Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.
| Version: | 0.1.5 |
| Imports: | torch |
| Published: | 2026-01-08 |
| DOI: | 10.32614/CRAN.package.engression |
| Author: | Xinwei Shen [aut], Nicolai Meinshausen [aut, cre] |
| Maintainer: | Nicolai Meinshausen <meinshausen at stat.math.ethz.ch> |
| BugReports: | https://github.com/xwshen51/engression/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/xwshen51/engression/ |
| NeedsCompilation: | no |
| CRAN checks: | engression results |
| Reference manual: | engression.html , engression.pdf |
| Package source: | engression_0.1.5.tar.gz |
| Windows binaries: | r-devel: engression_0.1.5.zip, r-release: engression_0.1.5.zip, r-oldrel: engression_0.1.5.zip |
| macOS binaries: | r-release (arm64): engression_0.1.5.tgz, r-oldrel (arm64): engression_0.1.5.tgz, r-release (x86_64): engression_0.1.5.tgz, r-oldrel (x86_64): engression_0.1.5.tgz |
| Old sources: | engression archive |
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