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'))

Peer review:

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:

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 342 downloads 2 exports 13 dependencies

Last updated 1 years agofrom:7378a400f2. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-winNOTENov 16 2024
R-4.5-linuxNOTENov 16 2024
R-4.4-winOKNov 16 2024
R-4.4-macOKNov 16 2024
R-4.3-winOKNov 16 2024
R-4.3-macOKNov 16 2024

Exports:iterPlotPLmixed

Dependencies:bootlatticelme4MASSMatrixminqanlmenloptrnumDerivoptimxpracmaRcppRcppEigen

PLmixed: An Introduction

Rendered fromPLmixed_intro.Rmdusingknitr::rmarkdownon Nov 16 2024.

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