WeMix
Weighted Mixed-Effects Models Using Multilevel Pseudo Maximum Likelihood Estimation
Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013)
- Version4.0.3
- R version≥ 3.5.0
- LicenseGPL-2
- Needs compilation?No
- Last release11/03/2023
Documentation
Team
Paul Bailey
Emmanuel Sikali
Show author detailsRolespdrBlue Webb
Show author detailsRolesAuthorClaire Kelley
Show author detailsRolesAuthorTrang Nguyen
Show author detailsRolesAuthorHuade Huo
Show author detailsRolesAuthorSteve Walker
Show author detailsRolesCopyright holderDoug Bates
Show author detailsRolesCopyright holderEric Buehler
Show author detailsRolesContributorChristian Christrup Kjeldsen
Show author detailsRolesContributor
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- Depends2 packages
- Imports4 packages
- Suggests7 packages
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