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Enhanced documentation for the class remfpca by
providing more detailed descriptions of parameters.
Renamed the argument alpha to
smooth_tuning in the remfpca R6 class to better reflect its
role in controlling smoothness.
The new following functions are replaced to GitHub load data:
Introduced a joint_power() function as an
alternative method to address smoothness issues in MFPCA, and a
sequential_power() to tackle both sparsity and smoothness
issues.
Added electrical_power_data and
motion_sense_data as example datasets for multivariate
functional data, with variables observed over a one-dimensional
domain.
Added a detailed example for the class remfpca,
demonstrating the regularized MFPCA approaches.
Added plotScores() function for visualizing
functional principal component scores.
Introduced reconstructCurve() function to
reconstruct original curves using estimated FPCs.
Enhanced plotMFPCA() with options for better
customization and additional plotting styles.
Added examples to all exported functions for improved usability.
Extended estimateMFPCA() with new argument
scale = TRUE/FALSE to control scaling before FPCA.
Improved internal documentation and inline comments for better maintainability.
Added more extensive unit tests for core functions.
In these updated functions, upon downloading the data files from GitHub into a temporary directory (not the global environment), the target objects are now returned within the function. This modification allows users to save the data into an arbitrary variable of their choice.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.