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

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: 38.61906

cat("Variance of Total Duration:", results$total_variance, "\n")
#> Variance of Total Duration: 19.95962

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

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.