Package index
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MLM_estimators() - Cluster RCT with multi-level modeling
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aggregate_data() - Aggregate data to the cluster level.
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aggregation_estimators() - Estimate ATEs for cluster RCT using data aggregation.
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as.data.frame(<clusterRCTstats>) - Cast cluster RCT info result to data.frame
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compare_methods() - Compare different estimators for a (possibly blocked) cluster randomized trial.
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count_covariates() - Calculate number of level 1 and level 2 variables
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describe_clusterRCT() - Describe a cluster RCT dataset
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design_based_estimators() - Estimate ATE via design-based approach
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design_based_estimators_individual() - Estimate ATE via design-based approach
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fakeBrokeCRT - Fake Broken Cluster Randomized Trial data
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fakeCRT - Fake Cluster Randomized Trial data
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fakeCRT2 - Fake Cluster Randomized Trial data (set 2)
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gee_estimators() - Estimate ATEs for a cluster RCT using a GEE approach
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generate_all_interacted_estimates() - Average block-level estimates to get overall ATE
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get_estimand() - Get the estimand for a given method.
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get_structure() - Given data, return a data frame with the structure of the data
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identify_singleton_blocks() - Identify all tx and all co blocks
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interacted_linear_model_estimators() - Estimate ATEs for a cluster RCT using linear model
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is.clusterRCTstats() - Is object a clusterRCTstats object?
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is_nested() - Check if one grouping variable nested in another
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linear_model_estimators() - Estimate ATEs for a cluster RCT using linear model
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make_block_table() - Make table of statistics by block
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make_regression_formula() - Make a canonical regression formula, possibly with control variables.
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method_characteristics() - Get table of characteristics of all the methods implemented in this package.
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middleton_aronow_estimator() - Estimate ATE via Middleton & Aronow unbiased approach
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patch_data_set() - Fill in missing values and convert data to canonical form
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patch_singleton_blocks() - Drop or combine all-tx or all-co blocks
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print(<clusterRCTstats>) - Pretty print result from describe cluster RCT call
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rerandomize() - Return new treatment assignment based on passed one.
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run_rerandomize_simulation() - Run a finite sample simulation by permuting treatment assignment labels within each block.
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summarize_simulation_results() - Summarize set of compare_methods results across estimators