diff --git a/.github/workflows/native-backend-ci.yml b/.github/workflows/native-backend-ci.yml
index 7cf16abd..c7414763 100644
--- a/.github/workflows/native-backend-ci.yml
+++ b/.github/workflows/native-backend-ci.yml
@@ -40,6 +40,9 @@ jobs:
       - name: Run parity from committed R cache
         run: NNS_R_CACHE_ONLY=1 python -m pytest -q tests/parity
 
+      - name: Run plotting color-fidelity tests
+        run: python -m pytest -q tests/plotting
+
       - name: Run vignette examples
         run: |
           if [ -f tests/docs/test_vignette_examples.py ]; then
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 29fd7cd3..ebbebc88 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -80,8 +80,26 @@ jobs:
           name: sdist
           path: dist/*.tar.gz
 
-  publish_testpypi:
+  check_metadata:
     needs: [build_wheels, build_sdist]
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          path: dist
+          merge-multiple: true
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Validate metadata and README rendering (twine check)
+        run: |
+          # Pin modern tooling so PEP 639 License-Expression / Metadata 2.4 is
+          # recognized regardless of any preinstalled system 'packaging'.
+          python -m pip install -U pip "twine>=6.1" "packaging>=24.2"
+          python -m twine check --strict dist/*
+
+  publish_testpypi:
+    needs: [build_wheels, build_sdist, check_metadata]
     if: github.event_name == 'workflow_dispatch' && inputs.publish == 'testpypi'
     runs-on: ubuntu-latest
     environment: testpypi
@@ -97,7 +115,7 @@ jobs:
           repository-url: https://test.pypi.org/legacy/
 
   publish_pypi:
-    needs: [build_wheels, build_sdist]
+    needs: [build_wheels, build_sdist, check_metadata]
     if: github.event_name == 'push' || (github.event_name == 'workflow_dispatch' && inputs.publish == 'pypi')
     runs-on: ubuntu-latest
     environment: pypi
diff --git a/README.md b/README.md
index 23a76c1c..71cde3dd 100644
--- a/README.md
+++ b/README.md
@@ -39,6 +39,10 @@ The public package is Python-native and does not call R at runtime. Some core ke
 pip install ovvo-nns
 ```
 
+This includes the matplotlib plotting API (`nns.plotting`); matplotlib is a
+regular dependency and is imported lazily, so `import nns` stays light. See
+[`docs/plot_parity_policy.md`](docs/plot_parity_policy.md).
+
 Use the package as `nns`:
 
 ```python
@@ -138,7 +142,7 @@ Important boundaries:
 - Stochastic exact stream parity is not expected because Python paths use NumPy random generation.
 - Factor and class ordering should be passed explicitly when ordering matters.
 - Direct raw-factor `nns_m_reg(..., factor_2_dummy=True)` is intentionally guarded. Use `prepare_factor_predictors(...)` before `nns_m_reg(...)`.
-- Plotting arguments are generally ignored and data is returned instead.
+- Compute functions' `plot` arguments are ignored and data is returned instead; visual plotting is a separate API in `nns.plotting`, color/element-faithful to R but not pixel-diffed.
 
 See [behavior conventions](docs/conventions.md) for detailed compatibility notes.
 
diff --git a/docs/plot_parity_policy.md b/docs/plot_parity_policy.md
index ee37d2f3..4ac91129 100644
--- a/docs/plot_parity_policy.md
+++ b/docs/plot_parity_policy.md
@@ -2,15 +2,36 @@
 
 ## Summary
 
-Graphics-device artifacts are **intentionally not compared** in CI parity. The
-parity suite validates the **returned values** of NNS functions, never the
-generated plots, PDFs, or other graphics-device output.
+Graphics-device artifacts are **intentionally not pixel-compared** in CI
+parity. The parity suite validates the **returned values** of NNS functions,
+never byte-/pixel-identical plot, PDF, or other graphics-device output.
 
 This is a deliberate, permanent policy decision — not an unresolved migration
 blocker. R plotting and Python plotting use different graphics stacks, and a
 faithful value-level port does not require byte-identical (or pixel-identical)
 plot artifacts.
 
+## A visual plotting API now exists (`nns.plotting`)
+
+The Python port now ships a plotting API in the `nns.plotting` subpackage. It is
+**color/element-faithful to R but not pixel-diffed**: tests assert *artist
+colors and which element they sit on*, never rendered images.
+
+- matplotlib is a regular dependency of the package (no optional extra). It is
+  still imported lazily inside each plot function — never at package top level —
+  so `import nns` stays light and does not pull matplotlib in.
+- Each `plot_*` function takes an already-computed NNS result (or the same raw
+  inputs) plus a keyword `ax=None`, returns the `Axes`/`Figure`, and **never**
+  calls `plt.show()`. The compute functions' `plot=False` default behavior is
+  untouched; plotting is a separate opt-in call.
+- Colors are pinned in `nns.plotting.palette` to the exact R `grDevices` hex
+  used by `tools/NNS/R/*.R`. R and matplotlib agree on `steelblue`/`red` but
+  **disagree** on `green` (R `#00FF00` vs mpl `#008000`) and `grey` (R `#BEBEBE`
+  vs mpl `#808080`); the palette pins those so the port stays faithful.
+- Plotting tests live in `tests/plotting/`, run on the headless `Agg` backend,
+  and assert `mcolors.to_hex(...)` of line/scatter/patch artists — **no**
+  pixel/PDF comparison.
+
 ## What is compared
 
 - Numeric return values (scalars, vectors, matrices, nested result dicts) from
@@ -46,9 +67,9 @@ any `*.pdf`, `plot3d`, or `rgl`; the CI workflow
 (`.github/workflows/native-backend-ci.yml`) runs only the invariant suite, the
 cache-only parity suite, `ruff`, `mypy`, and `python -m build`.
 
-## When (and only when) image comparison would be in scope
+## Image comparison remains out of scope
 
-Image or PDF comparison would only be considered if and when the Python package
-grows a real, first-class plotting API that needs validation. There is no such
-API today. Until one exists, no PDF/image comparison is attempted, and adding
-one is explicitly out of scope.
+Even though a first-class plotting API (`nns.plotting`) now exists, image or PDF
+comparison is still **out of scope**. The API is validated by asserting artist
+colors and the element each color sits on (faithful to R's `col=` usage), which
+is sufficient for a value-level port. No PDF/image diffing is attempted.
diff --git a/pyproject.toml b/pyproject.toml
index 5e8c93b1..41eb3cc9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,6 +33,7 @@ keywords = [
 ]
 urls = { "Homepage" = "https://github.com/OVVO-Financial/NNS-python", "Repository" = "https://github.com/OVVO-Financial/NNS-python", "Issues" = "https://github.com/OVVO-Financial/NNS-python/issues", "Source" = "https://github.com/OVVO-Financial/NNS-python", "Project" = "https://github.com/OVVO-Financial/NNS-python" }
 dependencies = [
+    "matplotlib>=3.7",
     "numpy",
     "scipy",
 ]
diff --git a/src/nns/plotting/__init__.py b/src/nns/plotting/__init__.py
new file mode 100644
index 00000000..9bf9f266
--- /dev/null
+++ b/src/nns/plotting/__init__.py
@@ -0,0 +1,69 @@
+"""matplotlib plotting API for NNS, faithful to R NNS ``plot = TRUE``.
+
+matplotlib ships as a regular dependency of the package, but it is imported
+lazily inside each plot function so ``import nns`` stays light.
+
+Design contract for every ``plot_*`` function:
+
+* Accept an already-computed NNS result (or the same raw inputs) plus a keyword
+  ``ax=None`` and return the matplotlib ``Axes`` (or ``Figure`` for 3-D).
+* Never call ``plt.show()`` -- the caller controls display and saving.
+* Be **color/element-faithful** to R (see :mod:`nns.plotting.palette`), not
+  pixel-diffed.
+
+Colors are pinned in :mod:`nns.plotting.palette`; the only same-named colors
+that must *not* be trusted from matplotlib are ``green`` (-> ``#00FF00``) and
+``grey`` (-> ``#BEBEBE``).
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from nns.plotting import palette as palette
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from nns.plotting.anova import plot_nns_anova as plot_nns_anova
+    from nns.plotting.arma import plot_nns_arma as plot_nns_arma
+    from nns.plotting.arma import plot_nns_arma_optim as plot_nns_arma_optim
+    from nns.plotting.causation import plot_nns_causation as plot_nns_causation
+    from nns.plotting.copula import plot_nns_copula as plot_nns_copula
+    from nns.plotting.differentiation import plot_nns_diff as plot_nns_diff
+    from nns.plotting.dominance import plot_fsd as plot_fsd
+    from nns.plotting.dominance import plot_ssd as plot_ssd
+    from nns.plotting.dominance import plot_tsd as plot_tsd
+    from nns.plotting.normalization import plot_nns_norm as plot_nns_norm
+    from nns.plotting.partial_moments import plot_nns_cdf as plot_nns_cdf
+    from nns.plotting.regression import plot_nns_part as plot_nns_part
+    from nns.plotting.regression import plot_nns_reg as plot_nns_reg
+    from nns.plotting.seasonality import plot_nns_seas as plot_nns_seas
+
+_EXPORTS = {
+    "plot_nns_anova": ("nns.plotting.anova", "plot_nns_anova"),
+    "plot_nns_arma": ("nns.plotting.arma", "plot_nns_arma"),
+    "plot_nns_arma_optim": ("nns.plotting.arma", "plot_nns_arma_optim"),
+    "plot_nns_causation": ("nns.plotting.causation", "plot_nns_causation"),
+    "plot_nns_copula": ("nns.plotting.copula", "plot_nns_copula"),
+    "plot_nns_diff": ("nns.plotting.differentiation", "plot_nns_diff"),
+    "plot_fsd": ("nns.plotting.dominance", "plot_fsd"),
+    "plot_ssd": ("nns.plotting.dominance", "plot_ssd"),
+    "plot_tsd": ("nns.plotting.dominance", "plot_tsd"),
+    "plot_nns_norm": ("nns.plotting.normalization", "plot_nns_norm"),
+    "plot_nns_cdf": ("nns.plotting.partial_moments", "plot_nns_cdf"),
+    "plot_nns_part": ("nns.plotting.regression", "plot_nns_part"),
+    "plot_nns_reg": ("nns.plotting.regression", "plot_nns_reg"),
+    "plot_nns_seas": ("nns.plotting.seasonality", "plot_nns_seas"),
+}
+
+__all__ = sorted((*_EXPORTS, "palette"))
+
+
+def __getattr__(name: str) -> Any:
+    if name not in _EXPORTS:
+        raise AttributeError(f"module 'nns.plotting' has no attribute {name!r}")
+    from importlib import import_module
+
+    module_name, attr_name = _EXPORTS[name]
+    value = getattr(import_module(module_name), attr_name)
+    globals()[name] = value
+    return value
diff --git a/src/nns/plotting/_mpl.py b/src/nns/plotting/_mpl.py
new file mode 100644
index 00000000..78bdb339
--- /dev/null
+++ b/src/nns/plotting/_mpl.py
@@ -0,0 +1,57 @@
+"""Lazy matplotlib loading for the plotting API.
+
+matplotlib is a regular dependency of this package, but it is still imported
+lazily (never at package import time) so ``import nns`` stays light. Every plot
+function calls :func:`require_mpl` to import it on demand.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, cast
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+_INSTALL_HINT = (
+    "matplotlib is required for nns.plotting but could not be imported; "
+    "reinstall ovvo-nns to restore it (`pip install --force-reinstall ovvo-nns`)."
+)
+
+
+def require_mpl() -> Any:
+    """Import and return the ``matplotlib.pyplot`` module, or raise ImportError."""
+    try:
+        import matplotlib.pyplot as plt
+    except ImportError as exc:  # pragma: no cover - exercised via test monkeypatch
+        raise ImportError(_INSTALL_HINT) from exc
+    return plt
+
+
+def horizontal_boxplot(ax: Axes, data: Any, **kwargs: Any) -> Any:
+    """``ax.boxplot`` rendered horizontally, compatible across matplotlib versions.
+
+    ``vert=`` was deprecated for ``orientation=`` in matplotlib 3.11; prefer the
+    new keyword when present and fall back to the old one for >= 3.7.
+    """
+    import matplotlib
+
+    version = tuple(int(p) for p in matplotlib.__version__.split(".")[:2])
+    if version >= (3, 11):
+        return ax.boxplot(data, orientation="horizontal", **kwargs)
+    return ax.boxplot(data, vert=False, **kwargs)
+
+
+def resolve_ax(ax: Axes | None) -> Axes:
+    """Return ``ax`` if given, otherwise create a fresh Axes.
+
+    Plot functions never call ``plt.show()``; they return the Axes/Figure so the
+    caller controls display and saving.
+    """
+    if ax is not None:
+        return ax
+    plt = require_mpl()
+    _, new_ax = plt.subplots()
+    return cast("Axes", new_ax)
+
+
+__all__ = ["require_mpl", "resolve_ax"]
diff --git a/src/nns/plotting/anova.py b/src/nns/plotting/anova.py
new file mode 100644
index 00000000..9eda0f38
--- /dev/null
+++ b/src/nns/plotting/anova.py
@@ -0,0 +1,46 @@
+"""Plot for ``nns_anova`` (R: ANOVA.R)."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting import palette
+from nns.plotting._mpl import horizontal_boxplot, resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def plot_nns_anova(
+    groups: Sequence[Any],
+    *,
+    medians: bool = False,
+    ax: Axes | None = None,
+) -> Axes:
+    """Plot ANOVA group boxplots, faithful to R ``NNS.ANOVA(..., plot = TRUE)``.
+
+    * first box: ``steelblue``; remaining boxes: ``rainbow(n - 1)``
+    * grand mean/median reference line (vertical): ``red``
+    """
+    ax = resolve_ax(ax)
+    arrays = [np.asarray(g, dtype=np.float64) for g in groups]
+    n = len(arrays)
+
+    bp = horizontal_boxplot(ax, arrays, patch_artist=True)
+    rest = palette.rainbow(n - 1)
+    facecolors = ["steelblue", *[tuple(c) for c in rest]]
+    for patch, color in zip(bp["boxes"], facecolors, strict=True):
+        patch.set_facecolor(color)
+
+    centers = [float(np.median(a)) if medians else float(np.mean(a)) for a in arrays]
+    grand = float(np.mean(centers))
+    ax.axvline(grand, color="red", linewidth=4)
+    ax.set_title("NNS ANOVA")
+    ax.set_xlabel("Grand Median" if medians else "Grand Mean")
+    return ax
+
+
+__all__ = ["plot_nns_anova"]
diff --git a/src/nns/plotting/arma.py b/src/nns/plotting/arma.py
new file mode 100644
index 00000000..88b487ba
--- /dev/null
+++ b/src/nns/plotting/arma.py
@@ -0,0 +1,121 @@
+"""Plots for ``nns_arma`` and ``nns_arma_optim`` (R: ARMA.R, ARMA_optim.R)."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting import palette
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def _estimates_and_bounds(
+    forecast: Any,
+) -> tuple[np.ndarray, np.ndarray | None, np.ndarray | None]:
+    """Split an nns_arma return into (estimates, lower, upper)."""
+    if isinstance(forecast, Mapping):
+        est = np.asarray(forecast["Estimates"], dtype=np.float64)
+        lower = upper = None
+        for key, value in forecast.items():
+            if key.startswith("Lower"):
+                lower = np.asarray(value, dtype=np.float64)
+            elif key.startswith("Upper"):
+                upper = np.asarray(value, dtype=np.float64)
+        return est, lower, upper
+    return np.asarray(forecast, dtype=np.float64), None, None
+
+
+def plot_nns_arma(
+    forecast: Any,
+    original: Any,
+    *,
+    training_set: int | None = None,
+    ax: Axes | None = None,
+) -> Axes:
+    """Plot an ``nns_arma`` forecast, faithful to R ``NNS.ARMA(..., plot = TRUE)``.
+
+    * original series: ``steelblue`` line
+    * prediction-interval band: pink at alpha ``0.5``
+    * forecast line + connector segment: ``red``
+    * training-set markers (filled diamonds): pure green
+    """
+    ax = resolve_ax(ax)
+    ov = np.asarray(original, dtype=np.float64)
+    n = ov.size
+    start = int(training_set) if training_set is not None else n
+    est, lower, upper = _estimates_and_bounds(forecast)
+    h = est.size
+
+    # x positions are 1-based to match R's plot indices.
+    ov_x = np.arange(1, n + 1)
+    fc_x = np.arange(start + 1, start + h + 1)
+
+    # Original series: steelblue.
+    ax.plot(ov_x, ov, color="steelblue", linewidth=2)
+
+    # Prediction-interval band: pink at alpha 0.5.
+    if lower is not None and upper is not None:
+        ax.fill_between(fc_x, lower, upper, color=palette.PINK,
+                        alpha=palette.CI_ALPHA_ARMA, linewidth=0.0)
+
+    # Forecast line + connector segment back to the last observed point: red.
+    ax.plot(fc_x, est, color="red", linewidth=2)
+    if 1 <= start <= n:
+        ax.plot([start, start + 1], [ov[start - 1], est[0]], color="red", linewidth=2)
+
+    # Training-set markers: pure-green diamonds.
+    if 1 <= start <= n:
+        ax.scatter([start], [ov[start - 1]], color=palette.GREEN, marker="D")
+    ax.scatter([start + h], [est[-1]], color=palette.GREEN, marker="D")
+
+    ax.legend(["Original", f"Forecast {h} period(s)"], loc="upper left", frameon=False)
+    ax.set_title("NNS.ARMA Forecast")
+    return ax
+
+
+def plot_nns_arma_optim(
+    result: Mapping[str, Any],
+    original: Any,
+    *,
+    ax: Axes | None = None,
+) -> Axes:
+    """Plot an ``nns_arma_optim`` result, faithful to R ``NNS.ARMA.optim``.
+
+    * original series: ``steelblue`` line
+    * confidence band: ``steelblue`` at alpha ``0.5``
+    * predicted/model line (dashed): ``red``
+    """
+    ax = resolve_ax(ax)
+    ov = np.asarray(original, dtype=np.float64)
+    n = ov.size
+    results = np.asarray(result["results"], dtype=np.float64)
+    h = results.size
+    ov_x = np.arange(1, n + 1)
+    fc_x = np.arange(n + 1, n + h + 1)
+
+    ax.plot(ov_x, ov, color="steelblue", linewidth=2)
+
+    lower = result.get("lower.pred.int")
+    upper = result.get("upper.pred.int")
+    if lower is not None and upper is not None:
+        ax.fill_between(
+            fc_x,
+            np.asarray(lower, dtype=np.float64),
+            np.asarray(upper, dtype=np.float64),
+            color="steelblue",
+            alpha=palette.CI_ALPHA_ARMA,
+            linewidth=0.0,
+        )
+
+    ax.plot(fc_x, results, color="red", linewidth=2, linestyle="--")
+    ax.legend(["Variable", "Internal Validation"], loc="upper left", frameon=False)
+    ax.set_title("NNS.ARMA Forecast")
+    return ax
+
+
+__all__ = ["plot_nns_arma", "plot_nns_arma_optim"]
diff --git a/src/nns/plotting/causation.py b/src/nns/plotting/causation.py
new file mode 100644
index 00000000..a2e904d3
--- /dev/null
+++ b/src/nns/plotting/causation.py
@@ -0,0 +1,38 @@
+"""Plot for ``nns_causation`` (R: Uni_Causation.R)."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def _standardize(v: np.ndarray) -> np.ndarray:
+    sd = np.std(v)
+    if sd == 0:
+        return np.asarray(v - np.mean(v), dtype=np.float64)
+    return np.asarray((v - np.mean(v)) / sd, dtype=np.float64)
+
+
+def plot_nns_causation(x: Any, y: Any, *, ax: Axes | None = None) -> Axes:
+    """Plot the standardized X/Y series used by ``NNS.causation``.
+
+    Faithful to R's first causation panel: the Y series is ``red`` and the X
+    series is ``steelblue``, with the legend ordered ``[X (steelblue), Y (red)]``.
+    """
+    ax = resolve_ax(ax)
+    xs = _standardize(np.asarray(x, dtype=np.float64))
+    ys = _standardize(np.asarray(y, dtype=np.float64))
+    ax.plot(xs, color="steelblue", linewidth=3, label="X")
+    ax.plot(ys, color="red", linewidth=3, label="Y")
+    ax.legend(loc="upper center", ncol=2)
+    ax.set_ylabel("STANDARDIZED")
+    return ax
+
+
+__all__ = ["plot_nns_causation"]
diff --git a/src/nns/plotting/copula.py b/src/nns/plotting/copula.py
new file mode 100644
index 00000000..445eab91
--- /dev/null
+++ b/src/nns/plotting/copula.py
@@ -0,0 +1,57 @@
+"""Plot for ``nns_copula`` (R: Copula.R)."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting._mpl import require_mpl, resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def _orthant_colors(data: np.ndarray) -> list[str]:
+    """Color each row by orthant (Copula.R:46-48).
+
+    * all dimensions <= their column mean -> ``red`` (lower orthant)
+    * all dimensions  > their column mean -> ``green`` (upper orthant)
+    * otherwise -> ``steelblue``
+    """
+    means = data.mean(axis=0)
+    below = (data <= means).all(axis=1)
+    above = (data > means).all(axis=1)
+    colors = np.full(data.shape[0], "steelblue", dtype=object)
+    colors[below] = "red"
+    colors[above] = "#00FF00"  # R pure green (mpl "green" would be #008000)
+    return colors.tolist()
+
+
+def plot_nns_copula(x: Any, *, ax: Axes | None = None) -> Axes:
+    """Scatter the copula input matrix with R's orthant coloring (Copula.R).
+
+    Supports 2-D (scatter) and 3-D (``n == 3``) inputs. Lower-orthant points
+    (all dimensions below their mean) are ``red``, upper-orthant points are pure
+    green, and the rest are ``steelblue``.
+    """
+    data = np.asarray(x, dtype=np.float64)
+    if data.ndim != 2 or data.shape[1] < 2:
+        raise ValueError("plot_nns_copula expects a 2-D array with >= 2 columns.")
+    colors = _orthant_colors(data)
+    n = data.shape[1]
+
+    if n == 3:
+        if ax is None:
+            plt = require_mpl()
+            fig = plt.figure()
+            ax = fig.add_subplot(projection="3d")
+        ax.scatter(data[:, 0], data[:, 1], data[:, 2], color=colors)
+        return ax
+
+    ax = resolve_ax(ax)
+    ax.scatter(data[:, 0], data[:, 1], color=colors)
+    return ax
+
+
+__all__ = ["plot_nns_copula"]
diff --git a/src/nns/plotting/differentiation.py b/src/nns/plotting/differentiation.py
new file mode 100644
index 00000000..51552d4e
--- /dev/null
+++ b/src/nns/plotting/differentiation.py
@@ -0,0 +1,79 @@
+"""Plot for ``nns_diff`` numerical differentiation (R: Numerical_Differentiation.R)."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting import palette
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def plot_nns_diff(
+    f: Callable[[Any], Any],
+    point: float,
+    *,
+    h: float | None = None,
+    ax: Axes | None = None,
+) -> Axes:
+    """Plot ``NNS.diff``'s ``f(x)`` / y-intercept-range panel (Numerical_Differentiation.R).
+
+    Reconstructs the initial finite-step geometry (R uses ``h``, not the inferred
+    step, in this panel):
+
+    * function curve: ``azure4``
+    * zero reference lines: ``grey`` (R ``grey`` == ``#BEBEBE``, not mpl gray)
+    * center point ``f(point)``: pure green
+    * the two finite-step bound points/segments swap ``steelblue`` <-> ``red``
+      according to which secant y-intercept (``B1`` vs ``B2``) is higher.
+    """
+    ax = resolve_ax(ax)
+    point = float(point)
+    h = abs(point) * 0.1 + 0.01 if h is None else float(h)
+
+    f_x = float(f(point))
+    f_lower = float(f(point - h))  # f.x.h.lower
+    f_upper = float(f(point + h))  # f.x.h.upper
+
+    left_slope = (f_x - f_lower) / h
+    right_slope = (f_upper - f_x) / h
+    b1 = f_x - left_slope * point
+    b2 = f_x - right_slope * point
+    high_b = max(b1, b2)
+    lower_color = "steelblue" if b1 == high_b else "red"
+    upper_color = "red" if b1 == high_b else "steelblue"
+
+    # Function curve: azure4 (no matplotlib name -> pinned hex).
+    lo = min(point - 100 * h, point + 100 * h, 0.0)
+    hi = max(point - 100 * h, point + 100 * h, 0.0)
+    xs = np.linspace(lo, hi, 1000)
+    ax.plot(xs, np.asarray([f(v) for v in xs], dtype=np.float64), color=palette.AZURE4, linewidth=2)
+
+    # Zero reference lines: R grey (#BEBEBE).
+    ax.axhline(0.0, color=palette.GREY)
+    ax.axvline(0.0, color=palette.GREY)
+
+    # Center point: pure green.
+    ax.scatter([point], [f_x], color=palette.GREEN, marker="o")
+
+    # Finite-step bound points (filled) and secant y-intercept markers (open).
+    ax.scatter([point - h], [f_lower], color=lower_color, marker="o")
+    ax.scatter([point + h], [f_upper], color=upper_color, marker="o")
+    ax.scatter([0.0, 0.0], [b1, b2], facecolors="none",
+               edgecolors=[lower_color, upper_color], marker="o")
+
+    # Dashed secant segments from each y-intercept to its bound point.
+    ax.plot([0.0, point - h], [b1, f_lower], color=lower_color, linestyle="--")
+    ax.plot([0.0, point + h], [b2, f_upper], color=upper_color, linestyle="--")
+
+    ax.set_ylabel("f(x)")
+    ax.set_title("f(x) and initial y-intercept range")
+    return ax
+
+
+__all__ = ["plot_nns_diff"]
diff --git a/src/nns/plotting/dominance.py b/src/nns/plotting/dominance.py
new file mode 100644
index 00000000..553c644b
--- /dev/null
+++ b/src/nns/plotting/dominance.py
@@ -0,0 +1,78 @@
+"""Plots for FSD / SSD / TSD stochastic-dominance tests (R: FSD.R, SSD.R, TSD.R).
+
+In every case the X curve is ``red`` and the Y curve is ``steelblue`` with the
+legend ordered ``[X (red), Y (steelblue)]`` -- matching the R ``col=`` usage.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.core import lpm, lpm_ratio
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def _dominance_plot(
+    x: Any,
+    y: Any,
+    *,
+    title: str,
+    ylabel: str,
+    curve: Callable[[np.ndarray, np.ndarray], np.ndarray],
+    ax: Axes | None,
+) -> Axes:
+    ax = resolve_ax(ax)
+    x = np.asarray(x, dtype=np.float64)
+    y = np.asarray(y, dtype=np.float64)
+    combined = np.sort(np.concatenate([x, y]))
+    lpm_x = curve(combined, x)
+    lpm_y = curve(combined, y)
+    ax.plot(combined, lpm_x, color="red", linewidth=3)
+    ax.plot(combined, lpm_y, color="steelblue", linewidth=3)
+    ax.legend(["X", "Y"], loc="upper left")
+    ax.set_title(title)
+    ax.set_ylabel(ylabel)
+    return ax
+
+
+def plot_fsd(x: Any, y: Any, *, type: str = "discrete", ax: Axes | None = None) -> Axes:
+    """Plot the FSD test: cumulative distributions (X red, Y steelblue)."""
+    degree = 0.0 if str(type).lower() == "discrete" else 1.0
+    return _dominance_plot(
+        x, y,
+        title="FSD",
+        ylabel="Probability of Cumulative Distribution",
+        curve=lambda t, v: np.asarray(lpm_ratio(degree, t, v), dtype=np.float64),
+        ax=ax,
+    )
+
+
+def plot_ssd(x: Any, y: Any, *, ax: Axes | None = None) -> Axes:
+    """Plot the SSD test: area of cumulative distributions (X red, Y steelblue)."""
+    return _dominance_plot(
+        x, y,
+        title="SSD",
+        ylabel="Area of Cumulative Distribution",
+        curve=lambda t, v: np.asarray(lpm(1.0, t, v), dtype=np.float64),
+        ax=ax,
+    )
+
+
+def plot_tsd(x: Any, y: Any, *, ax: Axes | None = None) -> Axes:
+    """Plot the TSD test: area of cumulative distributions (X red, Y steelblue)."""
+    return _dominance_plot(
+        x, y,
+        title="TSD",
+        ylabel="Area of Cumulative Distribution",
+        curve=lambda t, v: np.asarray(lpm(2.0, t, v), dtype=np.float64),
+        ax=ax,
+    )
+
+
+__all__ = ["plot_fsd", "plot_ssd", "plot_tsd"]
diff --git a/src/nns/plotting/normalization.py b/src/nns/plotting/normalization.py
new file mode 100644
index 00000000..3d369f1f
--- /dev/null
+++ b/src/nns/plotting/normalization.py
@@ -0,0 +1,57 @@
+"""Plot for ``nns_norm`` (R: Normalization.R)."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Literal
+
+import numpy as np
+
+from nns.plotting import palette
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def _series_colors(n: int) -> list[Any]:
+    """First series ``steelblue``; remaining series follow ``rainbow(n)``."""
+    rainbow = [tuple(c) for c in palette.rainbow(n)]
+    return ["steelblue", *rainbow][:n]
+
+
+def plot_nns_norm(
+    x: Any,
+    *,
+    chart_type: Literal["l", "b"] = "l",
+    ax: Axes | None = None,
+) -> Axes:
+    """Plot the input series for ``NNS.norm``, faithful to Normalization.R.
+
+    * line chart (``chart_type="l"``): first series ``steelblue``, rest ``rainbow``
+    * boxplot chart (``chart_type="b"``): raw boxes ``grey``, normalized ``rainbow``
+    """
+    ax = resolve_ax(ax)
+    data = np.asarray(x, dtype=np.float64)
+    if data.ndim == 1:
+        data = data.reshape(-1, 1)
+    n = data.shape[1]
+
+    if chart_type == "b":
+        from nns.norm import nns_norm
+
+        normalized = np.asarray(nns_norm(data), dtype=np.float64)
+        columns = [data[:, j] for j in range(n)] + [normalized[:, j] for j in range(n)]
+        bp = ax.boxplot(columns, patch_artist=True)
+        rainbow = [tuple(c) for c in palette.rainbow(n)]
+        facecolors = [palette.GREY] * n + rainbow
+        for patch, color in zip(bp["boxes"], facecolors, strict=True):
+            patch.set_facecolor(color)
+        return ax
+
+    colors = _series_colors(n)
+    for j in range(n):
+        ax.plot(data[:, j], color=colors[j], linewidth=2)
+    return ax
+
+
+__all__ = ["plot_nns_norm"]
diff --git a/src/nns/plotting/palette.py b/src/nns/plotting/palette.py
new file mode 100644
index 00000000..18cdd7d6
--- /dev/null
+++ b/src/nns/plotting/palette.py
@@ -0,0 +1,59 @@
+"""R ``grDevices`` colors, pinned to exact hex for plot parity with R NNS.
+
+R and matplotlib agree on ``steelblue`` and ``red`` but **disagree** on
+``green`` and ``grey``/``gray``. To stay faithful to the ``col=`` usage in
+``tools/NNS/R/*.R`` we pin every non-trivial color to its exact R hex value.
+
+Fidelity traps (do **not** trust matplotlib's same-named color):
+
+* R ``green`` is pure green ``#00FF00`` (matplotlib ``green`` is ``#008000``).
+* R ``grey``/``gray`` is ``#BEBEBE`` (matplotlib ``gray`` is ``#808080``).
+
+The literal names ``"steelblue"`` and ``"red"`` are identical in both stacks
+and may be used as-is; everything else should reference the constants here.
+"""
+
+from __future__ import annotations
+
+import colorsys
+
+# -- Colors that are identical in R and matplotlib (safe to use by name) ----
+STEELBLUE = "#4682B4"  # R "steelblue" == mpl "steelblue"
+RED = "#FF0000"  # R "red"       == mpl "red"
+BLUE = "#0000FF"  # R "blue"      == mpl "blue"
+PINK = "#FFC0CB"  # R rgb(1, 192/255, 203/255) CI band == mpl "pink"
+
+# -- Colors where the same name means something DIFFERENT in matplotlib -----
+GREEN = "#00FF00"  # R "green" is PURE green; mpl "green" is #008000 -> use this
+GREY = "#BEBEBE"  # R "grey"/"gray"; mpl "gray" is #808080 -> DIFFERENT
+AZURE4 = "#838B8B"  # R "azure4" (no matplotlib name)
+
+# -- Confidence/prediction-interval fill alphas R uses ----------------------
+CI_ALPHA_REG = 0.375  # NNS.reg pink band: rgb(1, 192/255, 203/255, alpha = 0.375)
+CI_ALPHA_ARMA = 0.5  # NNS.ARMA pink band & NNS.ARMA.optim steelblue band: alpha = 0.5
+
+
+def rainbow(n: int) -> list[tuple[float, float, float]]:
+    """Emulate R's ``grDevices::rainbow(n)`` (HSV with ``s = v = 1``).
+
+    R sweeps hues ``0, 1/n, ..., (n-1)/n`` at full saturation and value. This
+    is *not* identical to ``plt.cm.rainbow``; use this for color parity on the
+    multi-series plots (``NNS.norm``, ``NNS.ANOVA``).
+    """
+    if n <= 0:
+        return []
+    return [colorsys.hsv_to_rgb(i / n, 1.0, 1.0) for i in range(n)]
+
+
+__all__ = [
+    "AZURE4",
+    "BLUE",
+    "CI_ALPHA_ARMA",
+    "CI_ALPHA_REG",
+    "GREEN",
+    "GREY",
+    "PINK",
+    "RED",
+    "STEELBLUE",
+    "rainbow",
+]
diff --git a/src/nns/plotting/partial_moments.py b/src/nns/plotting/partial_moments.py
new file mode 100644
index 00000000..7409104b
--- /dev/null
+++ b/src/nns/plotting/partial_moments.py
@@ -0,0 +1,52 @@
+"""Plot for ``nns_cdf`` / VaR overlays (R: Partial_Moments.R)."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting import palette
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def plot_nns_cdf(
+    result: Mapping[str, Any],
+    *,
+    target: float | None = None,
+    ax: Axes | None = None,
+) -> Axes:
+    """Plot an ``nns_cdf`` result, faithful to R ``NNS.CDF(..., plot = TRUE)``.
+
+    * step CDF line + points: ``steelblue``
+    * VaR/target segments (dashed): ``red``
+    * VaR point (at the target): pure green
+    """
+    ax = resolve_ax(ax)
+    function = result["Function"]
+    x = np.asarray(function["x"], dtype=np.float64)
+    colname = next(k for k in function if k != "x")
+    fx = np.asarray(function[colname], dtype=np.float64)
+
+    # Step CDF + points: steelblue.
+    ax.step(x, fx, where="post", color="steelblue", linewidth=2)
+    ax.scatter(x, fx, color="steelblue", marker="o")
+
+    target_value = np.asarray(result.get("target.value", []), dtype=np.float64).reshape(-1)
+    if target is not None and target_value.size:
+        pv = float(target_value[0])
+        # Dashed red VaR segments down from the curve and across to the y-axis.
+        ax.plot([target, target], [0.0, pv], color="red", linestyle="--", linewidth=2)
+        ax.plot([float(x.min()), target], [pv, pv], color="red", linestyle="--", linewidth=2)
+        # Pure-green target point.
+        ax.scatter([target], [pv], color=palette.GREEN, marker="o")
+
+    ax.set_ylabel(colname)
+    return ax
+
+
+__all__ = ["plot_nns_cdf"]
diff --git a/src/nns/plotting/regression.py b/src/nns/plotting/regression.py
new file mode 100644
index 00000000..b2ed8ff6
--- /dev/null
+++ b/src/nns/plotting/regression.py
@@ -0,0 +1,108 @@
+"""Plots for ``nns_reg`` and ``nns_part`` (R: Regression.R, Partition_Map.R)."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting import palette
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def _sorted_xy(x: Any, y: Any) -> tuple[np.ndarray, np.ndarray]:
+    x = np.asarray(x, dtype=np.float64)
+    y = np.asarray(y, dtype=np.float64)
+    order = np.argsort(x)
+    return x[order], y[order]
+
+
+def plot_nns_reg(
+    result: Mapping[str, Any],
+    *,
+    ax: Axes | None = None,
+    ci: bool = True,
+    point_est: Any = None,
+) -> Axes:
+    """Plot an ``nns_reg`` result, faithful to R ``NNS.reg(..., plot = TRUE)``.
+
+    Element colors (Regression.R):
+
+    * data scatter (open circles): ``steelblue``
+    * confidence-interval band fill: pink at alpha ``0.375``
+    * regression points (filled squares) + connecting line: ``red``
+    * point estimates (filled diamonds) + extrapolation segments: pure green
+    """
+    ax = resolve_ax(ax)
+    fitted = result["Fitted.xy"]
+    x = np.asarray(fitted["x"], dtype=np.float64)
+    y = np.asarray(fitted["y"], dtype=np.float64)
+
+    # 1. Data scatter: open circles, steelblue.
+    ax.scatter(x, y, facecolors="none", edgecolors="steelblue", marker="o")
+
+    # 2. Confidence-interval band: pink polygon at alpha 0.375 (if available).
+    if ci and "conf.int.pos" in fitted and "conf.int.neg" in fitted:
+        idx = np.argsort(x)
+        pos = np.asarray(fitted["conf.int.pos"], dtype=np.float64)[idx]
+        neg = np.asarray(fitted["conf.int.neg"], dtype=np.float64)[idx]
+        mask = np.isfinite(pos) & np.isfinite(neg)
+        if mask.any():
+            ax.fill_between(
+                x[idx][mask],
+                neg[mask],
+                pos[mask],
+                color=palette.PINK,
+                alpha=palette.CI_ALPHA_REG,
+                linewidth=0.0,
+            )
+
+    # 3. Regression points: red filled squares + red dashed connecting line.
+    rp = result["regression.points"]
+    rpx, rpy = _sorted_xy(rp["x"], rp["y"])
+    finite = np.isfinite(rpx) & np.isfinite(rpy)
+    ax.plot(rpx[finite], rpy[finite], color="red", linewidth=2, linestyle="--")
+    ax.scatter(rpx[finite], rpy[finite], color="red", marker="s")
+
+    # 4. Point estimates: pure-green filled diamonds (+ extrapolation segments).
+    pe_y = np.asarray(result.get("Point.est", []), dtype=np.float64)
+    if point_est is not None and pe_y.size:
+        pe_x = np.asarray(point_est, dtype=np.float64).reshape(-1)
+        ax.scatter(pe_x, pe_y, color=palette.GREEN, marker="D", s=80)
+        if pe_x.size and rpx[finite].size:
+            hi = pe_x > x.max()
+            for px, py in zip(pe_x[hi], pe_y[hi], strict=True):
+                ax.plot([px, rpx[finite][-1]], [py, rpy[finite][-1]],
+                        color=palette.GREEN, linestyle="--")
+            lo = pe_x < x.min()
+            for px, py in zip(pe_x[lo], pe_y[lo], strict=True):
+                ax.plot([px, rpx[finite][0]], [py, rpy[finite][0]],
+                        color=palette.GREEN, linestyle="--")
+
+    ax.set_title(f"NNS Order = {max(1, int(result.get('order', 1) or 1))}")
+    return ax
+
+
+def plot_nns_part(result: Mapping[str, Any], *, ax: Axes | None = None) -> Axes:
+    """Plot an ``nns_part`` result, faithful to R ``NNS.part(..., plot = TRUE)``.
+
+    Scatter is ``steelblue``; regression points (filled squares) are ``red``.
+    """
+    ax = resolve_ax(ax)
+    dt = result["dt"]
+    x = np.asarray(dt["x"], dtype=np.float64)
+    y = np.asarray(dt["y"], dtype=np.float64)
+    ax.scatter(x, y, color="steelblue")
+
+    rp = result["regression.points"]
+    rpx = np.asarray(rp["x"], dtype=np.float64)
+    rpy = np.asarray(rp["y"], dtype=np.float64)
+    ax.scatter(rpx, rpy, color="red", marker="s", linewidths=2)
+    return ax
+
+
+__all__ = ["plot_nns_part", "plot_nns_reg"]
diff --git a/src/nns/plotting/seasonality.py b/src/nns/plotting/seasonality.py
new file mode 100644
index 00000000..2798c173
--- /dev/null
+++ b/src/nns/plotting/seasonality.py
@@ -0,0 +1,43 @@
+"""Plot for ``nns_seas`` (R: Seasonality_Test.R)."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from nns.plotting._mpl import resolve_ax
+
+if TYPE_CHECKING:  # pragma: no cover - typing only
+    from matplotlib.axes import Axes
+
+
+def plot_nns_seas(result: Mapping[str, Any], *, ax: Axes | None = None) -> Axes:
+    """Plot an ``nns_seas`` result, faithful to R ``NNS.seas(..., plot = TRUE)``.
+
+    * component-series CV points: ``steelblue``
+    * best period (enlarged point) + overall-CV reference line (dashed) + label: ``red``
+    """
+    ax = resolve_ax(ax)
+    periods = result["all.periods"]
+    period = np.asarray(periods["Period"], dtype=np.float64)
+    cv = np.asarray(periods["Coefficient.of.Variation"], dtype=np.float64)
+
+    # All component-series CV points: steelblue.
+    ax.scatter(period, cv, color="steelblue", marker="o")
+
+    # Best period is the first (table keyed ascending by CV): enlarged red point.
+    if period.size:
+        ax.scatter([period[0]], [cv[0]], color="red", marker="o", s=80)
+
+    overall_cv = float(np.asarray(periods["Variable.Coefficient.of.Variation"])[0])
+    if np.isfinite(overall_cv):
+        ax.axhline(overall_cv, color="red", linestyle="--")
+
+    ax.set_xlabel("Period")
+    ax.set_ylabel("Component Series CV")
+    return ax
+
+
+__all__ = ["plot_nns_seas"]
diff --git a/tests/plotting/__init__.py b/tests/plotting/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/plotting/conftest.py b/tests/plotting/conftest.py
new file mode 100644
index 00000000..4a84b82b
--- /dev/null
+++ b/tests/plotting/conftest.py
@@ -0,0 +1,7 @@
+"""Force the non-interactive Agg backend for all plotting tests (no display)."""
+
+from __future__ import annotations
+
+import matplotlib
+
+matplotlib.use("Agg")
diff --git a/tests/plotting/test_lazy_import.py b/tests/plotting/test_lazy_import.py
new file mode 100644
index 00000000..98d25c0c
--- /dev/null
+++ b/tests/plotting/test_lazy_import.py
@@ -0,0 +1,37 @@
+"""matplotlib is a regular dependency but still imported lazily, never eagerly."""
+
+from __future__ import annotations
+
+import builtins
+
+import pytest
+
+import nns.plotting._mpl as mpl_helper
+
+
+def test_require_mpl_returns_pyplot() -> None:
+    plt = mpl_helper.require_mpl()
+    assert hasattr(plt, "subplots")
+
+
+def test_require_mpl_raises_clear_error(monkeypatch: pytest.MonkeyPatch) -> None:
+    real_import = builtins.__import__
+
+    def fake_import(name: str, *args: object, **kwargs: object) -> object:
+        if name.startswith("matplotlib"):
+            raise ImportError("No module named 'matplotlib'")
+        return real_import(name, *args, **kwargs)  # type: ignore[arg-type]
+
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+    with pytest.raises(ImportError, match=r"matplotlib is required for nns\.plotting"):
+        mpl_helper.require_mpl()
+
+
+def test_importing_nns_core_does_not_import_matplotlib() -> None:
+    # The core package must not pull matplotlib in at import time.
+    import importlib
+
+    src = importlib.import_module("nns").__file__ or ""
+    with open(src, encoding="utf-8") as handle:
+        text = handle.read()
+    assert "matplotlib" not in text
diff --git a/tests/plotting/test_palette.py b/tests/plotting/test_palette.py
new file mode 100644
index 00000000..78aa0963
--- /dev/null
+++ b/tests/plotting/test_palette.py
@@ -0,0 +1,52 @@
+"""The palette pins exact R grDevices hex -- especially the fidelity traps."""
+
+from __future__ import annotations
+
+import matplotlib.colors as mcolors
+
+from nns.plotting import palette
+
+
+def test_safe_names_match_matplotlib() -> None:
+    # steelblue and red are identical in R and matplotlib.
+    assert mcolors.to_hex(palette.STEELBLUE) == "#4682b4"
+    assert mcolors.to_hex("steelblue") == "#4682b4"
+    assert mcolors.to_hex(palette.RED) == "#ff0000"
+    assert mcolors.to_hex("red") == "#ff0000"
+
+
+def test_green_trap() -> None:
+    # R "green" is PURE green; matplotlib "green" is #008000 -- they DIFFER.
+    assert palette.GREEN == "#00FF00"
+    assert mcolors.to_hex("green") == "#008000"
+    assert mcolors.to_hex(palette.GREEN) != mcolors.to_hex("green")
+
+
+def test_grey_trap() -> None:
+    # R "grey"/"gray" is #BEBEBE; matplotlib "gray" is #808080 -- they DIFFER.
+    assert palette.GREY == "#BEBEBE"
+    assert mcolors.to_hex("gray") == "#808080"
+    assert mcolors.to_hex(palette.GREY) != mcolors.to_hex("gray")
+
+
+def test_azure4_pinned() -> None:
+    assert mcolors.to_hex(palette.AZURE4) == "#838b8b"
+
+
+def test_pink_band_matches_r_rgb() -> None:
+    # R uses rgb(1, 192/255, 203/255) for the CI band == matplotlib "pink".
+    assert mcolors.to_hex(palette.PINK) == "#ffc0cb"
+
+
+def test_ci_alphas() -> None:
+    assert palette.CI_ALPHA_REG == 0.375
+    assert palette.CI_ALPHA_ARMA == 0.5
+
+
+def test_rainbow_matches_r_hsv() -> None:
+    # R rainbow(n): HSV with s=v=1 and hues 0, 1/n, ... -> for n=3, RGB primaries.
+    rgb = palette.rainbow(3)
+    assert rgb[0] == (1.0, 0.0, 0.0)
+    assert rgb[1] == (0.0, 1.0, 0.0)
+    assert rgb[2] == (0.0, 0.0, 1.0)
+    assert palette.rainbow(0) == []
diff --git a/tests/plotting/test_plots.py b/tests/plotting/test_plots.py
new file mode 100644
index 00000000..0b9bfef8
--- /dev/null
+++ b/tests/plotting/test_plots.py
@@ -0,0 +1,230 @@
+"""Color/element fidelity tests for nns.plotting.
+
+These assert the *colors* and *which element they sit on* -- matching the R
+``col=`` usage -- rather than doing any pixel/PDF comparison (see
+``docs/plot_parity_policy.md``).
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable, Iterator
+from typing import Any
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+from numpy.typing import NDArray
+
+import nns
+from nns.plotting import (
+    plot_fsd,
+    plot_nns_anova,
+    plot_nns_arma,
+    plot_nns_arma_optim,
+    plot_nns_causation,
+    plot_nns_cdf,
+    plot_nns_copula,
+    plot_nns_diff,
+    plot_nns_norm,
+    plot_nns_part,
+    plot_nns_reg,
+    plot_nns_seas,
+    plot_ssd,
+    plot_tsd,
+)
+
+STEELBLUE = "#4682b4"
+RED = "#ff0000"
+GREEN = "#00ff00"
+PINK = "#ffc0cb"
+GREY = "#bebebe"
+AZURE4 = "#838b8b"
+
+
+def _hex(color: Any) -> str:
+    return mcolors.to_hex(color)
+
+
+def line_hexes(ax: object) -> set[str]:
+    return {_hex(line.get_color()) for line in ax.get_lines()}  # type: ignore[attr-defined]
+
+
+def collection_face_hexes(ax: object) -> set[str]:
+    out: set[str] = set()
+    for coll in ax.collections:  # type: ignore[attr-defined]
+        for row in coll.get_facecolor():
+            if len(row):
+                out.add(_hex(row))
+    return out
+
+
+def collection_edge_hexes(ax: object) -> set[str]:
+    out: set[str] = set()
+    for coll in ax.collections:  # type: ignore[attr-defined]
+        for row in coll.get_edgecolor():
+            if len(row):
+                out.add(_hex(row))
+    return out
+
+
+def patch_face_hexes(ax: object) -> set[str]:
+    return {_hex(p.get_facecolor()) for p in ax.patches}  # type: ignore[attr-defined]
+
+
+Array = NDArray[np.float64]
+
+
+@pytest.fixture(autouse=True)
+def _close_figs() -> Iterator[None]:
+    yield
+    plt.close("all")
+
+
+@pytest.fixture
+def reg_xy() -> tuple[Array, Array]:
+    rng = np.random.default_rng(0)
+    x = np.sort(rng.normal(size=50))
+    y = 2.0 * x + rng.normal(scale=0.3, size=50)
+    return x, y
+
+
+def test_plot_nns_reg_colors(reg_xy: tuple[Array, Array]) -> None:
+    x, y = reg_xy
+    point_est = np.array([3.0, -3.0])
+    result = nns.nns_reg(x, y, confidence_interval=0.95, point_est=point_est)
+    ax = plot_nns_reg(result, point_est=point_est)
+
+    # steelblue open-circle scatter (edge colored, face transparent)
+    assert STEELBLUE in collection_edge_hexes(ax)
+    # pink CI band
+    assert PINK in collection_face_hexes(ax)
+    # red regression-point line + red squares
+    assert RED in line_hexes(ax)
+    assert RED in collection_face_hexes(ax)
+    # pure-green point-estimate diamonds + extrapolation segments
+    assert GREEN in collection_face_hexes(ax)
+    assert GREEN in line_hexes(ax)
+
+
+def test_plot_nns_part_colors(reg_xy: tuple[Array, Array]) -> None:
+    x, y = reg_xy
+    result = nns.nns_part(x, y)
+    ax = plot_nns_part(result)
+    faces = collection_face_hexes(ax)
+    assert STEELBLUE in faces
+    assert RED in faces
+
+
+@pytest.fixture
+def series() -> Array:
+    rng = np.random.default_rng(1)
+    return np.cumsum(rng.normal(size=60)) + 20.0
+
+
+def test_plot_nns_arma_colors(series: Array) -> None:
+    forecast = nns.nns_arma(series, h=6, pred_int=0.95, seasonal_factor=False)
+    ax = plot_nns_arma(forecast, series)
+    lines = line_hexes(ax)
+    assert STEELBLUE in lines  # original series
+    assert RED in lines  # forecast + connector
+    faces = collection_face_hexes(ax)
+    assert PINK in faces  # prediction band
+    assert GREEN in faces  # training-set markers
+
+
+def test_plot_nns_arma_optim_colors(series: Array) -> None:
+    result = nns.nns_arma_optim(
+        series[:40], h=6, seasonal_factor=[1], pred_int=0.95, print_trace=False
+    )
+    ax = plot_nns_arma_optim(result, series[:40])
+    lines = line_hexes(ax)
+    assert STEELBLUE in lines
+    assert RED in lines
+    # steelblue confidence band fill
+    assert STEELBLUE in collection_face_hexes(ax)
+
+
+def test_plot_nns_cdf_colors(series: Array) -> None:
+    result = nns.nns_cdf(series, target=20.0)
+    ax = plot_nns_cdf(result, target=20.0)
+    assert STEELBLUE in line_hexes(ax)  # step CDF
+    assert STEELBLUE in collection_face_hexes(ax)  # points
+    assert RED in line_hexes(ax)  # VaR segments
+    assert GREEN in collection_face_hexes(ax)  # VaR point
+
+
+@pytest.mark.parametrize("fn", [plot_fsd, plot_ssd, plot_tsd])
+def test_plot_dominance_colors(fn: Callable[..., Any]) -> None:
+    rng = np.random.default_rng(3)
+    x = rng.normal(size=80)
+    y = rng.normal(loc=0.5, size=80)
+    ax = fn(x, y)
+    lines = line_hexes(ax)
+    assert RED in lines  # X curve
+    assert STEELBLUE in lines  # Y curve
+
+
+def test_plot_nns_anova_colors() -> None:
+    rng = np.random.default_rng(4)
+    groups = [rng.normal(size=30), rng.normal(loc=1, size=30), rng.normal(loc=2, size=30)]
+    ax = plot_nns_anova(groups)
+    # first box steelblue, grand-mean line red
+    assert STEELBLUE in patch_face_hexes(ax)
+    assert RED in line_hexes(ax)
+
+
+def test_plot_nns_causation_colors() -> None:
+    rng = np.random.default_rng(5)
+    x = rng.normal(size=60)
+    y = np.roll(x, 1) + rng.normal(scale=0.1, size=60)
+    ax = plot_nns_causation(x, y)
+    lines = line_hexes(ax)
+    assert STEELBLUE in lines  # X series
+    assert RED in lines  # Y series
+
+
+def test_plot_nns_norm_line_colors() -> None:
+    rng = np.random.default_rng(6)
+    x = np.column_stack([rng.normal(size=40), rng.normal(loc=5, size=40)])
+    ax = plot_nns_norm(x, chart_type="l")
+    # first series steelblue
+    assert STEELBLUE in line_hexes(ax)
+
+
+def test_plot_nns_norm_boxplot_colors() -> None:
+    rng = np.random.default_rng(7)
+    x = np.column_stack([rng.normal(size=40), rng.normal(loc=5, size=40)])
+    ax = plot_nns_norm(x, chart_type="b")
+    # raw boxes are grey
+    assert GREY in patch_face_hexes(ax)
+
+
+def test_plot_nns_seas_colors(series: Array) -> None:
+    result = nns.nns_seas(series, plot=False)
+    ax = plot_nns_seas(result)
+    assert STEELBLUE in collection_face_hexes(ax)  # component points
+    assert RED in collection_face_hexes(ax)  # best-period point
+    assert RED in line_hexes(ax)  # overall-CV reference line
+
+
+def test_plot_nns_diff_colors() -> None:
+    ax = plot_nns_diff(lambda z: z**2, 5.0)
+    assert AZURE4 in line_hexes(ax)  # function curve
+    assert GREY in line_hexes(ax)  # zero reference lines
+    assert GREEN in collection_face_hexes(ax)  # center point
+    bounds = collection_face_hexes(ax) | collection_edge_hexes(ax)
+    assert STEELBLUE in bounds and RED in bounds  # swapped bound points
+
+
+def test_plot_nns_copula_colors() -> None:
+    rng = np.random.default_rng(8)
+    data = rng.normal(size=(120, 2))
+    ax = plot_nns_copula(data)
+    faces = collection_face_hexes(ax)
+    assert RED in faces  # lower-orthant points
+    assert STEELBLUE in faces  # mixed-orthant points
diff --git a/uv.lock b/uv.lock
index 9424fa43..b8da99c9 100644
--- a/uv.lock
+++ b/uv.lock
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 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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