Changes in version 0.1.0 (2026-06-23)                  

Initial CRAN release

New features

  - lmi_impute() — unified imputation entry point. Automatically selects
    between the LLM engine (Anthropic Claude API) and the offline
    statistical engine based on API key availability.
  - lmi_impute_offline() — fully self-contained imputation using
    nineteen algorithms implemented in base R. No internet connection or
    API key required.
  - lmi_diagnose() — local missingness audit: reports missing counts,
    percentages, and column types without any API calls.
  - lmi_set_api_key() / lmi_get_api_key() — session-scoped API key
    management via options() or the ANTHROPIC_API_KEY environment
    variable.
  - lmi_set_model() / lmi_get_model() — select the Anthropic Claude
    model used for LLM imputation.
  - lmi_export() — write the imputed data frame and audit trail to CSV
    or RDS files.
  - lmi_methods() — print a formatted catalogue of all available offline
    imputation methods with usage guidance.
  - S3 methods print.lmi_result(), summary.lmi_result(), and
    as.data.frame.lmi_result() for the lmi_result class.
  - Nineteen offline imputation algorithms:
      - Classical: mean, median, mode, LOCF, NOCB, hot-deck, PMM
      - Regression: linear, Lasso (coordinate descent), Ridge (closed
        form), Bayesian Ridge (evidence approximation)
      - Kernel/neighbour: KNN (scaled Euclidean), SVR (RBF kernel)
      - Tree/ensemble: decision tree (CART), random forest, gradient
        boosting
      - Iterative: MissForest
      - Matrix completion: PCA imputation (iterative SVD), SoftImpute
        (nuclear-norm minimisation)