Addresses a follow-up NOTE from win-builder R-devel on cevcmm 0.1.2 asking that arXiv preprints be cited via their arXiv DOI form rather than the plain URL form. Metadata-only change; no code changes.
<https://arxiv.org/abs/2511.12732> to
<doi:10.48550/arXiv.2511.12732>, as required by CRAN
policy for arXiv preprints.Addresses comments from CRAN reviewer Konstanze Lauseker on the initial submission of cevcmm 0.1.1.
\value sections to the
fixef and ranef S3 generic function
documentation, describing the return-value contract dispatched to
method-specific documentation.\dontrun{} to \donttest{}
in the plot.vcmm_fit example.inst/validation and
inst/benchmarks from the CRAN tarball. These are
development-time scripts (the Day 7-18 validation harness and
micro-benchmarks used during package development) that write files and
change par() / options() without resetting.
They remain available in the GitHub repository for reference but are no
longer shipped to CRAN users.PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) to
src/Makevars for BLAS / LAPACK / Fortran linking on Linux.
The equivalent flags were already present in
src/Makevars.win for Windows. Fixes install failure with
undefined symbol: dpotrf_ on CRAN’s Debian pretest.First public release.
vcmm() is the main entry point: a single call that
builds the spline design, computes sufficient statistics, and fits a
Varying Coefficient Mixed-Effects Model. The default
method = "auto" routes between the iterative
sufficient-statistics estimator (SS) and the one-step
communication-efficient surrogate-likelihood estimator (CSL) of Jalili
and Lin (2025), based on the problem size.Three structures, all selectable via re_cov:
re_cov = "diag": independent random effects with shared
variance \(\sigma_\alpha^2\).re_cov = "kronecker": \(\Sigma_\alpha = \Sigma_{\text{left}} \otimes
\Sigma_{\text{right}}\), with \(\Sigma_{\text{left}}\) (q_left
x q_left) estimated and \(\Sigma_{\text{right}}\) (G x
G) held at its initial value. Designed for
origin-destination flow data.re_cov = "separable": same internal Kronecker machinery
as the kronecker mode, but with the q_left argument
required (no default) and the right-side \(\Omega_G\) held fixed at its initial
value.node_summary() computes a sufficient-statistics summary
on a slice of data; the resulting vcmm_ss object is
independent of the slice size (a few KB regardless of \(n_k\)) and can be shipped between
machines.+.vcmm_ss adds summaries together;
Reduce("+", list_of_summaries) aggregates an arbitrary
number.init_accumulator() plus repeated calls to
accumulate_stats() provides a streaming alternative for
chunks that arrive over time.fit_from_summaries() accepts a single summary, a list
of summaries, or an accumulator, and returns a fit bit-equivalent to a
pooled vcmm() call on the same data (Theorem 1 of Jalili
and Lin, 2025).rowSums(Z)
(e.g. origin-destination indicators), fit_from_summaries()
takes a rowsum_constant argument so the same
identifiability shift vcmm() applies automatically can be
reproduced in the distributed setting.Standard generics work on the vcmm_fit class:
print(), summary(), coef(),
fixef(), ranef(), vcov(),
nobs(), logLik(), AIC(),
BIC(), predict(), plot(), plus
the package-specific helper varying_coef() for evaluating
\(\hat\beta_k(t)\) at new \(t\) values.
vcov() returns the asymptotic Wald variance from the
prior-augmented Hessian inverse. which = "beta" (default),
"alpha", or "both".predict() supports both subject-specific (with
Z) and marginal predictions, with optional pointwise
standard errors via se.fit.varying_coef() evaluates \(\hat\beta_k(t)\) on an arbitrary grid with
optional pointwise standard errors.plot.vcmm_fit() shows three diagnostic panels
(which = 1, 2, 3): varying coefficient with CI band;
residuals vs fitted and vs \(t\);
random-effect diagnostics that adapt to the chosen re_cov
(Q-Q plot for diag, heatmap of the estimated \(\Sigma_{\text{left}}\) for kronecker and
separable).Suggests:), enabled with
options(cevcmm.use_rspectra = TRUE) for the dense
high-dimensional case.inst/extdata/od_migration.csv: a 3000-row simulated
origin-destination migration dataset (10 regions over 30 years, all 100
OD pairs observed annually). Used by the od-migration
vignette.getting-started — simulate, fit, and inspect on a
simple diagonal-random-effects example.distributed-fitting — split data across multiple nodes
and recover the same fit; covers both list-of-summaries and
streaming-accumulator patterns.od-migration — Kronecker covariance for
origin-destination flow data using the bundled CSV example.Jalili, L. and Lin, L.-H. (2025). Scalable and Communication-Efficient Varying Coefficient Mixed Effect Models: Methodology, Theory, and Applications. arXiv:2511.12732; under review at Journal of the American Statistical Association.