Package: smoothic 1.2.0

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>.

Authors:Meadhbh O'Neill [aut, cre], Kevin Burke [aut]

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NEWS

# Install 'smoothic' in R:
install.packages('smoothic', repos = c('https://meadhbh-oneill.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/meadhbh-oneill/smoothic/issues

Datasets:

On CRAN:

3.70 score 1 stars 3 scripts 274 downloads 3 exports 56 dependencies

Last updated 1 years agofrom:1b831dc2ca. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winNOTENov 18 2024
R-4.5-linuxNOTENov 18 2024
R-4.4-winOKNov 18 2024
R-4.4-macOKNov 18 2024
R-4.3-winOKNov 18 2024
R-4.3-macOKNov 18 2024

Exports:plot_effectsplot_pathssmoothic

Dependencies:briocallrclicolorspacecpp11crayondata.tabledescdiffobjdigestdplyrevaluatefansifarverfsgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigpkgloadpraiseprocessxpspurrrR6RColorBrewerrlangrprojrootscalesstringistringrtestthattibbletidyrtidyselecttoOrdinalutf8vctrsviridisLitewaldowithr

Smooth Generalized Normal Distribution

Rendered fromsgnd-boston.Rmdusingknitr::rmarkdownon Nov 18 2024.

Last update: 2023-07-31
Started: 2022-11-17