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Changelog

Source: NEWS.md

cevcmm 0.1.3 (2026-07-16)

CRAN reviewer feedback (round 2)

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.

  • DESCRIPTION: change the arXiv reference from <https://arxiv.org/abs/2511.12732> to <doi:10.48550/arXiv.2511.12732>, as required by CRAN policy for arXiv preprints.

cevcmm 0.1.2 (2026-07-07)

CRAN reviewer feedback (round 1)

Addresses comments from CRAN reviewer Konstanze Lauseker on the initial submission of cevcmm 0.1.1.

  • DESCRIPTION: expand the “SVD” acronym on first use to “Singular Value Decomposition”.
  • DESCRIPTION: reformat the reference to Jalili and Lin (2025) in the CRAN-preferred autolinking form (later refined to DOI form in 0.1.3).
  • R/methods.R: add \value sections to the fixef and ranef S3 generic function documentation, describing the return-value contract dispatched to method-specific documentation.
  • R/plot.R: change \dontrun{} to \donttest{} in the plot.vcmm_fit example.
  • .Rbuildignore: exclude 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.

cevcmm 0.1.1 (2026-07-07)

Bug fixes

  • Add explicit 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.

cevcmm 0.1.0 (2026-07-01)

First public release.

Features

Main fit function

  • 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.

Random-effect covariance

Three structures, all selectable via re_cov:

  • re_cov = "diag": independent random effects with shared variance σα2\sigma_\alpha^2.
  • re_cov = "kronecker": Σα=Σleft⊗Σright\Sigma_\alpha = \Sigma_{\text{left}} \otimes \Sigma_{\text{right}}, with Σleft\Sigma_{\text{left}} (q_left x q_left) estimated and Σright\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 ΩG\Omega_G held fixed at its initial value.

Distributed and streaming API

  • 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 nkn_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).
  • For designs with constant 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.

S3 methods

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 β̂k(t)\hat\beta_k(t) at new tt 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 β̂k(t)\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 tt; random-effect diagnostics that adapt to the chosen re_cov (Q-Q plot for diag, heatmap of the estimated Σleft\Sigma_{\text{left}} for kronecker and separable).

Performance backends

  • RcppArmadillo backends for the hot paths: sufficient-statistics cross-products, Cholesky / LU / Moore-Penrose inversion. Falls back cleanly to R-only implementations if the C++ build is unavailable.
  • Optional truncated-SVD path via RSpectra (in Suggests:), enabled with options(cevcmm.use_rspectra = TRUE) for the dense high-dimensional case.

Bundled data

  • 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.

Vignettes

  • 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.

Reference

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.