dScoreTest
Debiased Score Tests for Goodness of Fit and Model Comparison
Debiased (Neyman-orthogonalized) score tests for assessing whether a semiparametric or parametric regression model is well-specified and for comparing nested models. The test employs a hunt-and-test strategy: on a held-out hunt sample, it fits the null model and uses machine learning to find a direction in which the null model's score seems positive; on an independent test sample, it assesses the significance of the score in the hunted direction. The test employs orthogonalization to eliminate the bias from estimating the null model, yielding a test statistic that is asymptotically standard normal under the null without requiring a parametric form for the alternative. Methods are provided for 'glm', 'lm' and 'mgcv::gam' fits as well as for detecting heterogeneous treatment effects. The methodology is described in Dhawan, Guo and Shah (2026) <doi:10.48550/arXiv.2607.28861>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | dScoreTest_1.0.0.tar.gz |
382.5 KiB |
1.0.0 |
rolling linux/noble R-4.5 | dScoreTest_1.0.0.tar.gz |
382.4 KiB |
1.0.0 |
rolling source/ R- | dScoreTest_1.0.0.tar.gz |
264.8 KiB |
1.0.0 |
latest linux/jammy R-4.5 | dScoreTest_1.0.0.tar.gz |
382.5 KiB |
1.0.0 |
latest linux/noble R-4.5 | dScoreTest_1.0.0.tar.gz |
382.4 KiB |
1.0.0 |
latest source/ R- | dScoreTest_1.0.0.tar.gz |
264.8 KiB |
1.0.0 |
2026-04-23 source/ R- | dScoreTest_1.0.0.tar.gz |
0 B |