From b9f01cac2d455ad2655e9bed4a5c57805c35cb93 Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 14 Jun 2026 18:26:12 +0000 Subject: [PATCH] Release 1.0.1: fix README/metadata, sole author Fred Viole 1.0.0 is already published to PyPI (immutable), so the corrected README and metadata ship as 1.0.1. - README: remove the stale 'status alpha' badge, set Current version to 1.0.1 and Public API status to Stable, and refer to the R NNS 13.0+ package consistently (any release after 13.0 is covered). - pyproject: bump to 1.0.1; make Fred Viole the sole author and maintainer (the PyPI page was attributing authorship to the no-email entries). - nns.__version__ bumped to 1.0.1 (enforced by test_version_sync). --- README.md | 11 +++++------ pyproject.toml | 4 +--- src/nns/__init__.py | 2 +- 3 files changed, 7 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 9b4948fe..87a89c06 100644 --- a/README.md +++ b/README.md @@ -5,9 +5,8 @@ [![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. @@ -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. @@ -134,7 +133,7 @@ See [API status](docs/api_status.md) for implemented, partial, guarded, and know ## 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: @@ -201,7 +200,7 @@ The default parity suite is cache-backed and does not require `Rscript`. `Rscrip ## 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) diff --git a/pyproject.toml b/pyproject.toml index 1d71613e..27ab04c3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,14 +1,12 @@ [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" }, diff --git a/src/nns/__init__.py b/src/nns/__init__.py index de784844..4d561bb5 100644 --- a/src/nns/__init__.py +++ b/src/nns/__init__.py @@ -4,7 +4,7 @@ from nns.pm_matrix import pm_matrix as pm_matrix -__version__ = "1.0.0" +__version__ = "1.0.1" _EXPORTS = { "FactorDesign": ("nns.regression", "FactorDesign"),