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docketscape 0.1.0

Initial Release

docketscape provides a statistical modeling framework for decomposing court delay into substantive legal complexity (“what a case is”) and institutional congestion (“where the case sits”) using network representations and survival analysis.

Core Features

• Case Standardization & Censoring (clean_cases):
  • Validates case-level filing and decision dates.
  • Handles right-censored pending matters relative to observation cutoffs.
  • Explicitly flags invalid records (e.g. decisions preceding filing, filings after observation end) without silent deletion.
  • Masks durations to NA for invalid cases to prevent non-positive durations in downstream models.
• Statutory Network Representation (build_section_graph):
  • Constructs undirected section co-occurrence networks from case citations.
  • Employs sparse matrix crossproducts (Matrix::crossprod) for scalable graph construction.
  • Preserves edge weight invariants (strictly positive integer case co-occurrence counts).
• Profile Diffusion (diffuse_profiles):
  • Smooths sparse statutory citation profiles across co-cited sections via one-step convex combination diffusion.
  • Enforces row-stochasticity (unit row sums, \(L_1 = 1.0\)) across all tuning values \(\alpha \in [0, 1]\).
• Case Similarity Graph (build_case_graph):
  • Constructs \(k\)-nearest-neighbour case graphs using inverse document frequency (IDF) weighted cosine similarities.
  • Uses chunked sparse evaluation and integer-ranked tie-breaking, scaling to large dockets within minimal memory.
  • Enforces graph invariants: no self-loops, no multigraph edges, maximum similarity weights on mutual edges.
• Neighbourhood Survival & Relative Lateness (neighbourhood_survival, relative_lateness):
  • Computes weighted Kaplan-Meier survival curves from legally similar peers.
  • Evaluates relative lateness percentiles (\(1 - S(T)\)) anchored in empirical peer timelines.
  • Guards against extrapolation: flags unestimable cases when durations exceed peer follow-up.
• Institutional Congestion Indexing (court_load):
  • Measures baseline court docket load at the monthly filing interval.
  • Implements fast \(O(N \log N)\) findInterval() event tracking.
• Semi-Parametric Delay Modeling (fit_delay_model, decompose_delay):
  • Fits Cox proportional hazards regression on legal scores and baseline court load.
  • Handles small sample sizes (\(N < 3\) or zero events) gracefully without numerical crashes.
  • Decomposes linear predictors into legal and court load absolute contribution shares.
• Leakage-Aware Validation & Auditing (validate_models, project_validation_legal_score, check_*):
  • Enforces strict chronological temporal holdouts preventing future-neighbour leakage.
  • Projects validation cases into training representations without validation-to-validation contamination.
  • Provides diagnostic audits: check_future_neighbours(), check_court_overlap(), and check_near_duplicates().

Methodological Safeguards

• Observational and non-causal framework.
• Does not rank judges, evaluate judicial performance, or assess judicial diligence.
• Contains zero bundled empirical DDL records; all package examples and tests use synthetic data structures.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.