{ "_id": "6a3ab67c44e65c5976a8b2d2", "Package": "llmimpute", "Type": "Package", "Title": "Missing Data Imputation via Language Models and Statistics", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Sadikul\", \"Islam\", email = \"sadikul.islamiasri@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-2924-7122\")),\nperson(\"Rajesh\", \"Kaushal\", role = \"aut\"))", "Description": "Provides missing data imputation through two complementary\nengines: a large language model engine that communicates with\nthe 'Anthropic' 'Claude' application programming interface for\ncontext-aware semantic imputation, and a fully self-contained\noffline engine implementing nineteen statistical and machine\nlearning algorithms entirely in base R with no additional\npackage dependencies. Offline methods include mean, median,\nmode, last observation carried forward, next observation\ncarried backward, hot-deck, predictive mean matching, k-nearest\nneighbours, ordinary least-squares regression, Lasso with\ncoordinate descent, Ridge with closed-form solution, Bayesian\nRidge regression with evidence approximation following MacKay\n(1992), support vector regression with a radial basis function\nkernel, classification and regression trees, random forests,\ngradient boosting, iterative random forest imputation,\nprincipal component analysis imputation via iterative singular\nvalue decomposition, and nuclear-norm minimisation via singular\nvalue thresholding. When no API key is available the package\nautomatically falls back to the offline engine, ensuring full\noperation in environments without internet access. Every\nimputed value is accompanied by a confidence score and a\nplain-language reasoning string, producing reproducible audit\ntrails. 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