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Merged
OVVO-Financial merged 1 commit into from
Jun 14, 2026
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Release 1.0.1: fix README/metadata, sole author Fred Viole #24

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11 changes: 5 additions & 6 deletions README.md
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[![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/)
[![License](https://img.shields.io/badge/license-GPL--3.0--only-blue)](LICENSE)
[![Status](https://img.shields.io/badge/status-alpha-orange)](docs/api_status.md)

`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.
`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.

Expand All @@ -23,12 +22,12 @@ NNS is built around partial moments, the lower and upper components of variance,
|---|---|
| Distribution package | `ovvo-nns` |
| Import package | `nns` |
| Current version | `1.0.0a0` |
| 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 | Alpha, parity-focused |
| 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.
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## 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.
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:

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## 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](docs/benchmarks.md) for current measurements and commands.

## Attribution
Upstream R package and reference implementation: [OVVO-Financial/NNS](https://github.com/OVVO-Financial/NNS)
4 changes: 1 addition & 3 deletions pyproject.toml
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[project]
name = "ovvo-nns"
version = "1.0.0"
version = "1.0.1"
description = "Python port of nonlinear nonparametric statistics from R NNS"
readme = "README.md"
requires-python = ">=3.11"
license = "GPL-3.0-only"
authors = [
{ name = "Fred Viole", email = "ovvo.open.source@gmail.com" },
{ name = "Roberto Spadim" },
{ name = "Rasheed Khoshnaw" },
]
maintainers = [
{ name = "Fred Viole", email = "ovvo.open.source@gmail.com" },
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2 changes: 1 addition & 1 deletion src/nns/__init__.py
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from nns.pm_matrix import pm_matrix as pm_matrix

__version__ = "1.0.0"
__version__ = "1.0.1"

_EXPORTS = {
"FactorDesign": ("nns.regression", "FactorDesign"),
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