Paired Sign Test Inference for KK Designs with Ordinal Response
Source:R/inference_ordinal_paired_sign_test.R
InferenceOrdinalPairedSignTest.RdFits the classical paired sign test for ordinal responses under a KK
matching-on-the-fly design. For each matched pair \(i\) with treated
member response \(Y_{i,T}\) and control member response \(Y_{i,C}\),
only the sign of the within-pair difference \(Y_{i,T} - Y_{i,C}\) is
used; tied pairs (\(Y_{i,T} = Y_{i,C}\)) are dropped from the effective
sample. The estimand is \(\theta = P(Y_T > Y_C \mid \text{pair
untied})\), and the reported treatment effect is \(\hat\beta_T = \hat p -
0.5\), where \(\hat p\) is the sample proportion of untied pairs favoring
treatment; \(\beta_T = 0\) corresponds to \(\theta = 0.5\) (no
directional preference). The standard error is the usual binomial-proportion
formula \(\sqrt{\hat p (1 - \hat p) / n_{\text{eff}}}\), where
\(n_{\text{eff}}\) is the number of untied pairs.
Reservoir (unmatched) subjects are not included — this is a purely
within-pair test, unlike the IVWC-style classes elsewhere in the KK family
that combine matched-pair and reservoir information.
likelihood_tier = "none" (supports_likelihood_tests() is hard
FALSE): only Wald inference on the proportion scale is exposed.
Bootstrap and jackknife are deliberately unsupported and throw explicit
errors (see approximate_bootstrap_distribution_beta_hat_T() and
approximate_jackknife_distribution_beta_hat_T()), since subject-level
resampling or deletion would violate the matched-pair design's dependence
structure; randomization inference (compute_rand_two_sided_pval())
remains available since it permutes treatment assignment within the design's
own randomization mechanism rather than resampling subjects. Requires a KK
matching-on-the-fly design (DesignSeqOneByOneKK14/KK21) or
DesignFixedBinaryMatch; a design with no discordant (untied) pairs is
cached as nonestimable for the standard error (point estimate 0) or
fully nonestimable, per harden.
References
Dixon, W. J., and Mood, A. M. (1946). "The Statistical Sign Test." Journal of the American Statistical Association, 41(236), 557-566, doi:10.2307/2280577 , for the classical paired sign test; Kapelner, A. and Krieger, A. M. (2014). "Matching on-the-fly: Sequential allocation with higher power and efficiency." Biometrics, 70(2), 378-388, doi:10.1111/biom.12148 , for the KK matching-on-the-fly design this class is built for.
See also
Sign test (Wikipedia).
Super class
Inference -> InferenceOrdinalPairedSignTest
Methods
Public methods
InferenceOrdinalPairedSignTest$compute_estimate_with_bootstrap_weights()InferenceOrdinalPairedSignTest$compute_asymp_confidence_interval()InferenceOrdinalPairedSignTest$compute_asymp_two_sided_pval()InferenceOrdinalPairedSignTest$approximate_bootstrap_distribution_beta_hat_T()InferenceOrdinalPairedSignTest$approximate_jackknife_distribution_beta_hat_T()
+ inherited public methods from Inference
Inference$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$supports()
InferenceOrdinalPairedSignTest$new()
Uses the shared randomization-test two-sided p-value
contract; see InferenceRand.
Pinned from plain InferenceRand (not InferenceRandCI)
per the established ordinal-class precedent – Zhang dispatch is
incidence-only.
Initialize inference for the paired sign test on within-pair
response differences \(Y_{i,T} - Y_{i,C}\); see
InferenceOrdinalPairedSignTest
for the model form. Requires a KK matching-on-the-fly design
(DesignSeqOneByOneKK14/KK21) or
DesignFixedBinaryMatch. Does not compute the sign-test statistic;
that is deferred to the first call to compute_estimate() or a
method that requires it.
Usage
InferenceOrdinalPairedSignTest$new(
des_obj,
model_formula = NULL,
verbose = FALSE,
smart_cold_start_default = NULL
)Arguments
des_objA completed KK matching-on-the-fly design object.
model_formulaOptional formula for covariate adjustment. If
NULL(default), the formula from the design object is used and its pre-computed design matrix is reused. If a formula is provided, a new design matrix is constructed from the design's imputed covariates.verboseWhether to print progress messages.
smart_cold_start_defaultWhether to use smart cold start values.
rNumber of randomization draws.
deltaNull treatment effect.
transform_responsesOptional response transformation.
na.rmWhether to drop non-finite draws.
show_progressWhether to show a progress bar.
permutationsOptional pre-computed permutations.
zero_one_logit_clampClamp for logit transforms.
InferenceOrdinalPairedSignTest$compute_estimate()
Computes the pair-sign counts (pos/neg, ties
dropped) from the design's matched-pair structure and returns
\(\hat\beta_T = \hat p - 0.5\), where \(\hat p\) is the proportion
of untied pairs favoring treatment. If every pair is tied, the
estimate is 0 (no directional preference) and the fit is
cached as standard-error-nonestimable (or fully nonestimable when
harden = FALSE).
InferenceOrdinalPairedSignTest$compute_estimate_with_bootstrap_weights()
Recomputes \(\hat\beta_T\) under subject/block-level
bootstrap weights (Bayesian-bootstrap draw weights, expanded to row
level via
private$expand_subject_or_block_weights_to_row_weights()): for
each matched pair, a weighted vote is cast toward whichever member has
the higher response, using the mean bootstrap weight of the pair's two
rows; \(\hat\beta_T^{(w)}\) is the weighted proportion of
treatment-favoring pairs minus 0.5. No standard error is
computed (s_beta_hat_T is always NA). Pairs with no
discordant (untied) weighted votes are cached as nonestimable.
InferenceOrdinalPairedSignTest$compute_asymp_confidence_interval()
Wald confidence interval for \(\beta_T = \theta - 0.5\)
(equivalently, for \(\theta = P(Y_T > Y_C \mid \text{pair
untied})\)), using the binomial-proportion standard error; see
InferenceAsymp for the shared Wald
contract. Fits (computes the pair-sign counts) first if not already
cached.
InferenceOrdinalPairedSignTest$compute_asymp_two_sided_pval()
Two-sided Wald test of \(H_0: \theta = 0.5\) (equal
chance of favoring treatment vs. control among untied pairs) against
\(H_1: \theta \ne 0.5\), using the binomial-proportion standard
error; see InferenceAsymp for the
shared Wald contract. Only delta = 0 is supported (the sign
test's null is fixed at no directional preference; a non-zero
delta throws).
InferenceOrdinalPairedSignTest$approximate_bootstrap_distribution_beta_hat_T()
Uses the shared nonparametric bootstrap distribution contract; see
InferenceNonParamBootstrap.
Note that Bootstrap is disabled for this class as subject-level resampling violates the
matched-pair design constraint.
Usage
InferenceOrdinalPairedSignTest$approximate_bootstrap_distribution_beta_hat_T(
B = 501,
show_progress = TRUE,
debug = FALSE,
bootstrap_type = NULL
)InferenceOrdinalPairedSignTest$approximate_jackknife_distribution_beta_hat_T()
Creates the jackknife distribution of the estimate for the treatment effect. Note that Jackknife is disabled for this class as subject-level deletion violates the matched-pair design constraint.
Examples
set.seed(1)
x_dat <- data.frame(
x1 = c(-1.2, -0.7, -0.2, 0.3, 0.8, 1.3, 1.8, 2.3),
x2 = c(0, 1, 0, 1, 0, 1, 0, 1)
)
seq_des <- DesignSeqOneByOneKK21$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 <- InferenceOrdinalPairedSignTest$
new(seq_des, verbose = FALSE)
infer
#> <InferenceOrdinalPairedSignTest>
#> Inherits from: <Inference>
#> Public:
#> approximate_bayesian_bootstrap_distribution_beta_hat_T: function (...)
#> approximate_bootstrap_distribution_beta_hat_T: function (B = 501, show_progress = TRUE, debug = FALSE, bootstrap_type = NULL)
#> 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 (subject_or_block_weights, estimate_only = FALSE)
#> 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_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_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, verbose = FALSE, smart_cold_start_default = NULL)
#> 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_custom_randomization_statistic_cpp: function (fn)
#> set_custom_randomization_statistic_function: function (custom_randomization_statistic_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
#> allocate_resampling_sizes_by_stratum: function (...)
#> analyze_custom_randomization_statistic: 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_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_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_kk_bootstrap_worker_design_caches: function (worker_priv)
#> 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_basic_kk_match_data_impl: function ()
#> compute_basic_match_data: function ()
#> 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_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_kk_bootstrap_debug_with_reused_worker: function (kk_boot_draws, kk_boot_context)
#> compute_kk_bootstrap_distribution_with_reused_workers: function (kk_boot_draws, kk_boot_context, actual_cores, show_progress)
#> compute_kk_bootstrap_worker_estimate: function (worker_state)
#> 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_kk_bootstrap_context: function (y, dead, w, X, m, n_reservoir)
#> create_kk_bootstrap_worker_state: function (kk_boot_context)
#> 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: DesignSeqOneByOneKK21, DesignSeqOneByOneKK14, DesignSeqOneByOne, Design, R6
#> des_obj_priv_int: environment
#> design_compatibility_reason: function (des_obj)
#> 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_lightweight_custom_randomization_statistic: function (lightweight_custom_context, y, w, dead, cpp_fn_override = NULL)
#> 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)
#> 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_compiled_cpp_stat: 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_information_preferences_impl: function ()
#> get_supported_testing_types_impl: function ()
#> get_w_signed: function (w)
#> harden: TRUE
#> has_general_censoring: FALSE
#> has_match_structure: TRUE
#> 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 (...)
#> init_kk_passthrough: function (des_obj)
#> invert_ci_to_find_two_sided_pval_for_treatment_effect: function (delta = 0)
#> invert_rand_bootstrap_test_bisection: function (...)
#> is_KK: TRUE
#> is_a_asymp: function ()
#> is_a_kk_passthrough_design: 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")
#> kk_passthrough: TRUE
#> 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_kk_bootstrap_sample_into_worker: function (worker_state, sample_info)
#> 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: 0 0 0 0 0 0 0 0
#> 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")
#> 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: lbfgs
#> 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
#> 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
#> 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_bayesian_bootstrap: function (...)
#> supports_design_randomization_draw: TRUE
#> supports_design_resampling: TRUE
#> supports_design_resampling_replay: TRUE
#> supports_information_preference: function ()
#> supports_interval_or_left_censored_data: function ()
#> supports_likelihood_tests: function ()
#> supports_observed_information: 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 ()
#> use_reusable_kk_bootstrap_worker: function ()
#> validate_bootstrap_worker_state: function (...)
#> verbose: FALSE
#> w: 0 0 1 1 0 1 1 1
#> warned_no_parallel: FALSE
#> 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