NNS Python

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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.

The R package is the reference implementation and the source of truth for the statistical behavior, terminology, and canonical example curriculum. Python is native and does not call R at runtime.

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 1.6.0
Python >=3.11
Required runtime dependencies NumPy, SciPy, Matplotlib
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

Install

pip install ovvo-nns

Use the package as nns:

import nns

print(nns.__version__)

Published wheels are preferred. Source builds use scikit-build-core and nanobind for the optional native extension.

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])
print("LPM2:", lpm(2, 0.0, x))
print("UPM2:", upm(2, 0.0, x))

grid = np.linspace(-2.0, 2.0, 80)
print("nonlinear dependence:", nns_dep(grid, grid**2))

fit = nns_reg(grid, np.sin(grid), point_est=np.array([-1.0, 0.0, 1.0]))
print("point estimates:", fit["Point.est"])

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

See API status for implemented, partial, guarded, and known-gap paths.

Canonical examples

The R package's nine numbered vignettes define the canonical NNS curriculum. Python follows the same numbering, topic names, and statistical intent:

# Topic Python entry point
01 Overview 01_overview.py
02 Partial Moments 02_partial_moments.py
03 Correlation and Dependence 03_correlation_and_dependence.py
04 Normalization and Rescaling 04_normalization_and_rescaling.py
05 Sampling and Simulation 05_sampling_and_simulation.py
06 Comparing Distributions 06_comparing_distributions.py
07 Clustering and Regression 07_clustering_and_regression.py
08 Classification 08_classification.py
09 Forecasting 09_forecasting.py

The mapping is recorded in examples/vignettes/manifest.yml and validated in CI. The older unnumbered scripts remain as focused examples and backward-compatible entry points.

Run one canonical example:

uv run python examples/vignettes/02_partial_moments.py

Run all nine in R curriculum order:

uv run python examples/run_all_vignettes.py

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 and uses explicit Python errors for unsafe R coercions.

  • R is used for parity tests and local cache regeneration, not normal runtime.
  • Exact stochastic stream parity is not expected for every randomized path.
  • Factor and class ordering should be supplied explicitly when it matters.
  • Classification codes follow the R contract and start at 1.
  • Compute functions return values; plot=True adds Matplotlib rendering as a side effect without changing the statistical result.

See behavior conventions for detailed compatibility notes.

Documentation

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.

Authors and contributors

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

Attribution

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

Project links

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Key dates

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  • Released:
Newer release available (2.0.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for OVVOLabs from gravatar.com OVVOLabs

Credits

Author/Maintainer: Fred Viole

GitHub Statistics

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The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

GPL-3.0-only
View SPDX License List

Requires

Python >=3.11

Tags

statistics nonparametric partial moments regression forecasting nns

Classifiers

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