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:
engression_0.1.6.tar.gz
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
Last updated from:ffd38164a5. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 114 | ||
| source / vignettes | OK | 159 | ||
| linux-release-x86_64 | OK | 125 | ||
| wasm-release | OK | 123 |
Exports:engression
Dependencies:bitbit64callrclicorodescfarvergluejsonlitelabelinglifecyclemagrittrotelprocessxpsR6RColorBrewerRcpprlangsafetensorsscalestorchviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Engression Function | engression |
| Prediction Function for Engression Models | predict.engression |
| Print an Engression Model Object | print.engression |