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

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

n = 80
X = data.frame(
  baseline_rate = round(rgamma(n, 4, 1), 1),
  urban         = rbinom(n, 1, 0.5)
)
true_log_rr = -0.4   # treatment reduces the event rate by ~33%

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

mu = exp(0.5 + true_log_rr * w + 0.15 * X$baseline_rate + 0.3 * X$urban)
y = rpois(n, mu)
des$add_all_subject_responses(y)

inf = InferenceCountPoisson$new(des, verbose = FALSE)
inf$num_cores = 1L
inf$compute_estimate()                         # log rate ratio for treatment
inf$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)

## ----negbin-------------------------------------------------------------------
inf_nb = InferenceCountNegBin$new(des, verbose = FALSE)
inf_nb$num_cores = 1L
inf_nb$compute_estimate()
inf_nb$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$new(n = n, response_type = "count", verbose = FALSE)
for (i in seq_len(n)) {
  w_i = des_seq$add_one_subject_to_experiment_and_assign(X[i, , drop = FALSE])
  mu_i = exp(0.5 + true_log_rr * w_i + 0.15 * X$baseline_rate[i] + 0.3 * X$urban[i])
  des_seq$add_one_subject_response(i, rpois(1, mu_i))
}

inf_seq = InferenceCountPoisson$new(des_seq, verbose = FALSE)
inf_seq$num_cores = 1L
inf_seq$compute_estimate()
inf_seq$set_seed(1)
inf_seq$compute_rand_two_sided_pval(r = 200, show_progress = FALSE)