Add hosted documentation site (MkDocs Material, pkgdown-style)

.github/workflows/docs.yml

@@ -0,0 +1,60 @@
+name: Docs
+
+on:
+  push:
+    branches: [main]
+    paths:
+      - "docs/**"
+      - "mkdocs.yml"
+      - ".github/workflows/docs.yml"
+  workflow_dispatch:
+
+# Allow only one concurrent deployment, and let in-progress runs finish.
+concurrency:
+  group: pages
+  cancel-in-progress: false
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+jobs:
+  build:
+    name: Build site
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out repo
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install MkDocs
+        run: |
+          python -m pip install -U pip
+          python -m pip install \
+            "mkdocs-material>=9.5" \
+            "pymdown-extensions>=10"
+
+      - name: Build (strict)
+        run: mkdocs build --strict
+
+      - name: Upload Pages artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: site
+
+  deploy:
+    name: Deploy to GitHub Pages
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    steps:
+      - name: Deploy
+        id: deployment
+        uses: actions/deploy-pages@v4

.gitignore

@@ -23,3 +23,6 @@ tools/NNS/src/*.o
 
 # Stray R plotting artifact from headless regeneration
 Rplots.pdf
+
+# MkDocs build output
+/site/

README.md

@@ -4,6 +4,7 @@
 
 [![PyPI package](https://img.shields.io/badge/package-ovvo--nns-blue)](https://pypi.org/project/ovvo-nns/)
 [![Python](https://img.shields.io/badge/python-3.11%2B-blue)](https://www.python.org/)
+[![Docs](https://img.shields.io/badge/docs-ovvo--financial.github.io-blue)](https://ovvo-financial.github.io/NNS-python/)
 [![License](https://img.shields.io/badge/license-GPL--3.0--only-blue)](LICENSE)
 
 `ovvo-nns` brings Nonlinear Nonparametric Statistics to Python as the `nns` import package. It is a parity-focused port of the R `NNS` 13.0+ package, designed for real-world data that violate symmetry, linearity, or distributional assumptions.
@@ -178,6 +179,9 @@ uv run python examples/run_all_vignettes.py
 
 ## Documentation
 
+The full documentation site is hosted at
+**<https://ovvo-financial.github.io/NNS-python/>**.
+
 - [API reference manual](docs/api_reference.md)
 - [API status and known gaps](docs/api_status.md)
 - [Behavior conventions and intentional divergences](docs/conventions.md)

docs/index.md

@@ -0,0 +1,119 @@
+<img src="assets/nns_hex_sticker.png" width="150" alt="NNS hex sticker" style="border: none; outline: none; margin: 0; padding: 0; display: block;">
+
+[![PyPI package](https://img.shields.io/pypi/v/ovvo-nns?label=ovvo-nns&color=2780e3)](https://pypi.org/project/ovvo-nns/)
+[![Python](https://img.shields.io/badge/python-3.11%2B-2780e3)](https://www.python.org/)
+[![License](https://img.shields.io/badge/license-GPL--3.0--only-2780e3)](https://github.com/OVVO-Financial/NNS-python/blob/main/LICENSE)
+
+# NNS Python
+
+`ovvo-nns` brings **Nonlinear Nonparametric Statistics** to Python as the `nns`
+import package. It is a parity-focused port of the R `NNS` 13.0+ package,
+designed for real-world data that violate symmetry, linearity, or distributional
+assumptions.
+
+NNS is built around partial moments — the lower and upper components of variance —
+and uses them across nonlinear dependence, correlation, causation, regression,
+classification, forecasting, stochastic dominance, stochastic superiority, Monte
+Carlo simulation, and numerical differentiation workflows.
+
+!!! note "Origin"
+    NNS was created by Fred Viole as the companion R package to Viole, F. and
+    Nawrocki, D. (2013), *Nonlinear Nonparametric Statistics: Using Partial
+    Moments*. **Book (2nd Edition):** <https://ovvo-financial.github.io/NNS/book/>.
+    For a direct quantitative finance implementation of NNS, see
+    [OVVO Labs](https://www.ovvolabs.com).
+
+## Package at a glance
+
+| Item | Value |
+|---|---|
+| Distribution package | `ovvo-nns` |
+| Import package | `nns` |
+| Python | `>=3.11` |
+| Required runtime dependencies | NumPy, SciPy |
+| R required at runtime | No |
+| Native acceleration | Private, optional `nns._nnscore` kernels where available |
+| Public API status | Stable, parity-focused |
+| License | GPL-3.0-only |
+
+The public package is Python-native and does not call R at runtime. Some core
+kernels can use the private `_nnscore` extension when it is present, while public
+functions keep Python implementations and explicit fallback behavior.
+
+## Get started
+
+<div class="grid cards" markdown>
+
+-   :material-download: **[Install](install.md)**
+
+    `pip install ovvo-nns`, then `import nns`.
+
+-   :material-rocket-launch: **[Quick start](quick_start.md)**
+
+    Partial moments, dependence, regression, and forecasting in a few lines.
+
+-   :material-book-open-variant: **[API reference](api_reference.md)**
+
+    Function-by-function index with R `NNS` name crosswalks.
+
+-   :material-check-decagram: **[API status](api_status.md)**
+
+    Implemented, partial, guarded, and known-gap paths.
+
+</div>
+
+## Main API areas
+
+| Area | Representative functions |
+|---|---|
+| Partial moments | `lpm`, `upm`, `lpm_ratio`, `upm_ratio`, `pm_matrix` |
+| Classical moment helpers | `mean_pm`, `var_pm`, `skew_pm`, `kurt_pm`, `nns_moments` |
+| Dependence, correlation, copula | `nns_dep`, `nns_cor`, `nns_copula` |
+| Causation | `nns_causation`, `causal_matrix` |
+| Regression and classification | `nns_reg`, `nns_m_reg`, `nns_stack`, `nns_boost` |
+| Forecasting | `nns_seas`, `nns_arma`, `nns_arma_optim`, `nns_var` |
+| Distribution tools | `nns_cdf`, `nns_anova`, `nns_norm` |
+| Stochastic dominance | `fsd`, `ssd`, `tsd`, `nns_sd_cluster`, `sd_efficient_set` |
+| Stochastic superiority and simulation | `nns_ss`, `nns_mc`, `nns_meboot` |
+| Differentiation | `nns_diff`, `dy_dx`, `dy_d` |
+| Categorical helpers | `encode_factor_codes`, `factor_2_dummy`, `factor_2_dummy_fr`, `prepare_factor_predictors` |
+
+See [API status](api_status.md) for implemented, partial, guarded, and known-gap
+paths.
+
+## Design boundaries
+
+NNS Python prioritizes stable public behavior from installed R NNS 13.0+, not
+private helper parity. The package returns NumPy arrays and plain dictionaries
+rather than R `data.table` objects, uses explicit Python errors for several
+unsafe R coercions, and generally ignores plotting side effects.
+
+See [behavior conventions](conventions.md) for detailed compatibility notes and
+[parity with R NNS](parity.md) for the parity target and automation.
+
+## Links
+
+- [View on PyPI](https://pypi.org/project/ovvo-nns/)
+- [Browse source code](https://github.com/OVVO-Financial/NNS-python)
+- [Report a bug](https://github.com/OVVO-Financial/NNS-python/issues)
+
+## License
+
+[GPL-3.0-only](https://www.gnu.org/licenses/gpl-3.0.en.html)
+
+## Citation
+
+NNS is the companion to Viole, F. and Nawrocki, D. (2013),
+*Nonlinear Nonparametric Statistics: Using Partial Moments* (ISBN: 1490523995).
+2nd edition: <https://ovvo-financial.github.io/NNS/book/>.
+
+## Developers
+
+- **Fred Viole** — author and maintainer
+- **Roberto Spadim** — contributor
+- **Rasheed Khoshnaw** — contributor
+
+## Attribution
+
+Upstream R package and reference implementation:
+[OVVO-Financial/NNS](https://github.com/OVVO-Financial/NNS).

docs/install.md

@@ -0,0 +1,55 @@
+# Install
+
+## From PyPI
+
+```bash
+pip install ovvo-nns
+```
+
+The distribution package is **`ovvo-nns`**; the import package is **`nns`**:
+
+```python
+import nns
+
+print(nns.__version__)
+```
+
+Installing `ovvo-nns` includes the Matplotlib plotting API (`nns.plotting`).
+Matplotlib is a regular dependency and is imported lazily, so `import nns` stays
+light. See the [plot parity policy](plot_parity_policy.md) for details.
+
+## Requirements
+
+| Requirement | Value |
+|---|---|
+| Python | `>=3.11` (CPython 3.11, 3.12, 3.13, 3.14) |
+| Runtime dependencies | NumPy, SciPy, Matplotlib |
+| R at runtime | Not required |
+
+R is used only for parity tests and local cache regeneration, never at normal
+runtime.
+
+## Wheels vs. source builds
+
+Published wheels should be preferred when available. They ship the optional
+private native extension (`nns._nnscore`) prebuilt for supported platforms.
+
+Source builds compile the optional native extension with
+[`scikit-build-core`](https://scikit-build-core.readthedocs.io/) and
+[`nanobind`](https://nanobind.readthedocs.io/), which require a C++17 toolchain.
+Public APIs keep Python implementations and explicit fallback behavior, so the
+native extension remains a private, benchmark-backed implementation detail rather
+than a public API.
+
+## Development install
+
+```bash
+uv sync --group dev
+uv run pytest
+uv run ruff check .
+uv run mypy
+```
+
+The default parity suite is cache-backed and does not require `Rscript`.
+`Rscript` and the R `NNS` package are needed only when regenerating parity caches
+or running live R comparison scripts.

docs/quick_start.md

@@ -0,0 +1,75 @@
+# Quick start
+
+These snippets mirror the runnable
+[example vignettes](https://github.com/OVVO-Financial/NNS-python/tree/main/examples/vignettes),
+which are exercised in CI so they stay in sync with the package.
+
+## Partial moments
+
+```python
+import numpy as np
+from nns import lpm, upm
+
+x = np.array([-2.0, -1.0, 0.5, 3.0], dtype=np.float64)
+
+lower = lpm(degree=2, target=0.0, x=x)
+upper = upm(degree=2, target=0.0, x=x)
+
+print("lower partial moment:", lower)
+print("upper partial moment:", upper)
+```
+
+## Nonlinear dependence
+
+```python
+import numpy as np
+from nns import nns_cor, nns_dep
+
+grid = np.linspace(-2.0, 2.0, 80, dtype=np.float64)
+y = grid**2
+
+print("NNS dependence:", nns_dep(grid, y))
+print("NNS correlation:", nns_cor(grid, y))
+```
+
+## Nonlinear regression
+
+Fit a nonlinear regression and estimate new points:
+
+```python
+import numpy as np
+from nns import nns_reg
+
+x = np.linspace(-3.0, 3.0, 80, dtype=np.float64)
+y = np.sin(x) + 0.2 * x
+points = np.array([-1.5, 0.0, 1.5], dtype=np.float64)
+
+fit = nns_reg(x, y, point_est=points, confidence_interval=None)
+
+print("R2:", fit["R2"])
+print(np.column_stack((points, fit["Point.est"])))
+```
+
+## Forecasting
+
+Forecast a univariate series:
+
+```python
+import numpy as np
+from nns import nns_arma, nns_seas
+
+t = np.arange(1, 60, dtype=np.float64)
+series = 10.0 + np.sin(t / 3.0) + 0.05 * t
+
+seasonality = nns_seas(series, modulo=[3, 4, 6], mod_only=True)
+forecast = nns_arma(series, h=3, seasonal_factor=4, method="lin")
+
+print("best seasonal period:", seasonality["best.period"])
+print("forecast:", forecast)
+```
+
+## Next steps
+
+- Browse the full [API reference](api_reference.md).
+- Check the [API status](api_status.md) for partial, guarded, and known-gap paths.
+- Read the [behavior conventions](conventions.md) for intentional divergences from R.

docs/release_notes.md

@@ -0,0 +1,31 @@
+# Release notes
+
+The authoritative, per-version release history is published to:
+
+- **PyPI release history:** <https://pypi.org/project/ovvo-nns/#history>
+- **GitHub Releases:** <https://github.com/OVVO-Financial/NNS-python/releases>
+
+The currently published version is shown by the badge on the
+[home page](index.md) and by:
+
+```python
+import nns
+
+print(nns.__version__)
+```
+
+## Versioning
+
+`ovvo-nns` uses standard semantic-style version numbers (`MAJOR.MINOR.PATCH`).
+The public API is **stable and parity-focused**: documented public behavior is
+not expected to break across minor releases. Known partial, guarded, and
+known-gap paths are tracked on the [API status](api_status.md) page, and
+intentional divergences from R `NNS` are recorded in the
+[conventions](conventions.md).
+
+Version numbers are kept consistent across `pyproject.toml`, the package
+`__version__`, and the README. This is enforced in CI by
+`scripts/check_version_consistency.py`.
+
+For how releases are built, signed, and published, see the
+[release process](releasing.md).