NEWS.md
In eigencore: Certified Partial Eigenvalue and Singular Value Computation

eigencore 1.0.2

Complete the no-memory-profiling fix from 1.0.1. The CRAN benchmark vignette and the installed README benchmark now also request bench::mark() memory measurements only when capabilities("profmem") is true. The vignette's summary tables also report unavailable results instead of failing when a benchmark regime has no successful methods.
Add a regression test that audits every shipped benchmark entry point for unconditional memory = TRUE and exercises the package timing helper on the current R build.

eigencore 1.0.1

Fix an R CMD check ERROR on R builds without memory profiling (for example the r-devel Linux fedora flavors, which are configured without --enable-memory-profiling). The internal benchmark/validation timing helpers passed memory = TRUE to bench::mark(), which calls utils::Rprofmem() and aborts with "memory profiling is not available on this system" on those platforms. Memory measurement is now requested only when capabilities("profmem") is TRUE; elsewhere timing still runs and mem_alloc is reported as NA.
Benchmark smoke tests now use testthat::skip_on_cran() so they are skipped on CRAN as intended (the previous Sys.getenv("CRAN") guard never fired on the CRAN check farm).

eigencore 1.0.0

First CRAN release.

svd_partial() and eig_partial() compute the top-k singular triplets or eigenpairs of large dense, sparse (CSC), diagonal, banded/tridiagonal, and matrix-free operators through native C++ kernels.
Every result carries a numerical certificate: residuals for both singular relations, a backward-error bound, orthogonality loss, a labeled norm bound, and a single passed flag. Bounds that can only be estimated (for example stochastic norm estimates on centered sparse operators) are reported as estimates and never produce an unqualified passed.
Operator algebra — center(), scale_cols(), compose(), crossprod_operator(), linear_operator() — solves centered, scaled, and composed problems without forming dense matrices.
Transparent method selection: plan_solver() reports the chosen kernel before a solve, and fit$method names the path that actually ran. Problem classes without a production kernel carry explicit reference labels.
RSpectra-compatible wrappers eigs(), eigs_sym(), and svds() accept the same which codes and additionally return certificates.
Benchmarked against 'RSpectra', 'irlba', and 'PRIMME'; reproduce with Rscript inst/benchmarks/bench-readme.R.
Generalized eigen support: eig_full() for dense SPD and general pencils, generalized_schur() and generalized_svd() for dense QZ/GSVD, partial sparse general pencils with nonsingular diagonal B via transformed native Arnoldi, left eigenvectors and conditioning diagnostics on supported dense paths, and pencil_norm_scaled alpha/beta classification. Sparse SPD partial paths remain under eig_partial() / LOBPCG / B-orthogonal Lanczos; general sparse QZ and non-diagonal sparse B are explicit unsupported boundaries. The real dense GSVD path currently requires a linked LAPACK that provides the deprecated dggsvd routine.
The exported API is stable as of 1.0.0; breaking changes from here follow semantic versioning.