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fastFMM: Fast Functional Mixed Models using Fast Univariate Inference

Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2024) <doi:10.7554/eLife.95802.2>) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.

Version:0.4.0
Imports:lme4, parallel,cAIC4,magrittr,dplyr,mgcv,MASS,lsei,refund,stringr,Matrix,mvtnorm,progress,ggplot2,gridExtra,Rfast,lmeresampler, stats, methods
Suggests:knitr,rmarkdown,spelling
Published:2025-03-13
DOI:10.32614/CRAN.package.fastFMM
Author:Erjia Cui [aut, cre], Gabriel Loewinger [aut], Al Xin [ctb]
Maintainer:Erjia Cui <ecui at umn.edu>
BugReports:https://github.com/gloewing/fastFMM/issues
License:GPL (≥ 3)
URL:https://github.com/gloewing/fastFMM
NeedsCompilation:no
Language:en-US
Materials:README,NEWS
In views:FunctionalData
CRAN checks:fastFMM results

Documentation:

Reference manual:fastFMM.html ,fastFMM.pdf
Vignettes:fastFMM Vignette (source,R code)

Downloads:

Package source: fastFMM_0.4.0.tar.gz
Windows binaries: r-devel:fastFMM_0.4.0.zip, r-release:fastFMM_0.4.0.zip, r-oldrel:fastFMM_0.4.0.zip
macOS binaries: r-release (arm64):fastFMM_0.4.0.tgz, r-oldrel (arm64):fastFMM_0.4.0.tgz, r-release (x86_64):fastFMM_0.4.0.tgz, r-oldrel (x86_64):fastFMM_0.4.0.tgz
Old sources: fastFMM archive

Linking:

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