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fastFMM: Fast Functional Mixed Models using Fast Univariate Inference (FUI)

Repository Description

Repository for the development version of the R Package fastFMM. For more information, see the official fastFMM $\texttt{CRAN}$ site.

fastFMM R Package

Installation

Download the $\texttt{R}$ Package fastFMM by running the following command within $\texttt{R}$ or $\texttt{RStudio}$:

install.packages("fastFMM", dependencies = TRUE)

Alternatively, the development version of the $\texttt{R}$ Package fastFMM can be downloaded as follows:

library(devtools)
install_github("gloewing/fastFMM")

Package Usage

Repository Folders

  1. The 'R' folder contains the code of the package, including fui.R and plot_fui.R. The plot_fui.R is still under development and has not been widely tested.

  2. The 'vignettes' folder contains a vignette which shows how to use different arguments of the fui function. This vignette can also be viewed in the link above (under Package Usage).

Dataset Links

The example data set is available in the 'vignettes' folder under the name 'time_series.csv'.

Calling fastFMM from Python

See the README on the associated Github page photometry_FLMM for instructions on using fastFMM in Python through the Python packages rpy2 and fast_fmm_rpy2.

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Version

Install

install.packages('fastFMM')

Monthly Downloads

326

Version

0.4.0

License

GPL (>= 3)

Issues

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Maintainer

Erjia Cui

Last Published

March 13th, 2025

Functions in fastFMM (0.4.0)

pspline_setting

pspline.setting.R from refund
G_generate

Creates the design matrix that allows for estimation of G
all_crossterms

Create crossterms from two matrices
cov_nnls

Estimate non-negative diagonal terms on G matrix
fui

Fast Univariate Inference for Longitudinal Functional Models
G_estimate

Estimate covariance of random components G(s1, s2)
G_estimate_randint

Special case of estimating covariance of random components G(s1, s2)
unimm

Fit a univariate mixed model
select_knots

select_knots.R from refund package
plot_fui

Default FUI plotting