Package index
Selecting covariates
Post-double-selection LASSO: the outcome within each treatment arm, and each arm indicator on the candidates.
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lasso_select_covariates() - Select covariates by post-double-selection LASSO
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lasso_select_one() - Select covariates that predict one variable
Adjusted estimators
Fit the selected set. Each falls back to a simpler specification when selection returns nothing or the adjusted fit is degenerate.
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lm_lin_lasso() - Lin estimator on LASSO-selected covariates
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lm_robust_lasso() - Additive robust regression on LASSO-selected covariates
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lm_moderator_lasso() - Treatment-by-moderator model with LASSO-selected covariates
Attrition
Whether treatment predicts who is missing, allowing the pattern to differ across covariates.
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check_attrition_lasso() - Test for differential attrition with LASSO-selected covariates
What actually ran
A fallback changes what the estimate is, so it is recorded rather than hidden. Which accessor you need depends on whether you still hold the fit.
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adjustment()selected_covariates()fallback_reason() - What adjustment did a fit actually use?
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fallback_summary() - Summarize what a collection of fits actually did
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fallback_log()reset_fallback_log() - Record of what every fit did
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estimatrToolsestimatrTools-package - estimatrTools: Data-Driven Covariate Adjustment for the 'estimatr' Estimators