NEWS

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docketscape 0.1.0 (2026-11-13)

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.