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.
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 | 2.0.0 |
| 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=Trueadds Matplotlib rendering as a side effect without changing the statistical result.
See behavior conventions for detailed compatibility notes.
Documentation
- API reference
- API status and known gaps
- Behavior conventions
- Parity policy and cache regeneration
- Benchmarks
- Canonical 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.
Authors and contributors
- Fred Viole — author and maintainer
- Roberto Spadim — contributor
- Rasheed Khoshnaw — contributor
Attribution
Upstream R package and reference implementation: OVVO-Financial/NNS
1 maintainer
Release files for ovvo-nns 2.0.0
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Built distributions (wheels)
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Transparency logRelease files / ovvo_nns-2.0.0-cp311-cp311-musllinux_1_2_x86_64.whl
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Transparency logRelease files / ovvo_nns-2.0.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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Transparency logRelease files / ovvo_nns-2.0.0-cp311-cp311-macosx_11_0_arm64.whl
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| Tags | CPython 3.11 macOS 11.0+ ARM64 |
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Transparency logRelease files / ovvo_nns-2.0.0-cp311-cp311-macosx_10_14_x86_64.whl
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| Tags | CPython 3.11 macOS 10.14+ x86-64 |
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