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engression  

Engression Modelling
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", "0.1.4")



Attach the package and use:
library("engression")
Maintained by
Nicolai Meinshausen
[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 0.1.4, https://cran.r-project.org/web/packages/engression
Previous versions and publish date:
0.1.1 (2023-07-06 20:50), 0.1.3 (2023-09-15 18:02)
Other packages that cited engression R package
View engression citation profile
Other R packages that engression depends, imports, suggests or enhances
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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