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IFTPredictor  

Predictions Using Item-Focused Tree Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("IFTPredictor", "0.1.0")



Attach the package and use:
library("IFTPredictor")
Maintained by
Muditha L. Bodawatte Gedara
[Scholar Profile | Author Map]
First Published: 2025-02-13
Latest Update: 2025-02-13
Description:
This function predicts item response probabilities and item responses using the item-focused tree model. The item-focused tree model combines logistic regression with recursive partitioning to detect Differential Item Functioning in dichotomous items. The model applies partitioning rules to the data, splitting it into homogeneous subgroups, and uses logistic regression within each subgroup to explain the data. Differential Item Functioning detection is achieved by examining potential group differences in item response patterns. This method is useful for understanding how different predictors, such as demographic or psychological factors, influence item responses across subgroups.
How to cite:
Muditha L. Bodawatte Gedara (2025). IFTPredictor: Predictions Using Item-Focused Tree Models. R package version 0.1.0, https://cran.r-project.org/web/packages/IFTPredictor. Accessed 29 Mar. 2025.
Previous versions and publish date:
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Complete documentation for IFTPredictor
Functions, R codes and Examples using the IFTPredictor R package
Full IFTPredictor package functions and examples
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