{ "_id": "6a294470732311cd875ab571", "Package": "glmmPen", "Type": "Package", "Title": "High Dimensional Penalized Generalized Linear Mixed Models\n(pGLMM)", "Version": "1.5.4.8", "Date": "2024-08-29", "Authors@R": "c(\nperson(\"Hillary\", \"Heiling\", email = \"hmheiling@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Naim\", \"Rashid\", email = \"nur2@email.unc.edu\", role = c(\"aut\")),\nperson(\"Quefeng\", \"Li\", email = \"quefeng@email.unc.edu\", role = c(\"aut\")),\nperson(\"Joseph\", \"Ibrahim\", email = \"ibrahim@bios.unc.edu\", role = c(\"aut\")))", "Description": "Fits high dimensional penalized generalized linear mixed\nmodels using the Monte Carlo Expectation Conditional\nMinimization (MCECM) algorithm. The purpose of the package is\nto perform variable selection on both the fixed and random\neffects simultaneously for generalized linear mixed models. The\npackage supports fitting of Binomial, Gaussian, and Poisson\ndata with canonical links, and supports penalization using the\nMCP, SCAD, or LASSO penalties. The MCECM algorithm is described\nin Rashid et al. (2020) .\nThe techniques used in the minimization portion of the\nprocedure (the M-step) are derived from the procedures of the\n'ncvreg' package (Breheny and Huang (2011)\n) and 'grpreg' package (Breheny and\nHuang (2015) ), with appropriate\nmodifications to account for the estimation and penalization of\nthe random effects. 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