Package: IFTPredictor
Type: Package
Title: Predictions Using Item-Focused Tree Models
Version: 0.1.0
Author@R: c( person(given=c("Muditha","L."), family="Bodawatte Gedara",
        email="muditha.lakmali.1993@gmail.com", role=c("aut", "cre")),
        person(given=c("Barret","A."), family="Monchka",
        email="Barret.Monchka@umanitoba.ca", role="aut"),
        person(given=c("Lisa","M."), family="Lix",
        email="Lisa.Lix@umanitoba.ca", role="aut") )
Author: Muditha Bodawatte Gedara [aut, cre], Barret Mochka [aut], Lisa
        Lix [aut]
Maintainer: Muditha Bodawatte Gedara <muditha.lakmali.1993@gmail.com>
Description: This function predicts item response probabilities and
        item responses using the item-focused tree (IFT) model. The IFT
        model combines logistic regression with recursive partitioning
        to detect Differential Item Functioning (DIF) 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. DIF
        detection is achieved by examining potential group differences
        in item response patterns. This method is useful for
        understanding how different covariates, such as demographic or
        psychological factors, influence item responses across
        subpopulations.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: DIFtree
Suggests: devtools, testthat (>= 3.0.0)
RoxygenNote: 7.3.2
Config/testthat/edition: 3
Repository: https://mudithabo.r-universe.dev
Date/Publication: 2025-01-21 03:54:03 UTC
RemoteUrl: https://github.com/mudithabo/iftpredictor
RemoteRef: HEAD
RemoteSha: 120bf7f233b915ab4c39739b218ac591c647cde2
NeedsCompilation: no
Packaged: 2026-06-19 11:32:09 UTC; root