{ "_id": "6a3a52466265ec60d620eeab", "Package": "llmclean", "Type": "Package", "Title": "LLM-Assisted Data Cleaning with Multi-Provider Support", "Version": "0.1.1", "Date": "2026-06-01", "Authors@R": "c(\nperson(\"Sadikul\", \"Islam\",\nemail = \"sadikul.islamiasri@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2924-7122\")),\nperson(\"Rajesh\", \"Kaushal\",\nrole = \"aut\"))", "Maintainer": "Sadikul Islam ", "Description": "Detects and suggests fixes for semantic inconsistencies in\ndata frames by calling large language models (LLMs) through a\nunified, provider-agnostic interface. Supported providers\ninclude 'OpenAI' ('GPT-4o', 'GPT-4o-mini')\n, 'Anthropic' ('Claude')\n, 'Google' ('Gemini')\n, 'Groq' (free-tier 'LLaMA' and\n'Mixtral') , and local 'Ollama' models\n. The package identifies issues that\nrule-based tools cannot detect: abbreviation variants,\ntypographic errors, case inconsistencies, and malformed values.\nResults are returned as tidy data frames with column, row\nindex, detected value, issue type, suggested fix, and\nconfidence score. An offline fallback using statistical and\nfuzzy-matching methods is provided for use without any\napplication programming interface (API) key. Interactive fix\napplication with human review is supported via 'apply_fixes()'.\nMethods follow de Jonge and van der Loo (2013)\n\nand Chaudhuri et al. 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