From 51b018089ff7a73ddf0c62d673fe48e8c32bbea6 Mon Sep 17 00:00:00 2001
From: gitRasheed <rasheed.khoshnaw@gmail.com>
Date: Sat, 4 Jul 2026 19:20:24 +0100
Subject: [PATCH] Deduplicate native dispatch and warn on ignored R-compat
 parameters

- core.py: collapse lpm/upm/lpm_ratio/upm_ratio into one moment and one
  ratio implementation; numerics unchanged, including the degree-0
  equality convention and non-finite fallback behavior.
- _native.py: add native_fn(name) and use it everywhere, replacing 18
  per-call hasattr checks against the pinned extension. co_moments picks
  its kernel (and the d_lpm_v degree swap) in one resolver.
- Warn (UserWarning) when a caller sets an accepted-for-R-compatibility
  parameter that has no effect: ncores, nowcast, obj_fn in nns_var,
  location/dist/return_values/plot_regions in nns_m_reg, return_values
  in nns_reg. Console-verbosity flags (status, print_trace, messages,
  elaps) stay silent with a comment; NNS Python never prints.
- Remove dead code: the no-op _reject_deferred_paths stub and internal
  ncores=1 forwarding in nns_var/nns_m_reg.
- Update invariant tests to patch native_fn instead of nnscore.
- Refresh uv.lock.

Verified: full suite (cache-only) 2202 passed; the 3 practical-example
failures are pre-existing on clean main in this environment and green in
CI. Native and pure-Python paths spot-checked identical for all core and
co-moment functions. ruff and mypy --strict clean.
---
 src/nns/_helpers.py                           |  26 +-
 src/nns/_native.py                            |   7 +
 src/nns/arma.py                               |   7 +-
 src/nns/boost.py                              |   2 +-
 src/nns/central_tendencies.py                 |  14 +-
 src/nns/co_moments.py                         |  55 +-
 src/nns/copula.py                             |   8 +-
 src/nns/core.py                               | 113 ++--
 src/nns/dependence.py                         |  14 +-
 src/nns/diff.py                               |   2 +-
 src/nns/meboot.py                             |   2 +-
 src/nns/multivariate_regression.py            |  10 +-
 src/nns/part.py                               |   8 +-
 src/nns/pm_matrix.py                          |   8 +-
 src/nns/regression.py                         |  34 +-
 src/nns/stack.py                              |   6 +-
 src/nns/stochastic_superiority.py             |  10 +-
 src/nns/var.py                                |  11 +-
 .../test_native_original_src_coverage.py      |  19 +-
 uv.lock                                       | 485 +++++++++++++++++-
 20 files changed, 653 insertions(+), 188 deletions(-)

diff --git a/src/nns/_helpers.py b/src/nns/_helpers.py
index e333da76..6618aaa8 100644
--- a/src/nns/_helpers.py
+++ b/src/nns/_helpers.py
@@ -1,9 +1,27 @@
 from __future__ import annotations
 
+import warnings
+
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
+
+
+def _warn_unsupported(**was_set: bool) -> None:
+    """Warn about R-compatibility parameters that have no effect in NNS Python.
+
+    Each keyword names a parameter; pass True when the caller deviated from the
+    default and would therefore expect the parameter to do something.
+    """
+    ignored = [name for name, flag in was_set.items() if flag]
+    if ignored:
+        warnings.warn(
+            f"{', '.join(ignored)}: accepted for R NNS API compatibility "
+            "but not implemented in NNS Python; ignored.",
+            UserWarning,
+            stacklevel=3,
+        )
 
 
 def _fast_lm(x: NDArray[np.float64], y: NDArray[np.float64]) -> tuple[float, float]:
@@ -17,9 +35,9 @@ def _fast_lm(x: NDArray[np.float64], y: NDArray[np.float64]) -> tuple[float, flo
     if x_values.size == 0:
         raise ValueError("x and y must be non-empty.")
 
-    native = nnscore()
-    if native is not None and hasattr(native, "fast_lm"):
-        result = native.fast_lm(np.ascontiguousarray(x_values), np.ascontiguousarray(y_values))
+    native_fast_lm = native_fn("fast_lm")
+    if native_fast_lm is not None:
+        result = native_fast_lm(np.ascontiguousarray(x_values), np.ascontiguousarray(y_values))
         coef = result["coef"]
         return float(coef[0]), float(coef[1])
 
diff --git a/src/nns/_native.py b/src/nns/_native.py
index b071e530..7c3eb41b 100644
--- a/src/nns/_native.py
+++ b/src/nns/_native.py
@@ -15,3 +15,10 @@
 def nnscore() -> Any | None:
     """Return the optional private NNS-core extension module when available."""
     return cast(Any | None, _nnscore)
+
+
+def native_fn(name: str) -> Any | None:
+    """Return a callable from the NNS-core extension, or None when unavailable."""
+    if _nnscore is None:
+        return None
+    return getattr(_nnscore, name, None)
diff --git a/src/nns/arma.py b/src/nns/arma.py
index 5e55105f..71f7899d 100644
--- a/src/nns/arma.py
+++ b/src/nns/arma.py
@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._helpers import _fast_lm
+from nns._helpers import _fast_lm, _warn_unsupported
 from nns.co_moments import co_lpm, co_upm
 from nns.dependence import _gravity
 from nns.mc import nns_mc
@@ -32,7 +32,8 @@ def nns_arma_optim(
     plot: bool = False,
 ) -> dict[str, Any]:
     """Optimize seasonal factors for :func:`nns_arma` like R's ``NNS.ARMA.optim``."""
-    del ncores, print_trace
+    _warn_unsupported(ncores=ncores is not None)
+    del print_trace  # R console trace flag; NNS Python emits no console output.
 
     values = _as_variable(variable)
     original_values = values.copy()
@@ -365,7 +366,7 @@ def nns_arma(
     random_seed: int | None = None,
 ) -> NDArray[np.float64] | dict[str, NDArray[np.float64]]:
     """Autoregressive NNS forecast matching R's installed NNS.ARMA behavior."""
-    del seasonal_plot
+    del seasonal_plot  # R plotting side effect; NNS Python returns data instead.
 
     horizon = int(h)
     if horizon < 1:
diff --git a/src/nns/boost.py b/src/nns/boost.py
index da4c9019..a35f86d5 100644
--- a/src/nns/boost.py
+++ b/src/nns/boost.py
@@ -50,7 +50,7 @@ def nns_boost(
     factor_levels: Sequence[object] | Sequence[Sequence[object] | None] | None = None,
 ) -> BoostResult:
     """Deterministic NNS.boost port using real NNS.reg and NNS.stack internals."""
-    del status
+    del status  # R console progress flag; NNS Python emits no console output.
     type_value = _normalize_type(type)
     if balance:
         type_value = "class"
diff --git a/src/nns/central_tendencies.py b/src/nns/central_tendencies.py
index f89746e9..4cee52fa 100644
--- a/src/nns/central_tendencies.py
+++ b/src/nns/central_tendencies.py
@@ -5,7 +5,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.dependence import _quartiles_like_r_code, _simple_bin_counts
 
 
@@ -60,10 +60,10 @@ def nns_mode(
     if n == 0:
         return np.array([np.nan], dtype=np.float64) if multi else float("nan")
 
-    native = nnscore()
-    if native is not None and hasattr(native, "mode"):
+    native_mode = native_fn("mode")
+    if native_mode is not None:
         native_result = np.asarray(
-            native.mode(np.ascontiguousarray(finite), discrete, multi), dtype=np.float64
+            native_mode(np.ascontiguousarray(finite), discrete, multi), dtype=np.float64
         )
         if multi:
             return native_result
@@ -87,9 +87,9 @@ def nns_gravity(x: NDArray[np.float64], discrete: bool = False) -> float:
     if bool(np.all(finite == finite[0])):
         return float(finite[0])
 
-    native = nnscore()
-    if native is not None and hasattr(native, "gravity"):
-        return float(native.gravity(np.ascontiguousarray(finite), discrete))
+    native_gravity = native_fn("gravity")
+    if native_gravity is not None:
+        return float(native_gravity(np.ascontiguousarray(finite), discrete))
 
     value_range = float(abs(finite[-1] - finite[0]))
     if value_range == 0.0:
diff --git a/src/nns/co_moments.py b/src/nns/co_moments.py
index e2f99ac3..0e045160 100644
--- a/src/nns/co_moments.py
+++ b/src/nns/co_moments.py
@@ -1,9 +1,11 @@
 from __future__ import annotations
 
+from typing import Any
+
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.core import _as_degree, _as_targets
 
 
@@ -69,33 +71,19 @@ def _co_moment(
     degree_x = _as_degree(degree_x)
     degree_y = _as_degree(degree_y)
 
-    native = nnscore()
-    if (
-        native is not None
-        and x_targets.size > 0
-        and y_targets.size > 0
-        and _native_function_available(native, x_side, y_side)
-    ):
-        x_contig = np.ascontiguousarray(x_values)
-        y_contig = np.ascontiguousarray(y_values)
-        x_targets_contig = np.ascontiguousarray(x_targets)
-        y_targets_contig = np.ascontiguousarray(y_targets)
-        if x_side is _lower and y_side is _lower:
-            native_result = native.co_lpm_v(
-                degree_x, degree_y, x_contig, y_contig, x_targets_contig, y_targets_contig
-            )
-        elif x_side is _upper and y_side is _upper:
-            native_result = native.co_upm_v(
-                degree_x, degree_y, x_contig, y_contig, x_targets_contig, y_targets_contig
-            )
-        elif x_side is _upper and y_side is _lower:
-            native_result = native.d_lpm_v(
-                degree_y, degree_x, x_contig, y_contig, x_targets_contig, y_targets_contig
-            )
-        else:
-            native_result = native.d_upm_v(
-                degree_x, degree_y, x_contig, y_contig, x_targets_contig, y_targets_contig
-            )
+    native, swap_degrees = _native_co_moment(x_side, y_side)
+    if native is not None and x_targets.size > 0 and y_targets.size > 0:
+        first_degree, second_degree = (
+            (degree_y, degree_x) if swap_degrees else (degree_x, degree_y)
+        )
+        native_result = native(
+            first_degree,
+            second_degree,
+            np.ascontiguousarray(x_values),
+            np.ascontiguousarray(y_values),
+            np.ascontiguousarray(x_targets),
+            np.ascontiguousarray(y_targets),
+        )
         moments = np.asarray(native_result, dtype=np.float64).reshape(-1)
         if np.asarray(target_x).ndim == 0 and np.asarray(target_y).ndim == 0:
             return float(moments[0])
@@ -115,14 +103,15 @@ def _co_moment(
     return moments
 
 
-def _native_function_available(native: object, x_side: object, y_side: object) -> bool:
+def _native_co_moment(x_side: object, y_side: object) -> tuple[Any | None, bool]:
+    """Return the native kernel for a side pair and whether it swaps degree order."""
     if x_side is _lower and y_side is _lower:
-        return hasattr(native, "co_lpm_v")
+        return native_fn("co_lpm_v"), False
     if x_side is _upper and y_side is _upper:
-        return hasattr(native, "co_upm_v")
+        return native_fn("co_upm_v"), False
     if x_side is _upper and y_side is _lower:
-        return hasattr(native, "d_lpm_v")
-    return hasattr(native, "d_upm_v")
+        return native_fn("d_lpm_v"), True
+    return native_fn("d_upm_v"), False
 
 
 def _as_pair(
diff --git a/src/nns/copula.py b/src/nns/copula.py
index 7b814429..18514e12 100644
--- a/src/nns/copula.py
+++ b/src/nns/copula.py
@@ -7,7 +7,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.co_moments import _as_pair
 from nns.dependence import _dpm_nd
 from nns.pm_matrix import pm_matrix
@@ -48,10 +48,10 @@ def _copula(
     target: NDArray[np.float64],
     continuous: bool,
 ) -> float:
-    native = nnscore()
-    if native is not None and hasattr(native, "copula_nd"):
+    native_copula = native_fn("copula_nd")
+    if native_copula is not None:
         return float(
-            native.copula_nd(
+            native_copula(
                 np.ascontiguousarray(np.ravel(values, order="F")),
                 values.shape[0],
                 values.shape[1],
diff --git a/src/nns/core.py b/src/nns/core.py
index 04baa515..50496767 100644
--- a/src/nns/core.py
+++ b/src/nns/core.py
@@ -3,7 +3,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 
 
 def lpm(
@@ -11,30 +11,15 @@ def lpm(
     target: float | NDArray[np.float64],
     x: NDArray[np.float64],
 ) -> float | NDArray[np.float64]:
-    values = _as_1d_values(x)
-    targets = _as_targets(target)
-    degree = _as_degree(degree)
-
-    native = nnscore()
-    if (
-        native is not None
-        and hasattr(native, "lpm")
-        and targets.size > 0
-        and _native_safe(values, targets)
-    ):
-        native_result = native.lpm(
-            degree,
-            float(targets[0]) if np.asarray(target).ndim == 0 else targets,
-            np.ascontiguousarray(values),
-        )
-        return _result_for_target(np.asarray(native_result, dtype=np.float64).reshape(-1), target)
+    return _moment(degree, target, x, lower=True)
 
-    if degree == 0:
-        moments = np.mean(values <= targets[:, np.newaxis], axis=1)
-        return _result_for_target(moments, target)
 
-    moments = np.mean(np.maximum(0.0, targets[:, np.newaxis] - values) ** degree, axis=1)
-    return _result_for_target(moments, target)
+def upm(
+    degree: float,
+    target: float | NDArray[np.float64],
+    x: NDArray[np.float64],
+) -> float | NDArray[np.float64]:
+    return _moment(degree, target, x, lower=False)
 
 
 def lpm_ratio(
@@ -42,82 +27,59 @@ def lpm_ratio(
     target: float | NDArray[np.float64],
     x: NDArray[np.float64],
 ) -> float | NDArray[np.float64]:
-    values = _as_1d_values(x)
-    targets = _as_targets(target)
-    degree = _as_degree(degree)
+    return _ratio(degree, target, x, lower=True)
 
-    native = nnscore()
-    if (
-        native is not None
-        and hasattr(native, "lpm_ratio_v")
-        and targets.size > 0
-        and _native_safe(values, targets)
-    ):
-        native_result = native.lpm_ratio_v(
-            degree,
-            np.ascontiguousarray(targets),
-            np.ascontiguousarray(values),
-        )
-        return _result_for_target(np.asarray(native_result, dtype=np.float64).reshape(-1), target)
 
-    if degree == 0:
-        return lpm(degree, target, x)
-
-    lower = lpm(degree, target, x)
-    upper = upm(degree, target, x)
-    with np.errstate(invalid="ignore", divide="ignore"):
-        ratio = np.asarray(lower) / (np.asarray(lower) + np.asarray(upper))
-    return _result_for_target(np.asarray(ratio).reshape(-1), target)
+def upm_ratio(
+    degree: float,
+    target: float | NDArray[np.float64],
+    x: NDArray[np.float64],
+) -> float | NDArray[np.float64]:
+    return _ratio(degree, target, x, lower=False)
 
 
-def upm(
+def _moment(
     degree: float,
     target: float | NDArray[np.float64],
     x: NDArray[np.float64],
+    *,
+    lower: bool,
 ) -> float | NDArray[np.float64]:
     values = _as_1d_values(x)
     targets = _as_targets(target)
     degree = _as_degree(degree)
 
-    native = nnscore()
-    if (
-        native is not None
-        and hasattr(native, "upm")
-        and targets.size > 0
-        and _native_safe(values, targets)
-    ):
-        native_result = native.upm(
-            degree,
-            float(targets[0]) if np.asarray(target).ndim == 0 else targets,
-            np.ascontiguousarray(values),
-        )
+    native = native_fn("lpm" if lower else "upm")
+    if native is not None and targets.size > 0 and _native_safe(values, targets):
+        native_target = float(targets[0]) if np.asarray(target).ndim == 0 else targets
+        native_result = native(degree, native_target, np.ascontiguousarray(values))
         return _result_for_target(np.asarray(native_result, dtype=np.float64).reshape(-1), target)
 
     if degree == 0:
-        moments = np.mean(values > targets[:, np.newaxis], axis=1)
+        # R convention: equality with the target counts toward the lower moment.
+        grid = targets[:, np.newaxis]
+        moments = np.mean(values <= grid if lower else values > grid, axis=1)
         return _result_for_target(moments, target)
 
-    moments = np.mean(np.maximum(0.0, values - targets[:, np.newaxis]) ** degree, axis=1)
+    deviations = targets[:, np.newaxis] - values if lower else values - targets[:, np.newaxis]
+    moments = np.mean(np.maximum(0.0, deviations) ** degree, axis=1)
     return _result_for_target(moments, target)
 
 
-def upm_ratio(
+def _ratio(
     degree: float,
     target: float | NDArray[np.float64],
     x: NDArray[np.float64],
+    *,
+    lower: bool,
 ) -> float | NDArray[np.float64]:
     values = _as_1d_values(x)
     targets = _as_targets(target)
     degree = _as_degree(degree)
 
-    native = nnscore()
-    if (
-        native is not None
-        and hasattr(native, "upm_ratio_v")
-        and targets.size > 0
-        and _native_safe(values, targets)
-    ):
-        native_result = native.upm_ratio_v(
+    native = native_fn("lpm_ratio_v" if lower else "upm_ratio_v")
+    if native is not None and targets.size > 0 and _native_safe(values, targets):
+        native_result = native(
             degree,
             np.ascontiguousarray(targets),
             np.ascontiguousarray(values),
@@ -125,12 +87,13 @@ def upm_ratio(
         return _result_for_target(np.asarray(native_result, dtype=np.float64).reshape(-1), target)
 
     if degree == 0:
-        return upm(degree, target, x)
+        return _moment(degree, target, x, lower=lower)
 
-    lower = lpm(degree, target, x)
-    upper = upm(degree, target, x)
+    lower_moment = np.asarray(lpm(degree, target, x))
+    upper_moment = np.asarray(upm(degree, target, x))
+    numerator = lower_moment if lower else upper_moment
     with np.errstate(invalid="ignore", divide="ignore"):
-        ratio = np.asarray(upper) / (np.asarray(lower) + np.asarray(upper))
+        ratio = numerator / (lower_moment + upper_moment)
     return _result_for_target(np.asarray(ratio).reshape(-1), target)
 
 
diff --git a/src/nns/dependence.py b/src/nns/dependence.py
index 5dd3200d..9cc5c2ba 100644
--- a/src/nns/dependence.py
+++ b/src/nns/dependence.py
@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.co_moments import co_lpm, co_upm, d_lpm, d_upm
 
 
@@ -18,9 +18,9 @@ def nns_dep(
     """Return NNS nonlinear correlation and dependence for a pair of variables."""
     x_values, y_values = _as_pair(x, y)
 
-    native = nnscore()
-    if native is not None and hasattr(native, "nns_dep_pair"):
-        res = native.nns_dep_pair(
+    native_dep_pair = native_fn("nns_dep_pair")
+    if native_dep_pair is not None:
+        res = native_dep_pair(
             np.ascontiguousarray(x_values),
             np.ascontiguousarray(y_values),
             bool(asym),
@@ -287,10 +287,10 @@ def _as_nd_moment_inputs(
 
 
 def _gravity(x: NDArray[np.float64]) -> float:
-    native = nnscore()
-    if native is not None and hasattr(native, "gravity_exact"):
+    native_gravity_exact = native_fn("gravity_exact")
+    if native_gravity_exact is not None:
         values = np.ascontiguousarray(np.asarray(x, dtype=np.float64).reshape(-1))
-        return float(native.gravity_exact(values))
+        return float(native_gravity_exact(values))
     values = np.sort(x[np.isfinite(x)])
     n = values.size
     if n == 0:
diff --git a/src/nns/diff.py b/src/nns/diff.py
index 7f486f80..33f353a9 100644
--- a/src/nns/diff.py
+++ b/src/nns/diff.py
@@ -206,7 +206,7 @@ def dy_d(
     messages: bool = True,
 ) -> dict[str, NDArray[np.float64]]:
     """Partial derivative wrapper for R's ``dy.d_`` numeric matrix path."""
-    del messages
+    del messages  # R console message flag; NNS Python emits no console output.
     x_values = np.asarray(x, dtype=np.float64)
     if x_values.ndim != 2:
         raise ValueError("Please ensure (x) is a matrix or data.frame type object.")
diff --git a/src/nns/meboot.py b/src/nns/meboot.py
index b30b02ea..d16a012b 100644
--- a/src/nns/meboot.py
+++ b/src/nns/meboot.py
@@ -36,7 +36,7 @@ def nns_meboot(
     Stochastic draws use NumPy's RNG, so exact replicate parity with R is not
     expected. Deterministic diagnostics follow the installed R algorithm.
     """
-    del elaps
+    del elaps  # R elapsed-time printout flag; NNS Python emits no console output.
     values = np.asarray(x, dtype=np.float64)
     if values.ndim != 1:
         raise ValueError("x must be a 1D numeric vector.")
diff --git a/src/nns/multivariate_regression.py b/src/nns/multivariate_regression.py
index cf0ca5ff..dcb66b74 100644
--- a/src/nns/multivariate_regression.py
+++ b/src/nns/multivariate_regression.py
@@ -6,6 +6,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
+from nns._helpers import _warn_unsupported
 from nns.central_tendencies import nns_mode
 from nns.dependence import _gravity
 from nns.distance import KValue, nns_distance, nns_distance_path_single_bulk
@@ -40,7 +41,13 @@ def nns_m_reg(
     class_levels: list[object] | None = None,
 ) -> MRegResult:
     """Multivariate numeric regression matching R's NNS.M.reg path."""
-    del location, dist, return_values, plot_regions, ncores
+    _warn_unsupported(
+        location=location is not None,
+        dist=dist != "L2",
+        return_values=return_values is not False,
+        plot_regions=plot_regions,
+        ncores=ncores is not None,
+    )
     type_value = _normalize_type(type)
     x_values, y_values = _validate_inputs(
         x,
@@ -237,7 +244,6 @@ def _regression_points_matrix(
             noise_reduction=noise,
             plot=False,
             multivariate_call=True,
-            ncores=1,
         )
         points = np.asarray(result["x"], dtype=np.float64)
         columns.append(points)
diff --git a/src/nns/part.py b/src/nns/part.py
index b455c117..276d6e7a 100644
--- a/src/nns/part.py
+++ b/src/nns/part.py
@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.central_tendencies import _nearest_int_half_up_array, nns_mode
 from nns.dependence import _gravity
 
@@ -71,9 +71,9 @@ def nns_part(
     xonly = type is not None
     n = x_values.size
 
-    native = nnscore()
-    if native is not None and hasattr(native, "nns_part_off") and noise == "off":
-        res = native.nns_part_off(
+    native_part = native_fn("nns_part_off")
+    if native_part is not None and noise == "off":
+        res = native_part(
             np.ascontiguousarray(x_values),
             np.ascontiguousarray(y_values),
             xonly,
diff --git a/src/nns/pm_matrix.py b/src/nns/pm_matrix.py
index 946e8110..1de3e947 100644
--- a/src/nns/pm_matrix.py
+++ b/src/nns/pm_matrix.py
@@ -6,7 +6,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.core import _as_degree
 
 Target: TypeAlias = float | None | Literal["mean"] | NDArray[np.float64]
@@ -42,9 +42,9 @@ def pm_matrix(
 
     observations = values.shape[0]
 
-    native = nnscore()
-    if native is not None and hasattr(native, "pm_matrix"):
-        native_result = native.pm_matrix(
+    native_pm_matrix = native_fn("pm_matrix")
+    if native_pm_matrix is not None:
+        native_result = native_pm_matrix(
             lpm_degree,
             upm_degree,
             np.ascontiguousarray(targets),
diff --git a/src/nns/regression.py b/src/nns/regression.py
index 5420a653..9027f765 100644
--- a/src/nns/regression.py
+++ b/src/nns/regression.py
@@ -8,8 +8,8 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._helpers import _fast_lm, _is_fcl
-from nns._native import nnscore
+from nns._helpers import _fast_lm, _is_fcl, _warn_unsupported
+from nns._native import native_fn
 from nns.categorical import encode_factor_codes, factor_2_dummy_fr
 from nns.causation import _uni_caus
 from nns.central_tendencies import nns_mode
@@ -58,7 +58,10 @@ def nns_reg(
     factor_levels: Sequence[object] | Sequence[Sequence[object] | None] | None = None,
 ) -> dict[str, Any]:
     """Univariate numeric port of R's NNS.reg."""
-    del return_values, ncores
+    _warn_unsupported(
+        return_values=return_values is not True,
+        ncores=ncores is not None,
+    )
 
     if dim_red_method is not None:
         result = _nns_reg_dimred(
@@ -127,6 +130,8 @@ def nns_reg(
         )
         return result
 
+    # tau, threshold, n_best, and dist only apply to the dim-red and
+    # multivariate paths dispatched above, mirroring R's NNS.reg.
     del tau, threshold, n_best, dist
     x_values, y_values = _validate_univariate_inputs(
         x_for_dispatch,
@@ -138,12 +143,6 @@ def nns_reg(
     class_mode = type_value == "class" or _should_auto_classify(y_values)
     if class_mode:
         noise_reduction = "mode_class"
-    _reject_deferred_paths(
-        point_est=point_for_dispatch,
-        confidence_interval=confidence_interval,
-        smooth=smooth,
-        multivariate_call=multivariate_call,
-    )
     noise = _validate_noise_reduction(noise_reduction)
     point_values = _as_point_est(point_for_dispatch)
     result = _nns_reg_univariate_core(
@@ -260,9 +259,9 @@ def _nns_reg_univariate_core(
         and dep_order != "max"
         and not isinstance(order, str)
     ):
-        native = nnscore()
-        if native is not None and hasattr(native, "nns_reg_mv"):
-            res = native.nns_reg_mv(
+        native_reg_mv = native_fn("nns_reg_mv")
+        if native_reg_mv is not None:
+            res = native_reg_mv(
                 np.ascontiguousarray(x_values),
                 np.ascontiguousarray(y_values),
                 float(dependence),
@@ -905,17 +904,6 @@ def _dimred_order(x_star: NDArray[np.float64], y: NDArray[np.float64], order: Or
     return max(1, computed)
 
 
-def _reject_deferred_paths(
-    *,
-    point_est: NDArray[np.float64] | float | None,
-    confidence_interval: float | None,
-    smooth: bool,
-    multivariate_call: bool,
-    allow_smooth_fallback: bool = False,
-) -> None:
-    del point_est, confidence_interval, smooth, multivariate_call, allow_smooth_fallback
-
-
 def _validate_noise_reduction(value: str) -> NoiseReduction:
     noise = value.lower()
     if noise not in {"off", "mean", "median", "mode", "mode_class"}:
diff --git a/src/nns/stack.py b/src/nns/stack.py
index 57862376..702de542 100644
--- a/src/nns/stack.py
+++ b/src/nns/stack.py
@@ -7,6 +7,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
+from nns._helpers import _warn_unsupported
 from nns.categorical import _balance_class_training, _dense_factor_codes
 from nns.central_tendencies import nns_mode
 from nns.dependence import _gravity
@@ -51,7 +52,10 @@ def nns_stack(
     random_seed: int | None = None,
 ) -> StackResult:
     """Port of R's deterministic numeric/classification NNS.stack orchestration."""
-    del optimize_threshold, status, ncores
+    _warn_unsupported(ncores=ncores is not None)
+    # optimize_threshold defaults to True in R but the threshold search is not
+    # ported; status is R's console progress flag and NNS Python prints nothing.
+    del optimize_threshold, status
     type_value = _normalize_type(type)
     if balance:
         type_value = "class"
diff --git a/src/nns/stochastic_superiority.py b/src/nns/stochastic_superiority.py
index f2f44eee..752641fd 100644
--- a/src/nns/stochastic_superiority.py
+++ b/src/nns/stochastic_superiority.py
@@ -5,7 +5,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
-from nns._native import nnscore
+from nns._native import native_fn
 from nns.meboot import nns_meboot
 from nns.var import lpm_var, upm_var
 
@@ -73,11 +73,9 @@ def _stoch_superiority(
     if xs.size == 0 or ys.size == 0:
         raise ValueError("x and y must both have positive length.")
 
-    native = nnscore()
-    if native is not None and hasattr(native, "stochastic_superiority"):
-        return dict(
-            native.stochastic_superiority(np.ascontiguousarray(xs), np.ascontiguousarray(ys))
-        )
+    native_ss = native_fn("stochastic_superiority")
+    if native_ss is not None:
+        return dict(native_ss(np.ascontiguousarray(xs), np.ascontiguousarray(ys)))
 
     xs = np.sort(xs)
     ys = np.sort(ys)
diff --git a/src/nns/var.py b/src/nns/var.py
index 1f05fc2e..55c0cb77 100644
--- a/src/nns/var.py
+++ b/src/nns/var.py
@@ -8,6 +8,7 @@
 import numpy as np
 from numpy.typing import NDArray
 
+from nns._helpers import _warn_unsupported
 from nns.core import lpm_ratio, upm_ratio
 
 _R_OPTIMIZE_TOL = float(np.finfo(float).eps ** 0.25)
@@ -33,7 +34,12 @@ def nns_var(
     ``interpolated_and_extrapolated`` and ``names`` instead of returning a bare
     data frame as R does.
     """
-    del obj_fn, status, ncores, nowcast
+    _warn_unsupported(
+        obj_fn=obj_fn is not None,
+        ncores=ncores is not None,
+        nowcast=nowcast,
+    )
+    del status  # R console progress flag; NNS Python emits no console output.
 
     method = dim_red_method.lower()
     if method not in {"cor", "nns.dep", "nns.caus", "all"}:
@@ -140,7 +146,6 @@ def _var_interpolate_and_extrapolate(
                 order=None,
                 folds=5,
                 method=1,
-                ncores=1,
                 status=False,
             )["stack"]
             variable_interpolation[missing] = np.asarray(fill, dtype=np.float64)
@@ -149,7 +154,6 @@ def _var_interpolate_and_extrapolate(
                 complete[:, 0],
                 complete[:, 1],
                 order="max",
-                ncores=1,
                 point_est=np.asarray(missing, dtype=np.float64) + 1,
                 plot=False,
                 point_only=True,
@@ -175,7 +179,6 @@ def _var_interpolate_and_extrapolate(
                 h=h,
                 seasonal_factor=None if periods is None else periods,
                 negative_values=float(np.min(variable_interpolation)) < 0.0,
-                ncores=1,
             )
             forecast = np.asarray(result["results"], dtype=np.float64)
             univariate_columns.append(forecast)
diff --git a/tests/invariants/test_native_original_src_coverage.py b/tests/invariants/test_native_original_src_coverage.py
index 1d66edad..692bd1cd 100644
--- a/tests/invariants/test_native_original_src_coverage.py
+++ b/tests/invariants/test_native_original_src_coverage.py
@@ -39,9 +39,9 @@ def native() -> ModuleType:
 
 @pytest.fixture()
 def disable_native(monkeypatch: pytest.MonkeyPatch) -> Iterator[None]:
-    monkeypatch.setattr(core_module, "nnscore", lambda: None)
-    monkeypatch.setattr(co_moments_module, "nnscore", lambda: None)
-    monkeypatch.setattr(pm_matrix_module, "nnscore", lambda: None)
+    monkeypatch.setattr(core_module, "native_fn", lambda name: None)
+    monkeypatch.setattr(co_moments_module, "native_fn", lambda name: None)
+    monkeypatch.setattr(pm_matrix_module, "native_fn", lambda name: None)
     yield
 
 
@@ -153,7 +153,8 @@ def upm(*args: Any, **kwargs: Any) -> float:
 def test_public_lpm_upm_non_finite_values_use_fallback(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    monkeypatch.setattr(core_module, "nnscore", lambda: _FinitePartialMomentNative())
+    fake = _FinitePartialMomentNative()
+    monkeypatch.setattr(core_module, "native_fn", lambda name: getattr(fake, name, None))
     x = np.array([1.0, np.nan, 3.0], dtype=np.float64)
 
     assert np.isnan(lpm(1.0, 0.0, x))
@@ -206,8 +207,10 @@ def test_public_partial_moment_fallback_matches_native(
     del disable_native
     fallback = fallback_call()
     native_module = _native()
-    monkeypatch.setattr(core_module, "nnscore", lambda: native_module)
-    monkeypatch.setattr(co_moments_module, "nnscore", lambda: native_module)
+    monkeypatch.setattr(core_module, "native_fn", lambda name: getattr(native_module, name, None))
+    monkeypatch.setattr(
+        co_moments_module, "native_fn", lambda name: getattr(native_module, name, None)
+    )
     actual = native_call()
     np.testing.assert_allclose(actual, fallback)
 
@@ -220,7 +223,9 @@ def test_public_pm_matrix_fallback_matches_native(
     fallback = pm_matrix(1.0, 1.0, "mean", matrix, True, norm=False)
 
     native_module = _native()
-    monkeypatch.setattr(pm_matrix_module, "nnscore", lambda: native_module)
+    monkeypatch.setattr(
+        pm_matrix_module, "native_fn", lambda name: getattr(native_module, name, None)
+    )
     actual = pm_matrix(1.0, 1.0, "mean", matrix, True, norm=False)
 
     assert actual.keys() == fallback.keys()
diff --git a/uv.lock b/uv.lock
index ccd7719a..5b45cd50 100644
--- a/uv.lock
+++ b/uv.lock
@@ -51,6 +51,138 @@ wheels = [
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 ]
 
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