picR: Predictive Information Criteria for Model Selection

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".

Version: 1.0.0
Imports: stats
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), dplyr
Published: 2022-10-24
Author: Javier Flores ORCID iD [aut, cre]
Maintainer: Javier Flores <javenrflo.pro at pm.me>
BugReports: https://github.com/javenrflo/picR/issues
License: GPL (≥ 3)
URL: https://github.com/javenrflo/picR
NeedsCompilation: no
Citation: picR citation info
Materials: README NEWS
CRAN checks: picR results


Reference manual: picR.pdf


Package source: picR_1.0.0.tar.gz
Windows binaries: r-devel: picR_1.0.0.zip, r-release: picR_1.0.0.zip, r-oldrel: picR_1.0.0.zip
macOS binaries: r-release (arm64): picR_1.0.0.tgz, r-oldrel (arm64): picR_1.0.0.tgz, r-release (x86_64): picR_1.0.0.tgz, r-oldrel (x86_64): picR_1.0.0.tgz


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