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  "Title": "Maximum Likelihood Estimation of Latent Variable Models",
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  "Date": "2023-08-24",
  "Authors@R": "c(person(given = \"Björn\", family = \"Andersson\", role = c(\"aut\", \"cre\"), email = \"bjoern.h.andersson@gmail.com\", comment = c(ORCID = \"0000-0002-9007-2440\")),      person(given = \"Shaobo\", family = \"Jin\", role = c(\"aut\"), email = \"shaobo.jin@statistik.uu.se\", comment = c(ORCID = \"0000-0001-6538-3477\")), \t person(given = \"Maoxin\", family = \"Zhang\", role = c(\"ctb\"), email = \"maoxin.zhang@cemo.uio.no\"))",
  "Description": "Approximate marginal maximum likelihood estimation of\nmultidimensional latent variable models via adaptive quadrature\nor Laplace approximations to the integrals in the likelihood\nfunction, as presented for confirmatory factor analysis models\nin Jin, S., Noh, M., and Lee, Y. (2018)\n<doi:10.1080/10705511.2017.1403287>, for item response theory\nmodels in Andersson, B., and Xin, T. (2021)\n<doi:10.3102/1076998620945199>, and for generalized linear\nlatent variable models in Andersson, B., Jin, S., and Zhang, M.\n(2023) <doi:10.1016/j.csda.2023.107710>. Models implemented\ninclude the generalized partial credit model, the graded\nresponse model, and generalized linear latent variable models\nfor Poisson, negative-binomial and normal distributions.\nSupports a combination of binary, ordinal, count and continuous\nobserved variables and multiple group models.",
  "License": "GPL (>= 2)",
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    "Date": "2026-05-20 06:59:14 UTC",
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  "Author": "Björn Andersson [aut, cre]\n(<https://orcid.org/0000-0002-9007-2440>), Shaobo Jin [aut]\n(<https://orcid.org/0000-0001-6538-3477>), Maoxin Zhang [ctb]",
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      "title": "Generation of Observed Data From a Generalized Linear Latent Variable Model",
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