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  "Note": "-*- Encoding: utf-8 -*-",
  "Package": "stepmixr",
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  "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 <ce.giguere@gmail.com>",
  "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. Additional features include support\nfor covariates and distal outcomes, various simulation\nutilities, and non-parametric bootstrapping, which allows\ninference in semi-supervised and unsupervised settings.\nSoftware paper available at <doi:10.18637/jss.v113.i08>.",
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  "URL": "https://github.com/Labo-Lacourse/StepMixr",
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  "Repository": "https://labo-lacourse.r-universe.dev",
  "Date/Publication": "2025-07-28 04:25:11 UTC",
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  "_created": "2026-09-18T08:30:14.000Z",
  "_published": "2026-09-18T09:01:43.083Z",
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    "bootstrap_stats",
    "check_pystepmix_version",
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    "data_bakk_covariate",
    "data_bakk_response",
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    "random_nan",
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    "stepmix"
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      "title": "Non-parametric bootstrap of StepMix estimator.",
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        "bootstrap.stepmix.stepmix.StepMix"
      ]
    },
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      "title": "Non-parametric boostrap of StepMix estimator.",
      "topics": [
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        "bootstrap_stats.stepmix.stepmix.StepMix"
      ]
    },
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      "page": "Datasets",
      "title": "Series of function to simulate data.",
      "topics": [
        "bakk_measurements",
        "data_bakk_complete",
        "data_bakk_covariate",
        "data_bakk_response",
        "data_gaussian_diag",
        "data_generation_gaussian",
        "random_nan"
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      "title": "Fit a mixture using the stepmix python package.",
      "topics": [
        "fit",
        "identify_coef",
        "print.stepmix.stepmix.StepMix"
      ]
    },
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      "page": "install.stepmix",
      "title": "Install stepmix python package into python via reticulate.",
      "topics": [
        "check_pystepmix_version",
        "install.stepmix"
      ]
    },
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      "page": "mixed_descriptor",
      "title": "Utility function for mixture using mixed description.",
      "topics": [
        "mixed_descriptor"
      ]
    },
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      "page": "predict",
      "title": "Predict the membership (probabilities) using the fit of the stepmix python package.",
      "topics": [
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        "predict_proba",
        "predict_proba.stepmix.stepmix.StepMix"
      ]
    },
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      "title": "Save the fit of a mixture using the stepmix python package.",
      "topics": [
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        "savefit"
      ]
    },
    {
      "page": "stepmix",
      "title": "R interface to stepmix in StepMix python.",
      "topics": [
        "stepmix"
      ]
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