smoothic: Variable Selection Using a Smooth Information Criterion

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. <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>.

Version: 1.2.0
Depends: R (≥ 3.5.0)
Imports: data.table, dplyr, ggplot2, MASS, numDeriv, purrr, rlang, stringr, tibble, tidyr, toOrdinal
Suggests: knitr, rmarkdown
Published: 2023-08-22
Author: Meadhbh O'Neill [aut, cre], Kevin Burke [aut]
Maintainer: Meadhbh O'Neill <meadhbhon at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: smoothic results


Reference manual: smoothic.pdf
Vignettes: Smooth Generalized Normal Distribution


Package source: smoothic_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): smoothic_1.2.0.tgz, r-oldrel (arm64): smoothic_1.2.0.tgz, r-release (x86_64): smoothic_1.2.0.tgz, r-oldrel (x86_64): smoothic_1.2.0.tgz
Old sources: smoothic archive


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