Package: smoothic
Type: Package
Title: Variable Selection Using a Smooth Information Criterion
Version: 1.2.0
Depends: R (>= 3.5.0)
Authors@R: c(person("Meadhbh", "O'Neill", email = "meadhbhon@gmail.com", role = c("aut", "cre")), person("Kevin", "Burke", role = c("aut")))
Maintainer: Meadhbh O'Neill <meadhbhon@gmail.com>
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.
    <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>.
License: GPL-3
URL: https://github.com/meadhbh-oneill/smoothic,
        https://meadhbh-oneill.ie/smoothic/
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Imports: data.table, dplyr, ggplot2, MASS, numDeriv, purrr, rlang,
        stringr, tibble, tidyr, toOrdinal
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-08-22 11:01:21 UTC; meadh
Author: Meadhbh O'Neill [aut, cre],
  Kevin Burke [aut]
Repository: CRAN
Date/Publication: 2023-08-22 14:40:02 UTC
Built: R 4.2.0; ; 2023-08-27 04:26:42 UTC; unix
