Changelog • np

np 0.70-5

CRAN release: 2026-07-15

Corrected the heteroskedasticity-robust Ichimura index-coefficient covariance returned by vcov(npindex(..., gradients = TRUE)) when the model has more than one free index coefficient. Link-gradient and residual weights are now applied observation by observation. Fits with one free coefficient remain exactly unchanged, as do beta, bandwidth, objective, fitted-value, residual, and gradient results.
Repaired finite-support bounded continuous-kernel normalization for all supported Gaussian, Epanechnikov, uniform, and truncated-Gaussian kernels. The centered normalization now avoids tail cancellation, uses the platform C99 error function where appropriate, and preserves the uniform-density limit as the bandwidth tends to infinity.
Replaced the bounded distribution operator with the mathematically correct observation-centered truncated-kernel CDF. Analytic centered interval primitives and hoisted invariant work preserve exact support endpoints, numerical accuracy, and the finite-support large-bandwidth uniform limit.
Corrected transform.bounds = TRUE initialization for regression initial starts and multistarts and for later conditional-distribution multistarts. External bandwidth starts are now inverse-mapped before transformed Powell search; the public default remains FALSE.
Clarified optimizer summaries so objective-cache lookups, NOMAD point lookups, family-native R-optim refinement, and total function-evaluation accounting are labelled consistently without implying that their denominators are interchangeable.
Clarified bandwidth-object replay documentation across estimator families. Stored bandwidth objects retain search and fit metadata but do not silently materialize or expand training data; callers must provide data again where the documented replay route requires it.
Standardized the public nomad.opts contract across supported bandwidth selectors and hardened native NOMAD callback cleanup, nearest-neighbor cache release, and native allocation-dimension guards.
Reworked npindexbw() / npindex() internals after 0.70-4. Ichimura and Klein-Spady single-index objectives now reuse the established npreg leave-one-out backend where applicable, preserving the public estimator contract while materially improving high-dimensional and local-polynomial objective evaluation. The single-index formula/materialization routes were tightened so formula-selected variables, explicit bandwidth objects, and direct formula calls with explicit smoothing parameters reenter consistently.
Added user documentation for npindex() optimizer choice and beta interpretation. The documentation now gives practical guidance on when derivative-free Nelder-Mead remains a reasonable low-dimensional default and when BFGS is useful for higher-dimensional index searches, and it explains the relative interpretation of normalized single-index beta coefficients.
Hardened single-index fit, evaluation, plotting, and summary behavior. Bounded continuous-kernel options are now carried consistently through objective, fit, evaluation, variance, and bootstrap routes; Klein-Spady confusion-matrix output is guarded against out-of-range fitted values; and large-bandwidth shortcuts are restricted to kernel/order combinations whose constant-weight approximation was validated.
Repaired single-index plot bootstrap memory usage. Wild-bootstrap self-maps now avoid public-facing O(n^2) kernel-weight allocations for the ordinary large-sample plot route, while preserving the public fitted and plotting contracts.
Repaired generalized-nearest-neighbor local-polynomial derivative ownership across npreghat() and related public routes. Mixed-degree local-polynomial fits now route available derivative components through the correct owner, preserve apply == H %*% y contracts, and report unavailable derivative components consistently rather than silently applying the wrong operator.
Repaired local-constant derivative ownership in npreghat() so degree-zero local-polynomial derivative requests use the analytic local-constant derivative contract only when that contract is mathematically available. Scalar, matrix, and multi-column apply routes were validated separately.
Improved conditional mixed-degree local-polynomial gradients for npcdens() / npcdist() and public conditional-gradient accessors. The partial-availability contract now matches the regression-family policy: available components are returned, unavailable components are represented as NA, and incoherent metadata fails clearly.
Repaired formula/data reentry contracts across density, distribution, conditional density, conditional distribution, single-index, smooth coefficient, partially linear, quantile, conditional-mode, copula, and significance-test routes. Explicit estimator data= now overrides stored bandwidth-object data where that public call shape is supported, formula newdata is validated against fitted RHS variables, and direct formula calls with numeric smoothing parameters no longer misroute formula objects as native data.
Repaired unconditional density and distribution edge contracts. Ordered kernel-code selection is now consistent between conditional bandwidth selection and fitting where normalization is required, all-NA input is rejected before native calls, training and evaluation omission metadata are retained separately where needed, categorical zero-bandwidth standard-error handling is consistent across categorical configurations, and unsupported bandwidth-selection method codes fail clearly.
Clarified normal-reference bandwidth documentation for density and distribution routes. The rule-of-thumb formulas are documented as fast exploratory Silverman-style heuristics, not production substitutes for cross-validation or likelihood-based selection.
Hardened npcdistbw() normal-reference method handling so the R method code and native C method code stay aligned, avoiding accidental fall-through to an unintended bandwidth-selection branch.
Repaired and hardened public estimator contracts found during adversarial audits of npscoef, npplreg, npqreg, nplsqreg, npconmode, npcopula, npreg, npcdens, npcdist, npudens, and npudist. Repairs include npscoef iterated backfit behavior, partially-linear fit reentry, quantile inversion/clamping contracts, conditional-mode probability/tie handling, copula sample reentry, regression tree/large-h predicate alignment, and density/distribution formula/native argument consistency.
Plot-bootstrap memory hardening now covers single-index, conditional-mode, and partially-linear plot routes. The default plot evaluation grids remain linear in the training sample size, and explicit neval == ntrain style requests remain user-controlled.
Categorical-gradient and derivative workspace handling were repaired so no-gradient and gradient calls can be mixed safely in one session without stale native derivative workspace state affecting later calls.
Native shadow-object and cache lifecycles were hardened. Conditional-density shadow pointers, native objective-cache state, and regression large-h / large-lambda caches are cleared at the appropriate top-level lifecycle boundary so pointer-keyed helper state cannot leak across independent calls or datasets.
npksum() numeric-bandwidth dispatch now constructs data-aware bandwidth objects for mixed continuous/categorical data instead of falling back to a default-typed object. The serial and MPI packages now share the same public behavior for numeric smoothing parameters.
The local-polynomial regression CVKS low-support objective path now uses a named bandwidth-method code and aligns the R and C routing contracts, reducing the risk of drift between search metadata and native objective handling.
Expanded focused tests, demos, benchmarks, and release-protocol sentinels across the public exported surface. The release protocol now requires an explicit public-exported-surface inventory, estimator-family sentinels, documentation/demo/benchmark smoke coverage, and installed package proof before release-ready claims.

np 0.70-4

CRAN release: 2026-06-26

Hardened proactive C cleanup paths by clearing extended nearest-neighbor alias state in the shared estimator cleanup helper, routing selected bandwidth-constructor allocation and unsupported-method failures through existing cleanup labels, and guarding large np_kernelsum() allocation-size products before calling the legacy alloc_vecd(int) allocator.
Added nomad = "auto" for local-polynomial degree searches. The automatic policy uses exhaustive Powell-style degree search for small one-dimensional degree lattices where evidence showed it is more reliable than heuristic NOMAD restarts, while preserving NOMAD for larger or explicitly requested search surfaces.
Bandwidth and fit summaries now report cumulative search diagnostics more clearly. NOMAD cache output distinguishes repeated point lookups avoided by NOMAD from true objective computations; Powell summaries expose repeated objective lookups avoided by the package-side cache; hybrid NOMAD+Powell and exhaustive Powell timing labels are reported consistently.
Base-graphics plot scaling and legends have been hardened for multi-panel displays. Plots now honor active mfrow/cex behavior more consistently, use role-appropriate legend sizes, and draw factor legends with point glyphs matching the plotted estimates.
Fixed-bandwidth bias-corrected bootstrap plot intervals have been reworked across the supported plot families. Bias-corrected centers and intervals now share a common centering contract for regression, unconditional density and distribution, conditional density and distribution, single-index, partially linear, and smooth-coefficient plots where supported.
Pair/block/geometric bootstrap intervals in regression-style routes now use smooth-bootstrap bias correction when center = "bias-corrected" is requested; wild-bootstrap regression intervals retain the standard wild-bootstrap correction. Density and distribution routes use perturbation-based smooth-bootstrap bias correction. Gaussian, uniform, and second-order Epanechnikov perturbation kernels are supported; higher-order signed perturbation kernels fail closed.
Bias-corrected plot support now covers mixed-data unconditional density and distribution, mixed-data conditional density and distribution, and conditional gradient displays. Non-fixed/adaptive/generalized bandwidth bias correction and empirically unsupported npqreg bias correction remain fail-closed with explicit messages.
Bias-corrected plot payloads returned by output = "data" and output = "both" now consistently expose fitted values, bias-corrected values, gradients, gradient bias corrections, and interval payloads with the same centering contract used for rendering.
Derivative-order validation has been tightened across plot/gradient routes so unsupported derivative requests fail early or are represented consistently rather than silently plotting a lower-order derivative.
Single-index formula dispatch with explicit bandwidths has been repaired, and single-index bias-corrected plot centers, output payloads, and legends have been tightened.
The native loadNamespace("crs") call construction used by CRS-backed native search routes is now protected, resolving the RCHK protection finding without changing the native NOMAD search contract.

np 0.70-3

CRAN release: 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

CRAN release: 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

CRAN release: 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.