Type:	Package
Title:	Project Risk Analysis
Version:	0.3.0
Description:	Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Design Structure Matrices, and more.
Imports:	mc2d, minpack.lm, stats
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://paulgovan.github.io/PRA/, https://github.com/paulgovan/PRA
BugReports:	https://github.com/paulgovan/PRA/issues
Suggests:	ggplot2, knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-08-20 16:16:40 UTC; paulgovan
Author:	Paul Govan [aut, cre, cph]
Maintainer:	Paul Govan <paul.govan2@gmail.com>
Repository:	CRAN
Date/Publication:	2024-08-20 17:40:02 UTC

Actual Cost (AC).

Description

Actual Cost (AC).

Usage

ac(actual_costs, time_period)

Arguments

Value

The function returns the Actual Cost (AC) of work completed.

Examples

# Set the actual costs and current time period for a toy project.
actual_costs <- c(9000, 18000, 36000, 70000, 100000)
time_period <- 3

# Calculate the AC and print the results.
ac <- ac(actual_costs, time_period)
cat("Actual Cost (AC):", ac, "\n")

Contingency Calculation.

Description

Contingency Calculation.

Usage

contingency(sims, phigh = 0.95, pbase = 0.5)

Arguments

Value

The function returns the value of calculated contingency.

Examples

# Set the number os simulations and the task distributions for a toy project.
num_sims <- 10000
task_dists <- 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
)

# Set the correlation matrix for the correlations between tasks.
cor_mat <- matrix(c(
  1, 0.5, 0.3,
  0.5, 1, 0.4,
  0.3, 0.4, 1
), nrow = 3, byrow = TRUE)

# Run the Monte Carlo simulation.
results <- mcs(num_sims, task_dists, cor_mat)

# Calculate the contingency and print the results.
contingency <- contingency(results, phigh = 0.95, pbase = 0.50)
cat("Contingency based on 95th percentile and 50th percentile:", contingency)

Generate Correlation Matrix.

Description

Generate Correlation Matrix.

Usage

cor_matrix(num_samples = 100, num_vars = 5, dists = dists)

Arguments

Value

The function returns the correlation matrix for the distributions.

Examples

# List of probability distributions
dists <- list(
  normal = function(n) rnorm(n, mean = 0, sd = 1),
  uniform = function(n) runif(n, min = 0, max = 1),
  exponential = function(n) rexp(n, rate = 1),
  poisson = function(n) rpois(n, lambda = 1),
  binomial = function(n) rbinom(n, size = 10, prob = 0.5)
)

# Generate correlation matrix
cor_matrix <- cor_matrix(num_samples = 100, num_vars = 5, dists = dists)

# Print correlation matrix
print(cor_matrix)

Cost Performance Index (CPI).

Description

Cost Performance Index (CPI).

Usage

cpi(ev, ac)

Arguments

Value

The function returns the Cost Performance Index (CPI) of work completed.

Examples

# Set the BAC and actual % complete for an example project.
bac <- 100000
actual_per_complete <- 0.35

# Calcualte the EV
ev <- ev(bac, actual_per_complete)

# Set the actual costs and current time period and calculate the AC.
actual_costs <- c(9000, 18000, 36000, 70000, 100000)
time_period <- 3
ac <- ac(actual_costs, time_period)

# Calculate the CPI and print the results.
cpi <- cpi(ev, ac)
cat("Cost Performance Index (CPI):", cpi, "\n")

Cost Variance (CV).

Description

Cost Variance (CV).

Usage

cv(ev, ac)

Arguments

Value

The function returns the Cost Variance (CV) of work completed.

Examples

# Set the BAC and actual % complete for an example project.
bac <- 100000
actual_per_complete <- 0.35

# Calcualte the EV
ev <- ev(bac, actual_per_complete)

# Set the actual costs and current time period and calculate the AC.
actual_costs <- c(9000, 18000, 36000, 70000, 100000)
time_period <- 3
ac <- ac(actual_costs, time_period)

# Calculate the CV and print the results.
cv <- cv(ev, ac)
cat("Cost Variance (CV):", cv, "\n")

Earned Value (EV).

Description

Earned Value (EV).

Usage

ev(bac, actual_per_complete)

Arguments

Value

The function returns the Earned Value (EV) of work completed.

Examples

# Set the BAC and actual % complete for a toy project.
bac <- 100000
actual_per_complete <- 0.35

# Calculate the EV and print the results.
ev <- ev(bac, actual_per_complete)
cat("Earned Value (EV):", ev, "\n")

Fit a Sigmoidal Model.

Description

Fit a Sigmoidal Model.

Usage

fit_sigmoidal(data, x_col, y_col, model_type)

Arguments

Value

The function returns a list of results for the sigmoidal model.

Examples

# Set up a data frame of time and completion percentage data
data <- data.frame(time = 1:10, completion = c(5, 15, 40, 60, 70, 75, 80, 85, 90, 95))

# Fit a logistic model to the data.
fit <- fit_sigmoidal(data, "time", "completion", "logistic")

# Use the model to predict future completion times.
predictions <- predict_sigmoidal(fit, seq(min(data$time), max(data$time),
  length.out = 100), "logistic")

# Plot the results.
p <- ggplot2::ggplot(data, ggplot2::aes_string(x = "time", y = "completion")) +
  ggplot2::geom_point() +
  ggplot2::geom_line(data = predictions, ggplot2::aes(x = x, y = pred), color = "red") +
  ggplot2::labs(title = "Fitted Logistic Model", x = "time", y = "completion %") +
  ggplot2::theme_minimal()
p

Risk-based 'Grandparent' Design Structure Matrix (DSM).

Description

Risk-based 'Grandparent' Design Structure Matrix (DSM).

Usage

grandparent_dsm(S, R)

Arguments

Value

The function returns the Risk-based 'Grandparent' DSM 'G' giving the number of risks shared between each task.

Examples

# Set the S and R matrices and print the results.
S <- matrix(c(1, 1, 0, 0, 1, 0, 0, 1, 1), nrow = 3, ncol = 3)
R <- matrix(c(1, 1, 1, 1, 0, 0), nrow = 2, ncol = 3)
cat("Resource-Task Matrix:\n")
print(S)
cat("\nRisk-Resource Matrix:\n")
print(R)
# Calculate the Risk-based Grandparent Matrix and print the results.
risk_dsm <- grandparent_dsm(S, R)
cat("\nRisk-based 'Grandparent' DSM:\n")
print(risk_dsm)

Monte Carlo Simulation.

Description

Monte Carlo Simulation.

Usage

mcs(num_sims, task_dists, cor_mat = NULL)

Arguments

Value

The function returns a list of the total mean, variance, standard deviation, and percentiles for the project.

Examples

# Set the number of simulations and task distributions for a toy project.
num_sims <- 10000
task_dists <- 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
)

# Set the correlation matrix for the correlations between tasks.
cor_mat <- matrix(c(
  1, 0.5, 0.3,
  0.5, 1, 0.4,
  0.3, 0.4, 1
), nrow = 3, byrow = TRUE)

# Run the Monte Carlo sumulation and print the results.
results <- mcs(num_sims, task_dists, cor_mat)
cat("Mean Total Duration:", results$total_mean, "\n")
cat("Variance of Total Variance:", results$total_variance, "\n")
cat("Standard Deviation of Total Duration:", results$total_sd, "\n")
cat("5th Percentile:", results$percentiles[1], "\n")
cat("Median (50th Percentile):", results$percentiles[2], "\n")
cat("95th Percentile:", results$percentiles[3], "\n")
hist(results$total_distribution, breaks = 50, main = "Distribution of Total Project Duration",
  xlab = "Total Duration", col = "skyblue", border = "white")

Resource-based 'Parent' Design Structure Matrix (DSM).

Description

Resource-based 'Parent' Design Structure Matrix (DSM).

Usage

parent_dsm(S)

Arguments

Value

The function returns the Resource-based 'Parent' DSM 'P' giving the number of resources shared between each task.

Examples

# Set the S matrix for a toy project and print the results.
s <- matrix(c(1, 1, 0, 0, 1, 0, 0, 1, 1), nrow = 3, ncol = 3)
cat("Resource-Task Matrix:\n")
print(s)

# Calculate the Resource-based Parent DSM and print the results.
resource_dsm <- parent_dsm(s)
cat("\nResource-based 'Parent' DSM:\n")
print(resource_dsm)

Predict a Sigmoidal Function.

Description

Predict a Sigmoidal Function.

Usage

predict_sigmoidal(fit, x_range, model_type)

Arguments

Value

The function returns a table of results containing the time and predicted values.

Examples

# Set up a data frame of time and completion percentage data
data <- data.frame(time = 1:10, completion = c(5, 15, 40, 60, 70, 75, 80, 85, 90, 95))

# Fit a logistic model to the data.
fit <- fit_sigmoidal(data, "time", "completion", "logistic")

# Use the model to predict future completion times.
predictions <- predict_sigmoidal(fit, seq(min(data$time), max(data$time),
  length.out = 100), "logistic")

# Plot the results.
p <- ggplot2::ggplot(data, ggplot2::aes_string(x = "time", y = "completion")) +
  ggplot2::geom_point() +
  ggplot2::geom_line(data = predictions, ggplot2::aes(x = x, y = pred), color = "red") +
  ggplot2::labs(title = "Fitted Logistic Model", x = "time", y = "completion %") +
  ggplot2::theme_minimal()
p

Planned Value (PV).

Description

Planned Value (PV).

Usage

pv(bac, schedule, time_period)

Arguments

Value

The function returns the Planned Value (PV) of work completed.

Examples

# Set the BAC, schedule, and current time period for a toy project.
bac <- 100000
schedule <- c(0.1, 0.2, 0.4, 0.7, 1.0)
time_period <- 3

# Calculate the PV and print the results.
pv <- pv(bac, schedule, time_period)
cat("Planned Value (PV):", pv, "\n")

Sensitivity Analysis.

Description

Sensitivity Analysis.

Usage

sensitivity(task_dists, cor_mat = NULL)

Arguments

Value

The function returns a vector of sensitivity results with respect to each task.

Examples

# Set the task distributions for a toy project.
task_dists <- list(
  list(type = "normal", mean = 10, sd = 2),  # Task A: Normal distribution
  list(type = "triangular", a = 5, b = 15, c = 10),  # Task B: Triangular distribution
  list(type = "uniform", min = 8, max = 12)  # Task C: Uniform distribution
)

# Set the correlation matrix between the tasks.
cor_mat <- matrix(c(
  1, 0.5, 0.3,
 0.5, 1, 0.4,
  0.3, 0.4, 1
), nrow = 3, byrow = TRUE)

# Calculate the sensitivity of each task and print the results
sensitivity_results <- sensitivity(task_dists, cor_mat)
cat("Sensitivity of the variance in total cost with respect to the variance in each task cost:\n")
print(sensitivity_results)

# Build a vertical barchart and display the results.
data <- data.frame(
  Tasks = c('A', 'B', 'C'),
  Sensitivity = sensitivity_results
)
barplot(height=data$Sensitivity, names=data$Tasks, col='skyblue',
        horiz=TRUE, xlab = 'Sensitivity', ylab = 'Tasks')

Second Moment Analysis.

Description

Second Moment Analysis.

Usage

smm(mean, var, cor_mat = NULL)

Arguments

Value

The function returns a list of the total mean, variance, and standard deviation for the project.

Examples

# Set the mean vector, variance vector, and correlation matrix for a toy project.
mean <- c(10, 15, 20)
var <- c(4, 9, 16)
cor_mat <- matrix(c(
  1, 0.5, 0.3,
  0.5, 1, 0.4,
  0.3, 0.4, 1
), nrow = 3, byrow = TRUE)

# Use the Second Moment Method to estimate the results for the project.
result <- smm(mean, var, cor_mat)
print(result)

Schedule Performance Index (SPI).

Description

Schedule Performance Index (SPI).

Usage

spi(ev, pv)

Arguments

Value

The function returns the Schedule Performance Index (SPI) of work completed.

Examples

# Set the BAC, schedule, and current time period for an example project.
bac <- 100000
schedule <- c(0.1, 0.2, 0.4, 0.7, 1.0)
time_period <- 3

# Calculate the PV.
pv <- pv(bac, schedule, time_period)

# Set the actual % complete and calculate the EV.
actual_per_complete <- 0.35
ev <- ev(bac, actual_per_complete)

# Calculate the SPI and print the results.
spi <- spi(ev, pv)
cat("Schedule Performance Index (SPI):", spi, "\n")

Schedule Variance (SV).

Description

Schedule Variance (SV).

Usage

sv(ev, pv)

Arguments

Value

The function returns the Schedule Variance (SV) of work completed.

Examples

# Set the BAC, schedule, and current time period for an example project.
bac <- 100000
schedule <- c(0.1, 0.2, 0.4, 0.7, 1.0)
time_period <- 3

# Calculate the PV.
pv <- pv(bac, schedule, time_period)

# Set the actual % complete and calculate the EV.
actual_per_complete <- 0.35
ev <- ev(bac, actual_per_complete)

# Calculate the SV and print the results.
sv <- sv(ev, pv)
cat("Schedule Variance (SV):", sv, "\n")