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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".
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