Randomeffectsmodeller

Randomeffectsmodeller is a software library and toolkit designed to fit random effects models, including linear mixed models and generalized linear mixed models. It emphasizes straightforward specification of fixed and random effects, support for diverse data structures, and transparent estimation procedures.

Key features include support for maximum likelihood and restricted maximum likelihood estimation, as well as Bayesian inference via MCMC or Laplace approximations. The toolkit handles unbalanced designs and supports both nested and crossed random effects, along with various link functions for GLMMs. It accepts data in standard tabular formats and integrates with common data science ecosystems, providing options for data input, model specification, and result retrieval.

Model specification typically uses a formula-like syntax to define fixed effects and random effects structure. The software returns estimates for fixed effects, variance components, and conditional modes of random effects, along with fitted values and predictions. Diagnostic tools include likelihood-based tests, information criteria such as AIC and BIC, residual analyses, and cross-validation facilities to compare competing models.

Implementation and ecosystem aspects include bindings or interfaces for popular languages, such as Python and R, enabling seamless use with data frames and existing analysis pipelines. Results can be exported in common formats (CSV, JSON) for reporting, reproducibility, or downstream processing. Randomeffectsmodeller is distributed as an open-source project with a permissive license, fostering community contributions and ongoing development.

See also: mixed effects models, hierarchical modeling, Bayesian hierarchical modeling.