REVIEW 1 minor 6 references
regcorr: An R Package for Regression Models of Pearson Correlation Coefficients
T0 review · 0 major / 1 minor · reviewed 2026-06-28 · grok-4.3
Pith's one-line read The regcorr package provides regression models that link Pearson correlation coefficients to covariates for bivariate responses.
desk verdict This is a software paper that packages the 2023 Dufera et al. likelihood framework into a lightweight R package for regressing bivariate correlations on covariates. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The regression link that expresses the correlation parameter as a function of covariates, maximized via Newton-Raphson on the bivariate likelihood.
What would settle it
Simulate data from the model with known nonzero covariate effects on correlation, then check whether the package recovers those effects and their significance at the rates implied by the bootstrap procedure.
Extended reading notes
Core claim
The paper claims that the regcorr package implements a likelihood-based regression in which the Pearson correlation coefficient is expressed as a function of a linear predictor of covariates, supports bivariate normal and bivariate Bernoulli responses, performs estimation via Newton-Raphson, supplies data generators, and provides bootstrap-based inference for the significance and power of covariate effects on the correlation.
Load-bearing premise
The likelihood framework accurately describes how covariates affect the correlation parameter and the package implements that framework without errors.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript describes the regcorr R package, which implements regression models linking a Pearson correlation coefficient to a linear predictor of covariates. It supports bivariate normal and bivariate Bernoulli responses, uses Newton-Raphson for maximum-likelihood estimation, includes data generators for simulations, and provides a bootstrap subroutine for assessing covariate effects on correlations. The implementation follows the likelihood framework of Dufera, Liu, and Xu (2023) and is distributed on CRAN with minimal dependencies and no compiled code.
Significance. If the implementation faithfully realizes the cited framework, the package supplies a lightweight, accessible tool for modeling heterogeneous associations in bivariate data. The inclusion of simulation generators and bootstrap routines directly supports reproducibility of power and significance assessments, which is a practical strength for applied work in fields where correlation strength varies with covariates.
minor comments (1)
- The abstract states that the package 'exposes' the Dufera-Liu-Xu framework; a brief explicit statement in the computational-design section confirming that the score equations and Hessian match those in the 2023 reference would strengthen the claim of faithful implementation.
Simulated Author's Rebuttal
We thank the referee for their positive assessment of the manuscript, the accurate summary of the regcorr package, and the recommendation to accept. We are pleased that the practical strengths for applied work in modeling heterogeneous correlations were recognized.
Circularity Check
No significant circularity; external framework implemented
full rationale
The paper is a software description whose core contribution is an R package exposing the likelihood framework of Dufera, Liu, and Xu (2023). This is an external citation with non-overlapping authors; the package adds Newton-Raphson routines, data generators, and bootstrap tools but does not derive or redefine the underlying statistical model. No equations, ansatzes, or uniqueness claims reduce to self-citation chains or fitted inputs by construction. The derivation chain is therefore self-contained against the cited external reference.
Assumptions & free parameters
Cite this review
Pith. "Pith review of regcorr: An R Package for Regression Models of Pearson Correlation Coefficients." pith.science (2026). https://pith.science/paper/VO22R4EV
@misc{pith2026260605676,
author = {Pith},
title = {Pith review of: regcorr: An R Package for Regression Models of Pearson Correlation Coefficients},
year = {2026},
howpublished = {\url{https://pith.science/paper/VO22R4EV}},
note = {Machine review of arXiv:2606.05676}
}
read the original abstract
Pearson's correlation coefficient is commonly used as a single-number summary of association between two responses. In many applications, however, the strength of association is itself heterogeneous and may vary with demographic, biological, experimental, or environmental covariates. The regcorr package implements regression models in which a Pearson correlation coefficient is linked to a linear predictor of covariates. The package supports bivariate normal responses and bivariate Bernoulli responses, provides Newton-Raphson estimation routines, includes data generators for simulation studies, and supplies a bootstrap-based subroutine for assessing the significance and power of covariate effects. The implementation follows the likelihood-based framework of Dufera, Liu, and Xu (2023) and exposes it through a lightweight R interface with no compiled code and minimal dependencies. This paper describes the statistical model, the computational design of regcorr, reproducible usage examples, and practical guidance for interpreting covariate-dependent correlations. The package is available from the Comprehensive R Archive Network at https://CRAN.R-project.org/package=regcorr under the MIT license.
Reference graph
Works this paper leans on
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[1]
Dufera, A. G., Liu, T., and Xu, J. (2023). Regression mode ls of Pearson correlation coefficient. Statistical Theory and Related Fields , 7(2), 97–106. doi:10.1080/24754269.2023.2164970
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[2]
Bartlett, R. F. (1993). Linear modelling of Pearson’s pr oduct moment correlation coefficient: An application of Fisher’s z-transformation. Journal of the Royal Statistical Society: Series D, 42(1), 45–53
1993
-
[3]
Liu, Q., Li, C., Wang, V., and Shepherd, B. E. (2018). Cova riate-adjusted Spearman rank correlation with probability-scale residuals. Biometrics, 74(2), 595–605
2018
-
[4]
Qaqish, B. F. (2003). A family of multivariate binary dis tributions for simulating correlated binary variables with specified marginal means and correlat ions. Biometrika, 90(2), 455–463
2003
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[5]
K., and McFadden, D
Newey, W. K., and McFadden, D. (1994). Large sample estim ation and hypothesis testing. In Handbook of Econometrics , volume 4, 2111–2245. Elsevier
1994
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[6]
R: A Language and Environment for Statistical Computing
R Core Team. R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. 8
Reviewed June 28, 2026 · model on record in the stance chip above.
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