Hierarchical Effect and Variance Atlases

Python-facing port of hierarchical-effect-atlas.Rmd from the frozen R gp3bayes 0.5.0 reference. The statistical and governance framing below follows the canonical vignette; executable Python workflows use the mapped APIs listed later.

The expanded group-level layer can inspect raw posterior deviations, rank uncertainty, and baseline latent variance partitioning.

Rank probabilities and variance fractions remain descriptive posterior quantities; they do not establish substantive group importance or causal variance attribution.

Python API mapping

• gp3bayespy.group_effect_draws_table
• gp3bayespy.group_effect_rank_probability_table
• gp3bayespy.plot_group_effect_distribution
• gp3bayespy.plot_group_effect_rank_probability
• gp3bayespy.plot_random_intercept_variance_partition
• gp3bayespy.random_intercept_variance_partition

Python usage

import gp3bayespy as gp

# All functions listed above are available from the package root.
# Use help(gp.<function>) or the API reference for the exact Python signature.

Visual companion

These figures complement the canonical ported narrative and are generated from package functions.

Group effects. Deterministic visual companion generated from the Python package.

Variance components. Deterministic visual companion generated from the Python package.

The figures remain descriptive evidence and do not create automatic inferential decisions.