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(
  person("Jorge", "Villalba", email = "jvillalba@utb.edu.co", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2888-9660")),
  person("Humberto", "Llinas", email = "hllinas@uninorte.edu.co", role = c("aut"), comment = c(ORCID = "0000-0002-2976-5109")),
  person("Omar", "Fabregas", email = "ofabregas@uninorte.edu.co", role = c("aut"), comment = c(ORCID = "0000-0001-6853-6280"))
  )
Author: Jorge Villalba [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-2888-9660>), Humberto Llinas [aut]
        (ORCID: <https://orcid.org/0000-0002-2976-5109>), Omar Fabregas
        [aut] (ORCID: <https://orcid.org/0000-0001-6853-6280>)
Maintainer: Jorge Villalba <jvillalba@utb.edu.co>
Description: When the values of the outcome variable Y are either 0 or
        1, the function lsm() calculates the estimation of the log
        likelihood in the saturated model. This model is characterized
        by Llinas (2006, ISSN:2389-8976) in section 2.3 through the
        assumptions 1 and 2. The function LogLik() works (almost
        perfectly) when the number of independent variables K is high,
        but for small K it calculates wrong values in some cases. For
        this reason, when Y is dichotomous and the data are grouped in
        J populations, it is recommended to use the function lsm()
        because it works very well for all K.
Depends: R (>= 3.5.0)
Imports: stats, dplyr (>= 1.0.0), ggplot2 (>= 1.0.0)
Encoding: UTF-8
License: MIT + file LICENSE
RoxygenNote: 7.3.1
LazyLoad: yes
LazyData: yes
NeedsCompilation: no
Packaged: 2026-07-17 04:54:37 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2025-06-02 17:30:01 UTC
RemoteUrl: https://github.com/cran/lsm
RemoteRef: HEAD
RemoteSha: 3d296488529fcdd92e359b59e2c6af5d0ecd4628