Work

SRMERS: Semi-Parametric Shape-Restricted Fixed/Mixed Effect(s) Regression Spline

Year: 2026

Type: dataset

Abstract: Select the most suitable shape to describe the relationship between the exposure and the outcome among increasing, decreasing, convex, and concave shapes (Yin et al. (2021) <doi:10.1007/s13571-020-00246-7>); estimate the direct and indirect effects with prior knowledge on the relationship between the mediator and the outcome with binary exposure (Yin et al. (2024) <doi:10.1007/s13571-024-00336-w>); estimate the direct and indirect effects using linear regression-based approach (VanderWeele (2015, ISBN:9780199325870)).

DOI: 10.32614/cran.package.srmers

Authors: Qing Yin, Shyamal Das Peddada, Jennifer Joan Adibi, Jong-Hyeon Jeong

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W7123865834

Cites: 2

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