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
fastFMM 0.1.0