piwheels

snob-factor

Mixture modelling by Minimum Message Length (MML) using Factor Snob

Installation

In a virtualenv (see these instructions if you need to create one):

pip3 install snob-factor

Dependencies

• numpy
• pandas
• prettytable

Releases

Version	Released	Bookworm Python 3.11	Trixie Python 3.13	Files
2026.4.5	2026-03-19
snob_factor-2026.4.5-cp311-cp311-manylinux_2_36_armv7l.whl (280 KB) snob_factor-2026.4.5-cp313-cp313-manylinux_2_41_armv7l.whl (286 KB)
2026.4.4	2026-03-19
snob_factor-2026.4.4-cp311-cp311-manylinux_2_36_armv7l.whl (280 KB) snob_factor-2026.4.4-cp313-cp313-manylinux_2_41_armv7l.whl (287 KB)
2026.4.0	2026-03-17
2026.3.22	2026-03-09
2026.3.17	2026-03-08
2026.2.16	2026-03-01
snob_factor-2026.2.16-cp311-cp311-linux_armv7l.whl (1 MB) snob_factor-2026.2.16-cp311-cp311-linux_armv6l.whl (1 MB)
2024.2.4	2024-01-03
snob_factor-2024.2.4-cp311-cp311-linux_armv6l.whl (1 MB) snob_factor-2024.2.4-cp311-cp311-linux_armv7l.whl (1 MB) snob_factor-2024.2.4-cp39-cp39-linux_armv6l.whl (1 MB) snob_factor-2024.2.4-cp39-cp39-linux_armv7l.whl (1 MB)

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