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.

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.

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

• :material-download: Install

  pip install ovvo-nns, then import nns.

• :material-rocket-launch: Quick start

  Partial moments, dependence, regression, and forecasting in a few lines.

• :material-book-open-variant: API reference

  Function-by-function index with R NNS name crosswalks.

• :material-check-decagram: API status

  Implemented, partial, guarded, and known-gap paths.

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.

See behavior conventions for detailed compatibility notes and parity with R NNS for the parity target and automation.

Links

• View on PyPI
• Browse source code
• Report a bug

License

GPL-3.0-only

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.