Package: ISCA 0.1.0
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 OLS regression results for each cluster across iterations.
Authors:
ISCA_0.1.0.tar.gz
ISCA_0.1.0.zip(r-4.5)ISCA_0.1.0.zip(r-4.4)ISCA_0.1.0.zip(r-4.3)
ISCA_0.1.0.tgz(r-4.4-any)ISCA_0.1.0.tgz(r-4.3-any)
ISCA_0.1.0.tar.gz(r-4.5-noble)ISCA_0.1.0.tar.gz(r-4.4-noble)
ISCA_0.1.0.tgz(r-4.4-emscripten)ISCA_0.1.0.tgz(r-4.3-emscripten)
ISCA.pdf |ISCA.html✨
ISCA/json (API)
NEWS
| # Install 'ISCA' in R: |
| install.packages('ISCA', repos = c('https://ldrouhot.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/ldrouhot/isca/issues
- sim_data - Cross-sectional, artificial data on 1000 individuals
Last updated 2 months agofrom:19057b903c. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 02 2024 |
| R-4.5-win | OK | Nov 02 2024 |
| R-4.5-linux | OK | Nov 02 2024 |
| R-4.4-win | OK | Nov 02 2024 |
| R-4.4-mac | OK | Nov 02 2024 |
| R-4.3-win | OK | Nov 02 2024 |
| R-4.3-mac | OK | Nov 02 2024 |
Exports:%>%ISCA_clustertableISCA_modelingISCA_random_assignments
Dependencies:backportsbase64encbroombslibcachemcheckmateclasscliclustercolorspacecpp11data.tabledigestdplyre1071evaluatefansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigplyrproxypurrrR6rappdirsRColorBrewerRcpprlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ISCA Cluster Tables | ISCA_clustertable |
| ISCA Modeling | ISCA_modeling |
| ISCA Random Assignments per Subgroup | ISCA_random_assignments |
| Cross-sectional, artificial data on 1000 individuals | sim_data |
