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Changelog

Source: NEWS.md

geosmooth 0.2.0

Solver reliability

  • Hessian L1 ADMM uses an operator-scale initial penalty by default (admm.rho = NULL) and adapts it during its initial iterations. An explicit positive admm.rho still sets the initial value; use admm.adaptive.rho = FALSE to hold it fixed. Numerical stabilization now preserves the requested objective instead of adding an unintended ridge.

  • ADMM convergence also checks stationarity of the original objective. Incomplete fixed fits warn and print their status. CV excludes candidates with any unsuccessful fold and errors if none qualify or the selected full-data ADMM refit is incomplete. Fold diagnostics are retained, including on the no-eligible-candidate error. Generalized-lasso warnings are retained.

  • The shared PTTF L1 solver receives the same safeguards. Hessian L1 objective reporting now accounts for the penalty actually used with row scaling; the unscaled Hessian norm remains available separately.

  • Generalized-lasso Hessian paths now propose penalties only: all returned coefficients and CV candidates are solved by ADMM on the original objective. Finite path coefficients can be suboptimal for nearly rank-deficient operators; missing-label path coefficients can include an extra ridge. The path remains diagnostic, with its original objectives in solver$path.objective and the refined route labeled genlasso_admm_refined. Default Hessian ADMM stopping tolerances are now 1e-6 absolute and 1e-5 relative. Fitted values and selected penalties can change; difficult candidates may require a larger iteration cap.

Uncertainty and result interpretation

  • lps.pointwise.band() now estimates noise variance using residual noise degrees of freedom sum((I - S)^2) = n - 2*tr(S) + sum(S^2). The former n - tr(S) denominator is available as variance.method = "legacy". df still reports tr(S); residual.df is returned separately. Bands remain pointwise, conditional on a fixed smoother, and uncorrected for smoothing bias.
  • Regression families support fitted() and residuals() with their existing vector/matrix and missing-label contracts. Residuals at other evaluation coordinates are rejected. Tracked harmonic fitted values use fitted(); density mass stays $rho. No new out-of-sample prediction or reusable-refit contract is implied.
  • Lifting, synchronized lifting, density, prediction-synchronized, chart-kernel, and local-likelihood fits print bounded diagnostics. Structured summary() methods cover the main regression families. Unavailable convergence information is labeled explicitly, and density accounting and local fallbacks remain visible.
  • The existing guides now include interpreted first-fit and graph workflows, embedded figures with text alternatives, and clear latent/ambient synthetic views. plot.synthetic_dataset() adds named view, legend, and col controls while preserving automatic view selection. Point-color keys now describe the displayed values; region coloring requires region labels.
  • Graph adaptation uses dgraphs’ public distance API and accepts both transitional graph lists and the forthcoming dgraph representation, without restoring retired geometry re-exports.

Input validation and harmonic diagnostics

  • Density methods now reject misspelled, duplicated, or unsupported controls and unused terminal arguments. Logical controls require TRUE or FALSE. Recognized controls forwarded to subject-occupation smoothers remain supported.
  • Graph low-pass, density, and harmonic methods validate finite integer vertex indices before conversion. Fractional indices and iteration counts now error instead of silently selecting a different vertex or truncating a control.
  • Harmonic smoothing documents the actual boundary rule: a region vertex must have a neighbor outside the region; graph leaves are not automatically fixed. The basic function still leaves a boundary-free region unchanged, while the tracked function still relaxes it. Both now report explicit numerical status.
  • harmonic.smoother() reports extrema-stability detection separately from numerical convergence. stable_iteration is now NA_integer_ when no stability window was detected; check stability_detected before using it. It returns update/residual histories and actual recorded iteration numbers, always records the final state, and uses those numbers in plots. Stability of recorded extrema does not establish convergence or optimal smoothing.

API changes

This release reduces the explicit public API from 93 to 60 exported functions. The 35 retired names are replaced by dependency-qualified calls, a unified fitter, or two S3 generics.

  • Replace the six exported refit.*() functions with the S3 generic refit(object, y, ...), covering MALPS, both lifting trend filters, metric graph low-pass, quadratic Hessian, and Hessian L1 fits. In old calls, rename fitted.model to object and y.new to y. Method-specific controls, response-shape restrictions, selection behavior, and return classes are preserved. Former help names remain available.

  • Replace lps.smoother.matrix() and malps.smoother.matrix() with smoother.matrix(object, ...). LPS retains check.tol and its fixed-configuration requirements; MALPS retains max.n, allow.robust, and its conditional interpretation. The eight former entry points are no longer exported. The function-guide vignette documents all supported methods.

  • API consolidation (breaking change): retire the 25 transitional synthetic geometry and sampling re-exports. Call the same names and arguments with dgraphs::, for example dgraphs::synthetic.circle() and dgraphs::synthetic.sampling.uniform.interval(0, 1). Statistical recipe, truth, response, materialization, and validation functions remain in geosmooth, including its local synthetic.sampling.stratified().

  • Consolidate quadratic Hessian regression into fit.ssrhe.hessian.regression(). Existing fixed calls with lambda1 and lambda2 are unchanged. Replace the former .cv() and .gcv() fitters with lambda.selection = "cv" or "gcv" and penalty grids. The old function names are no longer exported; their help aliases explain migration.

  • CV settings now use cv.control = list(foldid = ..., cv.folds = ..., loss = "mse", selection = "min"); foldid replaces fold.id and cv.folds replaces nfolds. GCV trace settings use gcv.control = list(trace.method = "hutchinson", trace.n.probes = 50L, trace.seed = ...). Both control lists support support.selection, support.grid, and support.max.candidates (formerly the mode-specific support.cv.max.candidates / support.gcv.max.candidates). Nonempty controls for the wrong mode, unknown control entries, and simultaneous scalar/grid penalties are errors. CV still supports missing labels; GCV still requires a single fully observed response and strictly positive weights. Existing fit classes, CV/GCV tables, and refit support are preserved.

Dependencies

  • Delegate reusable synthetic geometry and point generation to dgraphs (>= 0.2.1.9000). Statistical recipes, responses, dataset identities and legacy G4 remain here. Geometry Lab and maintained geometry reference tools now live in the dgraphs development tree.

Documentation

  • Clarify that metric graph low-pass refits process response columns sequentially. The retained n.cores argument does not enable parallel execution, and per-column GCV results report n.cores.used = 1L.
  • Add “Finding your way around geosmooth”, a task-oriented vignette covering every exported function, supported prediction/refit workflows, and migration to the consolidated API. Link the guide from the package overview and README.
  • Update the synthetic-datasets vignette to use geometry and sampling from dgraphs. Both vignettes are distributed as HTML with the built package.

geosmooth 0.1.0

CRAN release: 2026-09-12

  • Initial CRAN release.
  • Provides local polynomial and model-averaged local polynomial smoothing, local polynomial lifting trend filtering, synchronized local polynomial lifting trend filtering, and occupation-density adapters.
  • Provides metric-graph low-pass filtering and Hessian-energy regression with compiled backends.
  • Provides reproducible synthetic geometry and response generators for method evaluation.
  • Makes synthetic-registry identity verification stable across supported R versions by excluding the serializer’s producer-version header from the checksum contract.