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
| Analysis requirement | lmmix |
lme4 and lmerTest
|
nlme |
mmrm |
|---|---|---|---|---|
| Gaussian random intercepts or slopes | Yes | Yes | Yes | No |
| Structured residual correlation |
id, cs, ar1,
toep, toep(k), un
|
Conditionally independent residuals | Correlation structures documented by
nlme
|
Repeated-measures covariance structures documented by
mmrm
|
| Random effects and correlated residuals in one model | Yes | No through the standard lmer
interface |
Yes | No |
| Satterthwaite inference | Yes | Yes through lmerTest
|
Not the package’s standard convention | Yes |
| Kenward-Roger inference | Yes for REML | Yes with supporting packages | Not the package’s standard convention | Yes |
| Type III tests and marginal means in the same package | Yes | Tests through lmerTest; means commonly
through emmeans
|
Commonly assembled from multiple interfaces | Model inference plus emmeans
interoperability |
| Large sparse crossed-effect focus | No | Yes | No general sparse crossed-effect claim | Subject-block computation |
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.