Package: ISCA
Version: 0.1.0
Author: Lucas Drouhot [aut, cre], Marion Späth [aut]
Maintainer: Lucas Drouhot <l.g.m.drouhot@uu.nl>
License: GPL (>= 3)
Title: Compare Heterogeneous Social Groups
Description: The Inductive Subgroup Comparison Approach ('ISCA') offers
        a way to compare groups that are internally differentiated and
        heterogeneous. It starts by identifying the social structure of
        a reference group against which a minority or another group is
        to be compared, yielding empirical subgroups to which minority
        members are then matched based on how similar they are. The
        modelling of specific outcomes then occurs within specific
        subgroups in which majority and minority members are matched.
        ISCA is characterized by its data-driven, probabilistic, and
        iterative approach and combines fuzzy clustering, Monte Carlo
        simulation, and regression Analysis. ISCA_random_assignments()
        assigns subjects probabilistically to subgroups.
        ISCA_clustertable() provides summary statistics of each cluster
        across iterations. ISCA_modeling provides OLS regression
        results for each cluster across iterations.
Encoding: UTF-8
RoxygenNote: 7.3.1
Depends: R (>= 4.3)
Imports: data.table (>= 1.15.4), stringr (>= 1.5.1), e1071 (>= 1.7-14),
        Hmisc (>= 5.1-3), broom (>= 1.0.6), dplyr (>= 1.1.4),
        tidyselect (>= 1.2.1), stats (>= 4.3.1), tibble (>= 3.2.1),
        plyr (>= 1.8.9), magrittr (>= 2.0.3)
Suggests: knitr, rmarkdown, tidyr (>= 1.3.0), testthat (>= 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2026-06-20 06:48:04 UTC; root
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev
Repository: https://ldrouhot.r-universe.dev
Date/Publication: 2024-09-26 15:19:22 UTC
RemoteUrl: https://github.com/ldrouhot/isca
RemoteRef: HEAD
RemoteSha: 19057b903c48b6e00d99653e0719a97dca9d1d77