{ "_id": "6a50a7b789c83ec243a2f1a8", "Package": "glmtrans", "Type": "Package", "Title": "Transfer Learning under Regularized Generalized Linear Models", "Version": "2.1.0", "Authors@R": "c(person(\"Ye\", \"Tian\", role = c(\"aut\", \"cre\"), email = \"ye.t@columbia.edu\"), person(\"Yang\", \"Feng\", role = \"aut\", email = \"yang.feng@nyu.edu\"))", "Description": "We provide an efficient implementation for two-step\nmulti-source transfer learning algorithms in high-dimensional\ngeneralized linear models (GLMs). The elastic-net penalized GLM\nwith three popular families, including linear, logistic and\nPoisson regression models, can be fitted. To avoid negative\ntransfer, a transferable source detection algorithm is\nproposed. We also provides visualization for the transferable\nsource detection results. The details of methods can be found\nin \"Tian, Y., & Feng, Y. (2023). Transfer learning under\nhigh-dimensional generalized linear models. 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