Package: veesa 0.1.6
veesa: Pipeline for Explainable Machine Learning with Functional Data
Implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.
Authors:
veesa_0.1.6.tar.gz
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veesa.pdf |veesa.html✨
veesa/json (API)
NEWS
# Install 'veesa' in R: |
install.packages('veesa', repos = c('https://goodekat.r-universe.dev', 'https://cloud.r-project.org')) |
- shifted_peaks - "Shifted Peaks" Simulated Dataset
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 months agofrom:bd51335b91. Checks:8 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Feb 17 2025 |
R-4.5-win | OK | Feb 17 2025 |
R-4.5-mac | OK | Feb 17 2025 |
R-4.5-linux | OK | Feb 17 2025 |
R-4.4-win | OK | Feb 17 2025 |
R-4.4-mac | OK | Feb 17 2025 |
R-4.3-win | OK | Feb 17 2025 |
R-4.3-mac | OK | Feb 17 2025 |
Exports:compute_pfiplot_pc_directionsprep_testing_dataprep_training_datasimulate_functions
Dependencies:askpassbase64encbslibcachemclicodacodetoolscolorspacecpp11crosstalkcurldata.tabledigestdoParalleldotCall64dplyrevaluatefansifarverfastmapfdasrvffieldsfontawesomeforcatsforeachfsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelpSolvemagrittrmapsMASSMatrixmemoisemgcvmimemunsellmvtnormnlmeopensslpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangrmarkdownsassscalesspamstringistringrsystibbletidyrtidyselecttinytextoleranceutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Obtain PC directions with centered warping functions | align_pcdirs |
Center warping functions | center_warping_funs |
Compute permutation feature importance (PFI) | compute_pfi |
Plot principal component directions | plot_pc_directions |
Align test data and apply fPCA using elastic method applied to training data | prep_testing_data |
Align training data and apply a method of elastic fPCA | prep_training_data |
"Shifted Peaks" Simulated Dataset | shifted_peaks |
Simulate example functional data | simulate_functions |