Package: REMLA
Title: Robust Expectation-Maximization Estimation for Latent Variable
        Models
Version: 1.2.0
Date: 2024-11-30
Authors@R: c(
    person("Bryan", "Ortiz-Torres", , "bortiztorres@wisc.edu", role = c("aut", "cre")),
    person("Kenneth", "Nieser", , "nieser@stanford.edu", role = "aut",
           comment = c(ORCID = "0000-0001-6003-1296"))
  )
Description: Traditional latent variable models assume that the
        population is homogeneous, meaning that all individuals in the
        population are assumed to have the same latent structure.
        However, this assumption is often violated in practice given
        that individuals may differ in their age, gender, socioeconomic
        status, and other factors that can affect their latent
        structure. The robust expectation maximization (REM) algorithm
        is a statistical method for estimating the parameters of a
        latent variable model in the presence of population
        heterogeneity as recommended by Nieser & Cochran (2023)
        <doi:10.1037/met0000413>. The REM algorithm is based on the
        expectation-maximization (EM) algorithm, but it allows for the
        case when all the data are generated by the assumed data
        generating model.
License: GPL (>= 3)
URL: https://github.com/knieser/REM
Depends: R (>= 4.0), GPArotation,geex
Imports: stats
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: knitr, lavaan, rmarkdown, testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-10-15 09:18:01 UTC; root
Author: Bryan Ortiz-Torres [aut, cre], Kenneth Nieser [aut]
        (<https://orcid.org/0000-0001-6003-1296>)
Maintainer: Bryan Ortiz-Torres <bortiztorres@wisc.edu>
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