Package: picR
Title: Predictive Information Criteria for Model Selection
Version: 1.0.1
Authors@R: 
    person("Javier", "Flores", , "javenrflo.pro@pm.me", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-1550-1655"))
Description: Computation of predictive information criteria (PIC) from select model object classes 
    for model selection in predictive contexts. In contrast to the more widely used Akaike 
    Information Criterion (AIC), which are derived under the assumption that target(s) of prediction 
    (i.e. validation data) are independently and identically distributed to the fitting data, the PIC
    are derived under less restrictive assumptions and thus generalize AIC to the more practically 
    relevant case of training/validation data heterogeneity. The methodology featured in this package is based on Flores (2021) <https://iro.uiowa.edu/esploro/outputs/doctoral/A-new-class-of-information-criteria/9984097169902771?institution=01IOWA_INST> "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.2.0
Imports: stats
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), dplyr
Config/testthat/edition: 3
URL: https://github.com/javenrflo/picR
BugReports: https://github.com/javenrflo/picR/issues
NeedsCompilation: no
Packaged: 2025-06-11 00:24:53 UTC; User
Author: Javier Flores [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-1550-1655>)
Maintainer: Javier Flores <javenrflo.pro@pm.me>
Repository: CRAN
Date/Publication: 2025-06-11 02:20:02 UTC
Built: R 4.6.0; ; 2026-04-25 21:09:41 UTC; unix
