Debiased Score Tests for Goodness of Fit and Model Comparison R package dScoreTest version 1.0.0 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>. Software Imports: grf, mgcv Suggests: knitr, rmarkdown, speff2trial, testthat (>= 3.0.0) F. Richard Guo <ricguo@umich.edu> Comprehensive R Archive Network (CRAN) F. Richard Guo [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-2081-7398>), Aditya Dhawan [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=dScoreTest/LICENSE) 2026-09-02 application/tgz https://CRAN.R-project.org/package=dScoreTest doi:10.32614/CRAN.package.dScoreTest en-US