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

## ----cic-example--------------------------------------------------------------
library(sccic)

# Load workers' comp data
if (requireNamespace("wooldridge", quietly = TRUE)) {
  data("injury", package = "wooldridge")

  y_00 <- injury$ldurat[injury$highearn == 0 & injury$afchnge == 0]
  y_01 <- injury$ldurat[injury$highearn == 0 & injury$afchnge == 1]
  y_10 <- injury$ldurat[injury$highearn == 1 & injury$afchnge == 0]
  y_11 <- injury$ldurat[injury$highearn == 1 & injury$afchnge == 1]

  # Continuous CIC (Theorem 3.1)
  result <- cic(y_00, y_01, y_10, y_11)
  print(result)

  # Discrete CIC (Theorem 4.1) — matches Athey and Imbens (2006)
  result_d <- cic(y_00, y_01, y_10, y_11, discrete = TRUE, boot = FALSE)
  print(result_d)
}

## ----sccic-example, warning=FALSE---------------------------------------------
if (requireNamespace("Synth", quietly = TRUE)) {
  data("basque", package = "Synth")

  # Reshape to wide format
  gdp <- reshape(basque[, c("regionno", "year", "gdpcap")],
                 idvar = "year", timevar = "regionno", direction = "wide")

  y_treated <- gdp[, "gdpcap.17"]  # Basque Country
  donor_cols <- grep("gdpcap\\.", names(gdp), value = TRUE)
  donor_cols <- donor_cols[!donor_cols %in% c("gdpcap.17", "gdpcap.1")]
  donors <- as.matrix(gdp[, donor_cols])

  valid <- complete.cases(y_treated, donors)
  result2 <- sc_cic(y_treated[valid], donors[valid, ],
                    treatment_period = 16, seed = 42)
  print(result2)
}

## ----boot-example, eval = FALSE-----------------------------------------------
#  result <- cic(y_00, y_01, y_10, y_11, boot = TRUE, boot_iters = 500, seed = 42)

