Package: fastFMM 0.3.0
Erjia Cui
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.
Authors:
fastFMM_0.3.0.tar.gz
fastFMM_0.3.0.zip(r-4.5)fastFMM_0.3.0.zip(r-4.4)fastFMM_0.3.0.zip(r-4.3)
fastFMM_0.3.0.tgz(r-4.4-any)fastFMM_0.3.0.tgz(r-4.3-any)
fastFMM_0.3.0.tar.gz(r-4.5-noble)fastFMM_0.3.0.tar.gz(r-4.4-noble)
fastFMM_0.3.0.tgz(r-4.4-emscripten)fastFMM_0.3.0.tgz(r-4.3-emscripten)
fastFMM.pdf |fastFMM.html✨
fastFMM/json (API)
NEWS
| # Install 'fastFMM' in R: |
| install.packages('fastFMM', repos = c('https://gloewing.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/gloewing/fastfmm/issues
Last updated 30 days agofrom:238da48f26. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 22 2024 |
| R-4.5-win | OK | Nov 22 2024 |
| R-4.5-linux | OK | Nov 22 2024 |
| R-4.4-win | OK | Nov 22 2024 |
| R-4.4-mac | OK | Nov 22 2024 |
| R-4.3-win | OK | Nov 22 2024 |
| R-4.3-mac | OK | Nov 22 2024 |
Dependencies:abindashbitopsbootcAIC4cliclustercolorspacecpp11crayondeSolvediagonalsdistributionaldplyrfansifarverfdafdsFNNforcatsgamm4genericsggdistggplot2ggrepelgluegridExtragrpreggtablehdrcdeHLMdiaghmsisobandjanitorkernlabKernSmoothkslabelinglatticelifecyclelme4lmeresamplerlocfitlseilubridatemagicmagrittrMASSMatrixmclustmgcvminqamulticoolmunsellmvtnormnlmenlmeUnloptrnumDerivpbspcaPPpillarpkgconfigplyrpracmaprettyunitsprogresspurrrquadprogR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratRCurlrefundreshape2RfastrlangRLRsimscalessnakecasestatmodstringistringrtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create crossterms from two matrices | all_crossterms |
| Estimate non-negative diagonal terms on G matrix | cov_nnls |
| Fast Univariate Inference for Longitudinal Functional Models | fui |
| Estimate covariance of random components G(s1, s2) | G_estimate |
| Special case of estimating covariance of random components G(s1, s2) | G_estimate_randint |
| Creates the design matrix that allows for estimation of G | G_generate |
| Default FUI plotting | plot_fui |
| pspline.setting.R from refund | pspline_setting |
| select_knots.R from refund package | select_knots |
| Fit a univariate mixed model | unimm |