Package: divoRce 1.0-2

Thomas Rusch

divoRce: Diagnosing Separation Phenomena in Latent Polyhedral Categorical Response Models

Utilities for checking and diagnosing separation phenomena (complete separation, quasi-complete separation, overlap) for latent polyhedral categorical response models including binary, nominal and ordinal data models with link construction as baseline-category, adjacent-category, cumulative, sequential (continuation-ratio), ordered stereotype and arbitrary link error distribution (including logit, probit, cauchit, cloglog, loglog, log amongst others) based on structure vectors as described in Sablica, Hornik, Rusch (2026) <doi:10.48550/arXiv.2610.08100> and Rusch, Sablica, Hornik (2026) <doi:10.48550/arXiv.2610.09951>. The package functionality includes a check for separation and a check for overlap with a linear program, a quick check for overlap, a sequential and a parallel check for overlap based on subsamples, detailed separation diagnostics such as identification of columns in the design matrix/structure vector matrix that lead to separation, identification of design matrix/structure vector matrix rows that lead to separation, the dimension of the recession cone and the structure vectors that are linearities. Calculations can be done with package 'rcdd' to employ arbitrary precision and various other linear program solvers can also be used via package 'ROI'. Implemented checks can be done before model fitting based on outcome and design matrix or structure vector matrix or via formula (pre-fit), or after model fitting for objects of class 'glm', 'multinom', 'polr', 'osm', 'clm', 'bracl', 'brmultinom'. Wrappers for other classes can be easily constructed by third parties via pre-fit functions.

Authors:Thomas Rusch [aut, cre], Lukas Sablica [aut], Kurt Hornik [aut]

divoRce_1.0-2.tar.gz
divoRce_1.0-2.zip(r-4.7-any)divoRce_1.0-2.zip(r-4.6-any)
divoRce_1.0-2.tar.gz(r-4.7-any)divoRce_1.0-2.tar.gz(r-4.6-any)
divoRce_1.0-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
divoRce/json (API)

# Install 'divoRce' in R:
install.packages('divoRce', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • Alligators - Untabeled alligator food choice data
  • csepdat1 - Toy data set with complete separation
  • csepdat2 - Toy data set with complete separation
  • csepdatm - Toy data set with complete separation
  • csepdato - Toy data set with complete separation
  • HDSS - Willingness to Share Health Data of Drexler
  • iris2 - Adapted Iris Dataset
  • nsduh2019 - A snapshot of the NSDUH 2019 data
  • ohiovoters - Voters in Ohio
  • ovldat1 - Toy data set with overlap.
  • ovldat2 - Toy data set with overlap.
  • ovldatm - Toy data set with overlap.
  • ovldato - Toy data set with overlap.
  • qcsepdat1 - Toy data set with quasi-complete separation
  • qcsepdat2 - Toy data set with quasi-complete separation
  • qcsepdatm - Toy data set with quasi-complete separation
  • qcsepdato - Toy data set with quasi-complete separation
  • Silvapulle - Psychiatric Cases Classification based on GHQ of Silvapulle
  • titanic3 - All people of age 3 in the Kaggle Titanic data set

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 9 exports 1 dependencies

Last updated from:6423ad28f2. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK120
source / vignettesOK160
linux-releaseOK114
windows-develOK64
windows-releaseOK85
wasm-releaseOK106

Exports:check_overlapcheck_separationdiagnose_separationlinearitiesmake_yxrecession_coneseparation_columnsseparation_rowsstructure_vectors

Dependencies:rcdd

Readme and manuals

Help Manual

Help pageTopics
Untabeled alligator food choice dataAlligators
Check overlapcheck_overlap check_overlap.bracl check_overlap.brmultinom check_overlap.character check_overlap.clm check_overlap.default check_overlap.factor check_overlap.formula check_overlap.glm check_overlap.integer check_overlap.logical check_overlap.matrix check_overlap.multinom check_overlap.numeric check_overlap.osm check_overlap.polr
Check separationcheck_separation check_separation.bracl check_separation.brmultinom check_separation.character check_separation.clm check_separation.default check_separation.factor check_separation.formula check_separation.glm check_separation.integer check_separation.logical check_separation.matrix check_separation.multinom check_separation.numeric check_separation.osm check_separation.polr
Toy data set with complete separationcsepdat1
Toy data set with complete separationcsepdat2
Toy data set with complete separationcsepdatm
Toy data set with complete separationcsepdato
Detailed separation diagnostic for all categorical outcomes.diagnose_separation diagnose_separation.bracl diagnose_separation.brmultinom diagnose_separation.character diagnose_separation.clm diagnose_separation.default diagnose_separation.factor diagnose_separation.formula diagnose_separation.glm diagnose_separation.integer diagnose_separation.logical diagnose_separation.matrix diagnose_separation.multinom diagnose_separation.numeric diagnose_separation.osm diagnose_separation.polr
Willingness to Share Health Data of Drexler (2026)HDSS
Adapted Iris Datasetiris2
Linearitieslinearities
This function is meant to set up a response variable and a design matrix from a formula-data combination for the pre-fit separation check functions.make_yx
A snapshot of the NSDUH 2019 datansduh2019
Voters in Ohioohiovoters
Toy data set with overlap.ovldat1
Toy data set with overlap.ovldat2
Toy data set with overlap.ovldatm
Toy data set with overlap.ovldato
Print method for sepmod classesprint.sepmod
Print method for sepmod_sl classesprint.sepmod_sl
Toy data set with quasi-complete separationqcsepdat1
Toy data set with quasi-complete separationqcsepdat2
Toy data set with quasi-complete separationqcsepdatm
Toy data set with quasi-complete separationqcsepdato
Recession cone calculationrecession_cone recession_cone.bracl recession_cone.brmultinom recession_cone.character recession_cone.clm recession_cone.default recession_cone.factor recession_cone.formula recession_cone.glm recession_cone.integer recession_cone.logical recession_cone.matrix recession_cone.multinom recession_cone.numeric recession_cone.osm recession_cone.polr
Identify separation columnsseparation_columns separation_columns.bracl separation_columns.brmultinom separation_columns.character separation_columns.clm separation_columns.default separation_columns.factor separation_columns.formula separation_columns.glm separation_columns.integer separation_columns.logical separation_columns.matrix separation_columns.multinom separation_columns.numeric separation_columns.osm separation_columns.polr
Identify separation rowsseparation_rows separation_rows.bracl separation_rows.brmultinom separation_rows.character separation_rows.clm separation_rows.default separation_rows.factor separation_rows.formula separation_rows.glm separation_rows.integer separation_rows.logical separation_rows.matrix separation_rows.multinom separation_rows.numeric separation_rows.osm separation_rows.polr
Psychiatric Cases Classification based on GHQ of Silvapulle (1981)Silvapulle
Structure Vector S3 Genericstructure_vectors structure_vectors.default structure_vectors.formula
All people of age 3 in the Kaggle Titanic data settitanic3