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sparsepca 1.0.0

Correctness and observable behavior

• Reject nonzero processed data whose squared norm underflows to zero. Extremely small measurements previously took the zero-data shortcut and returned zero loadings and scores with converged = TRUE. Rescale such inputs before fitting; genuine zero and constant centered inputs retain their existing results.
• Correct robspca()’s objective to use the complete residual X - X %*% B %*% t(A) - S. Previously the sparse error matrix was charged a penalty while omitted from the reconstruction loss, which could increase the recorded objective and terminate a fit prematurely. Thanks to Maria A. Sans-Fuentes for the report and correction. Robust fits with nonzero sparse residuals can now run longer and produce different fitted values; the intended optimization model is unchanged.
• Honor the q argument to rspca(); previous versions always used two power iterations. Calls with the default q = 2 retain that setting.
• Make rank-one, zero-data, rank-deficient, and single-variable cases well-defined, retain matrix dimensions, and validate unsupported inputs and invalid scalar arguments before numerical operations.
• Correct eigenvalues and sdev to report sequentially adjusted score energy and its square root. The previous values came from an auxiliary Procrustes SVD and were not the variances of the returned sparse scores. These field names and the summary matrix layout are retained; their numerical values can change for penalized fits. score_variance records marginal score energy separately. With centered data these quantities are adjusted and marginal sample variances, respectively.
• Compute var as the total squared energy of the processed data divided by n - 1. This is total sample variance when centering is enabled. For center = FALSE, it now consistently includes the column means, matching the uncentered score-energy convention.
• Retain all existing function names, argument positions and defaults, result classes, and existing result components. Sparse loading vectors remain in their fitted scale; they are not normalized after optimization.

New capabilities

• Add penalty = "l0" for hard thresholding, with the existing ridge penalty providing the ZeroNet variant when beta > 0. The default remains elastic net. The new arguments follow all existing arguments.
• Add B_init to initialize a fit from a finite loading matrix, and spca_path() to fit a sequence of penalties using a shared matrix factorization or randomized sketch. Paths retain the supplied order and report per-component nonzero counts and the union of selected variables. Penalty paths do not impose an exact requested cardinality.
• Add spca_target() to search for an approximate requested number of distinct variables across components. The bounded search shares one factorization or randomized sketch and uses warm starts. It selects the closest converged fit found, reports the achieved count and full search diagnostics, and returns no recommendation when every candidate fails to converge. Count tolerance is separate from objective tolerance; support need not change monotonically or attain the requested count.
• Add predict() methods for all three fitted classes. Predictions use stored centering and scaling, accept numeric data frames and Matrix inputs, and align a complete set of named variables with the training columns. Empty dimension names are normalized across supported Matrix versions. Calling predict() without new data returns the training scores.
• Support sparse Matrix input in rspca() with implicit centering and scaling. Dense sketches, loadings, and scores are still needed; spca() and robspca() explicitly use dense working matrices.
• Return iteration, objective-convergence, and effective-penalty diagnostics so users can inspect a fit instead of inferring convergence from printed output.

Maintenance and documentation

• Credit Maria A. Sans-Fuentes with her ORCID in the contributor metadata and a README contributor section. Add a short quick-start example and identify the source snapshot behind the retained performance tables.

• Extend the independent numerical tests to cover genuinely compressed sparse sketches, finite-difference reduced-objective gradients, correlated loading optimality conditions, active robust Huber optima, changes of measurement units, and numerical safeguard branches. Fault-injection experiments verify that the new safeguards detect their intended failures.

• Clarify that ordinary-PCA recovery at zero penalties assumes the default SVD initialization, and distinguish objective convergence from coefficient accuracy or first-order optimality of every returned factor.

• Avoid computing unused left singular vectors during deterministic initialization. Reuse thin matrix products during optimization instead of constructing a full reconstruction residual on every iteration. Near an exact fit, evaluate the residual directly in bounded blocks to avoid cancellation in differences of large squared norms.

• Preallocate objective histories, share common solver and preprocessing code, and remove duplicate products from robust updates. These changes preserve the intended optimization updates while reducing allocation and repeated work.

• Expand every help topic and add documentation for prediction, regularization paths, approximate feature targeting, parameter scaling, reconstruction, and the interpretation of correlated sparse component scores.

• Expand the README into a self-contained tutorial with the two-factor model, parameter guidance, held-out prediction, and reproducible iris, grouped-measurement, and corrupted-sensor applications. Recompute all five eigenface panels using 1.0.0, with provenance and interpretation, and retain the measured performance tables and figures. Add an iris example of selecting an approximate number of variables with spca_target().

• Deepen all eight help topics with practical guidance and independent examples. Check labeled README chunks and help examples against the installed public API, fail unexpected warnings, and record explicit installation skips and source provenance. Save new application results separately from their figure rendering.

• Add a package citation for the final 2020 SIAM publication and update author and maintainer metadata.

• Add numerical regression tests, package-check workflows, and reproducible benchmark and release-validation scripts. Development measurements and README assets are excluded from the source archive.

Dependency policy

Require rsvd 2.0.0 or later, with R 4.4.0 or later and Matrix 1.7-0 or later to match its dependency requirements. Earlier versions of R and these dependencies are no longer supported. The minimum-version CI job tests exact R 4.4.0, Matrix 1.7-0, rsvd 2.0.0, and testthat 3.3.2. Matrix does not need to be attached by the user. Suggest testthat 3.3.2 or later for running the tests; it is not a runtime dependency.

These 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.