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. 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.
- Version1.2.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release08/22/2023
Documentation
Team
Meadhbh O'Neill
Kevin Burke
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Last 30 days
This package has been downloaded 250 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 11 times.
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Last 365 days
This package has been downloaded 3,739 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 21, 2024 with 154 downloads.
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- Imports11 packages
- Suggests2 packages