On the recordRead 11 Oct 202624 facts
IDCeMPy
On the record
- Contributions
- The authors welcome contributors to help test IDCeMPy and add functionality.idcempy.readthedocs.io · read 11 Oct 2026
- Data import formats
- CSV, Stata (.dta), pandas DataFrameidcempy.readthedocs.io · read 24 Sep 2026
- Dependencies in examples
- The documentation examples use pandas, urllib, and matplotlib for importing and visualizing data.idcempy.readthedocs.io · read 11 Oct 2026
- Deployment
- desktopidcempy.readthedocs.io · read 24 Sep 2026
- Diagnostics
- It computes AIC, log-likelihood, and Vuong test statistics to assess model performance.idcempy.readthedocs.io · read 11 Oct 2026
- Estimation
- The package estimates models by maximum likelihood using Newton numerical optimization methods.idcempy.readthedocs.io · read 11 Oct 2026
- Inflated category options
- For GiMNL, users can choose the baseline, second, or third outcome category as the inflated category.idcempy.readthedocs.io · read 11 Oct 2026
- Input workflow
- The examples use pandas DataFrames and show CSV and Stata data imports.idcempy.readthedocs.io · read 11 Oct 2026
- Installation
- The documentation gives installation instructions through pip and from the project's GitHub repository.idcempy.readthedocs.io · read 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.idcempy.readthedocs.io · read 11 Oct 2026
- Middle-inflated models
- It fits middle-inflated ordered probit models with or without correlated errors.idcempy.readthedocs.io · read 11 Oct 2026
- Model diagnostics
- It computes AIC, log-likelihood, and Vuong test statistics to assess model performance.idcempy.readthedocs.io · read 11 Oct 2026
- Multinomial models
- It fits generalized inflated multinomial logit models for three-outcome dependent variables.idcempy.readthedocs.io · read 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.idcempy.readthedocs.io · read 11 Oct 2026
- Performance limit
- The documentation says models with correlated errors take substantially longer to run than their counterparts without correlated errors.idcempy.readthedocs.io · read 11 Oct 2026
- Predictions and effects
- Its functions calculate fitted probabilities and marginal effects for split-stage and outcome-stage covariates.idcempy.readthedocs.io · read 11 Oct 2026
- Purpose
- IDCeMPy is a Python package for fitting and assessing inflated discrete choice models.idcempy.readthedocs.io · read 11 Oct 2026
- Regression models
- Yesidcempy.readthedocs.io · read 24 Sep 2026
