Package: catregs 1.3

catregs: Post-Estimation Functions for Generalized Linear Mixed Models

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

Authors:David Melamed [aut, cre]

catregs_1.3.tar.gz
catregs_1.3.zip(r-4.7)catregs_1.3.zip(r-4.6)catregs_1.3.zip(r-4.5)
catregs_1.3.tgz(r-4.6-any)catregs_1.3.tgz(r-4.5-any)
catregs_1.3.tar.gz(r-4.7-any)catregs_1.3.tar.gz(r-4.6-any)
catregs_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
catregs/json (API)

# Install 'catregs' in R:
install.packages('catregs', repos = c('https://dmmelamed.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dmmelamed/catregs/issues

Datasets:
  • ess - A subset of data from the European Social Survey
  • essUK - A subset of data from the European Social Survey
  • gss2016 - Data from the 2016 General Social Survey.
  • LF06art - Data to replicate Long and Freese's
  • LF06travel - Travel time example data for alternative-specific outcomes.
  • logan - Replication data for Logan's (1983) application of conditional logistic regression to mobility processes.
  • Mize19AH - Add-Health Data analzed in Mize
  • Mize19GSS - General Social Survey Data analzed in Mize
  • wagepan - Data to illustrate mixed effects regression models with serial correlation.

On CRAN:

Conda:

4.75 score 28 scripts 245 downloads 10 exports 0 dependencies

Last updated from:cd4a3079e6. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING164
source / vignettesOK233
linux-release-x86_64WARNING166
macos-release-arm64WARNING137
macos-oldrel-arm64WARNING115
windows-develWARNING115
windows-releaseWARNING108
windows-oldrelWARNING98
wasm-releaseOK137

Exports:compare.marginscount.fitdiagnfirst.diff.fittedlist.coefmargins.datmargins.dat.clogitmargins.desrubins.rulesecond.diff.fitted

Dependencies:

compare.margins

Rendered fromcompare.margins.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

count.fit

Rendered fromcount.fit.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

diagn

Rendered fromdaign.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

first.diff.fitted

Rendered fromfirst.diff.fitted.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

list.coef

Rendered fromlist.coef.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2022-10-17

margins.dat

Rendered frommargins.dat.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2022-10-17

margins.dat.clogit

Rendered frommargins.dat.clogit.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

margins.des

Rendered frommargins.des.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2022-10-17

rubins.rule

Rendered fromrubins.rule.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

second.diff.fitted

Rendered fromsecond.diff.fitted.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2025-11-06
Started: 2024-06-12

Readme and manuals

Help Manual

Help pageTopics
Compares two marginal effects (MEMs or AMEs). Estimate of uncertainty is from a simulated draw from a normal distribution.compare.margins
Fits four different count models and compares them.count.fit
Computes diagnostics for generalized linear models.diagn
A subset of data from the European Social Surveyess
A subset of data from the European Social SurveyessUK
Computes the first difference in fitted values, or a series of first differences. Inference in supported via the delta method or bootstrapping.first.diff.fitted
Data from the 2016 General Social Survey.gss2016
Data to replicate Long and Freese's (2006) count models (pp354-414)LF06art
Travel time example data for alternative-specific outcomes.LF06travel
Transform glm and mixed model objects into model summaries that include coefficients, standard errors, exponentiated coefficients, confidence intervals and percent change.list.coef
Replication data for Logan's (1983) application of conditional logistic regression to mobility processes.logan
Add model predictions, standard errors and confidence intervals to a design matrix for a model object.margins.dat
Computes predicted probabilities for conditional and rank-order/exploded logistic regression models. Inference is based upon simulation techniques (requires the MASS package). Alternatively, bootstrapping is an option for conditional logistic regression models.margins.dat.clogit
Creates a design matrix of idealized data for illustrating model predictions.margins.des
Add-Health Data analzed in Mize (2019)Mize19AH
General Social Survey Data analzed in Mize (2019)Mize19GSS
Aggregate Standard Errors using Rubin's Rule.rubins.rule
Computes the second difference in fitted values. Inference in supported via the delta method or bootstrapping.second.diff.fitted
Data to illustrate mixed effects regression models with serial correlation.wagepan