Variable Selection Using a Smooth Information Criterion
Overview
Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. doi:10.48550/arXiv.2212.07317. This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". doi:10.1007/s11222-023-10204-8.
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- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 11
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- Help pages citing references
- 64%
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Dependencies
Installing smoothic adds 36 packages (18 with compiled code, 36 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
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Package metadata
- First published
- 2021-10-27
- Total releases
- 5 / 5 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 45 KB / 6 files
- Download size
- 184 KB
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