Package: REMLA 1.1

REMLA: Robust Expectation-Maximization Estimation for Latent Variable Models

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.

Authors:Bryan Ortiz-Torres [aut, cre], Kenneth Nieser [aut]

REMLA_1.1.tar.gz
REMLA_1.1.zip(r-4.5)REMLA_1.1.zip(r-4.4)REMLA_1.1.zip(r-4.3)
REMLA_1.1.tgz(r-4.4-any)REMLA_1.1.tgz(r-4.3-any)
REMLA_1.1.tar.gz(r-4.5-noble)REMLA_1.1.tar.gz(r-4.4-noble)
REMLA_1.1.tgz(r-4.4-emscripten)REMLA_1.1.tgz(r-4.3-emscripten)
REMLA.pdf |REMLA.html
REMLA/json (API)

# Install 'REMLA' in R:
install.packages('REMLA', repos = c('https://bryansaul.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/knieser/rem/issues

On CRAN:

2.30 score 171 downloads 3 exports 14 dependencies

Last updated 6 months agofrom:e1d97c0405. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 09 2024
R-4.5-winNOTEOct 09 2024
R-4.5-linuxNOTEOct 09 2024
R-4.4-winOKOct 09 2024
R-4.4-macOKOct 09 2024
R-4.3-winOKOct 09 2024
R-4.3-macOKOct 09 2024

Exports:controlREMREM_CFAREM_EFA

Dependencies:bootgeexGPArotationlatticelme4MASSMatrixminqanlmenloptrnumDerivRcppRcppEigenrootSolve

REMLA_tutorial

Rendered fromREMLA_tutorial.Rmdusingknitr::rmarkdownon Oct 09 2024.

Last update: 2024-03-27
Started: 2024-03-27