Implementation of Cross-Validated Kernel Ensemble (CVEK), a flexible modeling framework for robust nonlinear regression and hypothesis testing based on ensemble learning with kernel-ridge estimators (Jeremiah et al. (2017) <arXiv:1710.01406> and Wenying et al. (2018) <arXiv:1811.11025>). It allows user to conduct nonlinear regression with minimal assumption on the function form by aggregating nonlinear models generated from a diverse collection of kernel families. It also provides utilities to test for the estimated nonlinear effect under this ensemble estimator, using either the asymptotic or the bootstrap version of a generalized score test.
| Version: | 0.1-1 |
| Depends: | R (≥ 3.6.0), MASS, limSolve |
| Suggests: | testthat, knitr, rmarkdown, ggplot2, ggrepel |
| Published: | 2020-11-25 |
| Author: | Wenying Deng [aut, cre], Jeremiah Zhe Liu [ctb] |
| Maintainer: | Wenying Deng <wdeng at g.harvard.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | CVEK results |
| Reference manual: | CVEK.pdf |
| Vignettes: |
Using the CVEK R package |
| Package source: | CVEK_0.1-1.tar.gz |
| Windows binaries: | r-devel: CVEK_0.1-1.zip, r-release: CVEK_0.1-1.zip, r-oldrel: CVEK_0.1-1.zip |
| macOS binaries: | r-release: CVEK_0.1-1.tgz, r-oldrel: CVEK_0.1-1.tgz |
| Old sources: | CVEK archive |
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