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5/10 means Regression Models and Utilities for Repeated Measures and Panel Data • panelr is borderline visible. AI bots can crawl your site but your structured-data signals are thin. You are at risk of being skipped when buyers ask AI for a recommendation.
Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via Stan. Supports estimation of asymmetric effects models via
Industry: Technology
panelr.jacob-long.com1
Structured Data
8
Content Structure
5
Entity Clarity
3
E-E-A-T Signals
8
Technical AEO
5
AI Discoverability
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Scored by Engagemii on June 2, 2026. Methodology: engagemii.com/aeo/methodology
Source URL: https://engagemii.com/aeo/brands/panelr-jacob-long
Cite this score: Engagemii (2026). "AEO Score for Regression Models and Utilities for Repeated Measures and Panel Data • panelr." Retrieved from https://engagemii.com/aeo/brands/panelr-jacob-long
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