ISCA: Compare Heterogeneous Social Groups
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 Ordinary Least Squares regression results for each cluster across iterations. For further details please see Drouhot (2021) <doi:10.1086/712804>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.3) |
| Imports: |
data.table (≥ 1.16.0), stringr (≥ 1.5.1), e1071 (≥ 1.7-16), Hmisc (≥ 5.1-3), broom (≥ 1.0.7), 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) |
| Published: |
2024-10-02 |
| DOI: |
10.32614/CRAN.package.ISCA |
| Author: |
Lucas Drouhot [aut, cre],
Marion Späth [aut] |
| Maintainer: |
Lucas Drouhot <l.g.m.drouhot at uu.nl> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
ISCA results |
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