iop: Inflated Ordered Probit and Logit Models

Estimation, inference, and quantities of interest for ordered probit and ordered logit models whose outcome contains an inflated category: a single ordered category (bottom, middle, top, or any other) that mixes observations generated by the ordered process with observations generated by a distinct split-population process. Fits the zero-inflated ordered probit of Harris and Zhao (2007) <doi:10.1016/j.jeconom.2007.01.002> and its middle- and top-inflated extensions (Bagozzi and Mukherjee 2012 <doi:10.1093/pan/mps020>; Bagozzi, Hill, Moore and Mukherjee 2015 <doi:10.1177/0022002713520530>; Bagozzi, Joo and Mukherjee 2024 <doi:10.1093/fpa/orae006>), generalized to an arbitrary inflated category and to the logit link, with optional correlated errors for the probit form, plus the standard ordered probit and logit and their partial proportional-odds (non-parallel) variants on the same footing. Provides analytic, robust, and cluster-robust standard errors, survey weights and offsets, model comparison (Vuong, likelihood-ratio, information criteria), regime-specific predicted probabilities and first differences, simulation for residual diagnostics, and tidy/table-package integration. The likelihood, its gradient, and the bivariate-normal probabilities are implemented in C++.

Version:	0.1.0
Depends:	R (≥ 4.0)
Imports:	Rcpp, stats, graphics, methods, numDeriv, MASS
LinkingTo:	Rcpp
Suggests:	ordinal, VGAM, mvtnorm, pbivnorm, sandwich, DHARMa, testthat (≥ 3.0.0), knitr, rmarkdown, broom, modelsummary, texreg, parallel, AER
Published:	2026-09-03
DOI:	10.32614/CRAN.package.iop (may not be active yet)
Author:	Benjamin E. Bagozzi [aut, cre]
Maintainer:	Benjamin E. Bagozzi <bagozzib at udel.edu>
BugReports:	https://github.com/bagozzib/iop/issues
License:	GPL-3
URL:	https://github.com/bagozzib/iop, https://bagozzib.github.io/iop/
NeedsCompilation:	yes
Citation:	iop citation info
Materials:	README, NEWS
CRAN checks:	iop results

Documentation:

Reference manual:	iop.html , iop.pdf
Vignettes:	Getting started with iop (source, R code) The model, identification, and estimation (source, R code) Panel data: random intercepts, fixed effects, and the Mundlak device (source, R code) Predicted probabilities, first differences, and marginal effects (source, R code)

Downloads:

Package source:	iop_0.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

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