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catregs (version 1.3)

Post-Estimation Functions for Generalized Linear Mixed Models

Description

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).

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Version

Install

install.packages('catregs')

Monthly Downloads

217

Version

1.3

License

GPL-2 | GPL-3

Maintainer

David Melamed

Last Published

December 18th, 2025

Functions in catregs (1.3)

Creates a design matrix of idealized data for illustrating model predictions.

Add model predictions, standard errors and confidence intervals to a design matrix for a model object.

Fits four different count models and compares them.

Add-Health Data analzed in Mize (2019)

Computes diagnostics for generalized linear models.

first.diff.fitted

Computes the first difference in fitted values, or a series of first differences. Inference in supported via the delta method or bootstrapping.

compare.margins

Compares two marginal effects (MEMs or AMEs). Estimate of uncertainty is from a simulated draw from a normal distribution.

Transform glm and mixed model objects into model summaries that include coefficients, standard errors, exponentiated coefficients, confidence intervals and percent change.

Data to replicate Long and Freese's (2006) count models (pp354-414)

Data to illustrate mixed effects regression models with serial correlation.

General Social Survey Data analzed in Mize (2019)

Replication data for Logan's (1983) application of conditional logistic regression to mobility processes.

Data from the 2016 General Social Survey.

A subset of data from the European Social Survey

A subset of data from the European Social Survey

Travel time example data for alternative-specific outcomes.

Aggregate Standard Errors using Rubin's Rule.

second.diff.fitted

Computes the second difference in fitted values. Inference in supported via the delta method or bootstrapping.

margins.dat.clogit

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.