Package: gilmour
Type: Package
Title: The Interpretation of Adjusted Cp Statistic
Version: 0.1.1
Authors@R: c(
    person(
      "Josef", "Dolejs",
      email = "josef.dolejs@uhk.cz",
      role = c("aut", "cre"),
      comment = c(ORCID = "0000-0002-3224-585X")
    )
  )
    #> [1] "Josef Dolejs <josef.dolejs@uhk.cz> [aut, cre] (ORCID <https//orcid.org/0000-0002-3224-585X>)"
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
Description: Several methods may be found for selecting a subset of regressors from a set of k candidate variables in multiple linear regression.
        One possibility is to evaluate all possible regression models and comparing them using Mallows's Cp statistic (Cp) according to Gilmour original study.
        Full model is calculated, all possible combinations of regressors are generated, adjusted Cp for each submodel are computed, and the submodel with the minimum adjusted value Cp (ModelMin) is calculated.
        To identify the final model, the package applies a sequence of hypothesis tests on submodels nested within ModelMin, following the approach outlined in Gilmour's original paper.
        For more details see the help of the function final_model() and the original study (1996) <doi:10.2307/2348411>.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Depends: R (>= 3.5)
NeedsCompilation: no
Packaged: 2025-07-06 04:56:05 UTC; djose
Author: Josef Dolejs [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-3224-585X>)
Maintainer: Josef Dolejs <josef.dolejs@uhk.cz>
Repository: CRAN
Date/Publication: 2025-07-10 13:50:02 UTC
Built: R 4.3.3; ; 2025-07-10 13:52:29 UTC; unix
