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picR  

Predictive Information Criteria for Model Selection
View on CRAN: Click here


Download and install picR package within the R console
Install from CRAN:
install.packages("picR")

Install from Github:
library("remotes")
install_github("cran/picR")

Install by package version:
library("remotes")
install_version("picR", "1.0.0")



Attach the package and use:
library("picR")
Maintained by
Javier Flores
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-10-24
Latest Update: 2022-10-24
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) "A new class of information criteria for improved prediction in the presence of training/validation data heterogeneity".
How to cite:
Javier Flores (2022). picR: Predictive Information Criteria for Model Selection. R package version 1.0.0, https://cran.r-project.org/web/packages/picR. Accessed 29 Jan. 2025.
Previous versions and publish date:
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Other packages that cited picR R package
View picR citation profile
Other R packages that picR depends, imports, suggests or enhances
Complete documentation for picR
Functions, R codes and Examples using the picR R package
Some associated functions: PIC.lm . PIC.mlm . PIC . 
Some associated R codes: PIC.R . utils.R .  Full picR package functions and examples
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