NEWS

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np 0.70-3 (2026-06-04)

• Added nplsqreg()/nplsqregbw() as a location-scale quantile-regression front end with formula/data and bandwidth-object workflows, scalar/vector tau, prediction, residual extraction, summaries, and plot routes built on the shared quantile plotting engine.
• Supported MADS/NOMAD-backed bandwidth-search routes now use the final native crs NOMAD C API rather than the retired legacy snomadr() fallback. This covers the promoted regression, density, distribution, conditional density, conditional distribution, smooth-coefficient, single-index, partially linear, and location-scale quantile search surfaces where those routes support native NOMAD/MADS. The runtime dependency on crs is now declared in Imports, while LinkingTo remains for the native header.
• Native NOMAD routes now preserve progress best-record reporting, expose cache/evaluation diagnostics, honor explicit start and option controls, and reject unsupported or indeterminate cache-off settings before solver entry. Inadmissible GLP degree candidates are guarded before expensive evaluator work.
• npindexbw(..., method = "ichimura", regtype = c("ll", "lp")) now reuses the established local-polynomial regression objective evaluator for fixed-degree and NOMAD degree-search routes. Focused sentinel runs preserved selected objective payloads while materially reducing runtime for local-linear and local-polynomial Ichimura single-index bandwidth searches.
• options(np.tree = "auto") is now the default tree mode. In auto mode, continuous kd-tree routes are enabled only for bounded-support continuous kernels ("epanechnikov" and "uniform"); np.tree = TRUE remains the explicit force-on override and np.tree = FALSE remains the force-off diagnostic path.
• Powell bandwidth searches now expose package-side repeated-candidate objective caching through options(np.objective.cache = TRUE/FALSE). The cache remains enabled by default and is scoped to one bandwidth solve, so it can reuse exact candidates across Powell restarts without carrying state across datasets or later calls. Continuous-only generalized/adaptive nearest-neighbor routes also retain their integer nearest-neighbor objective cache under the same switch; NOMAD solver caching and extended-NN distance reuse remain separate mechanisms.
• Continuous large-bandwidth shortcut evaluations can now be disabled with options(np.largeh = FALSE), and discrete near-upper-bandwidth shortcut evaluations can now be disabled with options(np.largelambda = FALSE). Both remain enabled by default. These switches are intended for diagnostic timing and reproducibility studies that need to separate tree effects from large-bandwidth and large-lambda fast paths without changing the canonical dense/tree objective machinery.
• Local-polynomial regression cross-validation now uses a leaner hot symmetric weighted-sum loop. Fixed-bandwidth npregbw(..., regtype = "lp", bwmethod = "cv.ls") objective probes show substantially faster local-polynomial CV evaluation while preserving objective values to numerical precision; adjacent density bandwidth probes preserve their objective values as well.
• Shared weighted outer-product accumulation in npksum() now uses a guarded BLAS dgemm route when the operation is dense, non-permuted, and memory-bounded. Focused fixed-bandwidth probes preserve objective values to numerical precision while substantially accelerating high-basis local-polynomial regression and smooth-coefficient objective rows; small and scalar routes remain on the established loop path.
• Unconditional density least-squares cross-validation now uses a leaner fixed-bandwidth Gaussian convolution loop. Fixed-bandwidth npudensbw(..., bwmethod = "cv.ls") objective probes preserve objective values exactly in the focused validation rows while materially reducing the convolution portion of the objective calculation. Conditional-density least-squares objective probes inherit the same fixed-bandwidth Gaussian convolution improvement.
• Non-Gaussian scalar-bandwidth convolution helpers now hoist the response bandwidth power outside the inner loop, improving fixed-bandwidth least-squares density cross-validation with compact-support kernels while preserving objective values exactly in focused probes.
• Continuous-kernel vector helpers now reuse the loop-invariant signed inverse bandwidth scale inside their inner loops. Focused density, conditional density, and regression objective probes preserved objective values exactly while reducing repeated scaling work in shared C hot paths.
• Conditional density and conditional distribution least-squares cross-validation now use a size-aware row-block policy for local-polynomial objective evaluation. The accepted route keeps the bounded-quadrature cap unchanged, bounds transient memory by sample size, and preserves objective values to numerical precision while materially reducing evaluator overhead for fixed-bandwidth CVLS probes.
• Local-polynomial conditional density maximum-likelihood cross-validation now uses the same bounded-memory block machinery for fixed and generalized nearest-neighbor bandwidths. Focused npcdensbw(..., bwmethod = "cv.ml", regtype = "lp") probes preserve objective values and selected bandwidths to numerical precision while reducing objective and full-search runtime.
• Large-sample categorical-only regression now has a profile-compressed execution route controlled by options(np.categorical.compress = TRUE), which is enabled by default. This categorical route is independent of options(np.tree). For local constant categorical regression, repeated predictor profiles are compressed before fitting, prediction/evaluation, standard errors, gradients where meaningful, bandwidth search, hat-helper use, and plot bootstrap helpers. This preserves the established dense-route numerical contract while greatly reducing work for large samples with many repeated factor/ordered predictor combinations.
• Categorical-only unconditional density routes now use the same profile-compression idea when options(np.categorical.compress = TRUE) is enabled. The fixed-bandwidth fit/evaluation route preserves dense-route fitted/evaluation values while avoiding repeated computation over identical categorical profiles, and the bandwidth-search route now uses the same compressed support representation for all-categorical data. As with other flat categorical search surfaces, selected smoothing parameters may drift by optimizer-path amounts while preserving the objective scale.
• Categorical-only conditional density and conditional distribution bandwidth searches now honor options(np.categorical.compress = TRUE). The promoted route preserves the objective value to numerical precision while allowing harmless optimizer-path drift in selected smoothing parameters, especially near upper-bound or large-bandwidth regions where the objective is flat.
• Ordered-only unconditional distribution bandwidth search and fit/evaluation routes also use profile compression when options(np.categorical.compress = TRUE) is enabled. The bandwidth-search route preserves the objective value to numerical precision while allowing harmless optimizer-path drift in selected smoothing parameters; fitted distribution values and standard errors are preserved while avoiding repeated computation over identical ordered profiles.
• Fixed-bandwidth local-constant npscoef() fits now use categorical-profile compression when all Z variables are categorical and options(np.categorical.compress = TRUE) is enabled. The route preserves fitted means, coefficient surfaces, asymptotic mean standard errors, and coefficient/gradient standard errors for training and evaluation fits while avoiding repeated work over duplicate Z profiles. The corresponding npscoefhat(output = "apply") path and count-based plot-bootstrap helper use the same profile compression without changing the explicit full-matrix output = "matrix" contract.
• Internal categorical-profile and large-bandwidth caches are now cleared at the relevant top-level density, distribution, conditional-density, conditional-distribution, and regression cleanup points. These caches are keyed by call-local row pointers, so clearing them per .Call prevents stale same-process state from leaking across unrelated data sets.
• Formula variables whose names contain dots, such as y.irr ~ x, are no longer mistaken for the formula wildcard . in conditional density and conditional distribution bandwidth routes. The conditional-density bandwidth formula route also now expands the actual wildcard form y ~ . using the supplied data frame, matching the conditional-distribution route.

np 0.70-2 (2026-05-15)

• npqreg() is now a fully fledged quantile-regression front end. It supports the formula/data workflow, internally computes npcdistbw() bandwidths when a bandwidth object is not supplied, accepts scalar or vector tau, reuses selected bandwidths for additional quantiles in plot(), and exposes the usual S3 surface: fitted(), predict(), predict(..., se.fit=TRUE), se(), gradients(), summary(), print(), quantile(), and plot().
• npqreg() prediction now honors the standard newdata workflow while preserving native exdat precedence for compatibility with existing np call surfaces. Formula-based prediction validates that new data contain the required right-hand-side variables.
• npqreg() plotting has been expanded for vector quantiles, level/gradient displays, ordered predictors, user-specified legends, and object-fed plotting of additional tau values without recomputing cross-validation.
• npconmode() is now a first-class conditional-mode estimator. It supports formula/data and bandwidth-object workflows, forwards bandwidth-selection options to npcdensbw(), propagates local polynomial and NOMAD metadata, and exposes fitted(), predict(), summary(), print(), gradients(), and plot() methods.
• npconmode() now supports optional class-probability matrices and level-specific probability gradients. For non-local-constant fits, probabilities are normalized to be non-negative and to sum to one across the discrete response support before modal classification.
• npconmode() now fails early for non-categorical responses and validates formula-based newdata against the original right-hand-side variables.
• npconmode() plotting now supports object-fed class-probability slices and two-dimensional probability surfaces, optional rgl rendering, and probability-level asymptotic intervals where defined. Surface bootstrap intervals for class probabilities remain intentionally deferred.
• npcopula() is now a first-class copula estimator. It supports formula/data and bandwidth-object workflows, automatic two-dimensional probability grids, explicit u evaluation grids, and ordinary extractable object components including $bws.
• npcopula() now provides fitted(), predict(), predict(..., se.fit=TRUE), se(), summary(), print(), as.data.frame(), and richer plot() methods. Plotting supports base persp, image, and optional rgl rendering, with asymptotic and bootstrap intervals for copula surfaces where defined.
• npcopula() explicit-grid evaluation now uses the direct estimator route, preserving numerical results while avoiding the severe runtime growth of the previous expanded-grid path when users request larger probability grids.
• The automatic local-polynomial NOMAD controls have been split into explicit restart toggles: powell.remin for Powell restarts and nomad.remin for the second NOMAD hot start. This preserves the Powell Numerical Recipes restart default while allowing NOMAD hot starts to be controlled separately.
• Deprecated legacy remin remains accepted by npregbw() and npreg() with a warning and is mapped to the modern powell.remin/nomad.remin controls where appropriate, preserving downstream compatibility while documenting the new spelling.
• Hat-operator helpers now support an additional constraint-oriented output route for objects needed by shape-constrained quadratic programming workflows, avoiding reimplementation of local-polynomial hat-matrix construction in user examples.
• Local-polynomial derivative support has been broadened across the conditional estimator family. npreg(), npcdens(), and npcdist() now honor gradient.order more consistently for fitted, evaluated, predicted, and plotted objects when the selected polynomial degree is high enough, including vector derivative orders over continuous predictors and tensor/additive/Bernstein local-polynomial bases.
• Core and semiparametric S3 prediction paths have been hardened around newdata, native evaluation-argument precedence, formula RHS validation, and se.fit handling.
• Front-end/bandwidth argument hygiene has been tightened so estimator-only controls such as proper are not forwarded into bandwidth selectors that do not accept them.
• Documentation has been refreshed for the promoted npqreg(), npconmode(), and npcopula() workflows, including the local-polynomial NOMAD route, probability/gradient outputs, plot controls, and examples that use the streamlined interfaces.
• The pre-release validation suite was expanded with focused hostile argument tests, S3 contract tests, installed/tarball proof scripts, and cross-package parity checks for the newly promoted estimator families.

np 0.70-1 (2026-05-02)

• The default multistart cap for bandwidth selection now follows min(2, p) across the core estimator families, replacing the older min(5, p) cap. This includes automatic LP degree-search calls when search.engine="nomad" or "nomad+powell" and nmulti is not supplied explicitly.
• The univariate boundary density helper npuniden.boundary() now defaults to nmulti=1.
• The empirical studies supporting this change are documented under benchmarks/validation/.
• LP-capable front ends now accept nomad=TRUE as a documented convenience preset for the recommended automatic NOMAD local-polynomial route. Missing settings expand to the same long-form LP/NOMAD defaults documented in the bandwidth help pages, and regression formula calls such as npreg(y ~ x, nomad = TRUE) now carry that shortcut through the internally computed bandwidth path.