Package: engression 0.1.4
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 for nonlinear regression?" by Xinwei Shen and Nicolai Meinshausen (2023). Also supports classification (experimental). <arxiv:2307.00835>.
Authors:
engression_0.1.4.tar.gz
engression_0.1.4.tar.gz(r-4.5-noble)engression_0.1.4.tar.gz(r-4.4-noble)
engression_0.1.4.tgz(r-4.4-emscripten)engression_0.1.4.tgz(r-4.3-emscripten)
engression.pdf |engression.html✨
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 1 years agofrom:cdb9e25d1c. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 17 2024 |
| R-4.5-linux | OK | Dec 17 2024 |
Exports:engression
Dependencies:bitbit64callrclicorodescellipsisgluejsonlitemagrittrprocessxpsR6Rcpprlangsafetensorstorchwithr
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 |