gibbsTI

R Package: Calibrated Gibbs Posteriors for Tolerance Intervals

gibbsTI provides tools for constructing one-sided and two-sided nonparametric tolerance intervals using calibrated Gibbs posteriors.

⚠️ Note: This package is currently under active development. The API is subject to change, and you may encounter bugs. Please report any issues or feedback via the GitHub Issue Tracker.

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Installation

You can install the development version of gibbsTI from GitHub.

1. Prerequisites

Since you are installing from source, ensure you have a functional build environment:

• Windows: Rtools
• macOS: Xcode command line tools

2. Install via remotes

Run the following commands in your R console:

# Install the remotes package if you don't have it
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}

# Install gibbsTI
remotes::install_github("tpourmohamad/gibbsTI")

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Usage

library(gibbsTI)

set.seed(1)
x <- rnorm(50)

# One-sided upper 95% content / 90% confidence tolerance interval,
# with the Gibbs learning rate chosen by GPC calibration (the default).
fit <- gibbsTI(x, side = "one", type = "upper",
               content = 0.95, confidence = 0.90)
print(fit)
summary(fit)
plot(fit)

# Two-sided interval
fit2 <- gibbsTI(x, side = "two", content = 0.95, confidence = 0.90)
summary(fit2)

# Skip calibration and use a fixed learning rate
fit3 <- gibbsTI(x, side = "one", type = "upper",
                eta_method = "fixed", eta = 1)

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Citation

If you use gibbsTI, please cite:

Pourmohamad, T., Richardson, R., & Sansó, B. (2026). "Calibrated Bayesian Nonparametric Tolerance Intervals." Technometrics, 1–27. https://doi.org/10.1080/00401706.2026.2699103

You can also run citation("gibbsTI") in R.