socialverse 0.2.2

A structured, dependency-annotated function registry for the social sciences and humanities — the omicverse registry mechanism, ported off AnnData onto a light StudyState vocabulary.

socialverse

A structured, dependency-annotated function registry for the social sciences and humanities.

socialverse ports the mechanism that makes omicverse's agent capability work — not its data model. In AI-for-biology, what lets an agent plan a real analysis without hallucinating the API is ov.registry: every function is registered with a machine-readable contract (requires / produces / prerequisites / auto_fix), so an agent queries the registry instead of guessing. AnnData is only the vocabulary that contract speaks in.

Social data is not commensurable (a survey ≠ a corpus ≠ a network), so there is no "AnnData for social science" and there never will be. So socialverse keeps the registry and drops the container: the 12-slot StudyState is a light vocabulary — not a data matrix — that requires/produces speak in.

The spine is the registry, not the container. Define the vocabulary first, register the federated tools against it, and an agent can plan, chain, verify, and auto-fix.

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Install

pip install -e .            # minimal (numpy + pandas)
pip install -e ".[full]"    # + statsmodels/scipy/networkx/matplotlib to run every chain
pip install -e ".[dev]"     # + pytest

Everything domain-specific (linearmodels, spaCy, lxml, pyfixest, python-docx, …) is federated and lazy-imported — a chain degrades gracefully if its backend is absent.

Query the registry (the whole point)

import socialverse as sv

sv.registry.find("双重差分")          # fuzzy search (Chinese / English / abbrev / tool name)
sv.registry.get_prerequisites("did")  # what does DID require & produce? who satisfies each slot?
sv.registry.resolve_plan("sv.pl.forest")   # order the chain to reach a target

Coming from R / Stata / SPSS?

Search by the command name you already know — every function carries py-<command> aliases drawn from Stata, R, and SPSS (the py- marks the Python reimplementation):

sv.registry.get("py-lmer")             # R lme4::lmer      -> sv.tl.multilevel
sv.registry.get("py-stcox")            # Stata stcox       -> sv.tl.survival
sv.registry.get("py-svyglm")           # R survey::svyglm  -> sv.tl.survey_estimate
sv.registry.find("mixed")              # bare command also fuzzy-matches

164 such aliases across the registry map mixed/lmer, stcox/coxph, svyset/ svydesign, sem/lavaan, mirt, rdrobust, ergm, truthTable, lagsarlm, oaxaca, … onto their socialverse equivalents (see socialverse/_compat_aliases.py).

get_prerequisites("did") returns the same shape as omicverse's, so OmicOS's registry_lookup tool can consume a socialverse registry unchanged:

{
  "function": "socialverse.tl.did",
  "required_functions": ["parallel_trends"],
  "requires":  {"design": ["panel_id","time","treatment"], "identification": ["parallel_trends"]},
  "produces":  {"models": ["did","twfe"], "diagnostics": ["robustness"]},
  "auto_fix":  "escalate",
  "satisfied_by": {"identification.parallel_trends": ["parallel_trends"], "...": ["declare_design"]}
}

Run a chain — grounded, not guessed

import socialverse as sv
from socialverse import datasets

st = sv.StudyState()
st.write("estimand", "target", "ATT")           # the one user-supplied input
df = datasets.load_did_panel()

sv.pp.ingest(st, data=df)
sv.pp.declare_design(st, panel_id="firm_id", time="year",
                     treatment="treat_post", first_treated="first_treated")
sv.tl.parallel_trends(st)                        # must pass before DID is called causal
sv.tl.did(st)                                    # TWFE ATT + cluster-robust SE
sv.pl.forest(st)                                 # publication figure

print(st.summary())        # slots populated + a full provenance ledger

Call sv.tl.did(st) on an unprepared state and the registry refuses, telling you exactly which slot is missing and which function produces it — the leiden-before- neighbors guard, ported to social science:

socialverse.tl.did cannot run — unmet requires:
  - identification.parallel_trends (produced by: parallel_trends)
Query registry.get_prerequisites(...) or registry.resolve_plan(...) to plan the chain.

The StudyState vocabulary (12 slots)

The social-science analog of AnnData's obs / var / obsm / uns. Every contract speaks only in these slots (validated at registration):

slot	holds
sources	raw inputs: datasets, corpora, manuscripts, .bib, scans
design	sampling frame, weights, strata, PSU, panel_id, time, treatment/timing
variables	codebook, outcome, exposure, controls, scales, constructs
corpus	documents, coding units, dfm, TEI
codes	qualitative codebook, coded segments, themes, theme map
estimand	ATT / prevalence / association + target population (user-given)
identification	DAG, parallel-trends, IV validity, exclusion, positivity
models	DID/TWFE, event-study, weighted regression, topic model, network, field map
diagnostics	pretrend, balance, robustness matrix, reliability α, sensitivity
evidence	claim→quote/citation links, quote-trace index, verified .bib, provenance
governance	IRB, consent, PII-redaction status, data-use licence, AI-use disclosure
artifacts	figures, tables, DOCX/PDF, TEI-XML, apparatus, reproducible scripts

Namespaces (two axes, like omicverse)

• phase: sv.pp (prepare) · sv.tl (analyze) · sv.pl (plot/render)
• social-science axes: sv.gov (governance gates) · sv.lit (literature & citation)

Governance is a first-class axis — in social science, ethics/licence/PII/AI-disclosure gate almost every analysis, so they are registered functions with their own contracts, not an afterthought.

Method coverage (64 registered functions)

Each family is a real, tested implementation (pure numpy/scipy/statsmodels, with the champion backend lazy-imported when present) — see docs/CONTRACT_CARDS.md.

• measurement: EFA (exploratory factor analysis), scale reliability (Cronbach α / McDonald ω / ICC), inter-rater reliability (Cohen/Fleiss κ, Krippendorff α) — complements the existing CFA/SEM/IRT
• regression base: GLM (glm covers OLS / logit / probit / Poisson), multinomial (mlogit), ordered (ologit), average marginal effects (margins)
• causal / quasi-experimental: TWFE-DiD, event-study, RDD (local-linear), synthetic control, IV / 2SLS (iv_regress), propensity-score matching / IPW (psm), causal mediation (mediation)
• econometrics: 8-step replication pipeline (emits reproducible R/Stata scripts)
• complex survey: design-based weighted estimation (strata/PSU/weights)
• psychometrics: CFA, SEM (path fallback), IRT (2PL) — reliability, fit indices
• longitudinal: multilevel/HLM (MixedLM), survival/event-history (Cox PH, KM)
• spatial: Moran's I / LISA, spatial-lag (SAR) regression with impacts
• networks: descriptives, ERGM (MPLE), SAOM co-evolution (descriptive)
• set-theoretic: fsQCA (truth-table + Quine-McCluskey minimization)
• demography: life tables, Kitagawa / Oaxaca decomposition
• text / DH: corpus building, topic coding, OCR→TEI, philology collation, stylometry (Burrows's Delta)
• qualitative: reflexive thematic analysis, quote-traceability, theory lenses
• governance / literature: ethics/licence/AI-disclosure gates · search, citation-verify, review

Built-in analysis chains (auto-derived from requires ↔ produces)

• causal: ingest → declare_design → parallel_trends → did → event_study → forest
• quasi: ingest → rdd → rdd_plot · synthetic_control → synth_path
• survey: ingest → declare_design → design_survey → survey_estimate → survey_dist
• psychometrics: ingest → cfa → sem · irt
• longitudinal: ingest → multilevel · survival → km_curve
• spatial: ingest → spatial_autocorr → spatial_regression → moran_scatter
• qualitative: build_corpus → redact_pii → code_themes → trace_quotes → reflexive_memo → theme_map
• text / philology: ocr_tei → build_corpus → philology_collate → tei_encode · stylometry → dendrogram
• networks: build_network → ergm · saom
• QCA / demography: qca · life_table → decomposition
• literature / citation: search_free → zotero_bridge → citation_manage → verify_citations → manuscript_review
• governance (cross-cutting): data_use_check · ethics_check · redact_pii · ai_use_disclosure

How it maps to OmicOS

This package is the concrete instantiation of the humanities_social domain's registry table: its 54 registered functions cover all 26 humanities_social skills plus the quantitative method families a social-science审稿 pipeline needs. An OmicOS agent points its registry_lookup at sv.registry and gets the same grounding it gets from ov.registry in the bio domain — query, plan, chain, auto-fix.

Design notes

• Registry first, tools second. Contracts are the spine; implementations are federated wrappers over the field's best tools (statsmodels, linearmodels, pyfixest, networkx, spaCy, lxml …), never rewrites.
• Provenance is built in. Every registered call records params + slots touched into state.provenance — the reproducible/auditable "evidence spine".
• Fail-soft. A missing optional backend degrades one chain, never the import.

Licence: CC-BY-4.0.