Package: PLmixed 0.1.7

PLmixed: Estimate (Generalized) Linear Mixed Models with Factor Structures

Utilizes the 'lme4' and 'optimx' packages (previously the optim() function from 'stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>. Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models.

Authors:Minjeong Jeon [aut], Nicholas Rockwood [aut, cre]

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PLmixed.pdf |PLmixed.html
PLmixed/json (API)

# Install 'PLmixed' in R:
install.packages('PLmixed', repos = c('https://njrockwood.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • IRTsim - Simulated multilevel IRT dataset.
  • JUDGEsim - Simulated Multi-rater Multi-response dataset.
  • KYPSitemsim - Simulated KYPS item-level dataset.
  • KYPSsim - Simulated KYPS dataset.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 8 scripts 321 downloads 2 exports 16 dependencies

Last updated 2 years agofrom:7378a400f2. Checks:6 OK, 3 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 16 2025
R-4.5-winNOTEMar 16 2025
R-4.5-macNOTEMar 16 2025
R-4.5-linuxNOTEMar 16 2025
R-4.4-winOKMar 16 2025
R-4.4-macOKMar 16 2025
R-4.4-linuxOKMar 16 2025
R-4.3-winOKMar 16 2025
R-4.3-macOKMar 16 2025

Exports:iterPlotPLmixed

Dependencies:bootlatticelme4MASSMatrixminqanlmenloptrnumDerivoptimxpracmarbibutilsRcppRcppEigenRdpackreformulas

PLmixed: An Introduction

Rendered fromPLmixed_intro.Rmdusingknitr::rmarkdownon Mar 16 2025.

Last update: 2022-06-22
Started: 2017-08-31