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rbcc 0.2.0

rbcc implements risk-based statistical process control charts in the presence of measurement uncertainty. The package evaluates all four possible decision outcomes—correct acceptance, false control, missed control, and correct control—and can optimize chart limits by minimizing their total cost.

Available charts

• Shewhart X-bar, R, and S charts: rbcc() and rbcc_opt()
• Moving-average charts: rbmacc() and rbmacc_opt()
• EWMA charts: rbewmacc() and rbewmacc_opt()
• CUSUM charts: rbcusumcc() and rbcusumcc_opt()
• Multivariate Hotelling T-squared charts: rbmcc() and rbmcc_opt()

Version 0.2.0 substantially refactors the computational core. Optimization no longer rebuilds a complete chart for every trial value. The default exact threshold optimizer exploits the piecewise-constant structure of the decision cost and is deterministic. Bounded numerical optimization and optional parallel grid search remain available.

Example

library(rbcc)

set.seed(42)
X <- rnorm(80, mean = 10, sd = 0.5)
UC <- rnorm(80, mean = 0, sd = 0.05)
C <- c(1, 5, 60, 5)

fit <- rbcc_opt(X, UC, C, n = 1, type = "xbar")
print(fit)

s <- summary(fit)  # no console output
s$cost0

p <- plot(fit)     # no drawing during assignment
p                  # draws the ggplot object

Optional parallel grid optimization

fit_grid <- rbcc_opt(
  X, UC, C,
  optimizer = "grid",
  parallel = TRUE,
  workers = 2,
  control = rbcc_control(grid_size = 401)
)

See citation("rbcc") for the methodological publications.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.