Key features:
- The Second Moment Method
- Monte Carlo Simulation
- Contingency Analysis
- Sensitivity Analysis
- Earned Value Management
- Learning Curves
- Design Structure Matrices
- 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.
First, load the package:
Next, set the number of simulations and describe probability distributions for 3 work packages:
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
)
Then, set the correlation matrix between the 3 work packages:
Finally, run the simulation using the mcs
function:
results <- mcs(num_simulations, task_distributions, correlation_matrix)
To calculate the mean of the total duration:
cat("Mean Total Duration:", results$total_mean, "\n")
#> Mean Total Duration: 38.70397
To calculate the variance of the total duration:
cat("Variance of Total Duration:", results$total_variance, "\n")
#> Variance of Total Duration: 19.46001
To build a histogram of the total duration:
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.