# npRmpi 0.70-3

- Added MPI-aware `nplsqreg()`/`nplsqregbw()` support for location-scale
  quantile regression, including formula/data and bandwidth-object workflows,
  scalar/vector `tau`, prediction, residual extraction, summaries, and plot
  routes built on the shared quantile plotting engine.
- Supported MPI MADS/NOMAD-backed bandwidth-search routes now use the final
  native `crs` NOMAD C API rather than the retired legacy `snomadr()`
  fallback. 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 in serial-equivalent and MPI-dispatched routes.
- `npindexbw(..., method = "ichimura", regtype = c("ll", "lp"))` now reuses
  the established local-polynomial regression objective evaluator, and MPI
  autodispatch uses a rank-0-driven objective service for the fixed-degree and
  NOMAD degree-search Ichimura local-polynomial routes. Focused sentinel runs
  preserved payloads while restoring useful scaling for the formerly flat
  local-linear and local-polynomial single-index rows.
- MPI fanout coverage has been extended for computationally heavy bootstrap
  workloads in specification, dependence, distribution-equality, quantile, and
  symmetry tests, and plot-bootstrap RNG streams now restore the
  serial-equivalent final state after MPI fanout.
- The shipped `npplreg` attach-mode demo now explicitly finalizes the master
  rank after successful attach shutdown, hardening release-sentinel teardown
  without changing estimator or runtime defaults.
- MPI auto-dispatch for `nplsqreg()` now materializes named method-level
  `...` arguments before worker fanout, preserving user-supplied `scale` and
  option values that arrive through S3 `..n` placeholders.
- `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. The option is synchronized to MPI workers in
  autodispatch sessions; 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 and are synchronized to MPI workers in
  autodispatch sessions. 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 in active MPI sessions match serial
  `np` objective values to numerical precision while substantially reducing
  local-polynomial CV evaluation time.
- 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 serial/MPI objective
  parity 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 preserve serial/MPI objective
  parity 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 in serial and MPI sessions.
- 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 in serial and MPI sessions while reducing objective and
  full-search runtime.
- Large-sample categorical-only regression now uses the MPI-safe
  profile-compressed route under `options(np.categorical.compress = TRUE)`,
  which is enabled by default. This categorical route is independent of
  `options(np.tree)`. Repeated predictor profiles are compressed before
  bandwidth search, fitting, prediction/evaluation, standard errors,
  hat-helper use, and plot bootstrap helpers, preserving the established
  dense-route numerical contract while reducing repeated work.
- 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. Very fast
  compressed routes may remain overhead-floor limited, so MPI acceleration is
  most useful once the uncompressed work would be genuinely long-running.
- 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 or MPI dispatch
  modes.
- Fixed `npcdens()` and `npcdist()` formula calls with explicit numeric
  smoothing parameters, such as `npcdist(y ~ x, data = dat, bws = c(.25,.25))`,
  so `npRmpi` preserves the established formula-to-bandwidth-object rewrite
  before MPI autodispatch.
- Hardened the `npudist()` formula route so formula calls are handled before
  MPI autodispatch.
- `npplreg()` now activates the already validated categorical regression
  compression path for its internal all-categorical `Z` regressions when
  `options(np.categorical.compress = TRUE)` is enabled, without requiring users
  to request continuous kd-tree acceleration through `options(np.tree)`.
- 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.
- Fixed MPI conditional-density and conditional-distribution NOMAD degree-search
  routes so Powell refinement and promoted wrappers such as `npconmode()` reach
  the intended bandwidth-object construction path rather than the pre-search
  autodispatch preflight used by non-degree-search routes.

# npRmpi 0.70-2

- `npqreg()` is now a fully fledged MPI-aware 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
  `npRmpi` 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. The fixed-bandwidth gradient path now uses the
  MPI-aware helper route.
- `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 MPI-fanned 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. The
  MPI implementation dispatches the corresponding conditional hat-apply
  helper work across the active worker pool where applicable.
- Core and semiparametric S3 prediction paths have been hardened around
  `newdata`, native evaluation-argument precedence, formula RHS
  validation, and `se.fit` handling while preserving `npRmpi` route
  independence.
- 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.
- MPI lifecycle and plotting routes received additional hardening,
  including soft `npRmpi.quit()` behavior, local object-fed plot
  computation where required, and explicit fanout of applicable
  bootstrap workloads.
- 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,
  route-aware MPI probes, and serial/MPI parity checks for the newly
  promoted estimator families.

# npRmpi 0.70-1

- The default multistart cap for bandwidth selection now follows
  `min(2, p)` across the mirrored 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 mirror change are documented in
  `np-master/benchmarks/validation/`, with a summary note kept in this
  repository's `benchmarks/validation/` folder.
- LP-capable front ends now accept `nomad=TRUE` as a documented
  convenience preset for the recommended automatic NOMAD
  local-polynomial route, mirroring the serial package defaults and
  help-page guidance.
