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. (2023) <doi:10.1101/2023.11.06.565896>) for fitting functional mixed models.

Version: 0.2.0
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: 2024-02-02
Author: Erjia Cui [aut, cre], Gabriel Loewinger [aut]
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
CRAN checks: fastFMM results

Documentation:

Reference manual: fastFMM.pdf
Vignettes: fastFMM Vignette

Downloads:

Package source: fastFMM_0.2.0.tar.gz
Windows binaries: r-prerel: fastFMM_0.2.0.zip, r-release: fastFMM_0.2.0.zip, r-oldrel: fastFMM_0.2.0.zip
macOS binaries: r-prerel (arm64): fastFMM_0.2.0.tgz, r-release (arm64): fastFMM_0.2.0.tgz, r-oldrel (arm64): fastFMM_0.2.0.tgz, r-prerel (x86_64): fastFMM_0.2.0.tgz, r-release (x86_64): fastFMM_0.2.0.tgz
Old sources: fastFMM archive

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