scR: Empirical Sample Complexity Bounds
Provides tools for estimating empirical sample complexity
bounds for supervised learning tasks. The package supports simulation-based
estimates of generalization curves, parametric extrapolation of empirical
sample complexity bounds, theoretical bounds based on Vapnik-Chervonenkis
dimension, and optional monotone Gaussian process extrapolation for users who
install the external 'cmdstanr' workflow. For more details, see Carter and
Choi (2024) <doi:10.31219/osf.io/evrcj>.
| Version: |
0.7.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr, furrr, future, ggplot2, Matrix, minpack.lm, parallel, parallelly, pbapply, plotly, progressr, stats, tidyr |
| Suggests: |
cmdstanr, posterior, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-06-23 |
| DOI: |
10.32614/CRAN.package.scR |
| Author: |
Perry Carter 
[aut, cre],
Dahyun Choi [aut] |
| Maintainer: |
Perry Carter <pjc504 at nyu.edu> |
| BugReports: |
https://github.com/pjesscarter/scR/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/pjesscarter/scR |
| NeedsCompilation: |
no |
| Additional_repositories: |
https://stan-dev.r-universe.dev |
| Materials: |
README, NEWS |
| CRAN checks: |
scR results |
Documentation:
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