## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(gpciIntCensor)

## ----data_prep----------------------------------------------------------------
# Define normal distribution
dist_norm <- dist_normal(mean = 10, sd = 1.5)

# Simulate interval-censored data
set.seed(123)
true_vals <- rnorm(30, mean = 10, sd = 1.5)
data_left <- true_vals - 0.25
data_right <- true_vals + 0.25

## ----fit_dist-----------------------------------------------------------------
dist_fitted <- fit_distribution_censor(data_left, data_right, dist_norm)
print(dist_fitted$params)

## ----capability_calc----------------------------------------------------------
fit_cap <- capability_censor(
  data_left = data_left,
  data_right = data_right,
  distribution = dist_norm,
  USL = 14,
  LSL = 6,
  target = 10,
  indices = c("Cpy", "Cp", "Cpk", "Cpm", "Cpmk", "Spmk", "CpTk", "CNpmc"),
  mode = "moments"
)

print(fit_cap)

## ----boot_ci_example----------------------------------------------------------
ci_res <- boot_ci_censor(
  fit = fit_cap,
  B = 100,
  alpha = c(0.10, 0.05, 0.01),
  method = "percentile",
  type = "nonparametric"
)

print(ci_res)

## ----diagnostics_example------------------------------------------------------
diag_res <- compute_diagnostics_censor(
  fit = fit_cap,
  true_params = list(mean = 10, sd = 1.5),
  true_indices = c(Cpy = 1.0, Cp = 1.33),
  B = 50
)

print(diag_res)

## ----plot_example, fig.width=7, fig.height=4----------------------------------
plot(fit_cap)