Type:	Package
Title:	Interactive Reliability Probability Plots
Version:	0.5
Description:	Build interactive Reliability Probability Plots with 'plotly' by Carson Sievert (2020) https://plotly.com/r/, an interactive web-based graphing library.
URL:	https://paulgovan.github.io/ReliaPlotR/, https://github.com/paulgovan/ReliaPlotR
BugReports:	https://github.com/paulgovan/ReliaPlotR/issues
License:	Apache License version 1.1 | Apache License version 2.0 [expanded from: Apache License]
Imports:	plotly, ReliaGrowR, WeibullR, WeibullR.ALT
Suggests:	knitr, rmarkdown, spelling, testthat (≥ 3.0.0)
Encoding:	UTF-8
RoxygenNote:	7.3.3
Config/testthat/edition:	3
Language:	en-US
NeedsCompilation:	no
Packaged:	2026-04-17 16:51:28 UTC; paulgovan
Author:	Paul Govan [aut, cre, cph]
Maintainer:	Paul Govan <paul.govan2@gmail.com>
Repository:	CRAN
Date/Publication:	2026-04-18 05:50:21 UTC

ReliaPlotR: Interactive Reliability Probability Plots

Description

Build interactive Reliability Probability Plots with 'plotly' by Carson Sievert (2020) https://plotly.com/r/, an interactive web-based graphing library.

Author(s)

Maintainer: Paul Govan paul.govan2@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://paulgovan.github.io/ReliaPlotR/

• https://github.com/paulgovan/ReliaPlotR

• Report bugs at https://github.com/paulgovan/ReliaPlotR/issues

Interactive ALT Probability Plot.

Description

Creates an interactive probability plot for an 'alt' object, overlaying one Weibull or lognormal fit line per stress level on a shared probability paper. The 'alt' object must have been processed through [WeibullR.ALT::alt.parallel()] before passing to this function.

Usage

plotly_alt(
  alt_obj,
  showConf = TRUE,
  showGrid = TRUE,
  main = "ALT Probability Plot",
  xlab = "Time to Failure",
  ylab = "Probability",
  gridCol = "lightgray",
  signif = 3,
  cols = NULL
)

Arguments

Value

A 'plotly' object representing the interactive ALT probability plot.

Examples

library(WeibullR.ALT)
d1 <- alt.data(c(248, 456, 528, 731, 813, 537), stress = 300)
d2 <- alt.data(c(164, 176, 289), stress = 350)
d3 <- alt.data(c(88, 112, 152), stress = 400)
obj <- alt.fit(
  alt.parallel(
    alt.make(list(d1, d2, d3), dist = "weibull", alt.model = "arrhenius", view_dist_fits = FALSE),
    view_parallel_fits = FALSE
  )
)
plotly_alt(obj)

Interactive Contour Plot

Description

This function creates an interactive contour plot for one or more 'wblr' objects, each assumed to have confidence contours generated via ‘method.conf = ’lrb''. The function overlays all contours in a single plot and displays their respective MLE point estimates.

Usage

plotly_contour(
  wblr_obj,
  main = "Contour Plot",
  xlab = "Eta",
  ylab = "Beta",
  showGrid = TRUE,
  cols = NULL,
  gridCol = "lightgray",
  signif = 3
)

Arguments

Value

A 'plotly' object representing the interactive contour plot.

Examples

library(WeibullR)
library(ReliaPlotR)

failures1 <- c(30, 49, 82, 90, 96)
failures2 <- c(20, 40, 60, 80, 100)
obj1 <- wblr.conf(wblr.fit(wblr(failures1), method.fit = "mle"), method.conf = "lrb")
obj2 <- wblr.conf(wblr.fit(wblr(failures2), method.fit = "mle"), method.conf = "lrb")
plotly_contour(list(obj1, obj2), main = "Overlayed Contours")

Interactive Duane Plot.

Description

This function creates an interactive Duane plot for a duane object. The plot includes options to customize the appearance, such as colors and grid visibility.

Usage

plotly_duane(
  duane_obj,
  showConf = TRUE,
  showGrid = TRUE,
  main = "Duane Plot",
  xlab = "Cumulative Time",
  ylab = "Cumulative MTBF",
  pointCol = "black",
  fitCol = "black",
  confCol = "black",
  gridCol = "lightgray",
  signif = 3
)

Arguments

Value

A 'plotly' object representing the interactive Duane plot.

Examples

library(ReliaGrowR)
times <- c(100, 200, 300, 400, 500)
failures <- c(1, 2, 1, 3, 2)
fit <- duane(times, failures)
plotly_duane(fit)

Interactive Exposure Plot.

Description

The function creates an interactive exposure plot for one or more 'exposure' objects. When a list of objects is provided the estimates are overlaid on the same plot, each rendered in a distinct color. The plot shows the cumulative event rate over time as a step function.

Usage

plotly_exposure(
  exposure_obj,
  showGrid = TRUE,
  main = "Exposure Plot",
  xlab = "Time",
  ylab = "Event Rate",
  fitCol = "black",
  gridCol = "lightgray",
  signif = 3,
  cols = NULL
)

Arguments

Value

A 'plotly' object representing the interactive exposure plot.

Examples

## Not run: 
library(ReliaGrowR)
ids <- c("A", "A", "A", "B", "B", "C", "C", "C", "C")
times <- c(50, 150, 350, 100, 300, 80, 200, 320, 450)
fit <- exposure(id = ids, time = times)
plotly_exposure(fit)

# Overlay two exposure objects
fit2 <- exposure(id = c("X", "X", "Y"), time = c(60, 220, 180))
plotly_exposure(list(fit, fit2), cols = c("steelblue", "tomato"))

## End(Not run)

Interactive Mean Cumulative Function Plot.

Description

The function creates an interactive Mean Cumulative Function (MCF) plot for one or more 'mcf' objects. When a list of objects is provided the models are overlaid on the same plot, each rendered in a distinct color. The MCF is rendered as a step function. Optional confidence bounds are shown as a shaded band around the estimate.

Usage

plotly_mcf(
  mcf_obj,
  showConf = TRUE,
  showGrid = TRUE,
  main = "Mean Cumulative Function Plot",
  xlab = "Time",
  ylab = "Mean Cumulative Function",
  fitCol = "black",
  confCol = "black",
  gridCol = "lightgray",
  signif = 3,
  cols = NULL
)

Arguments

Value

A 'plotly' object representing the interactive MCF plot.

Examples

## Not run: 
library(ReliaGrowR)
ids <- c("A", "A", "A", "B", "B", "C", "C", "C", "C")
times <- c(50, 150, 350, 100, 300, 80, 200, 320, 450)
fit <- mcf(id = ids, time = times)
plotly_mcf(fit)

# Overlay two MCF objects
fit2 <- mcf(id = c("X", "X", "Y"), time = c(60, 220, 180))
plotly_mcf(list(fit, fit2), cols = c("steelblue", "tomato"))

## End(Not run)

Interactive NHPP Plot.

Description

The function creates an interactive Non-Homogeneous Poisson Process (NHPP) plot for one or more 'nhpp' objects. When a list of objects is provided the models are overlaid on the same plot, each rendered in a distinct color. The plot includes cumulative events over time, the model fit, and optional confidence bounds. Vertical lines indicate change points if breakpoints are specified in the nhpp object.

Usage

plotly_nhpp(
  nhpp_obj,
  showConf = TRUE,
  showGrid = TRUE,
  main = "NHPP Plot",
  xlab = "Cumulative Time",
  ylab = "Cumulative Events",
  pointCol = "black",
  fitCol = "black",
  confCol = "black",
  gridCol = "lightgray",
  breakCol = "black",
  signif = 3,
  cols = NULL
)

Arguments

Value

A 'plotly' object representing the interactive NHPP plot.

Examples

## Not run: 
library(ReliaGrowR)
times <- c(100, 200, 300, 400, 500)
events <- c(1, 2, 1, 3, 2)
fit <- nhpp(time = times, event = events)
plotly_nhpp(fit)

# Piecewise model with a breakpoint
times2 <- c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)
events2 <- c(1, 2, 1, 1, 1, 2, 3, 1, 2, 4)
fit2 <- nhpp(time = times2, event = events2, breaks = 500)
plotly_nhpp(fit2, breakCol = "red")

# Overlay two models
plotly_nhpp(list(fit, fit2))

## End(Not run)

Interactive ALT Life-Stress Relationship Plot.

Description

Creates an interactive life-stress relationship (Arrhenius or Power Law) plot for an 'alt' object. Displays the characteristic-life estimates per stress level, the fitted relationship line, optional percentile bands with shading, and an optional goal-condition marker. The 'alt' object must have been processed through [WeibullR.ALT::alt.fit()] before passing to this function.

Usage

plotly_rel(
  alt_obj,
  showPerc = TRUE,
  showGoal = TRUE,
  showGrid = TRUE,
  main = "Life-Stress Relationship",
  xlab = "Stress",
  ylab = "Time to Failure",
  fitCol = "red",
  ptCol = "black",
  percCol = "blue",
  goalCol = "orange",
  gridCol = "lightgray",
  signif = 3,
  percentiles = c(10, 90)
)

Arguments

Value

A 'plotly' object representing the interactive life-stress plot.

Examples

library(WeibullR.ALT)
d1 <- alt.data(c(248, 456, 528, 731, 813, 537), stress = 300)
d2 <- alt.data(c(164, 176, 289), stress = 350)
d3 <- alt.data(c(88, 112, 152), stress = 400)
obj <- alt.fit(
  alt.parallel(
    alt.make(list(d1, d2, d3), dist = "weibull", alt.model = "arrhenius", view_dist_fits = FALSE),
    view_parallel_fits = FALSE
  )
)
plotly_rel(obj)

Interactive Reliability Growth Plot.

Description

The function creates an interactive reliability growth plot for one or more 'rga' objects. When a list of objects is provided the models are overlaid on the same plot, each rendered in a distinct color. The plot includes cumulative failures over time, the model fit, and optional confidence bounds. Vertical lines indicate change points if breakpoints are specified in the rga object.

Usage

plotly_rga(
  rga_obj,
  showConf = TRUE,
  showGrid = TRUE,
  main = "Reliability Growth Plot",
  xlab = "Cumulative Time",
  ylab = "Cumulative Failures",
  pointCol = "black",
  fitCol = "black",
  confCol = "black",
  gridCol = "lightgray",
  breakCol = "black",
  signif = 3,
  cols = NULL
)

Arguments

Value

A 'plotly' object representing the interactive reliability growth plot.

Examples

library(ReliaGrowR)
times <- c(100, 200, 300, 400, 500)
failures <- c(1, 2, 1, 3, 2)
rga <- rga(times, failures)
plotly_rga(rga)

times <- c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)
failures <- c(1, 2, 1, 1, 1, 2, 3, 1, 2, 4)
breakpoints <- 400
rga2 <- rga(times, failures, model_type = "Piecewise NHPP", breaks = breakpoints)
plotly_rga(rga2, fitCol = "blue", confCol = "blue", breakCol = "red")

# Overlay two models
rga3 <- rga(c(50, 150, 250, 350, 450), c(2, 1, 3, 1, 2))
plotly_rga(list(rga, rga3))

Interactive Probability Plot.

Description

This function creates an interactive probability plot for one or more wblr objects. When a list of objects is provided the models are overlaid on the same plot, each rendered in a distinct color. All objects must use the same distribution family. It can include confidence bounds, suspension data (single object only), and a results table.

Usage

plotly_wblr(
  wblr_obj,
  susp = NULL,
  showConf = TRUE,
  showSusp = TRUE,
  showGrid = TRUE,
  main = "Probability Plot",
  xlab = "Time to Failure",
  ylab = "Probability",
  probCol = "black",
  fitCol = "black",
  confCol = "black",
  intCol = "black",
  gridCol = "lightgray",
  signif = 3,
  cols = NULL
)

Arguments

Value

A 'plotly' object representing the interactive probability plot.

Examples

library(WeibullR)
library(ReliaPlotR)
failures <- c(30, 49, 82, 90, 96)
obj <- wblr.conf(wblr.fit(wblr(failures)))
plotly_wblr(obj)

suspensions <- c(100, 45, 10)
obj <- wblr.conf(wblr.fit(wblr(failures, suspensions)))
plotly_wblr(obj, suspensions,
  fitCol = "blue",
  confCol = "blue"
)
inspection_data <- data.frame(
  left = c(0, 6.12, 19.92, 29.64, 35.4, 39.72, 45.32, 52.32),
  right = c(6.12, 19.92, 29.64, 35.4, 39.72, 45.32, 52.32, 63.48),
  qty = c(5, 16, 12, 18, 18, 2, 6, 17)
)
suspensions <- data.frame(time = 63.48, event = 0, qty = 73)
obj <- wblr(suspensions, interval = inspection_data)
obj <- wblr.fit(obj, method.fit = "mle")
obj <- wblr.conf(obj, method.conf = "fm", lty = 2)
suspensions <- as.vector(suspensions$time)
plotly_wblr(obj,
  susp = suspensions, fitCol = "red", confCol = "red", intCol = "blue",
  main = "Parts Cracking Inspection Interval Analysis",
  ylab = "Cumulative % Cracked", xlab = "Inspection Time"
)
failures <- c(25, 30, 42, 49, 55, 67, 73, 82, 90, 96, 101, 110, 120, 132, 145)
fit <- wblr.conf(wblr.fit(wblr(failures), dist = "weibull3p"))
plotly_wblr(fit, fitCol = "darkgreen", confCol = "darkgreen")

# Overlay two Weibull models
obj2 <- wblr.conf(wblr.fit(wblr(c(20, 40, 60, 80, 100)), method.fit = "mle"),
                  method.conf = "lrb")
obj3 <- wblr.conf(wblr.fit(wblr(c(10, 30, 50, 70, 90)), method.fit = "mle"),
                  method.conf = "lrb")
plotly_wblr(list(obj2, obj3))