A very simple, inefficient implemention of Firth-penalized Logistic Regression for rare event data.
Project description
The author of this package has not provided a project description
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyfirth-0.0.5.tar.gz
(5.9 kB
view details)
Built Distribution
File details
Details for the file pyfirth-0.0.5.tar.gz.
File metadata
- Download URL: pyfirth-0.0.5.tar.gz
- Upload date:
- Size: 5.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | cc05115b49a98526d58df70f2a69fb4bd3ba9d2b04f90c9ac8c42864a03bed83 |
|
| MD5 | 0686597b4d534ec8052ab7f686bdd2fa |
|
| BLAKE2b-256 | b07bfea2235a00cd882ac44e3334e0fb7b4f6a19a88ca74db37beda25b41d010 |
File details
Details for the file pyfirth-0.0.5-py3-none-any.whl.
File metadata
- Download URL: pyfirth-0.0.5-py3-none-any.whl
- Upload date:
- Size: 5.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.9.13
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 352c7bb189b978169f8883ef7d5b64343eb711bdd376b7dd0e86561d690da726 |
|
| MD5 | 0b98e23212cad8177bb20c75750a928b |
|
| BLAKE2b-256 | 546aefeaf8c05012e86b20c8f2e69518afc972d754af6df34e6897d59640bb3c |
