## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(citestR)
can_mock <- requireNamespace("jsonlite", quietly = TRUE)

## ----mocked-example, eval = can_mock------------------------------------------
mock_json_response <- function(body, status = 200L) {
  function(req) {
    httr2::response(
      status_code = status,
      headers = list("Content-Type" = "application/json"),
      body = charToRaw(jsonlite::toJSON(body, auto_unbox = TRUE))
    )
  }
}

pkg_env <- citestR:::.pkg_env
old_process <- pkg_env$process
old_port <- pkg_env$port
old_base_url <- pkg_env$base_url
on.exit({
  pkg_env$process <- old_process
  pkg_env$port <- old_port
  pkg_env$base_url <- old_base_url
}, add = TRUE)

pkg_env$process <- list(is_alive = function() TRUE)
pkg_env$port <- 9999L
pkg_env$base_url <- "http://127.0.0.1:9999"

# Mock a ci_test response
mock_fit <- mock_json_response(list(
  model_id = "test-001",
  dataset_id = "ds-001",
  results = list(
    m = 0.12,
    B = 0.04,
    W_bar = 0.01,
    T = 0.05,
    t_k = 2.5,
    p_k = 0.017,
    p_2s = 0.034,
    df = 9
  )
))

example_data <- data.frame(
  Y  = c(1.2, -0.4, 0.7, 0.3, -1.1),
  X1 = c(NA, 0.5, -1.1, 0.8, NA),
  X2 = c(0.3, 1.4, -0.2, 0.6, 0.9)
)

result <- httr2::with_mocked_responses(mock_fit, {
  ci_test(example_data, y = "Y", m = 2L, n_folds = 2L)
})

result

## ----install, eval = FALSE----------------------------------------------------
# library(citestR)
# 
# # Creates a virtualenv called "citest_env" and installs the citest API backend
# install_backend(method = "pip")

## ----fit, eval = FALSE--------------------------------------------------------
# library(citestR)
# 
# # Example data frame with some missing values
# set.seed(42)
# n <- 500
# df <- data.frame(
#   Y  = rnorm(n),
#   X1 = rnorm(n),
#   X2 = rnorm(n),
#   X3 = rnorm(n)
# )
# # Introduce MAR missingness on X2
# df$X2[df$X1 > 0.5] <- NA
# 
# # Run the CI test (server starts automatically)
# result <- ci_test(
#   data       = df,
#   y          = "Y",
#   imputer    = "iterative",
#   classifier = "rf",
#   m          = 5L,
#   n_folds    = 5L
# )
# 
# result$results

## ----summary, eval = FALSE----------------------------------------------------
# summary_info <- get_summary(result$model_id)
# summary_info

## ----imputer-r2, eval = FALSE-------------------------------------------------
# r2 <- imputer_r2(result$model_id, mask_frac = 0.2, m_eval = 1L)
# r2$mean_r2
# r2$per_variable

## ----kappa, eval = FALSE------------------------------------------------------
# # Single kappa value
# compute_kappa(r2_x_z = 0.5, beta_yx = 0.3, gamma_x = 0.2)
# 
# # Full calibration table
# cal <- kappa_calibration_table()
# head(cal)
# 
# # Pivot for a fixed beta
# calibration_pivot(beta_yx = 0.3)

## ----simulate, eval = FALSE---------------------------------------------------
# sim <- simulate_data("single_mar", n = 300, ci = TRUE)
# sim$dataset_id
# sim$pct_missing

## ----stop, eval = FALSE-------------------------------------------------------
# stop_server()