blme: Bayesian Linear Mixed-Effects Models for R

blme is an R package for maximum a posteriori estimation of linear and generalized linear mixed-effects models in a Bayesian framework. It implements the methods of Chung et al. (2013) and extends the popular 'lme4' package by Bates et al. (2015). The package was authored by Vincent Dorie.

Use Cases

• Fit Bayesian linear mixed-effects models based on the implemented maximum a posteriori estimation.
• Perform Bayesian generalized linear mixed-effects modeling for non-Gaussian response data.
• Extend standard 'lme4' model fits with Bayesian priors and regularization.
• Apply the specific methodological framework from Chung et al. (2013) for penalized likelihood estimation.

Strengths

• Implements a specific, peer-reviewed methodological framework from Chung et al. (2013).
• Directly extends the widely-used and tested 'lme4' package for mixed-effects modeling.
• Provides a Bayesian estimation approach for a common class of statistical models.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Vincent Dorie

Collection Method

Software package implementing statistical methods.