Mark NNS Python as stable and document native acceleration

.github/workflows/native-backend-ci.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11", "3.12"]
+        python-version: ["3.11", "3.12", "3.13", "3.14"]
 
     steps:
       - name: Check out repo

docs/api_status.md

@@ -3,7 +3,7 @@
 This page summarizes the public NNS Python API surface, known gaps, guarded paths,
 and design boundaries.
 
-NNS Python is an alpha, parity-focused Python port of installed R NNS 13.0,
+NNS Python is a stable, parity-focused Python port of installed R NNS 13.0,
 implemented natively in Python on top of NumPy and SciPy. It does not
 wrap R, call the R package at runtime, or depend on compiled R/C++ shims. The
 goal is public input/output compatibility where R behavior is stable,
@@ -81,7 +81,7 @@ invariant, and property coverage.
 - Stochastic exact stream parity is not expected. Stochastic paths use NumPy RNG
   and are tested structurally/statistically.
 - Plotting side effects from R APIs are generally ignored; NNS Python returns data.
-- Stochastic-dominance performance work stays pure NumPy for alpha. The current
+- Stochastic-dominance performance work stays pure NumPy for now. The current
   implementation mirrors R's sorted-column/prefix-sum algorithm and adds
   Python-specific guard pruning, kept-only active-set scans for degree 2/3 and
   degree-1 continuous calls, and an exact order-statistic matrix for large
@@ -115,7 +115,8 @@ examples include:
 
 ## Release-Relevant Caveats
 
-- NNS Python is alpha. The public API is parity-focused but not declared stable.
+- The public API is stable and parity-focused. Behavior is not expected to break
+  across minor releases.
 - This is not full R parity yet.
 - `dy_d` scalar and vectorized point/distribution modes are covered on focused
   fixtures. Multi-row mixed derivative point matrices intentionally use

docs/conventions.md

@@ -2,10 +2,12 @@
 
 ## Build
 
-NNS Python is currently a pure-Python/NumPy/SciPy port. The earlier native extension
-scaffolding was removed after the core port demonstrated pure NumPy/SciPy parity
-and competitive performance. Reintroduce native code only as a deliberate future
-change backed by benchmarks.
+NNS Python is a Python-native NumPy/SciPy port with optional private native
+acceleration through `nns._nnscore` where available. Source builds use
+`scikit-build-core` and `nanobind` to compile the extension; published wheels
+should be preferred when available. Public APIs keep Python implementations and
+explicit fallback behavior, so native code remains a deliberate,
+benchmark-backed implementation detail rather than a public API.
 
 ## Degree-Zero Boundary
 
@@ -90,7 +92,7 @@ data-frame grouping or filtering. R remains faster on some large finance
 fixtures because its C++ path walks merged sorted thresholds in tight parallel
 loops with minimal temporaries; NNS Python instead uses NumPy order-statistic blocks,
 `searchsorted`, contiguous column storage, and early-exit scans to stay
-dependency-light and pure Python for alpha.
+dependency-light and pure Python.
 
 `nns_cdf` maps to R's `NNS.CDF` deterministic non-plotting paths. It is a
 partial-moment distribution wrapper rather than a textbook ECDF: `degree = 0`