NEWS

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llmimpute 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)