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README.md

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-license: bigscience-openrail-m
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+---
+license: bigscience-openrail-m
+size_categories:
+- 1K<n<10K
+---
+
+# rerandomization-benchmarks
+
+Replication dataset for the benchmark and diagnostic analyses in  
+**Goldstein, Jerzak, Kamat & Zhu (2025), _“fastrerandomize: Fast Rerandomization Using Accelerated Computing”_.**
+
+This repository hosts the aggregated simulation and benchmark results produced by the scripts:
+
+- `FastSRR_VaryNAndD.R` (simulation & benchmarking)
+- `FastRR_PlotFigs.R` (aggregation & plotting)
+
+from the accompanying software repository for the `fastrerandomize` R package.
+
+---
+
+## Project & Paper Links
+
+- **Paper (preprint):** <https://arxiv.org/abs/2501.07642>  
+- **Software repository:** <https://github.com/cjerzak/fastrerandomize-software>  
+- **Package name:** `fastrerandomize` (R)
+
+---
+
+##  What’s in this dataset?
+
+The dataset contains **simulation-based benchmark results** used to compare:
+
+- Different **hardware backends**  
+  - `M4-CPU` (Apple M4 CPU, via JAX/XLA)  
+  - `M4-GPU` (Apple M4 GPU / METAL)  
+  - `RTX4090` (NVIDIA CUDA GPU)  
+  - `BaseR` (non-accelerated R baseline)  
+  - `jumble` (the `jumble` package as an alternative rerandomization implementation)
+
+- Different **problem scales**  
+  - Sample sizes: `n_units ∈ {10, 100, 1000}`  
+  - Covariate dimensions: `k_covars ∈ {10, 100, 1000}`  
+  - Monte Carlo draw budgets: `maxDraws ∈ {1e5, 2e5}`  
+  - Exact vs approximate linear algebra: `approximate_inv ∈ {TRUE, FALSE}`  
+
+- Different **rerandomization specifications**  
+  - Acceptance probability targets (via `randomization_accept_prob`)  
+  - Use or non-use of fiducial intervals (`findFI`)  
+
+Each row corresponds to a particular Monte Carlo configuration and summarizes:
+
+1. **Design & simulation settings** (e.g., `n_units`, `k_covars`, `maxDraws`, `treatment_effect`)  
+2. **Performance metrics** (e.g., runtime for randomization generation and testing)  
+3. **Statistical diagnostics** (e.g., p-value behavior, coverage, FI width)  
+4. **Hardware & system metadata** (CPU model, number of cores, OS, etc.)
+
+These data were used to:
+
+- Produce the **runtime benchmark figures** (CPU vs GPU vs baseline R / `jumble`)  
+- Compute **speedup factors** and **time-reduction summaries**  
+- Feed into macros such as `\FRRMaxSpeedupGPUvsBaselineOverall`, `\FRRGPUVsCPUTimeReductionDthousandPct`, etc., which are then read from `./Figures/bench_macros.tex` in the paper.
+
+---
+
+##  Files & Structure
+
+*(Adjust this section to match exactly what you upload to Hugging Face; here is a suggested structure.)*
+
+- `VaryNAndD_main.csv`  
+  Aggregated benchmark/simulation results across all configurations used in the paper.
+
+- `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.  
+- `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.  
+- `Hardware` – Hardware / implementation label, recoded in `FastRR_PlotFigs.R` to:  
+  - `"M4-CPU"`  (was `"CPU"`)  
+  - `"M4-GPU"`  (was `"METAL"`)  
+  - `"RTX4090"` (was `"NVIDIA"`)  
+  - `"jumble"`  (was `"AltPackage"`)  
+  - `"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:
+
+- `t_GenerateRandomizations` – Time (seconds) spent generating randomization pools.  
+- `t_RandomizationTest` – Time (seconds) spent on randomization-based inference.  
+- `randtest_time` – Duplicated / convenience version of `t_RandomizationTest` in some contexts.  
+- `sysname`, `machine`, `hardware_version` – OS and machine-level metadata (`Sys.info()`).  
+- `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
+
+### In Python (via `datasets`)
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("YOUR_USERNAME/rerandomization-benchmarks", split="train")
+print(ds)
+print(ds.column_names)
+````
+
+Or directly with `pandas`:
+
+```python
+import pandas as pd
+
+df = pd.read_csv("VaryNAndD_main.csv")
+df.head()
+```
+
+### In R
+
+```r
+library(data.table)
+
+bench <- fread("VaryNAndD_main.csv")
+str(bench)
+
+# Example: reproduce summaries by hardware and problem size
+bench[, .(
+  mean_t_generate = mean(t_GenerateRandomizations, na.rm = TRUE),
+  mean_t_test     = mean(t_RandomizationTest, na.rm = TRUE)
+), by = .(Hardware, n_units, k_covars, maxDraws, approximate_inv)]
+```
+
+You can then:
+
+* Recreate runtime comparisons across hardware platforms.
+* Explore how acceptance probability, dimension, and sample size interact.
+* Use the timing information as inputs for your own design/planning calculations.
+
+---
+
+## Citation
+
+If you use this dataset, **please cite the main paper**:
+
+```bibtex
+@misc{goldstein2025fastrerandomizefastrerandomizationusing,
+      title        = {fastrerandomize: Fast Rerandomization Using Accelerated Computing},
+      author       = {Rebecca Goldstein and Connor T. Jerzak and Aniket Kamat and Fucheng Warren Zhu},
+      year         = {2025},
+      eprint       = {2501.07642},
+      archivePrefix= {arXiv},
+      primaryClass = {stat.CO},
+      url          = {https://arxiv.org/abs/2501.07642}
+}
+```
+
+If you refer specifically to this Hugging Face dataset (e.g., for meta-analysis or benchmarking), you may also add a line such as:
+
+> “We use the `rerandomization-benchmarks` dataset (Hugging Face) accompanying Goldstein et al. (2025).”
+
+---
+
+## ⚖️ License & Terms of Use
+
+* The **code** in the associated repository is licensed under **GPL-3.0**.
+* The **data** in this dataset are simulation outputs derived from that code and are provided for **research and educational use**.
+
+Please open an issue in the GitHub repository or contact the corresponding author if you have questions about reuse in other contexts.
+
+---
+
+## Contact
+
+For questions about the paper, software, or dataset:
+
+* Corresponding author: **Connor T. Jerzak** – [[email protected]](mailto:[email protected])
+* Issues & contributions: please use the GitHub repository issues page for `fastrerandomize`.
+
+---