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smoothic  

Variable Selection Using a Smooth Information Criterion
View on CRAN: Click here


Download and install smoothic package within the R console
Install from CRAN:
install.packages("smoothic")

Install from Github:
library("remotes")
install_github("cran/smoothic")

Install by package version:
library("remotes")
install_version("smoothic", "1.2.0")



Attach the package and use:
library("smoothic")
Maintained by
Meadhbh O'Neill
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-10-27
Latest Update: 2023-08-22
Description:
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>.
How to cite:
Meadhbh O'Neill (2021). smoothic: Variable Selection Using a Smooth Information Criterion. R package version 1.2.0, https://cran.r-project.org/web/packages/smoothic. Accessed 03 Dec. 2024.
Previous versions and publish date:
0.1.0 (2021-10-27 16:40), 1.0.0 (2022-12-15 18:30), 1.1.0 (2023-06-13 10:40)
Other packages that cited smoothic R package
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Other R packages that smoothic depends, imports, suggests or enhances
Complete documentation for smoothic
Functions, R codes and Examples using the smoothic R package
Some associated functions: bostonhouseprice . bostonhouseprice2 . citycrime . diabetes . pcancer . plot_effects . plot_paths . predict.smoothic . smoothic . sniffer . summary.smoothic . 
Some associated R codes: data.R . smoothic_functions.R .  Full smoothic package functions and examples
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