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This is the 1.0 long-term-support release. It resolves the remaining
open issues, fixes a correctness bug in predictInterval()
for nested random effects, repairs and extends the
shinyMer() explorer, adds a new plotREimpact()
visualization, refreshes the documentation, and tidies the test suite
for low-maintenance, long-term use.
predictInterval() gained a
new.levels argument for unobserved groups. When
newdata contains a grouping level that was not in the
fitted model, the default new.levels = "zero" keeps the
historical behavior (the random effect is dropped, so the prediction
rests on the fixed effects plus residual variation). The new
new.levels = "draw" instead samples each unobserved group’s
effect from the estimated random-effect covariance
(VarCorr), so the interval reflects between-group
uncertainty — the analogue of
brms::posterior_predict(allow_new_levels = TRUE).
Observations sharing an unobserved level share the sampled effect. The
default is unchanged, so existing results are identical.plotREimpact() function for visualizing
REimpact() output (#84, #85). Plots the
weighted-average fitted value for each expected-rank bin of a grouping
factor, with confidence intervals, faceted by case. Passing a named
list of REimpact() results overlays them on shared
axes so the influence of different grouping factors (or the same factor
across models) can be compared directly — previously this required
hand-assembling the data frames. Uses a clean
theme_minimal() look.plotFEsim() now highlights significant fixed
effects (#85). Terms whose interval excludes the null line are
drawn in solid black and the rest in grey, matching the convention
already used by plotREsim(), on a cleaner
theme_minimal() canvas with labelled axes.shinyMer() gained a “Model Summary” tab
(#78). The interactive explorer now opens a dedicated tab
summarizing the fitted model: the modelInfo() overview
(observations, grouping factors, AIC, residual sigma), the original
model call, the fixed-effect estimates, the random effect
variances/correlations (VarCorr), and the number of levels
per grouping factor (ngrps).shinyMer() can now restrict the Random /
Average draw to a subset of the data (#32). When the “Random
Obs” or “Average Obs” scenario is selected, a sidebar control lets you
pick a model-frame variable and value to filter on; the chosen case is
drawn from that subset (via the existing
draw(..., varList = ) machinery), enabling much richer
on-the-fly exploration of specific cases.hsb data (student SES vs. school-mean SES) that separates
within-group and contextual effects, mirroring the easystats
modelbased “effects in context” article (cited). It
demonstrates FEsim()/ plotFEsim(),
wiggle() + predictInterval(), and the new
plotREimpact() on a single, self-contained model.# (so they rendered as body text) and two
sub-sections were both numbered “Step 3c”; these are corrected. The
predictInterval()-vs-bootMer() comparison
figure, which no longer matched the surrounding text, has been
regenerated with the current code and now shows the methods agreeing
closely. A latent display() call (from arm,
which was not attached) was qualified to
arm::display().inst/CITATION now derives its version and
year from the package metadata (rather than hard-coding a stale
version), lists all package authors, and a citation footer points users
to the methodological foundations — Gelman and Hill (2007), the
arm package’s sim(), and lme4
(Bates et al. 2015). The package-level help page
(?merTools) gained matching “Influences and
acknowledgements” and “References” sections.Fixed two shinyMer() defects in the
“Substantive Effect” tab. The fixed-effect impact (“wiggle”)
plot crashed for any non-numeric fixed effect
(object 'newvals' not found), and the numeric/factor branch
used class(x) %in% ... inside if(), which
errors with “the condition has length > 1” for multi-class objects
(e.g. ordered factors) on R >= 4.2. Both are corrected
(is.numeric() dispatch, and factor/character values now
wiggle across their observed levels), and the per-case faceting index is
computed correctly. The app’s data table was also migrated from the
deprecated shiny::renderDataTable() to
DT::renderDT(), the server now declares a
session argument, and the substantive-effect interval uses
a correct two-sided width.
Fixed non-reproducible predictInterval() for
partially-observed nested / interaction random effects (#124).
For a model such as y ~ 1 + (1 | a / b), a prediction frame
mixing observed and unobserved levels of the interaction grouping factor
(a:b) returned seed-dependent point estimates, and batch
predictions disagreed with row-by-row predictions. The mapping from the
random-effect model matrix back to a grouping level used
max.col(), which returns a column even for an all-zero row
(an unobserved interaction level) and breaks the resulting tie at
random — so an unobserved level silently borrowed a randomly chosen
observed level’s random effect. All-zero rows are now detected and
routed through the existing new-level path, which zeroes that
random-effect term’s contribution so the prediction falls back to the
fixed effects plus any observed higher-level random effects, exactly as
the out-of-sample warning describes. Observed-level predictions are
bit-for-bit unchanged.
Restored single RNG stream in
predictInterval(). The refactor of
predictInterval() into component helpers inadvertently had
each helper (simulate_random_effects(),
simulate_fixed_effects()) call set.seed(seed)
internally when invoked from the main function, resetting the RNG stream
mid-call. This produced numerically different output for any
user-supplied seed compared to CRAN releases. Fixed by passing
seed = NULL from predictInterval() to the
helpers; the outer set.seed(seed) now pins a single,
sequential stream (sigma → random effects → fixed effects → residuals)
exactly as before the refactor. Verified against
origin/master with a 16-case harness: 14 of 16 cases are
now bit-for-bit identical to master, and the remaining 2 differ only by
the intentional GLMM binomial-residual simulation fix below.
Fixed parse error caused by debug statements inserted
mid-function call in predictInterval() (now correctly
handles the function call structure)
Fixed GLMM residual variance simulation to properly return NULL for all GLMM/NLMM models (no Gaussian residual variance exists for discrete distributions)
Added !is.null(sigma_vec) checks in
combine_components() to handle cases where GLMMs don’t have
Gaussian residual variance to add (prevents errors with
sigma_vec = NULL)
Removed seed parameter from
simulate_residual_variance() (seeds are now handled at the
predictInterval() level for reproducibility)
Updated test expectations to reflect that GLMMs return NULL from
simulate_residual_variance()
Replaced bare subbars() with
reformulas::subbars() in the random-effect prediction path
to resolve the deprecation warning from lme4, which has migrated
subbars to the reformulas package.
Fixed predictInterval() on models with
multiple random-effect term blocks per grouping factor (#118).
Models using the double-bar syntax ((x + y || g)), explicit
splits ((1|g) + (0 + x|g)), or mixed correlated +
uncorrelated specs previously failed with
Error in dimnames(reMatrix) <- *vtmp* : 'dimnames' applied to non-array
because lme4::ranef(..., condVar = TRUE) returns
postVar as a list of per-block arrays in those cases, and
the level-filtering code assumed a 3-D array.
simulate_random_effects() now normalizes the list to a
single block-diagonal array (zero off-diagonals between uncorrelated
blocks, preserving full covariance within correlated blocks) before
indexing, so the mvtnorm::rmvnorm() path sees the
mathematically correct joint posterior covariance. Correlated-only
models are unaffected (guarded by is.list()
check).
Fixed averageObs() /
findFormFuns() on matrix-LHS models (#83).
averageObs(gm1) previously errored on two-column binomial
GLMMs such as
glmer(cbind(successes, failures) ~ ..., family = binomial)
because collapseFrame() attempted to take the mean of the
matrix response column, and a latent bug in the weights-selection path
tried to index a (weights) column that does not exist in
the model frame for cbind specifications. Matrix response columns are
now detected and dropped before averaging. Behavior
change: for matrix-LHS models the returned frame no longer
contains the response column; for scalar-LHS models the response is
still included as before. Callers that key off column count or column
names from averageObs() should treat matrix-LHS output as
predictors-only. The output remains valid newdata for
predict() and predictInterval(), which ignore
the response column.
rnorm(N, yhat, sigma) from gamma-distributed sigmasimulate_glmm_response() (binomial/poisson/gamma)include.resid.var = TRUE and GLMM with
type = "probability": Simulates from conditional
distribution (theoretically correct for discrete distributions)include.resid.var = TRUE and GLMM with
type = "linear.prediction": Returns linear predictor
without Gaussian noise (correct behavior - GLMMs don’t have additive
Gaussian noise)tests/comparisons/predictInterval-regression.R, a
standalone cross-version numeric regression harness. It pins a canonical
set of LMM and GLMM inputs — covering which,
level, stat, ignore.fixed.terms,
fix.intercept.variance, and single-row-newdata cases — and
serializes predictInterval() output to an RDS bundle so two
package versions can be compared bit-for-bit via a diff
subcommand. Invoke it whenever touching simulation internals to confirm
the change does not silently alter user-facing numeric output. See the
README for a worked git worktree-based workflow.tests/testthat/helper-seed.R that sets
RNGversion("4.1.0") and an explicit RNGkind().
This prevents silent stream differences across R-oldrel / R-release /
R-devel on CI.Config/testthat/parallel: false). File-level
set.seed() behaves unpredictably under parallel workers
with separate RNG state, which was the root cause of several of the
intermittent CI failures observed during the 0.9.0 development
cycle.11213 for consistency,
preserving a single differing seed in two tests that explicitly assert
that different seeds produce different results.thetaExtract() test in
test-helpers.R from a brittle numeric-equality check
against a value calibrated to an older seed, into behavioral assertions
(type, length, bounds).actions/checkout to @v5 and set
FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true on both workflows
to address the GitHub Actions Node.js 20 deprecation (scheduled for
2026-09-16).predictInterval() into modular component
functions for improved maintainability and testability. The main
function now orchestrates five internal helper functions:
simulate_residual_variance() - Draws residual standard
deviation samples from the posteriorsimulate_fixed_effects() - Simulates fixed effect
predictions with proper variance-covariance handlingsimulate_random_effects() - Simulates random effect
contributions for all grouping factorscombine_components() - Combines fixed, random, and
residual variance componentssummarise_predictions() - Computes prediction intervals
from simulation resultspredictInterval()
function from ~520 lines to ~180 lines while preserving complete
backward compatibilitypredictInterval() function now clearly shows the high-level
algorithm flowsimulate_random_effects()predictInterval()vctrs package impacting
dplyr::bind_rows() usage in REsim (#133)vcov in the
merDeriv packageaes_string() calls (#127)broom to
broom.mixed because of upstream package reorganizationaverageObs could not be calculated
when model weights were specified in the original model (closes
#110)subBoot now works with glmerMod objects as
wellreMargins a new function that allows the user to
marginalize the prediction over breaks in the distribution of random
effect distributions, see ?reMargins and the new
reMargins vignette (closes #73)merModLists is now supported using
the future.apply package and the future_lapply
functions, optionallySuggests fieldpredictInterval() would return
a data.frame of the wrong dimensions when predicting a single row of
observations for a glmrstanarm dependencies in
the package vignettedontrun to donttest for
long-running examples (CRAN compliance)merModList objects (#92)merModList functions now
apply the Rubin correction for multiple imputationfixef and ranef generics for
merModList objectsfastdisp generic for merModListsummary generic for merModListprint generic for merModListmerModList including
examples and a new imputation vignettemodelInfo generic for merMod objects
that provides simple summary stats about a whole modelstd.error of a multiply
imputed merModList when calling
modelRandEffStatsREimpact where some column names in
newdata would prevent the prediction intervals from being
computed correctly. Users will now be warned.wiggle where documentation incorrectly
stated the arguments to the function and the documentation did not
describe function correctlyreadme.rmd to package graphics with the R
package, per CRANmerMod
has functions specified in the formula, the draw and
wiggle functions will check for this and attempt to respect
these variable transformations. Where this is not possible a warning
will be issued. Most common transformations are respected as long as the
the original variable is passed untransformed to the model.predictInterval that allows the user to
return the full interval, the fixed component, the random component, or
the fixed and each random component separately for each observationrstanarm to the VignetteexpectedRank output more tidy like
and allow function to calculate expected rank for all terms at once
plyr and replace with dplyrvarList will now throw an error if
== is used instead of =predictInterval did not included random
effects in calculations when newdata had more than 1000
rows and/or user specified parallel=TRUE. Note: fix was to
disable the .paropts option for
predictInterval … user can still specify for
temporary backward compatibility but this should be either
removed or fixed in the permanent solution.predictInterval when
only specific levels of a grouping factor are in newdata
with the colon specification of interactionslme4 to ensure compatibility with
latest changes.dplyr tbl and tbl_df
objects to data.frames when they are passed to
predictInterval and issue a warningnewdata in
predictInterval before failing if coercion is
unsuccessfulpredictInterval
which includes better handling of large numbers of parameters and
simulations, performance tweaks for added speed (~10x), and parallel
backend support (currently not optimized)probit models and limited support for
other glmm link functions, with warning (still do not know
how to handle sigma parameter for these)blmer objects from the
blme packagemerModList object for lists of
merMod objects fitted to subsets of a dataset, useful for
imputation or for working with extremely large datasetsprint method for merModList to mimic
output of summary.merModVarCorr method for merModListn.sims for the
predictInterval function from 100 to 1,000 to give better
coverage and reflect performance increaselevel in
predictInterval to be 0.8 instead of 0.95 to reflect that
0.95 prediction intervals are more conservative than most users
needpredictInterval to allow prediction intervals
from glmer and lmer objectsFEsim and REsim to extract
distributions of model parametersshinyMer an interactive shiny
application for exploring lmer and glmer
modelsexpectedRank function to interpret the
ordering of effectsREimpact to simulate the impact of grouping
factors on the outcomedraw function to allow user to explore a
specific observationwiggle function for user to build a simulated
set of counterfactual cases to exploreThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.