Engression Modelling
Overview
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.
- Fixed examples to check for torch backend availability via
torch::torch_is_installed()before running, avoiding errors on systems where the Lantern library is not installed. - Fixed a bug in
predict.engression()where parentheses were misplaced in a dimension check for quantile predictions. - Changed default
dropoutfrom 0.05/0.01 to 0.0 inengression()andengressionfit(), and updated documentation to match. - Removed reference to non-existent
engressionBaggedfunction in documentation.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 8 (5 internal)
- Examples that run
- 0%3 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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- 0%
- Median days to close, 50 most recently updated closed issues
- 44
- Median days to close, 50 most recently updated closed PRs
- 195
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- Claude Codefirst seen 2026-07-31Named in .gitignore: .claudeCommits: at least 35 crediting it in the message
- Claude Opus 5.535 commits · Oct 2026
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Package metadata
- First published
- 2023-07-06
- Total releases
- 5 / 3 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 8.8 KB
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