Fast Functional Mixed Models using Fast Univariate Inference
Fast block diagonal generator, taken from Matrix package examples
Estimate non-negative diagonal terms on G matrix
Organize covariance matrices
Fast Univariate Inference for Longitudinal Functional Models
Special case of estimating covariance of random components G(s1, s2)
Generic "G_estimate" dispatch
Generic "G_generate" G matrix setup
Generic "massmm" model fit
Create a new "fastFMM" object
Create a new "fastFMMconc" object
Default FUI plotting
select_knots.R from refund package
Generic "unimm" model fitting
Analytic variance calculation
Analytic variance calculation
Parallel variance calculation for non-concurrent models
Parallel variance calculation
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)) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.