IDCeMPy

Spec sheet

Purpose

IDCeMPy is a Python package for fitting and assessing inflated discrete choice models.Read 11 Oct 2026 · idcempy.readthedocs.io

Deployment

desktopRead 24 Sep 2026 · idcempy.readthedocs.io

Free plan

YesRead 24 Sep 2026 · idcempy.readthedocs.io

Contributions

The authors welcome contributors to help test IDCeMPy and add functionality.Read 11 Oct 2026 · idcempy.readthedocs.io

Data import formats

CSV, Stata (.dta), pandas DataFrameRead 24 Sep 2026 · idcempy.readthedocs.io

Dependencies in examples

The documentation examples use pandas, urllib, and matplotlib for importing and visualizing data.Read 11 Oct 2026 · idcempy.readthedocs.io

Diagnostics

It computes AIC, log-likelihood, and Vuong test statistics to assess model performance.Read 11 Oct 2026 · idcempy.readthedocs.io

Estimation

The package estimates models by maximum likelihood using Newton numerical optimization methods.Read 11 Oct 2026 · idcempy.readthedocs.io

Inflated category options

For GiMNL, users can choose the baseline, second, or third outcome category as the inflated category.Read 11 Oct 2026 · idcempy.readthedocs.io

Input workflow

The examples use pandas DataFrames and show CSV and Stata data imports.Read 11 Oct 2026 · idcempy.readthedocs.io

Installation

The documentation gives installation instructions through pip and from the project's GitHub repository.Read 11 Oct 2026 · idcempy.readthedocs.io

Intended use

The documentation describes using IDCeMPy when an outcome category has an excessive share of observations arising from distinct data-generating processes.Read 11 Oct 2026 · idcempy.readthedocs.io

Middle-inflated models

It fits middle-inflated ordered probit models with or without correlated errors.Read 11 Oct 2026 · idcempy.readthedocs.io

Model diagnostics

It computes AIC, log-likelihood, and Vuong test statistics to assess model performance.Read 11 Oct 2026 · idcempy.readthedocs.io

Multinomial models

It fits generalized inflated multinomial logit models for three-outcome dependent variables.Read 11 Oct 2026 · idcempy.readthedocs.io

Notable runtime limit

The documentation says correlated-error models run substantially longer than their counterparts without correlated errors, and runtime also depends on observation and covariate counts.Read 11 Oct 2026 · idcempy.readthedocs.io

Performance limit

The documentation says models with correlated errors take substantially longer to run than their counterparts without correlated errors.Read 11 Oct 2026 · idcempy.readthedocs.io

Predictions and effects

Its functions calculate fitted probabilities and marginal effects for split-stage and outcome-stage covariates.Read 11 Oct 2026 · idcempy.readthedocs.io

Regression models

YesRead 24 Sep 2026 · idcempy.readthedocs.io

Scripting support

YesRead 24 Sep 2026 · idcempy.readthedocs.io

Standard models

The package also includes standard ordered probit and three-category multinomial logit models.Read 11 Oct 2026 · idcempy.readthedocs.io

Statistical tests

YesRead 24 Sep 2026 · idcempy.readthedocs.io

Who it is for

The documentation presents it for researchers analyzing inflated categories in ordered or unordered polytomous outcomes, including smoking, immigration-attitude, and vote-choice examples.Read 11 Oct 2026 · idcempy.readthedocs.io

Zero-inflated models

It fits zero-inflated ordered probit models with or without correlated errors.Read 11 Oct 2026 · idcempy.readthedocs.io

Where Specifiction places it

• Statistical Analysis Software#96 of 1067.0