cody-special
High-precision error functions and normal distribution (Cody's algorithm)
Decision gist · record as of 2026-08-14
Yes, if you need high-precision error or normal distribution functions and prefer a lightweight, dependency-free implementation. The package is stable and permissively licensed. Install with caution if you expect active maintenance—it is aging with no recent development activity, so treat it as a mature, self-contained tool rather than an actively evolving library.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later (supports up to 3.13).
- Low install friction with no runtime dependencies.
- Package is aging (213 days since release) with a single recent commit but no active development signal; suitable for stable, self-contained use.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal obligations.
last release 2026-01-13 (213 days) · last repo commit 2026-01-13
0 known vulnerabilities (OSV.dev, 2026-08-14) · 158,052 downloads/mo, #10,737 on PyPI
Alternatives
Verify before relying
pip install cody-special
from cody_special import erf_cody, norm_cdf, inverse_norm_cdf
erf_cody(1.0) # ~ 0.8427
norm_cdf(0.0) # = 0.5
inverse_norm_cdf(0.5) # = 0.0- Numerical accuracy and precision claims relative to reference implementations or standard libraries.
- Performance characteristics compared to scipy.special or numpy equivalents.
- Whether the package is actively maintained or in maintenance-only mode given aging status.
What it is and what it does
cody-special is a pure-Python library that implements error functions and normal distribution functions using W.J. Cody's rational Chebyshev approximation method from 1969. It provides erf, erfc, erfcx (scaled complementary error function), and standard normal PDF, CDF, and inverse CDF functions. The package has no external runtime dependencies and installs as a simple wheel.
The library is designed for applications requiring high-precision special function evaluation—particularly in quantitative finance, statistics, and scientific computing where accurate error and normal distribution calculations are critical. It is a stable, single-release package with no known vulnerabilities, making it suitable for incorporation into production systems where the specific approximation method or numerical properties are required.
Use it for
- Computing error function values in signal processing or physics simulations requiring Cody's approximation method.
- Evaluating normal distribution CDF and quantiles in statistical analysis or hypothesis testing workflows.
- Quantitative finance applications needing precise inverse normal CDF for option pricing or risk models.
- Replacing scipy.special calls when a lightweight, dependency-free implementation is preferred.
- Implementing algorithms that explicitly require Cody's rational Chebyshev approximations for reproducibility.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need high-precision error or normal distribution functions and prefer a lightweight, dependency-free implementation.
The package is stable and permissively licensed. Install with caution if you expect active maintenance—it is aging with no recent development activity, so treat it as a mature, self-contained tool rather than an actively evolving library.
Install
cody-special on PyPI
Before you install
Low install friction with no runtime dependencies. Package is aging (213 days since release) with a single recent commit but no active development signal; suitable for stable, self-contained use.
Requires Python 3.9 or later (supports up to 3.13).
License in practice
MIT license permits unrestricted use, modification, and distribution in commercial and private projects with minimal obligations.
Quickstart
pip install cody-special
from cody_special import erf_cody, norm_cdf, inverse_norm_cdf
erf_cody(1.0) # ~ 0.8427
norm_cdf(0.0) # = 0.5
inverse_norm_cdf(0.5) # = 0.0Verify before relying
- Numerical accuracy and precision claims relative to reference implementations or standard libraries.
- Performance characteristics compared to scipy.special or numpy equivalents.
- Whether the package is actively maintained or in maintenance-only mode given aging status.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Aging 213 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 158,052 / month, #10,737 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: cody_special-1.0.0-py3-none-any.whl
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