Performs Ridit analysis (Relative to an Identified Distribution unit) for
comparing two groups on an ordinal scale. Every subject's category \(k\) is
converted to a ridit score — its relative rank position within the
reference distribution's empirical CDF: \(r_k = F_{\mathrm{ref}}(k{-}1)
+ \tfrac12 f_{\mathrm{ref}}(k)\), where \(F_{\mathrm{ref}}\) and
\(f_{\mathrm{ref}}\) are the reference group's empirical cumulative and
point probabilities. The reference distribution — controlled by the
reference constructor argument — may be the control arm
(default), the treatment arm, or the pooled sample. The
treatment effect is oriented as treated-versus-control stochastic superiority:
with a control or pooled reference it is the treated mean ridit minus
\(0.5\); with a treatment reference it is \(0.5\) minus the control
mean ridit. The latter orientation is essential because the reference
group's own mean ridit is \(0.5\) by construction. For a two-arm sample,
the control- and treatment-reference formulas are the same empirical
Mann-Whitney contrast, while retaining the requested group's scores for
inspection. The mean ridit has a direct interpretation as (an
estimate of) the probability that a randomly selected treated subject's
outcome exceeds a randomly selected control subject's outcome
(a Mann-Whitney-type stochastic superiority probability), similar in spirit
to InferenceOrdinalJonckheereTerpstraTest's
superiority measure but referenced against a chosen distribution rather than
always symmetric between the two arms. Standard errors and p-values come
from fast_ridit_analysis_cpp's asymptotic formula, not a resampling
approximation.
References
Bross, I. D. J. (1958). "How to Use Ridit Analysis." Biometrics, 14(1), 18-38, doi:10.2307/2527727 , for the ridit transformation and its interpretation used here.
Super class
Inference -> InferenceOrdinalRidit
Methods
+ inherited public methods from Inference
Inference$capabilities()Inference$compute_exact_confidence_interval()Inference$compute_exact_two_sided_pval_for_treatment_effect()Inference$duplicate()Inference$get_analysis_data()Inference$get_covariates()Inference$get_design_object()Inference$get_model_formula()Inference$get_nonestimable_reason()Inference$get_nonestimable_stage()Inference$get_optimization_alg()Inference$get_response()Inference$get_response_type()Inference$get_treatment()Inference$is_nonestimable()Inference$set_optimization_alg()Inference$set_seed()Inference$supports()
InferenceOrdinalRidit$new()
Uses the shared randomization two-sided p-value contract; see
InferenceRand.
Initialize a Ridit analysis inference object for a completed design with an ordinal, uncensored response.
Usage
InferenceOrdinalRidit$new(
des_obj,
model_formula = NULL,
reference = "control",
verbose = FALSE,
max_resample_attempts = 50L
)Arguments
des_objA DesignSeqOneByOne object whose entire n subjects are assigned and response y is recorded within.
model_formulaOptional formula for covariate adjustment. If
NULL(default), the formula from the design object is used and its pre-computed design matrix is reused. If a formula is provided, a new design matrix is constructed from the design's imputed covariates.referenceThe group to use as the "Identified Distribution" when constructing ridit scores. Must be one of "control", "treatment", or "pooled". The reported contrast is always oriented so positive values mean higher treated outcomes: treated mean minus 0.5 for control/pooled, and 0.5 minus control mean for treatment. Default is "control".
verboseA flag indicating whether messages should be displayed.
max_resample_attemptsMaximum number of times a single bootstrap replicate may be redrawn when the drawn sample fails validity screening. If all attempts fail the replicate is recorded as
NA, silently reducing the effectiveB. Must be a positive integer. Default50L.
InferenceOrdinalRidit$compute_estimate()
Returns the estimated treated-versus-control stochastic superiority contrast. This is treated mean ridit minus 0.5 for a control/pooled reference and 0.5 minus control mean ridit for a treatment reference (see class documentation).
InferenceOrdinalRidit$compute_estimate_with_bootstrap_weights()
Recomputes the ridit treatment estimate under subject/block
bootstrap weights: the reference distribution's category proportions
and every subject's ridit score are recomputed using the weights, then
the oriented weighted ridit contrast is returned. Used by the Bayesian bootstrap and related
weighted-resampling machinery. Always leaves the standard error and
degrees of freedom unavailable (NA) regardless of
estimate_only — this weighted path never computes the
asymptotic variance.
InferenceOrdinalRidit$get_mean_ridit_treatment()
Returns the mean ridit score among treated subjects (not
centered). With a treatment reference this is 0.5 by construction;
$compute_estimate() therefore uses the control mean instead.
InferenceOrdinalRidit$get_ridit_scores()
Returns each subject's individual ridit score (see class documentation for the ridit-score formula), in subject order.
InferenceOrdinalRidit$compute_asymp_confidence_interval()
Computes the asymptotic confidence interval for the
oriented ridit treatment effect, using
fast_ridit_analysis_cpp's asymptotic standard error.
InferenceOrdinalRidit$compute_asymp_two_sided_pval()
Computes a two-sided Wald p-value for the oriented ridit
contrast (i.e. delta = 0 tests the null of no group difference), using
fast_ridit_analysis_cpp's asymptotic standard error.
Examples
set.seed(1)
x_dat <- data.frame(
x1 = c(-1.2, -0.7, -0.2, 0.3, 0.8, 1.3, 1.8, 2.3),
x2 = c(0, 1, 0, 1, 0, 1, 0, 1)
)
seq_des <- DesignSeqOneByOneBernoulli$new(n = nrow(x_dat), response_type = "ordinal",
verbose = FALSE)
for (i in seq_len(nrow(x_dat))) {
seq_des$add_one_subject_to_experiment_and_assign(x_dat[i, , drop = FALSE])
}
seq_des$add_all_subject_responses(as.integer(c(1, 2, 2, 3, 3, 4, 4, 5)))
infer <- InferenceOrdinalRidit$
new(seq_des, verbose = FALSE)
infer
#> <InferenceOrdinalRidit>
#> Inherits from: <Inference>
#> Public:
#> approximate_bayesian_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_jackknife_distribution_beta_hat_T: function (unit = "auto")
#> approximate_m_out_of_n_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_rand_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_randomization_distribution_beta_hat_T: function (r = 501, delta = 0, transform_responses = "none", show_progress = TRUE,
#> approximate_subsampling_distribution_beta_hat_T: function (...)
#> capabilities: function ()
#> clone: function (deep = FALSE)
#> compute_asymp_confidence_interval: function (alpha = 0.05)
#> compute_asymp_two_sided_pval: function (delta = 0)
#> compute_bayesian_bootstrap_confidence_interval: function (...)
#> compute_bayesian_bootstrap_two_sided_pval: function (...)
#> compute_bootstrap_confidence_interval: function (...)
#> compute_bootstrap_two_sided_pval: function (...)
#> compute_estimate: function (estimate_only = FALSE)
#> compute_estimate_with_bootstrap_weights: function (...)
#> compute_exact_confidence_interval: function (...)
#> compute_exact_two_sided_pval_for_treatment_effect: function (...)
#> compute_jackknife_bias_estimate: function (unit = "auto")
#> compute_jackknife_estimate: function (unit = "auto")
#> compute_jackknife_std_error: function (unit = "auto")
#> compute_jackknife_wald_confidence_interval: function (alpha = 0.05, unit = "auto")
#> compute_jackknife_wald_two_sided_pval: function (delta = 0, unit = "auto")
#> compute_m_out_of_n_bootstrap_confidence_interval: function (...)
#> compute_m_out_of_n_bootstrap_two_sided_pval: function (...)
#> compute_rand_bootstrap_confidence_interval: function (...)
#> compute_rand_bootstrap_two_sided_pval: function (...)
#> compute_rand_confidence_interval: function (alpha = 0.05, r = 501, pval_epsilon = 0.005, show_progress = TRUE,
#> compute_rand_two_sided_pval: function (r = 501, delta = 0, transform_responses = "none", na.rm = TRUE,
#> compute_subsampling_confidence_interval: function (...)
#> compute_subsampling_sensitivity: function (...)
#> compute_subsampling_two_sided_pval: function (...)
#> compute_wald_confidence_interval: function (alpha = 0.05)
#> compute_wald_two_sided_pval: function (delta = 0)
#> duplicate: function (verbose = FALSE, make_fork_cluster = FALSE)
#> get_analysis_data: function ()
#> get_covariates: function ()
#> get_design_object: function ()
#> get_mean_ridit_treatment: function ()
#> get_mod: function ()
#> get_model_formula: function ()
#> get_nonestimable_reason: function ()
#> get_nonestimable_stage: function ()
#> get_optimization_alg: function ()
#> get_response: function ()
#> get_response_type: function ()
#> get_ridit_scores: function ()
#> get_summary: function ()
#> get_supported_bayesian_bootstrap_ci_types: function (...)
#> get_supported_bayesian_bootstrap_pval_types: function (...)
#> get_supported_bootstrap_ci_types: function (...)
#> get_supported_bootstrap_pval_types: function (...)
#> get_supported_rand_bootstrap_ci_types: function (...)
#> get_supported_rand_bootstrap_pval_types: function (...)
#> get_supported_testing_types: function ()
#> get_treatment: function ()
#> initialize: function (des_obj, model_formula = NULL, reference = "control",
#> is_nonestimable: function (type = c("any", "estimate", "se"))
#> num_cores: active binding
#> select_optimal_b_subsampling: function (...)
#> select_optimal_m_out_of_n_bootstrap: function (...)
#> set_optimization_alg: function (optimization_alg = NULL, allow_irls = private$optimization_alg_allow_irls,
#> set_seed: function (seed)
#> set_testing_type: function (testing_type = "wald")
#> supports: function (capability)
#> supports_rand_pval_for_incidence: function ()
#> Private:
#> X: -1.2 -0.7 -0.2 0.3 0.8 1.3 1.8 2.3 0 1 0 1 0 1 0 1
#> active_resampling_operation: NULL
#> add_rand_bootstrap_smooth_noise: function (...)
#> allocate_resampling_sizes_by_stratum: function (...)
#> any_censoring: FALSE
#> approximate_bayesian_bootstrap_statistics_beta_hat_T: function (...)
#> approximate_bayesian_jackknife_distribution_beta_hat_T: function (...)
#> approximate_bootstrap_statistics_beta_hat_T: function (...)
#> approximate_jackknife_distribution_beta_hat_T_private: function (...)
#> approximate_m_out_of_n_bootstrap_distribution_beta_hat_T_impl: function (...)
#> approximate_subsampling_distribution_beta_hat_T_impl: function (...)
#> assert_design_supports_randomization_draw: function (method_family)
#> assert_design_supports_resampling: function (method_family)
#> assert_design_supports_resampling_replay: function (method_family)
#> assert_exact_inference_params: function (type, args_for_type)
#> assert_jackknife_supported: function (unit = "auto")
#> assert_no_incidence_only_randomization_args: function (resp_type, type, args_for_type)
#> assert_valid_bootstrap_type: function (...)
#> bayesian_bootstrap_cache_key: function (...)
#> bayesian_bootstrap_ci_types: NULL
#> bayesian_bootstrap_pval_types: NULL
#> bayesian_bootstrap_sample_weight_draws: function (...)
#> bayesian_bootstrap_sample_weights: function (...)
#> bca_ci_core: function (...)
#> bca_pval_core: function (...)
#> begin_rand_worker_reuse_session: function ()
#> boot_distr_cache: NULL
#> bootstrap_ci_types: NULL
#> bootstrap_confidence_interval_extreme: function (...)
#> bootstrap_estimates_extreme: function (...)
#> bootstrap_extreme_ci_width_threshold: NULL
#> bootstrap_extreme_estimate_threshold: NULL
#> bootstrap_pval_types: NULL
#> bootstrap_replication_stats: function (...)
#> bootstrap_sample_indices: function (...)
#> bootstrap_subset_inference: function (...)
#> brt_mc_control: NULL
#> build_bayesian_bootstrap_context: function (...)
#> build_fast_randomization_worker_cache: function (prev_cache = NULL, preserve_cache_keys = character())
#> build_jackknife_deletion_draws: function (...)
#> build_randomization_ci_search_bounds: function (inf_obj, r, alpha, transform_arg, permutations, ci_search_control,
#> build_randomization_distribution_cache_key: function (r, delta, transform_responses, permutations)
#> build_resampling_draw_from_units: function (...)
#> cache_additive_delta_shift_distribution: function (t0s, r, delta, transform_responses, permutations)
#> cache_nonestimable_estimate: function (reason = "not_estimable")
#> cache_nonestimable_se: function (reason = "standard_error_unavailable")
#> cached_X_full_for_reduced: NULL
#> cached_design_matrix: NULL
#> cached_harden_for_design_matrix: NULL
#> cached_hardened_X_cov: NULL
#> cached_j_treat_for_reduced: NULL
#> cached_keep_for_reduced: NULL
#> cached_reduced_X: NULL
#> cached_values: list
#> cached_vc_params: NULL
#> cached_w_for_design_matrix: NULL
#> check_bootstrap_replicate_deadline: function (...)
#> check_rand_bootstrap_ci_deadline: function (...)
#> check_randomization_ci_deadline: function (ci_search_control = NULL, label = "Randomization CI bisection")
#> ci_bayesian_bca: function (...)
#> ci_bca: function (...)
#> ci_calibrated_bootstrap: function (...)
#> ci_from_boot_distribution: function (...)
#> ci_smoothed_bootstrap: function (...)
#> ci_studentized: function (...)
#> ci_symmetric_studentized: function (...)
#> clear_fit_warm_start: function ()
#> clear_likelihood_null_warm_cache: function ()
#> clear_likelihood_test_eval_cache: function ()
#> clear_nonestimable_state: function ()
#> closed_form_ci_from_affine_null_draws: function (...)
#> compute_additive_delta_shift_slopes: function (permutations, t0s = NULL)
#> compute_bayesian_bootstrap_distribution_with_reused_workers: function (...)
#> compute_bayesian_bootstrap_worker_estimate: function (...)
#> compute_bootstrap_distribution_with_reused_workers: function (...)
#> compute_bootstrap_worker_estimate: function (worker_state)
#> compute_bootstrap_worker_estimate_via_compute_treatment_estimate: function (...)
#> compute_brt_null_statistics_with_reused_workers: function (...)
#> compute_brt_null_statistics_with_se: function (...)
#> compute_ci_by_inverting_the_randomization_test_iteratively: function (r, l, u, pval_th, tol, transform_responses, lower,
#> compute_exact_confidence_interval_rand: function (type, alpha, args_for_type)
#> compute_exact_two_sided_pval_rand: function (type, delta, args_for_type)
#> compute_fast_bootstrap_distr: function (B, ...)
#> compute_fast_rand_bootstrap_distr: function (y0_full, rand_bootstrap_draws, delta, transform_responses,
#> compute_fast_randomization_distr: function (y, permutations, delta, transform_responses, zero_one_logit_clamp = .Machine$double.eps)
#> compute_fast_randomization_distr_via_reused_worker: function (y, permutations, delta, transform_responses, preserve_cache_keys = character(),
#> compute_jackknife_distribution_with_reused_workers: function (...)
#> compute_jackknife_summary: function (unit = "auto")
#> compute_m_out_of_n_bootstrap_confidence_interval_impl: function (...)
#> compute_m_out_of_n_bootstrap_two_sided_pval_impl: function (...)
#> compute_rand_bootstrap_ci_pval_cached: function (...)
#> compute_rand_bootstrap_distribution_with_reused_workers: function (...)
#> compute_randomization_ci_pval_cached: function (inf_obj, r, delta, transform_responses, permutations,
#> compute_randomization_distr_via_reused_worker_states: function (permutations, delta, transform_responses, actual_rand_cores,
#> compute_randomization_worker_estimate: function (worker_state)
#> compute_resampling_draw_distribution: function (...)
#> compute_reusable_bootstrap_worker_distribution: function (...)
#> compute_subsampling_confidence_interval_impl: function (...)
#> compute_subsampling_sensitivity_impl: function (...)
#> compute_subsampling_two_sided_pval_impl: function (...)
#> compute_subsampling_worker_estimate: function (...)
#> compute_treatment_estimate_during_randomization_inference: function (estimate_only = TRUE)
#> compute_two_sided_brt_pval_studentized: function (...)
#> compute_two_sided_brt_pval_with_sequential_mc: function (...)
#> compute_two_sided_pval_with_sequential_mc: function (t, r, delta, transform_responses, show_progress, permutations,
#> compute_two_sided_randomization_pval_band: function (t0s, t, conf_level)
#> compute_two_sided_randomization_pval_from_t0s: function (t0s, t)
#> compute_wald_confidence_interval_impl: function (alpha)
#> compute_wald_two_sided_pval_impl: function (delta)
#> compute_z_or_t_ci_from_s_and_df: function (alpha)
#> compute_z_or_t_two_sided_pval_from_s_and_df: function (delta)
#> create_bootstrap_worker_state: function ()
#> create_design_backed_bootstrap_worker_state: function (...)
#> create_design_matrix: function ()
#> create_reusable_bootstrap_worker: function (...)
#> current_bayesian_bootstrap_context: NULL
#> current_bayesian_bootstrap_subject_or_block_weights: NULL
#> dead: 1 1 1 1 1 1 1 1
#> des_obj: DesignSeqOneByOneBernoulli, DesignSeqOneByOne, Design, R6
#> des_obj_priv_int: environment
#> effective_parallel_cores: function (operation, requested_cores = self$num_cores)
#> end_rand_worker_reuse_session: function ()
#> ensure_mirai_daemons: function (n)
#> ensure_resampling_distribution_cache: function (operation)
#> estimate_bootstrap_worker: function (...)
#> evaluate_m_out_of_n_bootstrap_size: function (...)
#> evaluate_subsampling_size: function (...)
#> expand_bound: function (inf_obj, bound, est, r, transform_arg, permutations,
#> expand_rand_bootstrap_bound: function (...)
#> expand_subject_or_block_weights_to_row_weights: function (...)
#> extract_dollar_paths: function (expr)
#> finalize: function ()
#> fit_warm_start: NULL
#> fit_warm_start_enabled: TRUE
#> fit_warm_start_fisher: NULL
#> fit_warm_start_type: NULL
#> fit_warm_start_weights: NULL
#> fit_with_hardened_qr_column_dropping: function (X_full, fit_fun, fit_ok, required_cols = 1L, implicit_intercept = FALSE)
#> fixed_covariate_keep_cache: NULL
#> fork_cluster: NULL
#> generate_exchangeable_resampling_draws: function (...)
#> generate_permutations: function (r)
#> generate_rand_bootstrap_draws: function (...)
#> get_X: function ()
#> get_bootstrap_type: function (...)
#> get_brt_distribution_prefix: function (...)
#> get_cached_centered_resampling_pivot: function (...)
#> get_cached_resampling_distribution: function (operation, cache_key)
#> get_cluster_jackknife_ids: function (...)
#> get_complexity_tier: function ()
#> get_degrees_of_freedom: function ()
#> get_estimand_type: function ()
#> get_exchangeable_units: function (...)
#> get_fit_warm_start: function (type = c("beta", "params"))
#> get_fit_warm_start_fisher: function (expected_dim = NULL)
#> get_fit_warm_start_for_length: function (type = c("beta", "params"), expected_length = NULL)
#> get_fit_warm_start_weights: function (expected_n = NULL)
#> get_likelihood_null_warm_state: function (key)
#> get_likelihood_test_eval_cache: function ()
#> get_likelihood_test_eval_entry: function (testing_type, delta)
#> get_optimal_warm_start_config: function (expected_length, expected_fisher_dim = expected_length)
#> get_or_create_fork_cluster: function ()
#> get_randomization_ci_seed_candidates: function (inf_obj, alpha)
#> get_randomization_distribution_prefix: function (r, delta, transform_responses, show_progress, permutations,
#> get_resampling_block_ids: function (...)
#> get_resampling_cluster_ids: function (...)
#> get_resampling_draw_contract: function (operation)
#> get_resampling_strata_ids: function (...)
#> get_standard_error: function ()
#> get_supported_testing_types_impl: function ()
#> get_w_signed: function (w)
#> harden: TRUE
#> has_general_censoring: FALSE
#> has_match_structure: FALSE
#> has_private_method: function (method_name)
#> high_precision_confirm_and_refine_ci_bound: function (l, u, lower, r, transform_responses, permutations,
#> infer_original_se: function (...)
#> install_weighted_refit_isolation: function ()
#> invert_ci_to_find_two_sided_pval_for_treatment_effect: function (delta = 0)
#> invert_rand_bootstrap_test_bisection: function (...)
#> is_KK: FALSE
#> is_a_asymp: function ()
#> is_a_rand_ci: function ()
#> is_bernoulli_design: function ()
#> is_resampling_control_condition: function (...)
#> jack_distr_cache: NULL
#> jackknife_always_nonestimable: function ()
#> jackknife_block_size_gt_one_unsupported: function (unit = "auto")
#> jackknife_cache_key: function (unit = "auto")
#> last_weighted_refit: NULL
#> likelihood_null_warm_cache: NULL
#> likelihood_test_delta_key: function (testing_type, delta)
#> lin_xm_m_vec: NULL
#> lin_xm_structural: NULL
#> load_bayesian_bootstrap_draw_into_worker: function (...)
#> load_bayesian_bootstrap_weights_into_worker: function (...)
#> load_bootstrap_draw_into_worker: function (...)
#> load_bootstrap_sample_into_design_backed_worker: function (...)
#> load_bootstrap_sample_into_worker: function (worker_state, indices)
#> load_m_out_of_n_bootstrap_draw_into_worker: function (...)
#> load_non_param_bootstrap_draw_into_worker: function (...)
#> load_rand_bootstrap_assignment_into_worker: function (...)
#> load_rand_bootstrap_draw_into_worker: function (...)
#> load_randomization_draw_into_worker: function (worker_state, draw, delta, transform_responses, setup,
#> load_randomization_perm_into_worker: function (worker_state, perm_w, delta, transform_responses, y_delta,
#> load_resampling_draw_into_worker: function (operation, worker_state, draw, ...)
#> load_subsampling_draw_into_worker: function (...)
#> m: NULL
#> m_out_of_n_bootstrap_cache_key: function (...)
#> m_out_of_n_bootstrap_centered_pivot: function (...)
#> m_out_of_n_bootstrap_sample_indices: function (...)
#> mark_jackknife_nonestimable_if_block_unsupported: function (unit = "auto")
#> max_resample_attempts: 50
#> missing_bootstrap_ci: function (...)
#> model_formula: formula
#> n: 8
#> n_cpp_threads: function (n_work_items)
#> normalize_delta_for_cache: function (delta, resolution = NULL)
#> normalize_exact_inference_args: function (type, args_for_type = NULL, pval_epsilon = NULL)
#> normalize_jackknife_unit: function (unit = "auto")
#> normalize_likelihood_test_delta: function (delta)
#> normalize_randomization_ci_search_control: function (ci_search_control, r, pval_epsilon)
#> null_fit_warm_start_enabled: TRUE
#> num_cores_override: NULL
#> object_has_private_method: function (obj, method_name)
#> optimization_alg: NULL
#> optimization_alg_allow_irls: FALSE
#> optimization_alg_default: lbfgs
#> p: NULL
#> par_lapply: function (X, FUN, n_cores = self$num_cores, budget = 1L, show_progress = FALSE,
#> parallel_dispatch_policy: function (operation)
#> prob_T: 0.5
#> pval_bayesian_bca: function (...)
#> pval_bca: function (...)
#> rand_boot_draws_counter: NULL
#> rand_bootstrap_ci_conservative_count: NULL
#> rand_bootstrap_ci_timeout_deadline: function (...)
#> rand_bootstrap_ci_types: NULL
#> rand_bootstrap_draw_matrices: function (...)
#> rand_bootstrap_pval_types: NULL
#> rand_bootstrap_transform_code: function (...)
#> reduce_design_matrix_preserving_treatment: function (X_full)
#> reduce_design_matrix_preserving_treatment_fixed_covariates: function (X_full)
#> reduce_design_matrix_preserving_treatment_matrix: function (X_full)
#> reduce_treatment_only_design_fast: function (X_full)
#> reduced_design_keep_cache: NULL
#> reference: control
#> renumber_match_ids: function (...)
#> requires_blocking_design: function ()
#> resampling_centered_pval: function (...)
#> resampling_ci_from_centered_distribution: function (...)
#> resampling_effective_p: function (...)
#> resampling_error_to_na: function (...)
#> resampling_scaling_factor: function (...)
#> resampling_scaling_key: function (...)
#> resolve_dollar_path: function (expr)
#> resolve_jackknife_unit: function (unit = "auto")
#> resolve_resampling_size: function (...)
#> resolve_resampling_unit: function (...)
#> reusable_bootstrap_worker_enabled: TRUE
#> reused_worker_preserved_cache_keys: function ()
#> run_isolated_weighted_refit: function (...)
#> run_rand_bootstrap_iteration: function (...)
#> run_rand_bootstrap_iteration_with_se: function (...)
#> run_randomization_iteration: function (thread_des_obj, thread_inf_obj, perm_idx, permutations,
#> sample_exchangeable_unit_ids: function (...)
#> seed: NULL
#> select_optimal_b_subsampling_impl: function (...)
#> select_optimal_m_out_of_n_bootstrap_impl: function (...)
#> select_optimal_resample_size: function (...)
#> sequential_mc_band_excludes_threshold: function (t0s, t, threshold, conf_level)
#> sequential_mc_control_enabled: function (mc_ctrl)
#> set_cached_centered_resampling_pivot: function (...)
#> set_cached_resampling_distribution: function (operation, cache_key, value)
#> set_fit_warm_start: function (start, type = c("beta", "params"), fisher = NULL, weights = NULL,
#> set_likelihood_null_warm_state: function (key, delta, start)
#> set_likelihood_test_eval_entry: function (testing_type, delta, entry)
#> setup_randomization_template_and_shifts: function (delta, transform_responses, zero_one_logit_clamp = .Machine$double.eps)
#> shared: function (estimate_only = FALSE)
#> shift_randomization_responses: function (y, w, delta, transform_responses, response_type, inverse = FALSE,
#> should_use_design_randomization_for_incidence: function ()
#> should_use_zhang_incidence_randomization: function ()
#> smart_cold_start_default: TRUE
#> stable_signature: function (obj)
#> studentized_bootstrap_pivots: function (...)
#> studentized_interval_scale_unstable: function (...)
#> subsampling_cache_key: function (...)
#> subsampling_centered_pivot: function (...)
#> subsampling_sample_indices: function (...)
#> subset_permutations: function (permutations, indices)
#> supports_additive_delta_shift: function (transform_responses = "none")
#> supports_bayesian_bootstrap: function (...)
#> supports_design_randomization_draw: TRUE
#> supports_design_resampling: TRUE
#> supports_design_resampling_replay: TRUE
#> supports_interval_or_left_censored_data: function ()
#> supports_reusable_bootstrap_worker: function ()
#> sync_randomization_worker_state: function (thread_des_obj, thread_inf_obj)
#> try_cached_reduced_design_keep: function (X_full, keep = private$reduced_design_keep_cache)
#> use_reusable_bootstrap_worker: function ()
#> validate_bootstrap_worker_state: function (...)
#> verbose: FALSE
#> w: 0 0 1 1 0 1 1 1
#> warned_no_parallel: FALSE
#> weighted_refit_depth: 0
#> weighted_refit_impl: function (subject_or_block_weights, estimate_only = FALSE)
#> weighted_refit_is_nonestimable: function (type = "any")
#> weighted_refit_se: function ()
#> xm_m_vec: NULL
#> xm_structural: NULL
#> y: 1 2 2 3 3 4 4 5
#> y_L: NA NA NA NA NA NA NA NA
#> y_R: NA NA NA NA NA NA NA NA
#> y_temp: 1 2 2 3 3 4 4 5