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WeMix

Weighted Mixed-Effects Models Using Multilevel Pseudo Maximum Likelihood Estimation

v4.0.3 · Nov 2, 2023 · GPL-2

Description

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) <https://github.com/lme4/lme4pureR>).

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r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
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r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
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r-release-macos-arm64 OK
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r-release-windows-x86_64 OK
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OK r-devel-linux-x86_64-debian-gcc

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Package suggested but not available for checking: ‘glmmTMB’
OK r-oldrel-macos-x86_64

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OK r-oldrel-windows-x86_64

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OK r-patched-linux-x86_64

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OK r-release-linux-x86_64

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OK r-release-macos-arm64

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NOTE 13 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-oldrel-macos-arm64

package dependencies

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Dependencies Reverse dependencies lme4 numDeriv Matrix minqa matrixStats EdSurvey MLMusingR ILSAstats WeMix

Version History

new 4.0.3 Mar 10, 2026