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engression
View on CRAN: Click
here
Download and install engression package within the R console
Install from CRAN:
install.packages("engression")
Install from Github:
library("remotes")
install_github("cran/engression") Install by package version:
library("remotes")
install_version("engression", "0.1.5") Attach the package and use:
library("engression")
Maintained by
Nicolai Meinshausen
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-07-06
Latest Update: 2023-11-22
Description:
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).
.
How to cite:
Nicolai Meinshausen (2023). engression: Engression Modelling. R package version 0.1.5, https://cran.r-project.org/web/packages/engression. Accessed 29 Aug. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for engression
Functions, R codes and Examples using
the engression R package
Some associated functions: dftomat . energyloss . energylossall . energylossbeta . engression . engressionfit . predict.engression . print.engression .
Some associated R codes: dftomat.R . energyloss.R . energylossall.R . energylossbeta.R . engression.R . engressionfit.R . predict.engression.R . print.engression.R . Full engression package functions and examples
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