OVVO-Financial · OVVO-Financial · Jun 30, 2026 · Jun 30, 2026 · Jun 30, 2026 · Jun 30, 2026
diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@
 [![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)
+[![License](https://img.shields.io/badge/license-GPL--3.0--only-blue)](https://github.com/OVVO-Financial/NNS-python/blob/main/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.
 
@@ -23,7 +23,7 @@ NNS is built around partial moments, the lower and upper components of variance,
 |---|---|
 | Distribution package | `ovvo-nns` |
 | Import package | `nns` |
-| Current version | `1.0.7` |
+| Current version | `1.0.8` |
 | Python | `>=3.11` |
 | Required runtime dependencies | NumPy, SciPy |
 | R required at runtime | No |
@@ -41,7 +41,7 @@ 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).
+the [plot parity policy](https://ovvo-financial.github.io/NNS-python/plot_parity_policy/).
 
 Use the package as `nns`:
 
@@ -129,7 +129,7 @@ print("forecast:", forecast)
 | 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](docs/api_status.md) for implemented, partial, guarded, and known-gap paths.
+See [API status](https://ovvo-financial.github.io/NNS-python/api_status/) for implemented, partial, guarded, and known-gap paths.
 
 ## Design boundaries
 
@@ -143,27 +143,27 @@ Important boundaries:
 - Direct raw-factor `nns_m_reg(..., factor_2_dummy=True)` is intentionally guarded. Use `prepare_factor_predictors(...)` before `nns_m_reg(...)`.
 - Compute functions still return values, not figures; passing `plot=True` (where R has it) additionally renders a Matplotlib figure as a side effect via the `nns.plotting` layer, which is color/element-faithful to R but not pixel-diffed. The plot functions can also be called directly on a computed result.
 
-See [behavior conventions](docs/conventions.md) for detailed compatibility notes.
+See [behavior conventions](https://ovvo-financial.github.io/NNS-python/conventions/) for detailed compatibility notes.
 
 ## Examples
 
 Runnable, self-checking example scripts live in
-[`examples/vignettes`](examples/vignettes), mirroring the R NNS vignettes. They
+[`examples/vignettes`](https://github.com/OVVO-Financial/NNS-python/tree/main/examples/vignettes), mirroring the R NNS vignettes. They
 are exercised in CI by `tests/docs/test_vignette_examples.py`, so they stay in
 sync with the package.
 
 | Topic | Script |
 |---|---|
-| Overview | [`overview.py`](examples/vignettes/overview.py) |
-| Partial moments | [`partial_moments.py`](examples/vignettes/partial_moments.py) |
-| Descriptive and distributional tools | [`descriptive_distributional_tools.py`](examples/vignettes/descriptive_distributional_tools.py) |
-| Dependence and nonlinear association | [`dependence_nonlinear_association.py`](examples/vignettes/dependence_nonlinear_association.py) |
-| Normalization and rescaling | [`normalization_rescaling.py`](examples/vignettes/normalization_rescaling.py) |
-| Hypothesis, ANOVA and stochastic superiority | [`hypothesis_anova_stochastic_superiority.py`](examples/vignettes/hypothesis_anova_stochastic_superiority.py) |
-| Regression, boosting, stacking and causality | [`regression_boosting_stacking_causality.py`](examples/vignettes/regression_boosting_stacking_causality.py) |
-| Time series forecasting | [`time_series_forecasting.py`](examples/vignettes/time_series_forecasting.py) |
-| Simulation, bootstrap and risk-neutral | [`simulation_bootstrap_riskneutral.py`](examples/vignettes/simulation_bootstrap_riskneutral.py) |
-| Portfolio and stochastic dominance | [`portfolio_stochastic_dominance.py`](examples/vignettes/portfolio_stochastic_dominance.py) |
+| Overview | [`overview.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/overview.py) |
+| Partial moments | [`partial_moments.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/partial_moments.py) |
+| Descriptive and distributional tools | [`descriptive_distributional_tools.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/descriptive_distributional_tools.py) |
+| Dependence and nonlinear association | [`dependence_nonlinear_association.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/dependence_nonlinear_association.py) |
+| Normalization and rescaling | [`normalization_rescaling.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/normalization_rescaling.py) |
+| Hypothesis, ANOVA and stochastic superiority | [`hypothesis_anova_stochastic_superiority.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/hypothesis_anova_stochastic_superiority.py) |
+| Regression, boosting, stacking and causality | [`regression_boosting_stacking_causality.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/regression_boosting_stacking_causality.py) |
+| Time series forecasting | [`time_series_forecasting.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/time_series_forecasting.py) |
+| Simulation, bootstrap and risk-neutral | [`simulation_bootstrap_riskneutral.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/simulation_bootstrap_riskneutral.py) |
+| Portfolio and stochastic dominance | [`portfolio_stochastic_dominance.py`](https://github.com/OVVO-Financial/NNS-python/blob/main/examples/vignettes/portfolio_stochastic_dominance.py) |
 
 Run one example:
 
@@ -182,12 +182,12 @@ uv run python examples/run_all_vignettes.py
 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)
-- [Parity target, cache regeneration, and automation](docs/parity.md)
-- [Benchmarks](docs/benchmarks.md)
-- [Examples](examples/vignettes)
+- [API reference manual](https://ovvo-financial.github.io/NNS-python/api_reference/)
+- [API status and known gaps](https://ovvo-financial.github.io/NNS-python/api_status/)
+- [Behavior conventions and intentional divergences](https://ovvo-financial.github.io/NNS-python/conventions/)
+- [Parity target, cache regeneration, and automation](https://ovvo-financial.github.io/NNS-python/parity/)
+- [Benchmarks](https://ovvo-financial.github.io/NNS-python/benchmarks/)
+- [Examples](https://github.com/OVVO-Financial/NNS-python/tree/main/examples/vignettes)
 
 ## Development
 
@@ -208,7 +208,7 @@ The default parity suite is cache-backed and does not require `Rscript`. `Rscrip
 
 ## Benchmarks
 
-Benchmarks compare selected Python paths with installed R NNS 13.0+ baselines. Many core operations are faster in Python, while some large stochastic-dominance workloads remain faster in R because the R package uses compiled kernels for those paths. See [benchmarks](docs/benchmarks.md) for current measurements and commands.
+Benchmarks compare selected Python paths with installed R NNS 13.0+ baselines. Many core operations are faster in Python, while some large stochastic-dominance workloads remain faster in R because the R package uses compiled kernels for those paths. See [benchmarks](https://ovvo-financial.github.io/NNS-python/benchmarks/) for current measurements and commands.
 
 ## Authors and contributors
 

diff --git a/docs/api_reference.md b/docs/api_reference.md
@@ -40,9 +40,9 @@ The generator introspects `nns.__all__`, imports each public object, records sig
 | Co-moments | `co_upm` | `Co.UPM` | implemented | Pairwise co-upper partial moment. |
 | Co-moments | `d_lpm` | `D.LPM` | implemented | Pairwise distance lower partial moment. |
 | Co-moments | `d_upm` | `D.UPM` | implemented | Pairwise distance upper partial moment. |
-| Co-moments | `co_lpm_nd` | N-dimensional co-LPM path | implemented | N-dimensional co-lower partial moment wrapper. |
-| Co-moments | `co_upm_nd` | N-dimensional co-UPM path | implemented | N-dimensional co-upper partial moment wrapper. |
-| Co-moments | `dpm_nd` | N-dimensional DPM path | implemented | N-dimensional directional partial moment wrapper. |
+| Co-moments | `co_lpm_nd` | `Co.LPM_nD` | implemented | N-dimensional co-lower partial moment wrapper. |
+| Co-moments | `co_upm_nd` | `Co.UPM_nD` | implemented | N-dimensional co-upper partial moment wrapper. |
+| Co-moments | `dpm_nd` | `DPM_nD` | implemented | N-dimensional directional partial moment wrapper. |
 | Classical moments | `ecdf_pm` | ECDF partial-moment helper | implemented | Empirical distribution helper used by moment routines. |
 | Classical moments | `mean_pm` | `Mean` partial-moment helper | implemented | Mean via partial-moment conventions. |
 | Classical moments | `var_pm` | `Variance` partial-moment helper | implemented | Variance via partial-moment conventions. |
@@ -76,20 +76,20 @@ The generator introspects `nns.__all__`, imports each public object, records sig
 | Central tendencies | `nns_gravity` | `NNS.gravity` | implemented | NNS gravity center helper. |
 | Central tendencies | `nns_mode` | `NNS.mode` | implemented | NNS mode helper. |
 | Central tendencies | `nns_rescale` | `NNS.rescale` | implemented | Rescaling helper. |
-| Stochastic dominance | `fsd` | `FSD` | implemented | First-order stochastic dominance. |
-| Stochastic dominance | `ssd` | `SSD` | implemented | Second-order stochastic dominance. |
-| Stochastic dominance | `tsd` | `TSD` | implemented | Third-order stochastic dominance. |
-| Stochastic dominance | `fsd_uni` | `FSD.uni` | implemented | Univariate FSD wrapper. |
-| Stochastic dominance | `ssd_uni` | `SSD.uni` | implemented | Univariate SSD wrapper. |
-| Stochastic dominance | `tsd_uni` | `TSD.uni` | implemented | Univariate TSD wrapper. |
+| Stochastic dominance | `fsd` | `NNS.FSD` | implemented | First-order stochastic dominance. |
+| Stochastic dominance | `ssd` | `NNS.SSD` | implemented | Second-order stochastic dominance. |
+| Stochastic dominance | `tsd` | `NNS.TSD` | implemented | Third-order stochastic dominance. |
+| Stochastic dominance | `fsd_uni` | `NNS.FSD.uni` | implemented | Univariate FSD wrapper. |
+| Stochastic dominance | `ssd_uni` | `NNS.SSD.uni` | implemented | Univariate SSD wrapper. |
+| Stochastic dominance | `tsd_uni` | `NNS.TSD.uni` | implemented | Univariate TSD wrapper. |
 | Stochastic dominance | `nns_sd_cluster` | `NNS.SD.cluster` | implemented | Stochastic-dominance clustering. |
 | Stochastic dominance | `sd_efficient_set` | `SD.efficient.set` | implemented | Stochastic-dominance efficient set. |
 | Stochastic superiority | `nns_ss` | `NNS.SS` | implemented | Stochastic superiority. |
 | Simulation | `nns_mc` | `NNS.MC` | implemented | Monte Carlo helper. |
 | Simulation | `nns_meboot` | `NNS.meboot` | implemented | Maximum-entropy bootstrap helper. |
 | Differentiation | `nns_diff` | `NNS.diff` | implemented | Numerical differentiation. |
 | Differentiation | `dy_dx` | `dy.dx` | implemented | Scalar derivative helper. |
-| Differentiation | `dy_d` | `dy.d` | partial | Multivariate derivative helper. |
+| Differentiation | `dy_d` | `dy.d_` | partial | Multivariate derivative helper. |
 | VaR helpers | `lpm_var` | `LPM.VaR` | implemented | Lower partial-moment VaR helper. |
 | VaR helpers | `upm_var` | `UPM.VaR` | implemented | Upper partial-moment VaR helper. |
 
@@ -359,13 +359,13 @@ Rescales inputs using NNS conventions.
 
 #### `fsd`, `ssd`, `tsd`
 
-Closest R APIs: `FSD`, `SSD`, and `TSD`.
+Closest R APIs: `NNS.FSD`, `NNS.SSD`, and `NNS.TSD`.
 
 Compute first-, second-, and third-order stochastic dominance.
 
 #### `fsd_uni`, `ssd_uni`, `tsd_uni`
 
-Closest R APIs: `FSD.uni`, `SSD.uni`, and `TSD.uni`.
+Closest R APIs: `NNS.FSD.uni`, `NNS.SSD.uni`, and `NNS.TSD.uni`.
 
 Univariate wrappers for stochastic dominance workflows.
 
@@ -415,7 +415,7 @@ Scalar derivative helper for overall and pointwise evaluation modes.
 
 #### `dy_d`
 
-Closest R API: `dy.d`.
+Closest R API: `dy.d_`.
 
 Multivariate derivative helper. Scalar and vectorized point and distribution modes are covered. Multi-row mixed derivative point matrices use pointwise Python semantics rather than R's order-dependent list-matrix packing quirk.
 

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "ovvo-nns"
-version = "1.0.7"
+version = "1.0.8"
 description = "Python port of nonlinear nonparametric statistics from R NNS"
 readme = "README.md"
 requires-python = ">=3.11"
@@ -31,7 +31,7 @@ keywords = [
     "forecasting",
     "nns",
 ]
-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" }
+urls = { "Homepage" = "https://github.com/OVVO-Financial/NNS-python", "Repository" = "https://github.com/OVVO-Financial/NNS-python", "Source" = "https://github.com/OVVO-Financial/NNS-python", "Issues" = "https://github.com/OVVO-Financial/NNS-python/issues", "Documentation" = "https://ovvo-financial.github.io/NNS-python/" }
 dependencies = [
     "matplotlib>=3.7",
     "numpy",

diff --git a/scripts/generate_api_reference.py b/scripts/generate_api_reference.py
@@ -32,9 +32,9 @@ class ApiMeta:
     "co_upm": ApiMeta("Co-moments", "Co.UPM", "implemented", "Pairwise co-upper partial moment."),
     "d_lpm": ApiMeta("Co-moments", "D.LPM", "implemented", "Pairwise lower directional partial moment."),
     "d_upm": ApiMeta("Co-moments", "D.UPM", "implemented", "Pairwise upper directional partial moment."),
-    "co_lpm_nd": ApiMeta("Co-moments", "N-dimensional co-LPM path", "implemented", "N-dimensional co-lower partial moment wrapper."),
-    "co_upm_nd": ApiMeta("Co-moments", "N-dimensional co-UPM path", "implemented", "N-dimensional co-upper partial moment wrapper."),
-    "dpm_nd": ApiMeta("Co-moments", "N-dimensional DPM path", "implemented", "N-dimensional directional partial moment wrapper."),
+    "co_lpm_nd": ApiMeta("Co-moments", "Co.LPM_nD", "implemented", "N-dimensional co-lower partial moment wrapper."),
+    "co_upm_nd": ApiMeta("Co-moments", "Co.UPM_nD", "implemented", "N-dimensional co-upper partial moment wrapper."),
+    "dpm_nd": ApiMeta("Co-moments", "DPM_nD", "implemented", "N-dimensional directional partial moment wrapper."),
     "ecdf_pm": ApiMeta("Classical moments", "ECDF partial-moment helper", "implemented", "Empirical distribution helper used by moment routines."),
     "mean_pm": ApiMeta("Classical moments", "Mean partial-moment helper", "implemented", "Mean via partial-moment conventions."),
     "var_pm": ApiMeta("Classical moments", "Variance partial-moment helper", "implemented", "Variance via partial-moment conventions."),
@@ -68,20 +68,20 @@ class ApiMeta:
     "nns_gravity": ApiMeta("Central tendencies", "NNS.gravity", "implemented", "NNS gravity center helper."),
     "nns_mode": ApiMeta("Central tendencies", "NNS.mode", "implemented", "NNS mode helper."),
     "nns_rescale": ApiMeta("Central tendencies", "NNS.rescale", "implemented", "Rescaling helper."),
-    "fsd": ApiMeta("Stochastic dominance, superiority, and simulation", "FSD", "implemented", "First-order stochastic dominance."),
-    "ssd": ApiMeta("Stochastic dominance, superiority, and simulation", "SSD", "implemented", "Second-order stochastic dominance."),
-    "tsd": ApiMeta("Stochastic dominance, superiority, and simulation", "TSD", "implemented", "Third-order stochastic dominance."),
-    "fsd_uni": ApiMeta("Stochastic dominance, superiority, and simulation", "FSD.uni", "implemented", "Univariate FSD wrapper."),
-    "ssd_uni": ApiMeta("Stochastic dominance, superiority, and simulation", "SSD.uni", "implemented", "Univariate SSD wrapper."),
-    "tsd_uni": ApiMeta("Stochastic dominance, superiority, and simulation", "TSD.uni", "implemented", "Univariate TSD wrapper."),
+    "fsd": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.FSD", "implemented", "First-order stochastic dominance."),
+    "ssd": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.SSD", "implemented", "Second-order stochastic dominance."),
+    "tsd": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.TSD", "implemented", "Third-order stochastic dominance."),
+    "fsd_uni": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.FSD.uni", "implemented", "Univariate FSD wrapper."),
+    "ssd_uni": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.SSD.uni", "implemented", "Univariate SSD wrapper."),
+    "tsd_uni": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.TSD.uni", "implemented", "Univariate TSD wrapper."),
     "nns_sd_cluster": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.SD.cluster", "implemented", "Stochastic-dominance clustering."),
     "sd_efficient_set": ApiMeta("Stochastic dominance, superiority, and simulation", "SD.efficient.set", "implemented", "Stochastic-dominance efficient set."),
     "nns_ss": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.SS", "implemented", "Stochastic superiority."),
     "nns_mc": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.MC", "implemented", "Monte Carlo helper."),
     "nns_meboot": ApiMeta("Stochastic dominance, superiority, and simulation", "NNS.meboot", "implemented", "Maximum-entropy bootstrap helper."),
     "nns_diff": ApiMeta("Differentiation", "NNS.diff", "implemented", "Numerical differentiation."),
     "dy_dx": ApiMeta("Differentiation", "dy.dx", "implemented", "Scalar derivative helper."),
-    "dy_d": ApiMeta("Differentiation", "dy.d", "partial", "Multivariate derivative helper."),
+    "dy_d": ApiMeta("Differentiation", "dy.d_", "partial", "Multivariate derivative helper."),
     "lpm_var": ApiMeta("VaR helpers", "LPM.VaR", "implemented", "Lower partial-moment VaR helper."),
     "upm_var": ApiMeta("VaR helpers", "UPM.VaR", "implemented", "Upper partial-moment VaR helper."),
 }

diff --git a/src/nns/__init__.py b/src/nns/__init__.py
@@ -4,7 +4,7 @@
 
 from nns.pm_matrix import pm_matrix as pm_matrix
 
-__version__ = "1.0.7"
+__version__ = "1.0.8"
 
 _EXPORTS = {
     "FactorDesign": ("nns.regression", "FactorDesign"),