{ "_id": "6a0f7768acfb0bcc41c620af", "Note": "-*- Encoding: utf-8 -*-", "Package": "stepmixr", "Type": "Package", "Title": "Interface to 'Python' Package 'StepMix'", "Version": "0.1.3", "Date": "2025-07-04", "Authors@R": "c(\nperson(\"Éric\", \"Lacourse\", role=\"aut\"),\nperson(\"Roxane\", \"de la Sablonnière\", role=\"aut\"),\nperson(\"Charles-Édouard\", \"Giguère\", role=c(\"aut\", \"cre\"),\nemail = \"ce.giguere@gmail.com\"),\nperson(\"Sacha\", \"Morin\", role=\"aut\"),\nperson(\"Robin\", \"Legault\", role=\"aut\"),\nperson(\"Félix\", \"Laliberté\", role = \"aut\"),\nperson(\"Zsusza\", \"Bakk\", role=\"ctb\") )", "Author": "Éric Lacourse [aut], Roxane de la Sablonnière [aut],\nCharles-Édouard Giguère [aut, cre], Sacha Morin [aut], Robin\nLegault [aut], Félix Laliberté [aut], Zsusza Bakk [ctb]", "Maintainer": "Charles-Édouard Giguère ", "Description": "This is an interface for the 'Python' package 'StepMix'.\nIt is a 'Python' package following the scikit-learn API for\nmodel-based clustering and generalized mixture modeling (latent\nclass/profile analysis) of continuous and categorical data.\n'StepMix' handles missing values through Full Information\nMaximum Likelihood (FIML) and provides multiple stepwise\nExpectation-Maximization (EM) estimation methods based on\npseudolikelihood theory. 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