Package {ReliaPlotR}

Type:	Package
Title:	Interactive Reliability Probability Plots
Version:	0.7
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:	ellmer, jsonlite, knitr, mcptools, rmarkdown, spelling, testthat (≥ 3.0.0)
Encoding:	UTF-8
RoxygenNote:	7.3.3
VignetteBuilder:	knitr
Config/testthat/edition:	3
Language:	en-US
NeedsCompilation:	no
Packaged:	2026-06-03 20:43:49 UTC; paulgovan
Author:	Paul Govan [aut, cre, cph]
Maintainer:	Paul Govan <paul.govan2@gmail.com>
Repository:	CRAN
Date/Publication:	2026-06-04 05:11:36 UTC

ReliaPlotR: Interactive Reliability Plots with plotly

Description

ReliaPlotR creates interactive reliability probability plots using plotly. It wraps the WeibullR, WeibullR.ALT, and ReliaGrowR packages and provides a consistent interface for four reliability analysis domains:

Details

Life Data Analysis

[plotly_wblr()], [plotly_contour()]

Accelerated Life Testing

[plotly_alt()], [plotly_rel()]

Reliability Growth

[plotly_rga()], [plotly_duane()]

Repairable Systems

[plotly_nhpp()], [plotly_mcf()], [plotly_exposure()]

All functions accept a single fitted model object or a list of objects (for overlay plots) and return a plotly object.

Author(s)

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

See Also

Vignettes:

• vignette("weibull", package = "ReliaPlotR") — Life Data Analysis

• vignette("alt", package = "ReliaPlotR") — Accelerated Life Testing

• vignette("rga", package = "ReliaPlotR") — Reliability Growth Analysis

• vignette("repairable", package = "ReliaPlotR") — Repairable Systems Analysis

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

Details

Building an alt object requires three steps:

1. alt.make() — bundle the per-stress data sets and choose a distribution ("weibull" or "lognormal") and life-stress model ("arrhenius" or "power").

2. alt.parallel() — fit independent Weibull/lognormal models at each stress level (required before calling plotly_alt()).

3. alt.fit() — fit the global life-stress relationship (required for plotly_rel()).

The probability paper transformation (log y-axis for Weibull, normal quantile for lognormal) is the same as in [plotly_wblr()].

Value

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

See Also

[plotly_rel()] for the life-stress relationship plot using the same alt object.

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

Details

Confidence contours require a likelihood-ratio-based (method.conf = 'lrb') MLE fit (method.fit = 'mle'). The contour traces the locus of (\eta, \beta) pairs whose log-likelihood falls within a chi-squared critical value of the MLE peak, giving a joint confidence region for the two Weibull parameters. The MLE point estimate is shown as a marker at the center of each contour.

Value

A 'plotly' object representing the interactive contour plot.

See Also

[plotly_wblr()] for probability plots using the same wblr objects.

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 displays observed cumulative MTBF against cumulative test time on a log-log scale alongside the fitted Duane line and optional confidence bounds. Positive slope on the log-log scale indicates reliability growth.

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

Details

The Duane model fits a power-law relationship between cumulative MTBF and cumulative test time: \text{MTBF}_c = K \cdot T^\alpha. On a log-log plot this appears as a straight line with slope \alpha (the growth rate) and intercept \log(K). A slope near 0 indicates no growth; a slope near 0.5 is typical for a managed development program.

Value

A 'plotly' object representing the interactive Duane plot.

See Also

[plotly_rga()] for the Crow-AMSAA NHPP growth model on the same data.

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 instantaneous event rate (events per unit time per system at risk) as a step function, calculated from recurrence data by dividing the event count in each interval by the total system-time at risk during that interval.

Usage

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

Arguments

Details

Unlike [plotly_mcf()] which shows cumulative events, the exposure plot shows the instantaneous event rate: the number of events per unit time per system at risk in each interval. A flat exposure plot suggests a constant event rate (homogeneous Poisson process); a declining rate suggests improvement; a rising rate suggests degradation. Use this plot alongside [plotly_mcf()] to diagnose time-dependence in the event process.

Value

A 'plotly' object representing the interactive exposure plot.

See Also

[plotly_mcf()] for the cumulative view; [plotly_nhpp()] for a parametric NHPP model.

Examples

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"))

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

Details

The MCF is a nonparametric estimate of the expected cumulative number of events per system by time t. It is computed from recurrence data (multiple events per system) and rendered as a step function. The slope of the MCF at any point approximates the current event rate (repairs per unit time). Confidence bounds are computed using the Nelson variance estimator.

Value

A 'plotly' object representing the interactive MCF plot.

See Also

[plotly_nhpp()] to overlay a parametric Power Law model on the MCF; [plotly_exposure()] for the cumulative event rate.

Examples

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"))

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 shows the nonparametric Mean Cumulative Function (MCF) alongside the parametric 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 = "Mean Cumulative Function",
  pointCol = "black",
  fitCol = "black",
  confCol = "black",
  gridCol = "lightgray",
  breakCol = "black",
  signif = 3,
  cols = NULL
)

Arguments

Details

The nonparametric MCF is overlaid with the fitted parametric Power Law NHPP model, E[N(t)] = \lambda t^\beta. Use this plot to assess whether the parametric model fits the observed event history and to identify change points. Confidence bounds are based on the Fisher matrix approximation of the fitted parameters. For piecewise models, vertical dotted lines mark each breakpoint.

Value

A 'plotly' object representing the interactive NHPP plot.

See Also

[plotly_mcf()] for the nonparametric MCF alone; [plotly_rga()] for a Crow-AMSAA cumulative-failures view.

Examples

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, method = "LS")
plotly_nhpp(fit2, breakCol = "red")

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

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

Details

Two life-stress models are supported:

• Arrhenius: \eta = A \exp(E_a / (k T)), where stress is temperature in Kelvin and the x-axis uses a reciprocal scale.

• Power Law: \eta = A / S^n, where the x-axis is log-transformed.

The percentiles argument controls which quantile lines are drawn with shading between adjacent lines. The goal-condition marker (showGoal) is drawn when the alt object contains a $goal specification from alt.make().

Value

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

See Also

[plotly_alt()] for the ALT probability plot using the same alt object.

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

Details

The Crow-AMSAA (NHPP Power Law) model fits a Non-Homogeneous Poisson Process where the expected cumulative failures follow E[N(t)] = \lambda t^\beta. A \beta < 1 indicates reliability growth (decreasing failure rate), \beta > 1 indicates reliability degradation. For piecewise models, a separate set of parameters is fit within each interval defined by breaks, and vertical lines mark the change-point times.

Value

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

See Also

[plotly_duane()] for the Duane MTBF growth model on the same data.

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

Details

For Weibull distributions the y-axis uses a double-log transformation (log(-log(1 - F))) so that a straight line represents a perfect Weibull fit. For lognormal distributions the y-axis uses the normal quantile transformation (qnorm(F)), i.e., standard normal probability paper. Three-parameter Weibull (dist = 'weibull3p') shifts the x-axis by the estimated location (gamma) parameter before applying the same transformation.

Goodness-of-fit is reported as R² for rank-regression fits or log-likelihood for MLE fits.

Value

A 'plotly' object representing the interactive probability plot.

See Also

[plotly_contour()] for MLE parameter confidence contours.

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))

Launch a ReliaPlotR MCP Server.

Description

Starts a Model Context Protocol (MCP) server that exposes five reliability analysis tools to MCP clients such as Claude Code and Claude Desktop.

Usage

reliapltr_mcp_server(...)

Arguments

Details

'fit_weibull'

Fits a Weibull or lognormal distribution to time-to-failure data (optionally with right-censored suspensions) and returns parameter estimates and goodness-of-fit metrics.

'fit_alt'

Fits an Accelerated Life Test (ALT) model across multiple stress levels and returns per-stress-level parameters and the global life-stress relationship coefficients.

'plot_weibull'

Fits a Weibull or lognormal distribution and returns a Weibull probability plot as a plotly JSON string.

'plot_alt'

Fits an ALT model and returns probability and life-stress relationship plots as plotly JSON strings.

'plot_rga'

Fits a Crow-AMSAA reliability growth model and returns a cumulative failure plot as a plotly JSON string.

Both 'mcptools' (>= 0.2.0) and 'ellmer' must be installed. They are listed in 'Suggests' and are not automatically installed with ReliaPlotR. 'jsonlite' is also required for the 'fit_alt' tool.

**Registering with Claude Code:** “'bash claude mcp add -s user reliapltr – \ Rscript -e "ReliaPlotR::reliapltr_mcp_server()" “'

Alternatively, use the bundled launcher script: “'bash claude mcp add -s user reliapltr – \ Rscript /path/to/ReliaPlotR/mcp/server.R “'

Value

Called for its side effect of launching a blocking MCP server process.

Extract Tidy Parameter Estimates from an alt Object.

Description

Returns tidy data frames of per-stress-level parameter estimates and (when available) global life-stress relationship coefficients from a fitted 'alt' object. Useful for reporting ALT results in reproducible research workflows.

Usage

tidy_alt(alt_obj)

Arguments

Details

Two life-stress models are supported. For the Arrhenius model, \eta = \exp(C_1 + C_2 \cdot S) where S is stress (typically reciprocal temperature in 1/K). For the Power Law model, \eta = \exp(C_1) / S^{|C_2|}. Both coef1 and coef2 are on the log scale as returned by alt.fit().

Value

A named list with two elements:

'parallel'

A 'data.frame' with one row per stress level, containing columns 'stress', 'P1', 'P2', 'wt', and 'n_failures'. For Weibull models, 'P1' is the scale parameter \eta and 'P2' is the shape parameter \beta. For lognormal models, 'P1' is \mu_{log} and 'P2' is \sigma_{log}.

'relationship'

A one-row 'data.frame' with columns 'model', 'coef1', and 'coef2' (the global life-stress relationship coefficients), or 'NULL' with a message if 'alt.fit()' has not been called.

References

Nelson, W. B. (1990). Accelerated Testing: Statistical Models, Test Plans, and Data Analysis. Wiley.

See Also

[plotly_alt()] for the ALT probability plot, [plotly_rel()] for the life-stress relationship 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
  )
)
result <- tidy_alt(obj)
result$parallel
result$relationship

Extract Tidy Fitted Values from an rga Object.

Description

Returns a tidy data frame of fitted cumulative failure counts, confidence bounds, and (when available) Crow-AMSAA model coefficients from an 'rga' object. Suitable for exporting results for use in reproducible research workflows.

Usage

tidy_rga(rga_obj)

Arguments

Details

The Crow-AMSAA (NHPP Power Law) model gives the expected cumulative failures as E[N(t)] = \lambda t^\beta. A \beta < 1 indicates reliability growth (decreasing failure rate); \beta > 1 indicates degradation. The parameters are recovered from the fitted log-log linear model via coef(): \lambda = \exp(\text{intercept}) and \beta is the slope coefficient.

Value

A named list with two elements:

'fitted'

For a single object, a 'data.frame' with columns 'time', 'cum_failures', 'fitted', 'lower', 'upper'. For a list, a list of such data frames (one per object).

'params'

For a single object, a one-row 'data.frame' with columns 'lambda' and 'beta' (Crow-AMSAA Power Law parameters), or 'NULL' if the model coefficients cannot be extracted. For a list, a list of such data frames.

References

Crow, L. H. (1974). Reliability Analysis for Complex Repairable Systems. In Reliability and Biometry, SIAM, pp. 379-410.

See Also

[plotly_rga()] for the corresponding interactive reliability growth plot.

Examples

library(ReliaGrowR)
times <- c(100, 200, 300, 400, 500)
failures <- c(1, 2, 1, 3, 2)
obj <- rga(times, failures)
result <- tidy_rga(obj)
result$fitted
result$params

Extract Tidy Parameter Estimates from a wblr Object.

Description

Returns a tidy data frame of distribution parameter estimates, goodness-of-fit metrics, and (optionally) confidence bound data from one or more fitted 'wblr' objects. This makes it straightforward to use ReliaPlotR output in reproducible research workflows — export to CSV, include in tables, or compare multiple fits.

Usage

tidy_wblr(wblr_obj)

Arguments

Details

Parameters are returned on their natural scales. For Weibull and three-parameter Weibull (weibull3p) models, param1 is the shape parameter \beta and param2 is the scale parameter \eta; param3 is the location parameter \gamma for weibull3p (NA otherwise). For lognormal models, param1 is \mu_{log} and param2 is \sigma_{log}.

Goodness-of-fit is reported as R^2 (column value "R2") for rank-regression fits, or log-likelihood ("loglikelihood") for MLE fits.

Value

A named list with two elements:

'estimates'

A 'data.frame' with one row per 'wblr' object and columns 'dist', 'method_fit', 'param1_name', 'param1', 'param2_name', 'param2', 'param3_name', 'param3', 'gof_metric', 'gof_value', 'method_conf', 'n_failures'.

'bounds'

For a single object, a 'data.frame' of confidence bound data (columns 'Datum', 'unrel', 'Lower', 'Upper'), or 'NULL' if no confidence bounds were computed. For a list of objects, a list of such data frames (one per object).

References

Meeker, W. Q., and Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley.

See Also

[plotly_wblr()] for the corresponding interactive probability plot.

Examples

library(WeibullR)
failures <- c(30, 49, 82, 90, 96)
obj <- wblr.conf(wblr.fit(wblr(failures), method.fit = "mle"), method.conf = "lrb")
result <- tidy_wblr(obj)
result$estimates
result$bounds

# List of objects
obj2 <- wblr.conf(wblr.fit(wblr(c(20, 40, 60, 80, 100)), method.fit = "mle"),
                  method.conf = "lrb")
result2 <- tidy_wblr(list(obj, obj2))
result2$estimates