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The robust expectation maximization (REM) algorithm\nis a statistical method for estimating the parameters of a\nlatent variable model in the presence of population\nheterogeneity as recommended by Nieser & Cochran (2023)\n. The REM algorithm is based on the\nexpectation-maximization (EM) algorithm, but it allows for the\ncase when all the data are generated by the assumed data\ngenerating model.", "License": "GPL (>= 3)", "URL": "https://github.com/knieser/REM", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-28 10:08:57 UTC", "User": "root" }, "Author": "Bryan Ortiz-Torres [aut, cre], Kenneth Nieser [aut]\n()", "Maintainer": "Bryan Ortiz-Torres ", "Config/pak/sysreqs": "cmake make", "Repository": "https://bryansaul.r-universe.dev", "Date/Publication": "2024-12-05 21:30:45 UTC", "RemoteUrl": "https://github.com/cran/REMLA", "RemoteRef": "HEAD", "RemoteSha": "f62b2f323db14192efd937b71f3c6c21b6176683", "MD5sum": "fcc22f77a39451c21b31b33e5cce2c12", "_user": "bryansaul", "_type": "src", "_file": "REMLA_1.2.0.tar.gz", "_fileid": "cf61c15cccc158eddfe80772a64324f0f0c899bd86dd9128f1f7a3659a9a9b5f", 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