# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "ISCA" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'ISCA: Compare Heterogeneous Social Groups'
version: 0.1.0
doi: 10.32614/CRAN.package.ISCA
abstract: 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.
authors:
- family-names: Drouhot
  given-names: Lucas
  email: l.g.m.drouhot@uu.nl
- family-names: Späth
  given-names: Marion
repository: https://ldrouhot.r-universe.dev
commit: 19057b903c48b6e00d99653e0719a97dca9d1d77
date-released: '2024-07-28'
contact:
- family-names: Drouhot
  given-names: Lucas
  email: l.g.m.drouhot@uu.nl