Function reference • IsoXshift

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`IsoXshift()`	IsoXShift: Data-adaptive discovery of minimal interventions in a mixed exposure that efficiently result in a target outcome.
`NHANES_eurocim`	NHANES 2001-2002, POP Exposure on Telomere Length
`NIEHS_data_1`	Data 1 from the NIEHS mixtures workshop
`bound_precision()`	Bound Precision
`bound_propensity()`	Bound Generalized Propensity Score
`calc_CIs()`	Calculate Confidence Interals
`calc_basis_freq()`	Calculate the frequency basis functions are used in the folds
`calc_final_effect_mod_param()`	Calculates the Effect Modification Shift Parameter
`calc_final_ind_shift_param()`	Calculates the Individual Shift Parameter
`calc_final_joint_shift_param()`	Calculates the Joint Shift Parameter
`calc_intxn_results()`	Calculates the Interaction Parameter
`calc_joint_results()`	Calculate the Joint Parameter
`calc_mediation_param()`	Calculates the Mediation Shift Parameters
`calc_pooled_em_shifts()`	Compute the Pooled Shift Parameter Estimate From the Fold Specific Results For the Effect Modification Parameter
`calc_pooled_indiv_shifts()`	Compute the Pooled Shift Parameter Estimate From the Fold Specific Results
`calc_pooled_intxn_shifts()`	Compute the Pooled Interaction Shift Parameter Estimate From the Fold Specific Results
`calc_pooled_joint_med_shifts()`	Compute the Pooled Joint Mediation Shift Parameter Estimate From the Fold Specific Results
`calc_pooled_med_shifts()`	Compute the Pooled Mediation Shift Parameter Estimate From the Fold Specific Results
`calc_pvals()`	Calculate the p-values based on variance of the influence curve
`calculatePooledEstimate()`	Calculates the Inverse Variance Pooled Estimate Including Null Folds
`create_cv_folds()`	Stratified CV to insure balance (by one grouping variable, Y)
`create_sls()`	Create default Super Learner estimators for the data adaptive and nuisance parameters used in `IsoXshift`
`eif()`	Compute the Shift Parameter Estimate and the Efficient Influence Function
`est_Q_w_shifted_mediation()`	Estimate the Outcome Mechanism with Shifted A and Z
`est_hn()`	Estimate Auxiliary Covariate of Full Data Efficient Influence Function
`est_samp()`	Estimate Probability of Censoring by Two-Phase Sampling
`estimate_mediator()`	Estimate the Mediator Mechanism
`extract_vars_from_basis()`	Extract variables from the basis function search
`find_min_concentrations()`	Data adaptively discover the shift interventions that require minimal change to achieve target outcome
`fit_basis_estimators()`	Fit the Zeta Learner: A Highly Flexible Estimator Using Splines or Basis Functions
`fit_fluctuation()`	Fit One-Dimensional Fluctuation Model for Updating Initial Estimates
`indiv_stoch_shift_est_Q()`	Estimate the Outcome Mechanism
`indiv_stoch_shift_est_g_exp()`	Estimate the Exposure Mechanism via Generalized Propensity Score for One Exposure Variable
`joint_shift_additive()`	Joint Shift Additive Modified Treatment Policy
`joint_stoch_shift_est_Q()`	Estimate the Outcome Mechanism
`joint_stoch_shift_est_g_exp()`	Estimate the Exposure Mechanism via Generalized Propensity Score for Two Exposure Variable
`list_rules_party()`	Get rules from partykit object in rule fitting
`mc_integrand_q_g_r()`	Compute the Monte Carlo integrand for double integration in IsoXshift
`quad_integrand_q_g_r()`	Integrand Function for Q, g, r Model Adaptive Quadrature Double Integration in IsoXshift
`scale_to_original()`	Transform values from the unit interval back to their original scale
`scale_to_unit()`	Transform values by scaling to the unit interval
`shift_additive()`	Simple Additive Modified Treatment Policy
`simulate_complicated_mediation_data()`	DGP for testing IsoXshift with mediation (complicated!)
`simulate_data()`	DGP for testing IsoXshift without mediation
`simulate_mediation_data()`	DGP for testing IsoXshift with mediation
`tmle_exposhift()`	Targeted Minimum Loss Estimate of Counterfactual Mean under Stochastic Shift Intervention

Reference

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