Choosing lmmix and related R packages

Purpose

Several mature R packages fit Gaussian mixed or repeated-measures models, but their model and inference boundaries differ. This vignette places lmmix within that ecosystem. It is a selection guide, not a claim that one package is uniformly preferable.

The comparison is limited to documented capabilities that overlap with the current lmmix scope. For definitive details, consult the cited software papers and the documentation for the installed package versions.

Main distinctions

lme4 is designed for mixed-effects models and uses sparse matrix methods for random-effects computations (Bates et al. 2015). lmerTest adds Satterthwaite and Kenward-Roger fixed-effect inference to fitted lmer models (Kuznetsova et al. 2017). This combination is a strong choice when the covariance is driven by random effects and the residual errors are conditionally independent.

nlme supports random effects together with within-group correlation and variance functions (Pinheiro and Bates 2000; Pinheiro et al. 2025). It is a mature choice when those model structures and its inference conventions meet the analysis requirements.

mmrm targets marginal mixed models for repeated measures, with structured within-subject covariance and small-sample inference, but without Gaussian random effects in the fitted model (Sabanes Bove et al. 2026).

lmmix directly evaluates its profiled Gaussian ML or REML objective for models that may combine random effects and one structured residual covariance. Its post-processing layer supplies Satterthwaite or Kenward-Roger inference, type III tests, estimated marginal means, and pairwise contrasts from the same fitted object.

Capability matrix

The table summarizes interfaces rather than every possible extension. In particular, emmeans interoperates with several of these model classes and can provide a common marginal-means workflow.

Choosing a package

Use lme4 with lmerTest when the scientific covariance model is adequately represented by random effects with conditionally independent residual errors, especially for large sparse crossed designs.

Use nlme when you need its established combination of random effects, correlation structures, and estimated variance functions, or when an existing validated workflow depends on its inference conventions.

Use mmrm when the estimand and covariance specification are marginal, the primary dependence is within subject, and no latent Gaussian random effect is required.

Consider lmmix when the same model must contain Gaussian random effects and a supported structured residual covariance, and the analysis requires its integrated Satterthwaite or Kenward-Roger, type III, and marginal-means layer. The validation vignette documents the numerical comparisons that currently support this use.

Computational boundary

lmmix 0.4.0 detects independent connected components induced jointly by the random and repeated grouping structures. It factors those covariance blocks separately during likelihood evaluation. This favors subject-level and nested designs with many modest blocks.

The fitted object still retains a complete dense marginal covariance, and some inference calculations remain dense. Crossed random effects can connect all observations into one component. Therefore, lmmix does not currently replace the sparse large-scale role of lme4, and no universal maximum sample size is claimed. Benchmark the intended design, covariance structure, and inference request before committing to a large analysis.

Current boundaries that affect selection

lmmix currently fits univariate Gaussian responses only. It rejects rank-deficient fixed-effect matrices, estimates one repeated covariance structure per model, and does not estimate residual variance strata analogous to nlme::varIdent(). Known relative precision weights are supported. Its prediction intervals include fitted residual variance but not uncertainty in empirical random effects. The maintained list of limitations and planned work is available in the roadmap.