Appendix: R Quick Reference

This appendix lists every R package and function used in this book, organized by package, with the chapter(s) where each appears. Use it as a lookup reference when you want to find the right function quickly without having to search through the chapters.

Install all packages at once with:

install.packages(c(
  "ReliaLearnR",
  "WeibullR",
  "WeibullR.ALT",
  "ReliaGrowR",
  "DiagrammeR",
  "ReliaPlotR",
  "ReliaShiny"
))

ReliaLearnR

RAM (Reliability, Availability, and Maintainability) calculation helpers. All five functions accept scalar or vector inputs and return a single numeric scalar.

install.packages("ReliaLearnR")
library(ReliaLearnR)

Function	Description	Chapter
`rel(outageTime, totalTime)`	Reliability: $1 - \text{outage} / \text{total}$	Ch. 1
`avail(unavailTime, totalTime)`	Availability: $1 - \text{unavailable} / \text{total}$ (failures + maintenance)	Ch. 1
`mttf(failures, totalTime)`	Mean Time to Failure for non-repairable items	Ch. 1
`mtbf(failures, totalTime)`	Mean Time Between Failures for repairable systems	Ch. 1
`fr(failures, totalTime)`	Failure rate $\lambda = \text{failures} / \text{total time}$	Ch. 1

Example usage:

library(ReliaLearnR)

rel(outageTime = 10, totalTime = 5 * 365)   # reliability over 5 years

[1] 0.9945205

avail(unavailTime = 25, totalTime = 5 * 365) # availability with maintenance

[1] 0.9863014

mttf(failures = 5, totalTime = 100 * 5)      # MTTF for a fleet of 100 units

[1] 100

mtbf(failures = 5, totalTime = 45000)        # MTBF for a repairable machine

[1] 9000

fr(failures = 75, totalTime = 100 * 5000)    # failure rate (failures per hour)

[1] 0.00015

WeibullR

Life data analysis: Weibull probability plotting, distribution fitting, and confidence bounds.

install.packages("WeibullR")
library(WeibullR)

Function	Description	Chapter
`wblr(...)`	Create a Weibull object from time/event data	Ch. 3, 7
`wblr.fit(obj, ...)`	Fit a distribution (MLE or MRR) to a `wblr` object	Ch. 3, 7
`wblr.conf(obj, ...)`	Compute confidence bounds on a fitted `wblr` object	Ch. 3, 7
`MLEw2p(...)`	Fit a 2-parameter Weibull distribution via MLE directly	Ch. 3
`plot_contour(obj)`	Static likelihood ratio contour plot	Ch. 3

Example usage:

library(WeibullR)

failures <- c(500, 1200, 900, 1500, 750)
da <- wblr(x = failures, s = c(2000, 2000))  # 2 suspensions
da <- wblr.fit(da, method.fit = "mle")
da <- wblr.conf(da, method.conf = "lrb")
plot(da)

WeibullR.ALT

Accelerated Life Testing (ALT): fit life-stress relationships such as Arrhenius, Power Law, and Eyring to multi-stress datasets.

install.packages("WeibullR.ALT")
library(WeibullR.ALT)

Function	Description	Chapter
`NelsonData(name)`	Load built-in Nelson ALT example datasets	Ch. 5, 7
`alt.data(...)`	Prepare an ALT data object from raw time/event/stress data	Ch. 5, 7
`alt.make(...)`	Create an ALT model object (specify distribution and relationship)	Ch. 5, 7
`alt.parallel(obj)`	Constrain Weibull slopes to be equal across stress levels	Ch. 5, 7
`alt.fit(obj, ...)`	Fit the life-stress relationship to the ALT model	Ch. 5, 7

Example usage:

library(WeibullR.ALT)

nd <- NelsonData("E2")
da <- alt.data(nd)
mo <- alt.make(da, distribution = "Weibull", relationship = "Arrhenius")
mo <- alt.parallel(mo)
mo <- alt.fit(mo)
plot(mo)

ReliaGrowR

Reliability growth testing and repairable systems analysis: Crow-AMSAA, Duane, Power Law Process, and Mean Cumulative Function.

install.packages("ReliaGrowR")
library(ReliaGrowR)

Function	Description	Chapter
`rdt(...)`	Plan a Reliability Demonstration Test	Ch. 4
`rga(times, ...)`	Fit a Crow-AMSAA (NHPP Power Law) reliability growth model	Ch. 4, 7
`predict_rga(obj, ...)`	Forecast cumulative failures from a `rga` object	Ch. 4
`duane(times, ...)`	Fit a Duane reliability growth model	Ch. 4, 7
`nhpp(...)`	Fit a Power Law Process to repairable systems data	Ch. 6, 7
`mcf(...)`	Estimate the Mean Cumulative Function (Nelson-Aalen)	Ch. 6, 7
`exposure(...)`	Compute fleet exposure from multi-system event data	Ch. 6, 7

Example usage:

library(ReliaGrowR)

# Reliability growth
times <- c(9.2, 25, 61.5, 260, 300, 710, 916, 1010, 1220)
rga_fit <- rga(times, T = 1500)
plot(rga_fit)

# Repairable systems
events <- data.frame(id = c(1,1,1,2,2), time = c(100,300,500,200,450))
mcf_fit <- mcf(events)
plot(mcf_fit)

DiagrammeR

Graph and diagram visualisation: render Graphviz DOT diagrams for reliability block diagrams and fault trees.

install.packages("DiagrammeR")
library(DiagrammeR)

Function	Description	Chapter
`grViz(diagram)`	Render a Graphviz DOT string as an SVG diagram	Ch. 2

Example usage:

library(DiagrammeR)

grViz("
  digraph series {
    rankdir = LR
    node [shape = rectangle]
    A -> B -> C
  }
")

ReliaPlotR

Interactive, plotly-based versions of every static plot produced by WeibullR, WeibullR.ALT, and ReliaGrowR. Drop-in replacements that return plotly objects embeddable in Quarto documents and Shiny apps.

install.packages("ReliaPlotR")
library(ReliaPlotR)

Function	Description	Chapter
`plotly_wblr(obj)`	Interactive Weibull probability plot	Ch. 7
`plotly_contour(obj)`	Interactive likelihood ratio contour plot	Ch. 7
`plotly_duane(obj)`	Interactive Duane reliability growth plot	Ch. 7
`plotly_rga(obj)`	Interactive Crow-AMSAA plot	Ch. 7
`plotly_alt(obj)`	Interactive ALT probability plot	Ch. 7
`plotly_rel(obj)`	Interactive life-stress relationship plot	Ch. 7
`plotly_nhpp(obj)`	Interactive NHPP intensity (recurrence rate) plot	Ch. 7
`plotly_mcf(obj)`	Interactive Mean Cumulative Function plot	Ch. 7
`plotly_exposure(obj)`	Interactive fleet exposure plot	Ch. 7

Example usage:

library(ReliaPlotR)
library(WeibullR)

da <- wblr(x = c(500, 1200, 900, 1500, 750))
da <- wblr.fit(da, method.fit = "mle")
da <- wblr.conf(da, method.conf = "lrb")
plotly_wblr(da)   # interactive version of plot(da)

ReliaShiny

A point-and-click Shiny web application that wraps all of the above analyses in a browser-based UI — no coding required. Modules cover RAM, Life Data Analysis, Reliability Growth, ALT, Repairable Systems, and RBD.

install.packages("ReliaShiny")
library(ReliaShiny)

Function	Description	Chapter
`ReliaShiny()`	Launch the ReliaShiny web application in the default browser	Ch. 8

Example usage:

library(ReliaShiny)
ReliaShiny()

# Appendix: R Quick Reference {#sec-appendix .unnumbered} This appendix lists every R package and function used in this book, organized by package, with the chapter(s) where each appears. Use it as a lookup reference when you want to find the right function quickly without having to search through the chapters. Install all packages at once with: ```{r} #| eval: false install.packages(c( "ReliaLearnR", "WeibullR", "WeibullR.ALT", "ReliaGrowR", "DiagrammeR", "ReliaPlotR", "ReliaShiny" )) ``` --- ## ReliaLearnR RAM (Reliability, Availability, and Maintainability) calculation helpers. All five functions accept scalar or vector inputs and return a single numeric scalar. ```{r} #| eval: false install.packages("ReliaLearnR") library(ReliaLearnR) ``` | Function | Description | Chapter | |---|---|---| | `rel(outageTime, totalTime)` | Reliability: $1 - \text{outage} / \text{total}$ | Ch. 1 | | `avail(unavailTime, totalTime)` | Availability: $1 - \text{unavailable} / \text{total}$ (failures + maintenance) | Ch. 1 | | `mttf(failures, totalTime)` | Mean Time to Failure for non-repairable items | Ch. 1 | | `mtbf(failures, totalTime)` | Mean Time Between Failures for repairable systems | Ch. 1 | | `fr(failures, totalTime)` | Failure rate $\lambda = \text{failures} / \text{total time}$ | Ch. 1 | **Example usage:** ```{r} library(ReliaLearnR) rel(outageTime = 10, totalTime = 5 * 365) # reliability over 5 years avail(unavailTime = 25, totalTime = 5 * 365) # availability with maintenance mttf(failures = 5, totalTime = 100 * 5) # MTTF for a fleet of 100 units mtbf(failures = 5, totalTime = 45000) # MTBF for a repairable machine fr(failures = 75, totalTime = 100 * 5000) # failure rate (failures per hour) ``` --- ## WeibullR Life data analysis: Weibull probability plotting, distribution fitting, and confidence bounds. ```{r} #| eval: false install.packages("WeibullR") library(WeibullR) ``` | Function | Description | Chapter | |---|---|---| | `wblr(...)` | Create a Weibull object from time/event data | Ch. 3, 7 | | `wblr.fit(obj, ...)` | Fit a distribution (MLE or MRR) to a `wblr` object | Ch. 3, 7 | | `wblr.conf(obj, ...)` | Compute confidence bounds on a fitted `wblr` object | Ch. 3, 7 | | `MLEw2p(...)` | Fit a 2-parameter Weibull distribution via MLE directly | Ch. 3 | | `plot_contour(obj)` | Static likelihood ratio contour plot | Ch. 3 | **Example usage:** ```{r} #| eval: false library(WeibullR) failures <- c(500, 1200, 900, 1500, 750) da <- wblr(x = failures, s = c(2000, 2000)) # 2 suspensions da <- wblr.fit(da, method.fit = "mle") da <- wblr.conf(da, method.conf = "lrb") plot(da) ``` --- ## WeibullR.ALT Accelerated Life Testing (ALT): fit life-stress relationships such as Arrhenius, Power Law, and Eyring to multi-stress datasets. ```{r} #| eval: false install.packages("WeibullR.ALT") library(WeibullR.ALT) ``` | Function | Description | Chapter | |---|---|---| | `NelsonData(name)` | Load built-in Nelson ALT example datasets | Ch. 5, 7 | | `alt.data(...)` | Prepare an ALT data object from raw time/event/stress data | Ch. 5, 7 | | `alt.make(...)` | Create an ALT model object (specify distribution and relationship) | Ch. 5, 7 | | `alt.parallel(obj)` | Constrain Weibull slopes to be equal across stress levels | Ch. 5, 7 | | `alt.fit(obj, ...)` | Fit the life-stress relationship to the ALT model | Ch. 5, 7 | **Example usage:** ```{r} #| eval: false library(WeibullR.ALT) nd <- NelsonData("E2") da <- alt.data(nd) mo <- alt.make(da, distribution = "Weibull", relationship = "Arrhenius") mo <- alt.parallel(mo) mo <- alt.fit(mo) plot(mo) ``` --- ## ReliaGrowR Reliability growth testing and repairable systems analysis: Crow-AMSAA, Duane, Power Law Process, and Mean Cumulative Function. ```{r} #| eval: false install.packages("ReliaGrowR") library(ReliaGrowR) ``` | Function | Description | Chapter | |---|---|---| | `rdt(...)` | Plan a Reliability Demonstration Test | Ch. 4 | | `rga(times, ...)` | Fit a Crow-AMSAA (NHPP Power Law) reliability growth model | Ch. 4, 7 | | `predict_rga(obj, ...)` | Forecast cumulative failures from a `rga` object | Ch. 4 | | `duane(times, ...)` | Fit a Duane reliability growth model | Ch. 4, 7 | | `nhpp(...)` | Fit a Power Law Process to repairable systems data | Ch. 6, 7 | | `mcf(...)` | Estimate the Mean Cumulative Function (Nelson-Aalen) | Ch. 6, 7 | | `exposure(...)` | Compute fleet exposure from multi-system event data | Ch. 6, 7 | **Example usage:** ```{r} #| eval: false library(ReliaGrowR) # Reliability growth times <- c(9.2, 25, 61.5, 260, 300, 710, 916, 1010, 1220) rga_fit <- rga(times, T = 1500) plot(rga_fit) # Repairable systems events <- data.frame(id = c(1,1,1,2,2), time = c(100,300,500,200,450)) mcf_fit <- mcf(events) plot(mcf_fit) ``` --- ## DiagrammeR Graph and diagram visualisation: render Graphviz DOT diagrams for reliability block diagrams and fault trees. ```{r} #| eval: false install.packages("DiagrammeR") library(DiagrammeR) ``` | Function | Description | Chapter | |---|---|---| | `grViz(diagram)` | Render a Graphviz DOT string as an SVG diagram | Ch. 2 | **Example usage:** ```{r} #| eval: false library(DiagrammeR) grViz(" digraph series { rankdir = LR node [shape = rectangle] A -> B -> C } ") ``` --- ## ReliaPlotR Interactive, plotly-based versions of every static plot produced by WeibullR, WeibullR.ALT, and ReliaGrowR. Drop-in replacements that return `plotly` objects embeddable in Quarto documents and Shiny apps. ```{r} #| eval: false install.packages("ReliaPlotR") library(ReliaPlotR) ``` | Function | Description | Chapter | |---|---|---| | `plotly_wblr(obj)` | Interactive Weibull probability plot | Ch. 7 | | `plotly_contour(obj)` | Interactive likelihood ratio contour plot | Ch. 7 | | `plotly_duane(obj)` | Interactive Duane reliability growth plot | Ch. 7 | | `plotly_rga(obj)` | Interactive Crow-AMSAA plot | Ch. 7 | | `plotly_alt(obj)` | Interactive ALT probability plot | Ch. 7 | | `plotly_rel(obj)` | Interactive life-stress relationship plot | Ch. 7 | | `plotly_nhpp(obj)` | Interactive NHPP intensity (recurrence rate) plot | Ch. 7 | | `plotly_mcf(obj)` | Interactive Mean Cumulative Function plot | Ch. 7 | | `plotly_exposure(obj)` | Interactive fleet exposure plot | Ch. 7 | **Example usage:** ```{r} #| eval: false library(ReliaPlotR) library(WeibullR) da <- wblr(x = c(500, 1200, 900, 1500, 750)) da <- wblr.fit(da, method.fit = "mle") da <- wblr.conf(da, method.conf = "lrb") plotly_wblr(da) # interactive version of plot(da) ``` --- ## ReliaShiny A point-and-click Shiny web application that wraps all of the above analyses in a browser-based UI — no coding required. Modules cover RAM, Life Data Analysis, Reliability Growth, ALT, Repairable Systems, and RBD. ```{r} #| eval: false install.packages("ReliaShiny") library(ReliaShiny) ``` | Function | Description | Chapter | |---|---|---| | `ReliaShiny()` | Launch the ReliaShiny web application in the default browser | Ch. 8 | **Example usage:** ```{r} #| eval: false library(ReliaShiny) ReliaShiny() ```