Releases · Samrdreyfuss/graphdml

Documentation site (https://samrdreyfuss.github.io/graphdml/): guides, methodology,
validation, example datasets and an API reference generated from the docstrings.
graphdml.selftest() and make_signal_check(): a built-in check, on synthetic data with
planted effects and a null twin that has none, that the estimator finds real signals and
does not invent false ones. Reports bias, coverage, power and false-alarm rates against
fixed criteria, next to a network-blind baseline.
ATE (total effect): the average effect of treating every node vs none, reported first
in summary() and as a "total" row in summary_frame(), cluster_summary_frame() and
conf_int(), with model.ate_ and model.total_weights_. Pass total=False for the
direct and peer rows only.
Practitioner-facing labels in summary(): ATE (total), direct (ADE), peer (APE),
with a plain-language unit line for each and a note on z-statistics.
GraphData.with_values(..., feature_names=...); covariates may change their number of
columns.
Installation guide, badges and project links in the README and on PyPI.
README datasets table now says which datasets need outside data (LastFM downloads on
first use, the insurance experiment must be downloaded from openICPSR), and lists
make_toy_graph.

Repository polish: code of conduct, security policy, issue forms and a pull-request
template; py.typed marker so type checkers use graphdml's annotations; CI pulls the Neo4j
test image through a mirror, which fixes random failures from Docker Hub rate limits.
load_insurance_experiment explains where to get the data when the files are missing,
and fetch_lastfm_asia explains what to do when the download fails.

summary_frame() includes a "total" row by default when a peer exposure is fitted.
Removed the unused gnn and viz optional extras.

The placebo test in the test suite is seeded, so it no longer fails occasionally.

Full changelog: v0.1.0...v0.2.0

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