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

## ----setup--------------------------------------------------------------------
library(EDI)
set.seed(20260916)

n = 80
X = data.frame(
  age    = round(rnorm(n, 50, 10)),
  smoker = rbinom(n, 1, 0.3)
)
true_log_or = 0.9

## ----fixed--------------------------------------------------------------------
des = DesignFixedBernoulli$new(n = n, response_type = "incidence", verbose = FALSE)
des$add_all_subjects_to_experiment(X)
des$assign_w_to_all_subjects()
w = des$get_w()

p = plogis(-1.2 + true_log_or * w + 0.03 * (X$age - 50) + 0.6 * X$smoker)
y = rbinom(n, 1, p)
des$add_all_subject_responses(y)

inf = InferenceIncidLogRegr$new(des, verbose = FALSE)
inf$num_cores = 1L
inf$compute_estimate()                         # log odds ratio for treatment
inf$compute_asymp_confidence_interval(alpha = 0.05)
inf$compute_asymp_two_sided_pval()

## ----fixed-resampling---------------------------------------------------------
inf$set_seed(1)
inf$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)
inf$set_seed(1)
inf$compute_bootstrap_confidence_interval(alpha = 0.05, B = 200, show_progress = FALSE)

## ----gcomp--------------------------------------------------------------------
inf_rd = InferenceIncidGCompRiskDiff$new(des, verbose = FALSE)
inf_rd$num_cores = 1L
inf_rd$compute_estimate()
inf_rd$compute_asymp_confidence_interval(alpha = 0.05)

## ----suite--------------------------------------------------------------------
suite = InferenceSuite$new(des)
res = suite$run_all_inference(screen = TRUE, plots = FALSE, num_cores = 1L,
                              methods = c("wald", "score", "lik_ratio"), max_secs_per_class = 15)

## ----seq----------------------------------------------------------------------
des_seq = DesignSeqOneByOneKK14$new(n = n, response_type = "incidence", verbose = FALSE)
for (i in seq_len(n)) {
  w_i = des_seq$add_one_subject_to_experiment_and_assign(X[i, , drop = FALSE])
  p_i = plogis(-1.2 + true_log_or * w_i + 0.03 * (X$age[i] - 50) + 0.6 * X$smoker[i])
  des_seq$add_one_subject_response(i, rbinom(1, 1, p_i))
}

inf_seq = InferenceIncidKKGCompRiskDiff$new(des_seq, verbose = FALSE)
inf_seq$num_cores = 1L
inf_seq$compute_estimate()
inf_seq$compute_asymp_confidence_interval(alpha = 0.05)
inf_seq$set_seed(1)
inf_seq$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)