{ "_id": "6ab77e1599f7aa8c6aa314e8", "Package": "lsm", "Type": "Package", "Title": "Estimation of the log Likelihood of the Saturated Model", "Version": "0.2.1.5", "Date": "2025-06-02", "Authors@R": "c(\nperson(\"Jorge\", \"Villalba\", email = \"jvillalba@utb.edu.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-2888-9660\")),\nperson(\"Humberto\", \"Llinas\", email = \"hllinas@uninorte.edu.co\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-2976-5109\")),\nperson(\"Omar\", \"Fabregas\", email = \"ofabregas@uninorte.edu.co\", role = c(\"aut\"), comment = c(ORCID = \"0000-0001-6853-6280\"))\n)", "Author": "Jorge Villalba [aut, cre] (ORCID:\n), Humberto Llinas [aut]\n(ORCID: ), Omar Fabregas\n[aut] (ORCID: )", "Maintainer": "Jorge Villalba ", "Description": "When the values of the outcome variable Y are either 0 or\n1, the function lsm() calculates the estimation of the log\nlikelihood in the saturated model. This model is characterized\nby Llinas (2006, ISSN:2389-8976) in section 2.3 through the\nassumptions 1 and 2. The function LogLik() works (almost\nperfectly) when the number of independent variables K is high,\nbut for small K it calculates wrong values in some cases. 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