version 0.2.0

DESCRIPTION

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+Package: PRA
+Type: Package
+Title: Project Risk Analysis
+Version: 0.2.0
+Authors@R: person("Paul", "Govan", email = "paul.govan2@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-1821-8492"))
+Description: Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Bayesian methods, Design Structure Matrices, and more.
+Imports: mc2d, minpack.lm, stats
+License: MIT + file LICENSE
+Encoding: UTF-8
+RoxygenNote: 7.3.1
+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: 2024-07-01 19:10:44 UTC; paulgovan
+Author: Paul Govan [aut, cre, cph] (<https://orcid.org/0000-0002-1821-8492>)
+Maintainer: Paul Govan <paul.govan2@gmail.com>
+Repository: CRAN
+Date/Publication: 2024-07-02 15:20:05 UTC

LICENSE

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+YEAR: 2024
+COPYRIGHT HOLDER: PRA authors

MD5

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+f3dfd8bb3a057feee3822f4ad380c5f7 *DESCRIPTION
+4879028cc3c07e76839597de80bef7bf *LICENSE
+ba62e7de2c235a1e0a3db87ada4e3692 *NAMESPACE
+df0eb7833550cb601f115bfb2a1b2c98 *NEWS.md
+eda1a5c46983d31d67d39e859b2e9802 *R/contingency.R
+e5a18bbe46fd1989e17732860dd6adef *R/dsm.R
+acebe937f255b998c843656787585f20 *R/evm.R
+eb16018e7e8d7babb3e04ae5dbc50aab *R/mcs.R
+4478d5eca920e450b2ccf0e5dc6e231c *R/sensitivity.R
+4675f5042987c2eddb170725dd09329f *R/sigmoidal.R
+97419e0a7e75901ebfd81ed82563f2d2 *R/smm.R
+96b55370eff18dbf4b2f58d67893b4ca *README.md
+2fa4c1dcdc8dd0561f9ca13163dd8f69 *build/vignette.rds
+49e1af367dbdff6f8995b30457e236b8 *inst/CITATION
+f6f32b25d2144349f2eb65d916da76df *inst/doc/SMM.R
+edd4482435ba8998d639d9bff89c8955 *inst/doc/SMM.Rmd
+4f0f309aafc5cecf51e64b8e3f71cde4 *inst/doc/SMM.html
+60534aba5b9539f79defd8b232f8cd0d *inst/hexSticker.R
+3ab67df2c4b396a7ea04ee718a84b8f5 *inst/logo.png
+bd7f36901f382cf94d27b2fffac0097d *man/ac.Rd
+dc3ed6b0a66acd5911787ce046c1ce8e *man/contingency.Rd
+54fe8714504d68c54d18ec7e6b9b7140 *man/cpi.Rd
+70c47d9e725835986ec200e9bb9a0ad0 *man/cv.Rd
+79c99c41519d4a12f00b9068e6242736 *man/ev.Rd
+ab3dbdacc7b819c12919c6d53dafb91b *man/figures/README-example-1.png
+9b6e489b9820eb1098a7652d0bc3755a *man/fit_sigmoidal.Rd
+6ca712db5613fa8594afaeefeed8080c *man/grandparent_dsm.Rd
+c9fe3e1ea36b1db504a3233b9d5add4c *man/mcs.Rd
+9bdf49bcccbc1d93e85d6674a5370920 *man/parent_dsm.Rd
+3bddca46f0df9cae0092cd66e334044c *man/predict_sigmoidal.Rd
+d6fb73346f53028bc0a9b99a9f66c37b *man/pv.Rd
+66ff79a6638210b02111254d24d68052 *man/sensitivity.Rd
+5264f9c1a81d9461604fe0a8b229ba29 *man/smm.Rd
+74b5bfcc80444580165c8cb344fb395c *man/spi.Rd
+543e2ca89227e7b28e484a6eb1cccebd *man/sv.Rd
+75aeed517c63e50ccc1665a72fea8ecc *tests/testthat.R
+e38bc5411ad3b24faa419ae346b3614d *tests/testthat/test-contingency.R
+5f01ee28a2f63e2850d214687f404fac *tests/testthat/test-dsm.R
+6964ef21a2c874fea8c417cd60ac0d31 *tests/testthat/test-evm.R
+99f3786dcd8b1ed7090659ef248146ed *tests/testthat/test-mcs.R
+ee6d8b40b7c481ae4ec72d10b1b85481 *tests/testthat/test-sensitivity.R
+516d45ab0d367978615d811c241a491c *tests/testthat/test-sigmoidal.R
+9ecf04e602675a86e2760007cb763d76 *tests/testthat/test-smm.R
+edd4482435ba8998d639d9bff89c8955 *vignettes/SMM.Rmd

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(ac)
+export(contingency)
+export(cpi)
+export(cv)
+export(ev)
+export(fit_sigmoidal)
+export(grandparent_dsm)
+export(mcs)
+export(parent_dsm)
+export(predict_sigmoidal)
+export(pv)
+export(sensitivity)
+export(smm)
+export(spi)
+export(sv)
+import(mc2d)
+import(minpack.lm)
+import(stats)

NEWS.md

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+# PRA 0.2.0
+
+# PRA 0.1.0
+
+* Initial CRAN submission.

R/contingency.R

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+#' Contingency Calculation.
+#'
+#' @param sims List of results from a Monte Carlo simulation.
+#' @param phigh Percentile level for contingency calculation.
+#' @param pbase Base level for contingency calculation.
+#' @return The function returns the value of calculated contingency.
+#' @examples
+#' 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
+#' )
+#' cor_mat <- matrix(c(
+#'   1, 0.5, 0.3,
+#'   0.5, 1, 0.4,
+#'   0.3, 0.4, 1
+#' ), nrow = 3, byrow = TRUE)
+#' results <- mcs(num_sims, task_dists, cor_mat)
+#' contingency <- contingency(results, phigh = 0.95, pbase = 0.50)
+#' cat("Contingency based on 95th percentile and 50th percentile:", contingency)
+#' @export
+contingency <- function(sims, phigh = 0.95, pbase = 0.50) {
+  # Check for valid p-values
+  if (!is.numeric(phigh) || phigh < 0 || phigh > 1) {
+    stop("phigh must be between 0 and 1")
+  }
+  if (!is.numeric(pbase) || pbase < 0 || phigh > 1) {
+    stop("pbase must be between 0 and 1")
+  }
+  if (phigh < pbase) {
+    stop("phigh must be greater than pbase.")
+  }
+
+  # Extract the relevant percentiles from the simulation results
+  phigh_value <- stats::quantile(sims$total_distribution, probs = phigh)
+  pbase_value <- stats::quantile(sims$total_distribution, probs = pbase)
+
+  # Calculate the contingency
+  contingency <- phigh_value - pbase_value
+
+  # Return the contingency value
+  return(contingency)
+}

R/dsm.R

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+#' Resource-based 'Parent' Design Structure Matrix (DSM).
+#'
+#' @param S Resource-Task Matrix 'S' giving the links (arcs) between resources and tasks.
+#' @return The function returns the Resource-based 'Parent' DSM 'P' giving the number
+#' of resources shared between each task.
+#' @examples
+#' s <- matrix(c(1, 1, 0, 0, 1, 0, 0, 1, 1), nrow = 3, ncol = 3)
+#' cat("Resource-Task Matrix:\n")
+#' print(s)
+#' resource_dsm <- parent_dsm(s)
+#' cat("\nResource-based 'Parent' DSM:\n")
+#' print(resource_dsm)
+#' @export
+
+# Parent DSM function
+parent_dsm <- function(S) {
+
+  # Check if the matrix is square
+  if (ncol(S) != nrow(S)) {
+    stop("The Resource-Task Matrix must be square.")
+  }
+
+  # Multiply matrix S by the transpose of R
+  parent_dsm <- S %*% t(S)
+
+  return(parent_dsm)
+}
+
+
+#' Risk-based 'Grandparent' Design Structure Matrix (DSM).
+#'
+#' @param S Resource-Task Matrix 'S' giving the links (arcs) between resources and tasks.
+#' @param R Risk-Resource Matrix 'R' giving the links (arcs) between risks and resources.
+#' @return The function returns the Risk-based 'Grandparent' DSM 'G' giving the number
+#' of risks shared between each task.
+#' @examples
+#' 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)
+#' risk_dsm <- grandparent_dsm(S, R)
+#' cat("\nRisk-based 'Grandparent' DSM:\n")
+#' print(risk_dsm)
+#' @export
+
+# Grandparent DSM function
+grandparent_dsm <- function(S, R) {
+
+
+  # Check if matrix S is square
+  if (ncol(S) != nrow(S)) {
+    stop("Matrix S must be square.")
+  }
+
+  # Check if the matrices can be multiplied
+  if (ncol(S) != ncol(R)) {
+    stop("Number of columns in the Matrix S must be equal to the number
+         of columns in Matrix R.")
+  }
+
+  # Multiply matrix S by the transpose of R
+  t <- S %*% t(R)
+
+  # Multiply matrix T by the transpose of T
+  grandparent_dsm <- t %*% t(t)
+
+  return(grandparent_dsm)
+}

R/evm.R

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+#' Planned Value (PV).
+#'
+#' @param bac Budget at Completion (total planned budget).
+#' @param schedule Vector of planned work completion (in terms of percentage) at each time period.
+#' @param time_period Current time period.
+#' @return The function returns the Planned Value (PV) of work completed.
+#' @examples
+#' bac <- 100000
+#' schedule <- c(0.1, 0.2, 0.4, 0.7, 1.0)
+#' time_period <- 3
+#' pv <- pv(bac, schedule, time_period)
+#' cat("Planned Value (PV):", pv, "\n")
+#' @export
+pv <- function(bac, schedule, time_period) {
+
+  pv <- bac * schedule[time_period]
+  return(pv)
+}
+
+#' Earned Value (EV).
+#'
+#' @param bac Budget at Completion (total planned budget).
+#' @param actual_per_complete Actual work completion percentage.
+#' @return The function returns the Earned Value (EV) of work completed.
+#' @examples
+#' bac <- 100000
+#' actual_per_complete <- 0.35
+#'
+#' ev <- ev(bac, actual_per_complete)
+#' cat("Earned Value (EV):", ev, "\n")
+#' @export
+ev <- function(bac, actual_per_complete) {
+
+  ev <- bac * actual_per_complete
+  return(ev)
+}
+
+#' Actual Cost (AC).
+#'
+#' @param actual_costs Vector of actual costs incurred at each time period.
+#' @param time_period Current time period.
+#' @return The function returns the Actual Cost (AC) of work completed.
+#' @examples
+#' actual_costs <- c(9000, 18000, 36000, 70000, 100000)
+#' time_period <- 3
+#'
+#' ac <- ac(actual_costs, time_period)
+#' cat("Actual Cost (AC):", ac, "\n")
+#' @export
+ac <- function(actual_costs, time_period) {
+
+  ac <- actual_costs[time_period]
+  return(ac)
+}
+
+#' Schedule Variance (SV).
+#'
+#' @param ev Earned Value.
+#' @param pv Planned Value.
+#' @return The function returns the Schedule Variance (SV) of work completed.
+#' @examples
+#' bac <- 100000
+#' schedule <- c(0.1, 0.2, 0.4, 0.7, 1.0)
+#' time_period <- 3
+#' pv <- pv(bac, schedule, time_period)
+#' actual_per_complete <- 0.35
+#' ev <- ev(bac, actual_per_complete)
+#'
+#' sv <- sv(ev, pv)
+#' cat("Schedule Variance (SV):", sv, "\n")
+#' @export
+sv <- function(ev, pv) {
+
+  sv <- ev - pv
+  return(sv)
+}
+
+#' Cost Variance (CV).
+#'
+#' @param ev Earned Value.
+#' @param ac Actual Cost.
+#' @return The function returns the Cost Variance (CV) of work completed.
+#' @examples
+#' bac <- 100000
+#' actual_per_complete <- 0.35
+#' ev <- ev(bac, actual_per_complete)
+#' actual_costs <- c(9000, 18000, 36000, 70000, 100000)
+#' time_period <- 3
+#' ac <- ac(actual_costs, time_period)
+#'
+#' cv <- cv(ev, ac)
+#' cat("Cost Variance (CV):", cv, "\n")
+#' @export
+cv <- function(ev, ac) {
+
+  cv <- ev - ac
+  return(cv)
+}
+
+#' Schedule Performance Index (SPI).
+#'
+#' @param ev Earned Value.
+#' @param pv Planned Value.
+#' @return The function returns the Schedule Performance Index (SPI) of work completed.
+#' @examples
+#' bac <- 100000
+#' schedule <- c(0.1, 0.2, 0.4, 0.7, 1.0)
+#' time_period <- 3
+#' pv <- pv(bac, schedule, time_period)
+#' actual_per_complete <- 0.35
+#' ev <- ev(bac, actual_per_complete)
+#'
+#' spi <- spi(ev, pv)
+#' cat("Schedule Performance Index (SPI):", spi, "\n")
+#' @export
+spi <- function(ev, pv) {
+
+  spi <- ev / pv
+  return(spi)
+}
+
+#' Cost Performance Index (CPI).
+#'
+#' @param ev Earned Value.
+#' @param ac Actual Cost.
+#' @return The function returns the Cost Performance Index (CPI) of work completed.
+#' @examples
+#' bac <- 100000
+#' actual_per_complete <- 0.35
+#' ev <- ev(bac, actual_per_complete)
+#' actual_costs <- c(9000, 18000, 36000, 70000, 100000)
+#' time_period <- 3
+#' ac <- ac(actual_costs, time_period)
+#'
+#' cpi <- cpi(ev, ac)
+#' cat("Cost Performance Index (CPI):", cpi, "\n")
+#' @export
+cpi <- function(ev, ac) {
+
+  cpi <- ev / ac
+  return(cpi)
+}