Package: engression 0.1.6

Nicolai Meinshausen

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 through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

Authors:Xinwei Shen [aut], Nicolai Meinshausen [aut, cre]

engression_0.1.6.tar.gz
engression_0.1.6.zip(r-4.7-any)engression_0.1.6.zip(r-4.6-any)
engression_0.1.6.tar.gz(r-4.7-any)engression_0.1.6.tar.gz(r-4.6-any)
engression_0.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
engression/json (API)

# Install 'engression' in R:
install.packages('engression', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/xwshen51/engression/issues

On CRAN:

Conda:

1.30 score 5 scripts 386 downloads 1 exports 24 dependencies

Last updated from:ffd38164a5. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK123
source / vignettesOK170
linux-releaseOK164
windows-develOK67
windows-releaseOK88
wasm-releaseOK127

Exports:engression

Dependencies:bitbit64callrclicorodescfarvergluejsonlitelabelinglifecyclemagrittrotelprocessxpsR6RColorBrewerRcpprlangsafetensorsscalestorchviridisLitewithr