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iop
View on CRAN: Click
here
Download and install iop package within the R console
Install from CRAN:
install.packages("iop")
Install from Github:
library("remotes")
install_github("cran/iop") Install by package version:
library("remotes")
install_version("iop", "0.1.0") Attach the package and use:
library("iop")
Maintained by
Benjamin E. Bagozzi
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[Scholar Profile | Author Map]
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First Published: 2026-09-03
Latest Update: 2026-09-03
Description:
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++.
How to cite:
Benjamin E. Bagozzi (2026). iop: Inflated Ordered Probit and Logit Models. R package version 0.1.0, https://cran.r-project.org/web/packages/iop. Accessed 24 Oct. 2026.
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