ovvo-nns 1.0.2

Python port of nonlinear nonparametric statistics from R NNS

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.

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/

Implementation: For a direct quantitative finance implementation of NNS, see OVVO Labs

Package at a glance

Item	Value
Distribution package	ovvo-nns
Import package	nns
Current version	1.0.1
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.

Install

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.

Use the package as nns:

import nns

print(nns.__version__)

Source builds use scikit-build-core and nanobind for the optional native extension. Published wheels should be preferred when available.

Quick start

import numpy as np
from nns import lpm, nns_dep, nns_reg, 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)

Measure nonlinear dependence:

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))

Fit a nonlinear regression and estimate new points:

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"])))

Forecast a univariate series:

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)

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 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.

Important boundaries:

• R is used only for parity tests and local cache regeneration, not normal runtime use.
• 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(...).
• 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 for detailed compatibility notes.

Examples and notebooks

Runnable examples live in docs/examples:

Topic	Script
Partial moments	partial_moments.py
Dependence	dependence.py
Distributions and ANOVA	distributions_anova.py
Regression	regression.py
Classification	classification.py
Forecasting	forecasting.py

Run one example:

uv run python docs/examples/partial_moments.py

Run all script examples:

for example in docs/examples/*.py; do uv run python "$example"; done

Notebook workflows are also available under docs/examples/notebooks.

Documentation

• API status and known gaps
• Behavior conventions and intentional divergences
• Benchmarks
• Examples

Development

uv sync --group dev
uv run pytest
uv run ruff check .
uv run mypy

Run benchmark tests explicitly:

uv run pytest -n0 -m benchmark --benchmark-enable tests/benchmarks/

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.

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 for current measurements and commands.

Authors and contributors

• Fred Viole — author and maintainer
• Roberto Spadim — contributor
• Rasheed Khoshnaw — contributor

Attribution

Upstream R package and reference implementation: OVVO-Financial/NNS