- The power analysis vignette now includes an "Examining other metrics of performance" section showing bias, simulation variance, model-based variance, relative RMSE, and coverage curves alongside the existing power curves, and the summary chunk now uses
summarize_simulation()directly to demonstrate the full set of metrics it returns. Bias and RMSE are reported as a percentage of the outcome mean. summarize_simulation()now falls back to an uncorrected 95% CI when no t-statistics are available, so coverage andcorrect_rejection_rateare populated for methods like ASCM and CSA whose default inference does not produce a t-statistic.
sim_correction_factor(),sim_coverage(), andsim_correct_rejection_rate()now returnNA_real_(rather thannumeric(0)) when no test statistics are available, replacing a confusing zero-length result with an explicit missing value.
- Updated the autoeffect result extractors (
.extract_results_autoeffectand.se_adjust_cluster_autoeffect) to read the renamed tidymodels-style columns fromautoeffect::cumulative_effects()(lag,estimate,std.error,statistic,p.value). Requires autoeffect >= 0.2.11. (#43)
optic_simulation()now stops early with an informative message when the input data contains NAs in any required column:unit_var,time_var, the outcome (formula LHS) of any model, or any covariate (formula RHS minus treatment-construct terms) referenced by any model. Treatment columns are excluded because they are simulated by optic. This replaces a class of cryptic downstream errors (e.g. autoeffect's "spec_strategy out of sync" message) with a single upfront error that names the offending columns. (#41)
- Added a power analysis vignette demonstrating simulation-based power analysis for TWFE, debiased AR, ASCM, and CSA estimators using the
overdosesdataset. Addsaugsynthandggplot2toSuggests. (#40)
- Added simulation performance statistics functions:
sim_rejection_rate(),sim_bias(),sim_mse(),sim_correction_factor(),sim_coverage(),sim_type_s_error(),sim_correct_rejection_rate(), andsummarize_simulation()for computing Type I error, power, bias, coverage, and related metrics from Monte Carlo simulation output.
- Autoeffect model p-values now use the t-distribution (via
cumulative_effects()) instead of manually computing with the normal distribution. This fixes inflated Type I error rates for DAR models. (#38)
- Clustered standard error support for autoeffect models, delegating to
autoeffect::cumulative_effects(cluster=). Unsupported SE adjustments (huber, arellano) now warn and skip. (#37)
- Moved SE adjustment behavior into the model type registry, replacing if-else chain in
noconf_postmodel(). (#37)
- Autoeffect models now extract covariates from the formula RHS like reg/autoreg models, eliminating separate
x_formulaorcov_namesparameters. Deprecation warnings added for the old interface. (#36)
- Restored
match.arg()for type and call validation innew_optic_model()constructor. (#35)
- Added autoeffect model type support for debiased autoregressive treatment effect estimation. New model type
"autoeffect"integrates with theautoeffectpackage. (#34)