engression: Engression Modelling

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 for nonlinear regression?" by Xinwei Shen and Nicolai Meinshausen (2023). Also supports classification (experimental). <doi:10.48550/arXiv.2307.00835>.

Version: 0.1.4
Imports: torch
Published: 2023-11-22
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

Documentation:

Reference manual: engression.pdf

Downloads:

Package source: engression_0.1.4.tar.gz
Windows binaries: r-prerel: engression_0.1.4.zip, r-release: engression_0.1.4.zip, r-oldrel: engression_0.1.4.zip
macOS binaries: r-prerel (arm64): engression_0.1.4.tgz, r-release (arm64): engression_0.1.4.tgz, r-oldrel (arm64): engression_0.1.4.tgz, r-prerel (x86_64): engression_0.1.4.tgz, r-release (x86_64): engression_0.1.4.tgz
Old sources: engression archive

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