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nerfplus: Network-Assisted Random Forest+ (NeRF+)

Network-assisted Random Forest+ (NeRF+) is a flexible and interpretable machine learning model for incorporating network data alongside node-level covariate information. Briefly, NeRF+ extends a generalization of random forests (RF) called RF+ to the network-assisted regression setting by incorporating both a network cohesion penalty and network embeddings as additional covariates. Using this approach, NeRF+ inherits both the flexibility and interpretability of RFs while allowing researchers to easily incorporate network information in their model to further improve predictive performance. This package provides tools to fit and interpret NeRF+ models, as detailed in Tang, Levina, and Zhu (2025) <doi:10.48550/arXiv.2509.15611>.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: abind, caret, dplyr, furrr, glmnet, irlba, Matrix, purrr, ranger, Rcpp, rlang, stringr, tibble, tidyr, tidyselect, yardstick
LinkingTo: Rcpp, RcppArmadillo
Suggests: DT, fresh, ggplot2, ggraph, htmltools, igraph, kableExtra, knitr, patchwork, plotly, shiny, shinycssloaders, shinydashboard, shinydashboardPlus, shinyjs, shinyWidgets, testthat (≥ 3.0.0), tippy
Published: 2026-11-03
DOI: 10.32614/CRAN.package.nerfplus (may not be active yet)
Author: Tiffany Tang ORCID iD [aut, cre]
Maintainer: Tiffany Tang <ttang4 at nd.edu>
License: MIT + file LICENSE
URL: https://tiffanymtang.github.io/nerfplus/
NeedsCompilation: yes
Materials: README
CRAN checks: nerfplus results

Documentation:

Reference manual: nerfplus.html , nerfplus.pdf

Downloads:

Package source: nerfplus_1.0.0.tar.gz
Windows binaries: r-devel: not available, r-release: nerfplus_1.0.0.zip, r-oldrel: nerfplus_1.0.0.zip
macOS binaries: r-release (arm64): nerfplus_1.0.0.tgz, r-oldrel (arm64): nerfplus_1.0.0.tgz, r-release (x86_64): nerfplus_1.0.0.tgz, r-oldrel (x86_64): nerfplus_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=nerfplus to link to this page.

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.