fastFMM 1.0.0

• Added concurrent models to fui(), allowing for fitting data with both functional outcomes and functional covariates.
• Rewrote all standard functions to S3 generics to allow for the same functions to handle both non-concurrent and concurrent functionality. This may need to be refined into S4 and R6 to prevent strange function exports.
• Added documentation for various helpers, which are exported somewhat messily to allow for the main calculation of fui().
• Added datasets lick and d2pvt to demonstrate fui() in the vignettes fastFMM and d2pvt, respectively. These datasets replace the previously used synthetic data.
• Updated references to the concurrent model (Xin et al. (2025)) and the data (Jeong et al. (2022), Machen et al. (2025)).
• Minor bug fix in plot_fui()’s. Adding geom_segment() axes no longer rely on the deprecated ggplot2::aes_string() method.
• Setting parallel = TRUE now requires n_cores to be manually specified. This avoids problems with asking for too many simultaneous processes on high-performance clusters if the user does not strictly specify the number of available threads.

fastFMM 0.4.0

• Provided pointers to a Python package to call fastFMM from Python.
• Provided pointers to user guides written in Python.
• Updated reference/citations on documentation.

fastFMM 0.3.0

• Fixed bugs.
• Added (optional) parallelization of step 3.2 in analytic inference fui(), leading to substantial speed ups of fui().
• Added in parallelization functionality for PCs.
• Added in code to remove rows with missing functional outcome values and added in option to impute with longitudinal FPCA (experimental feature).
• Changed default method of moments estimator to MoM=1 (appears to perform comparably to MoM=2 but is much faster and less memory intensive).
• Removed some fui() arguments that were not in use.

fastFMM 0.2.0

• Fixed several bugs.

fastFMM 0.1.0

• Initial CRAN submission.