Developer API Reference

Programmatic usage of the Indexly inference engine.

The inference engine is modular and side-effect free.

Core entry point:

run_inference_engine(
    df,
    test: str,
    x: list[str] | None = None,
    y: str | None = None,
    group: str | None = None,
    interaction: list[str] | None = None,
    auto_route: bool = True,
    bootstrap: bool = False,
    correction: str | None = None,
    lag: int = 1,
    alpha: float = 0.05,
)

Returns:

InferenceResult

InferenceResult Model

Defined in models.py.

Fields:

Field Description
test_name Name of test
statistic Primary statistic
p_value P-value
effect_size Effect size (optional)
ci_low Lower CI
ci_high Upper CI
additional_table Optional DataFrame
metadata Structured context

Correlation Module

File: correlation.py

Functions:

pearson_corr(df, x, y, alpha=0.05)
spearman_corr(df, x, y)
lag_corr(df, x, y, lag=1)
correlation_matrix(df, columns)

Pearson uses Fisher Z CI.

T-Tests

File: ttest.py

run_ttest(df, y, group, auto_route=True, use_bootstrap=False)
run_paired_ttest(df, x1, x2, use_bootstrap=False)

ANOVA

File: anova.py

run_anova(df, y, group, auto_route=True)

Posthoc:

run_tukey(df, y, group)

Regression

File: regression.py

run_ols(df, y, x, interaction=None, auto_route=True, bootstrap_coefficients=False)

Mixed Effects

File: mixed_effects.py

run_mixed_effects(df, y, group)

Multiple Comparison Correction

File: multiple_corrections.py

apply_correction(result, method="bonferroni")

Supported:

  • bonferroni
  • holm
  • bh (Benjamini–Hochberg)

Bootstrap

File: bootstrap.py

Reusable bootstrap utilities for:

  • Coefficients
  • Confidence intervals

Formatting & Export

formatter.py → Console rendering exporter.py → Markdown / PDF reports

Extension Pattern

To add a new test:

  1. Implement function returning InferenceResult
  2. Register in run_inference_engine
  3. Add CLI test choice
  4. Update documentation

The engine is designed for strict modular extension.