Auditing a Frozen Neural Goodness-of-Fit Test for Logistic Regression: Validity, Certified Consistency and Local Power of DeepGOF-1

Abstract

Reproducibility archive (version 2) for the paper Auditing a Frozen Neural Goodness-of-Fit Test for Logistic Regression: Validity, Certified Consistency and Local Power of DeepGOF-1. Version 1.0.0 of the DeepGOF-1 archive is 10.5281/zenodo.22113220. DeepGOF-1 is a goodness-of-fit test for logistic regression whose statistic is a convolutional network trained once, offline, on simulated departures and shipped frozen (18,273 parameters). The network reads the fitted model's residual map, a 6×6 grid of standardized residual sums over the ranks of the two strongest covariates, and the p-value is the rank of the observed score inside the analyst's own parametric bootstrap (B = 199). Contents. R/, inst/extdata/: the deployable test in base R with the frozen weights; ties among covariate values are broken at random, once per call, as in ebrahim.gof 2.8.0. training/: the training corpus (8,400 labelled maps), its generator, the network and training loop, the export to R and the cross-language check. benchmark/: self-contained harnesses for the two simulation studies (the 60-cell design grid and the four settings of Liu et al. 2024), with a smoke test that regenerates datasets and reproduces the deposited p-values exactly. results/: every per-replicate p-value behind the benchmark tables and figures, and the FDIC application's results. theory/: scripts, raw simulation output and notes behind the theoretical results of the paper and of its Online Resource 1 (validity and the finite-sample size bound, the data-computable calibration-gap bound and its rate, conditional consistency and the blind cone, local asymptotic power and covariates that carry no signal, finite-sample power certificates, the Neyman–Pearson optimality gap, one corrupted record at small n, the added comparison tests, the simulation with two active covariates among ten, the real-design check, the tie-breaking study). bagoft/: the paired comparison with BAGofT: every per-replicate p-value, timings, the verification of the fast implementation and the one-line repair the released BAGofT code needs for a single-covariate model. application/fdic/: the bank-failure application, rebuilt from two keyless FDIC BankFind API calls. tables/: one script that recomputes every generated number of the paper's tables from the deposited files. figures/: the scripts of the paper's figures. README.md maps every table and figure of the paper and every result of Online Resource 1 to its files. The deployable test alone is also distributed as deepgof1() in the R package ebrahim.gof (version 2.8.0 or later). Code is MIT-licensed; data, results, corpora and weights are CC BY 4.0 (see LICENSE).