lme4 <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">
An R package for fitting linear, generalized, and nonlinear mixed-effects models using S4 classes and RcppEigen.
What is lme4 <img class="emoji" alt="heart" src="https://cdn.jsdelivr.net/gh/qinwf/awesome-R@3c66da6e291bcc0520b1649125b0bed750896a9a/heart.png" height="20" align="absmiddle" width="20">?
lme4 is an R package designed for fitting and analyzing mixed-effects models, including linear, generalized linear, and nonlinear variants. It solves the problem of modeling data with hierarchical or grouped structures, where observations are correlated, by providing efficient algorithms and flexible random effects specifications. The package is widely used in fields like psychology, ecology, and social sciences for advanced statistical analysis.
Statisticians, data scientists, and researchers in fields like psychology, biology, and social sciences who need to analyze hierarchical or longitudinal data with mixed-effects models. It is also suitable for R developers and analysts working on complex experimental designs.
Developers choose lme4 for its computational efficiency with large datasets, flexibility in handling nested and crossed random effects, and support for a wide range of model types. Its integration with RcppEigen ensures high performance, while the comprehensive feature set makes it a go-to tool for advanced mixed-effects modeling in R.
Overview
Mixed-effects models in R using S4 classes and methods with RcppEigen
Use Cases
Best For
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
