Update README.md

README.md

@@ -65,24 +65,19 @@ These data were used to:
 - `VaryNAndD_main.parquet` (optional)  
   Parquet version of the same table (faster to load in many environments).
 
-- `CODE/` (optional, if you choose to include)  
-  - `FastSRR_VaryNAndD.R`  
-  - `FastRR_PlotFigs.R`  
-  Exact R scripts used to generate the raw CSV files and figures.
-
 ---
 
 ## Main Columns (schema overview)
 
 Below is an overview of the most important columns you will encounter in `VaryNAndD_main.*`.  
-Names are taken directly from the R code (especially the `res <- as.data.frame(cbind(...))` section in `FastSRR_VaryNAndD.R` and the subsequent processing in `FastRR_PlotFigs.R`).
 
 ### Core design variables
 
 - `treatment_effect` – Constant treatment effect used in the simulation (e.g., `0.1`).  
 - `SD_inherent` – Baseline SD of potential outcomes (`SD_inherent` in `GenerateCausalData`).  
 - `n_units` – Total number of experimental units.  
-- `k_covars` – Number of covariates.  
+- `k_covars` – Number of covariates.
+- `prob_accept` – Target acceptance probability (`randomization_accept_prob`).  
 - `maxDraws` – Maximum number of candidate randomizations drawn (e.g., `1e5`, `2e5`).  
 - `findFI` – Logical (`TRUE`/`FALSE`): whether fiducial intervals were computed.  
 - `approximate_inv` – Logical (`TRUE`/`FALSE`): whether approximate inverse / stabilized linear algebra was used.  
@@ -94,44 +89,6 @@ Names are taken directly from the R code (especially the `res <- as.data.frame(c
   - `"BaseR"`   (pure R baseline)  
 - `monte_i` – Monte Carlo replication index.
 
-### Rerandomization configuration
-
-- `prob_accept` – Target acceptance probability (`randomization_accept_prob`).  
-- `accept_prob` – Same or related acceptance probability field (used within plotting code).  
-
-### Randomization-test & FI summaries
-
-These are typically aggregated across Monte Carlo replications and/or over covariate-dimension strata:
-
-- `p_value` – Mean p-value across replications, by `k_covars` and acceptance probability.  
-- `p_value_se` – Standard error of the above p-value estimates.  
-- `min_p_value` – Average minimum achievable p-value (`1/(1 + n_accepted)`), reflecting how many accepted randomizations were available.  
-- `number_successes` – Average number of accepted randomizations (per configuration).  
-- `tau_hat_mean` – Mean estimated treatment effect across replications.  
-- `tau_hat_var` – Variance of the estimated treatment effect across replications.  
-- `FI_lower_vec`, `FI_upper_vec` – Mean lower/upper endpoints of fiducial intervals.  
-- `FI_width` – Median width of the fiducial interval (where available).  
-- `truth_covered` – Average indicator for whether the interval covered the true treatment effect.
-
-### Estimator-selection diagnostics (acceptance-prob “minimization”)
-
-These summarize how well different strategies for choosing the optimal acceptance probability perform:
-
-- `colMeans_mean_p_value_matrix`, `colMeans_median_p_value_matrix`, `colMeans_modal_p_value_matrix` –  
-  Average p-value summaries used to define estimators of the “best” acceptance probability.  
-
-- `bias_select_p_via_mean`, `rmse_select_p_via_mean` –  
-  Bias and RMSE when selecting the acceptance probability based on the mean p-value.  
-
-- `bias_select_p_via_median`, `rmse_select_p_via_median` –  
-  Bias and RMSE when selecting the acceptance probability based on the median p-value.  
-
-- `bias_select_p_via_mode`, `rmse_select_p_via_mode` –  
-  Bias and RMSE when selecting the acceptance probability based on the modal p-value.  
-
-- `bias_select_p_via_baseline`, `rmse_select_p_via_baseline` –  
-  Bias and RMSE of a naive baseline strategy (e.g., choosing acceptance probability at random), used as a comparison.
-
 ### Timing and hardware metadata
 
 Timing quantities are used to produce the benchmark plots in the paper:
@@ -143,8 +100,6 @@ Timing quantities are used to produce the benchmark plots in the paper:
 - `nCores` – Number of CPU cores from `benchmarkme::get_cpu()`.  
 - `cpuModel` – CPU model name from `benchmarkme::get_cpu()`.
 
-> **Note:** Because the scripts were developed iteratively, some columns may appear duplicated or with slightly redundant naming (e.g., multiple `randtest_time`-like fields). For replication of the paper’s figures, these are harmless; users may drop redundant columns as needed.
-
 ---
 
 ## How to use the dataset
@@ -193,7 +148,7 @@ You can then:
 
 ## Citation
 
-If you use this dataset, **please cite the main paper**:
+If you use this dataset, please cite the paper:
 
 ```bibtex
 @misc{goldstein2025fastrerandomizefastrerandomizationusing,