release verion of PRA, use:

install_packages('PRA')

You can install the development version of PRA like so:

devtools::install_github('paulgovan/PRA')

Usage

Here is a basic example which shows you how to solve a common problem using Monte Carlo Simulation:

library(PRA)

num_simulations <- 10000
task_distributions <- list(
  list(type = "normal", mean = 10, sd = 2),  # Task A: Normal distribution
  list(type = "triangular", a = 5, b = 10, c = 15),  # Task B: Triangular distribution
  list(type = "uniform", min = 8, max = 12)  # Task C: Uniform distribution
)
correlation_matrix <- matrix(c(
  1, 0.5, 0.3,
  0.5, 1, 0.4,
  0.3, 0.4, 1
), nrow = 3, byrow = TRUE)

results <- mcs(num_simulations, task_distributions, correlation_matrix)
cat("Mean Total Duration:", results$total_mean, "\n")
#> Mean Total Duration: 38.61415

cat("Variance of Total Duration:", results$total_variance, "\n")
#> Variance of Total Duration: 19.97797

hist(results$total_distribution, breaks = 50, main = "Distribution of Total Project Duration", 
     xlab = "Total Duration", col = "skyblue", border = "white")

More Resources

Much of this package is based on the book Data Analysis for Engineering and Project Risk Managment by Ivan Damnjanovic and Ken Reinschmidt and comes highly recommended.

Code of Conduct

Please note that the PRA project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.