Abstract class for asymptotic inference.
Super classes
Inference -> InferenceRand -> InferenceRandCI -> InferenceNonParamBootstrap -> InferenceRandBootstrap -> InferenceRandBootstrapCI -> InferenceBayesianBootstrap -> InferenceJackknife -> InferenceAsymp
Methods
+ inherited public methods from InferenceJackknife
InferenceJackknife$approximate_jackknife_distribution_beta_hat_T()InferenceJackknife$compute_jackknife_bias_estimate()InferenceJackknife$compute_jackknife_estimate()InferenceJackknife$compute_jackknife_std_error()InferenceJackknife$compute_jackknife_wald_confidence_interval()InferenceJackknife$compute_jackknife_wald_two_sided_pval()+ inherited public methods from InferenceBayesianBootstrap
InferenceBayesianBootstrap$approximate_bayesian_bootstrap_distribution_beta_hat_T()InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval()InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval()InferenceBayesianBootstrap$compute_estimate_with_bootstrap_weights()InferenceBayesianBootstrap$get_supported_bayesian_bootstrap_ci_types()InferenceBayesianBootstrap$get_supported_bayesian_bootstrap_pval_types()+ inherited public methods from InferenceRandBootstrapCI
+ inherited public methods from InferenceRandBootstrap
+ inherited public methods from InferenceNonParamBootstrap
InferenceNonParamBootstrap$approximate_bootstrap_distribution_beta_hat_T()InferenceNonParamBootstrap$approximate_m_out_of_n_bootstrap_distribution_beta_hat_T()InferenceNonParamBootstrap$approximate_subsampling_distribution_beta_hat_T()InferenceNonParamBootstrap$compute_bootstrap_confidence_interval()InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval()InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_confidence_interval()InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_two_sided_pval()InferenceNonParamBootstrap$compute_subsampling_confidence_interval()InferenceNonParamBootstrap$compute_subsampling_sensitivity()InferenceNonParamBootstrap$compute_subsampling_two_sided_pval()InferenceNonParamBootstrap$get_supported_bootstrap_ci_types()InferenceNonParamBootstrap$get_supported_bootstrap_pval_types()InferenceNonParamBootstrap$select_optimal_b_subsampling()InferenceNonParamBootstrap$select_optimal_m_out_of_n_bootstrap()+ inherited public methods from InferenceRandCI
InferenceRandCI$compute_rand_confidence_interval()InferenceRandCI$compute_rand_two_sided_pval()+ inherited public methods from InferenceRand
+ 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$initialize()Inference$is_nonestimable()Inference$set_optimization_alg()Inference$set_seed()Inference$supports()
InferenceAsymp$compute_asymp_confidence_interval()
Computes an asymptotic confidence interval for the treatment
effect using the configured large-sample test. For the default Wald path,
the method first calls compute_estimate(), retrieves the
class-specific standard error, and forms a normal or t interval around
the estimate. Likelihood-backed subclasses may override the dispatch; see
InferenceAsympLik.
InferenceAsymp$compute_asymp_two_sided_pval()
Computes an asymptotic two-sided p-value for the treatment
effect using the configured large-sample test. For the default Wald path,
the method compares compute_estimate() to the null value
delta using the class-specific standard error and a normal or t
reference distribution. Likelihood-backed subclasses may override the
dispatch; see InferenceAsympLik.
InferenceAsymp$get_supported_testing_types()
Gets the asymptotic testing methods supported by this inference object.
InferenceAsymp$set_testing_type()
Sets the asymptotic testing method used by p-values and CIs.
This base (Wald-only) implementation accepts only "wald" and
rejects everything else with a clear message; likelihood-tier classes
override this with a richer version supporting score/gradient/lik_ratio
testing types (see InferenceAsympLik).
Without this base method, a Wald-only class (one composing only the
Wald component, e.g. a robust-sandwich or Bai-adjusted-t
estimator) has no set_testing_type() at all, so calling it
fails with an opaque "attempt to apply non-function" instead of a
clear rejection.
InferenceAsymp$compute_wald_two_sided_pval()
Computes the Wald two-sided p-value regardless of configured
testing type. This directly uses the treatment estimate, its standard
error, and the available degrees of freedom; compare with
compute_asymp_two_sided_pval() for configured-test dispatch.
InferenceAsymp$compute_wald_confidence_interval()
Computes the Wald confidence interval regardless of configured
testing type. This directly uses the treatment estimate, its standard
error, and the available degrees of freedom; compare with
compute_asymp_confidence_interval() for configured-test dispatch.
InferenceAsymp$compute_estimate()
Abstract method to compute the treatment-effect estimate. Concrete subclasses implement the model-specific calculation, such as an MLE coefficient, estimating-equation coefficient, standardized contrast, or rank/statistic-based treatment effect. Related p-value and interval methods call this method before using class-specific uncertainty estimates.
InferenceAsymp$get_mod()
Returns the model object from the last call that produced the treatment
estimate and SE. Calls compute_estimate() first if needed.
InferenceAsymp$get_summary()
Prints a summary of the model from the last call that produced the treatment estimate and SE.