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llmimpute 0.1.0

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)

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.
Health stats visible at Monitor.