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veesa  

Pipeline for Explainable Machine Learning with Functional Data
View on CRAN: Click here


Download and install veesa package within the R console
Install from CRAN:
install.packages("veesa")

Install from Github:
library("remotes")
install_github("cran/veesa")

Install by package version:
library("remotes")
install_version("veesa", "0.1.6")



Attach the package and use:
library("veesa")
Maintained by
Katherine Goode
[Scholar Profile | Author Map]
First Published: 2025-01-17
Latest Update: 2025-01-17
Description:
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.
How to cite:
Katherine Goode (2025). veesa: Pipeline for Explainable Machine Learning with Functional Data. R package version 0.1.6, https://cran.r-project.org/web/packages/veesa. Accessed 17 Feb. 2025.
Previous versions and publish date:
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Complete documentation for veesa
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Full veesa package functions and examples
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