IDCeMPy
The facts below are what the maker publishes about IDCeMPy, checked and dated. Our editors have not written about it yet.
The facts
Each one read from IDCeMPy's own pages, with the date it was checked.
What it does
- Contributions
- The authors welcome contributors to help test IDCeMPy and add functionality.checked 11 Oct 2026
- Data import formats
- CSV, Stata (.dta), pandas DataFramechecked 24 Sep 2026
- Dependencies in examples
- The documentation examples use pandas, urllib, and matplotlib for importing and visualizing data.checked 11 Oct 2026
- Deployment
- desktopchecked 24 Sep 2026
- Diagnostics
- It computes AIC, log-likelihood, and Vuong test statistics to assess model performance.checked 11 Oct 2026
- Estimation
- The package estimates models by maximum likelihood using Newton numerical optimization methods.checked 11 Oct 2026
- Inflated category options
- For GiMNL, users can choose the baseline, second, or third outcome category as the inflated category.checked 11 Oct 2026
- Input workflow
- The examples use pandas DataFrames and show CSV and Stata data imports.checked 11 Oct 2026
- Installation
- The documentation gives installation instructions through pip and from the project's GitHub repository.checked 11 Oct 2026
- Intended use
- The documentation describes using IDCeMPy when an outcome category has an excessive share of observations arising from distinct data-generating processes.checked 11 Oct 2026
- Middle-inflated models
- It fits middle-inflated ordered probit models with or without correlated errors.checked 11 Oct 2026
- Model diagnostics
- It computes AIC, log-likelihood, and Vuong test statistics to assess model performance.checked 11 Oct 2026
- Multinomial models
- It fits generalized inflated multinomial logit models for three-outcome dependent variables.checked 11 Oct 2026
- 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.checked 11 Oct 2026
- Performance limit
- The documentation says models with correlated errors take substantially longer to run than their counterparts without correlated errors.checked 11 Oct 2026
- Predictions and effects
- Its functions calculate fitted probabilities and marginal effects for split-stage and outcome-stage covariates.checked 11 Oct 2026
- Purpose
- IDCeMPy is a Python package for fitting and assessing inflated discrete choice models.checked 11 Oct 2026
- Regression models
- Yeschecked 24 Sep 2026
- Scripting support
- Yeschecked 24 Sep 2026
- Standard models
- The package also includes standard ordered probit and three-category multinomial logit models.checked 11 Oct 2026
- Statistical tests
- Yeschecked 24 Sep 2026
- 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.checked 11 Oct 2026
- Zero-inflated models
- It fits zero-inflated ordered probit models with or without correlated errors.checked 11 Oct 2026
Money
- Free plan
- Yeschecked 24 Sep 2026
