{
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  "Title": "Penalized Likelihood Factor Analysis via Nonconvex Penalty",
  "Version": "2.3.13",
  "Date": "2026-06-03",
  "Authors@R": "c(person(given = \"Kei\",\nfamily = \"Hirose\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mail@keihirose.com\",\ncomment = c(ORCID = \"0000-0001-9827-0356\")),\nperson(given = \"Michio\",\nfamily = \"Yamamoto\",\nrole = \"aut\",\nemail = \"yamamoto.michio.hus@osaka-u.ac.jp\"),\nperson(given = \"Haruhisa\",\nfamily = \"Nagata\",\nrole = \"aut\"))",
  "Description": "Computes the penalized maximum likelihood estimates of\nfactor loadings and unique variances for various tuning\nparameters. The pathwise coordinate descent along with EM\nalgorithm is used.  This package also includes a new graphical\ntool which outputs path diagram, goodness-of-fit indices and\nmodel selection criteria for each regularization parameter\n(Yamamoto, M., Hirose, K. and Nagata, H., 2017\n<doi:10.1007/s41237-016-0007-3>). The user can change the\nregularization parameter by manipulating scrollbars, which is\nhelpful to find a suitable value of regularization parameter.\nAs a penalty, we can choose either the minimax concave penalty\n(Hirose, K. and Yamamoto, M., 2015\n<doi:10.1007/s11222-014-9458-0>; Hirose, K. and Yamamoto, M.,\n2014 <doi:10.1016/j.csda.2014.05.011>) or the product-based\nelastic net penalty (Hirose, K. and Terada, Y., 2023\n<doi:10.1007/s11336-022-09868-4>).",
  "License": "GPL (>= 2)",
  "URL": "https://doi.org/10.1007/s11222-014-9458-0,\nhttps://doi.org/10.1016/j.csda.2014.05.011,\nhttps://doi.org/10.1007/s41237-016-0007-3,\nhttps://doi.org/10.1007/s11336-022-09868-4,\nhttps://keihirose.com",
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