lvimp: Perform Inference on Summaries of Longidutinal Algorithm-Agnostic Variable Importance

Calculate point estimates of and valid confidence intervals for longitudinal summaries of nonparametric, algorithm-agnostic variable importance measures. For more details, see Williamson et al. (2024) doi:10.48550/arXiv.2311.01638 .

Authors

Maintainer: Brian Williamson https://bdwilliamson.github.io/

Methodology authors:

• Brian D. Williamson

• Susan M. Shortreed

See Also

Manuscripts: (to appear)

Imports

The packages that we import either make internal code nice (dplyr, magrittr, tibble, rlang, data.table), or are used for estimating and performing inference on cross-sectional variable importance (vimp).

We suggest several other packages: ggplot2 and cowplot help with plotting variable importance estimates; testthat and covr help with unit tests; and knitr and rmarkdown help with the vignettes and examples.

See also

Useful links:

• https://bdwilliamson.github.io/lvimp/

• Report bugs at https://github.com/bdwilliamson/lvimp/issues

Author

Maintainer: Brian D. Williamson brian.d.williamson@kp.org (ORCID)