Package: fastFMM 1.0.1.9000

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>, Xin et al. (2025) <doi:10.7554/eLife.109428.1>) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.

Authors:Erjia Cui [aut], Gabriel Loewinger [aut], Al Xin [aut, cre]

fastFMM_1.0.1.9000.tar.gz
fastFMM_1.0.1.9000.zip(r-4.7-any)fastFMM_1.0.1.9000.zip(r-4.6-any)fastFMM_1.0.1.9000.zip(r-4.5-any)
fastFMM_1.0.1.9000.tgz(r-4.6-any)fastFMM_1.0.1.9000.tgz(r-4.5-any)
fastFMM_1.0.1.9000.tar.gz(r-4.7-any)fastFMM_1.0.1.9000.tar.gz(r-4.6-any)
fastFMM_1.0.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
fastFMM/json (API)

# Install 'fastFMM' in R:
install.packages('fastFMM', repos = c('https://awqx.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/awqx/fastfmm/issues

Datasets:
  • d2pvt - Machen et al. (2025) variable trial length data
  • lick - Jeong et al. (2022) licking behavior data

On CRAN:

Conda:

6.07 score 1 stars 39 scripts 460 downloads 10 exports 98 dependencies

Last updated from:1105934827. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK202
source / vignettesOK271
linux-release-x86_64OK197
macos-release-arm64OK144
macos-oldrel-arm64OK176
windows-develOK155
windows-releaseOK132
windows-oldrelOK142
wasm-releaseOK135

Exports:fuiG_estimateG_estimate_randintG_generatemassmmnew_fastFMMnew_fastFMMconcplot_fuiunimmvar_parallel

Dependencies:abindashbitopsbootcAIC4cliclustercolorspacecpp11crayondeSolvediagonalsdistributionaldplyrfarverfdafdsFNNforcatsgamm4genericsggdistggplot2ggrepelgluegridExtragrpreggtablehdrcdeHLMdiaghmsisobandjanitorkernlabKernSmoothkslabelinglatticelifecyclelme4lmeresamplerlocfitlseilubridatemagicmagrittrMASSMatrixmclustmgcvminqamulticoolmvtnormnlmenlmeUnloptrnumDerivpbspcaPPpillarpkgconfigplyrpracmaprettyunitsprogresspurrrquadprogR6rainbowrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRCurlRdpackreformulasrefundreshape2RfastrlangRLRsimS7scalessnakecasestatmodstringistringrtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithrzigg

Introduction to fastFMM model fitting
Introduction | Installation | Fast univariate inference (FUI) | Additional references | Data organization | Example data | Matrix columns or multiple vector columns | Scalar covariates | Functional covariates | Model-fitting | Non-concurrent FMM fitting | Concurrent FMM fitting | Plotting models | Advanced Options | Additional fui() arguments | Additional plot_fui() arguments | Troubleshooting | Common warning messages | References
Rendered fromfastFMM.Rmdusingknitr::rmarkdown

Last update: 2026-05-18
Started: 2025-07-22

Example fastFMM application to variable trial lengths
Introduction | Experimental design | Variable trial lengths | Data processing | Concurrent model with an interaction term | Interpretation and discussion | Plotting the models | Concurrent model coefficients | Limitations of the concurrent model | Comparing concurrent and non-concurrent models | Specification of the non-concurrent model | Non-concurrent coefficients | Comparing the non-concurrent and concurrent model | References
Rendered fromd2pvt.Rmdusingknitr::rmarkdown

Last update: 2026-05-18
Started: 2025-08-15