## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6,
  fig.height = 4,
  warning = FALSE,
  message = FALSE
)

## ----setup--------------------------------------------------------------------
library(gpci)
library(ggplot2)

## ----define-custom------------------------------------------------------------
# Define custom Weibull distribution template
custom_weibull <- define_distribution(
  name = "custom_weibull",
  cdf = function(x, shape, scale) pweibull(x, shape = shape, scale = scale),
  quantile = function(p, shape, scale) qweibull(p, shape = shape, scale = scale),
  params = list(shape = 2.0, scale = 10.0), # initial parameters
  support = c(0, Inf)
)

## ----test-pdf-----------------------------------------------------------------
# Theoretical PDF at x = 5 (using derived PDF)
do.call(custom_weibull$pdf, c(list(5), custom_weibull$params))

# Compare with the built-in dweibull:
dweibull(5, shape = 2, scale = 10)

## ----sim-skewed---------------------------------------------------------------
set.seed(42)
process_data <- rweibull(80, shape = 2.5, scale = 12.0)

## ----fit-custom---------------------------------------------------------------
fitted_weibull <- fit_distribution(
  data = process_data,
  dist = custom_weibull,
  method = "mle"
)

# Print fitted parameters
print(fitted_weibull$params)

## ----capability-robust--------------------------------------------------------
fit_robust <- capability(
  data = process_data,
  distribution = fitted_weibull,
  USL = 20,
  LSL = 3,
  target = 11,
  indices = c("Cp_q", "Cpk_q", "CNpk", "CpTk", "Spmk", "CNpmc"),
  mode = "quantile",
  fit = FALSE # Already fitted
)

print(fit_robust)

## ----plot-robust-density------------------------------------------------------
plot(fit_robust, type = "density")

## ----run-cv, eval = FALSE-----------------------------------------------------
# # Evaluate coverage calibration for percentile CIs
# cv <- boot_cv(
#   fit = fit_robust,
#   B2 = 50,           # Number of synthetic samples
#   B = 200,          # Bootstrap reps per synthetic sample
#   alpha = c(0.10, 0.05),
#   method = "percentile",
#   type = "parametric",
#   parallel = FALSE
# )
# 
# # Print validation metrics (bias, RMSE, empirical coverage vs. nominal)
# print(cv)