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To install the release verion of PRA, use:
install_packages('PRA')
You can install the development version of PRA like so:
::install_github('paulgovan/PRA') devtools
Here is a basic example which shows you how to solve a common problem using Monte Carlo Simulation.
First, load the package:
library(PRA)
Next, set the number of simulations and describe probability distributions for 3 work packages:
<- 10000
num_simulations <- list(
task_distributions 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:
<- matrix(c(
correlation_matrix 1, 0.5, 0.3,
0.5, 1, 0.4,
0.3, 0.4, 1
nrow = 3, byrow = TRUE) ),
Finally, run the simulation using the mcs
function:
<- mcs(num_simulations, task_distributions, correlation_matrix) results
To calculate the mean of the total duration:
cat("Mean Total Duration:", results$total_mean, "\n")
#> Mean Total Duration: 38.57388
To calculate the variance of the total duration:
cat("Variance of Total Duration:", results$total_variance, "\n")
#> Variance of Total Duration: 19.65191
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")
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.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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