cubmods

Statistical package: CUB models for ordinal responses.

This package is a first Python implementation of statistical methods for the models of the class CUB, proposed by Professor Domenico Piccolo, 2003.

It contains inferential methods for each family of the class CUB (with or without covariates), basic graphical tools, and methods to draw random samples from given models.

It has been implemented by Massimo Pierini in 2024. It is mainly based upon the CUB package in R, maintened by Prof.ssa Rosaria Simone.

Requirements

The package requires numpy, pandas, scipy and statsmodels.

Notice that these requirements are automatically installed using pip (read Installation section).

Installation

The latest stable version of the package can be installed via pip with

pip install cubmods

Alternatively, you can install/update to the latest build directly from GitHub main branch, but this could be unnstable. First, you need to have git installed (see Install Git for detailed instructions for Windows, macOS and Linux). After installing git you'll need to restart your computer (to update the PATHs) and then you can install cubmods from GitHub with

pip install -U git+https://github.com/maxdevblock/cubmods.git@main

All these pip commands, can also be run in a Spyder 6.x IPython console. A conda environment is strongly suggested.

Basic usage

# import libraries
import matplotlib.pyplot as plt
from cubmods.gem import draw, estimate

# draw a sample
drawn = draw(formula="ordinal ~ 0 | 0",
             m=10, pi=.7, xi=.2,
             n=500, seed=1)
print(drawn.summary())
drawn.plot()
plt.show()

# inferential method on drawn sample
mod = estimate(
    df=drawn.df,
    formula="ordinal~0|0",
    m=10,
    ass_pars={"pi": .7, "xi":.2}
)
print(mod.summary())
mod.plot()
plt.show()

Read the Documentation for further details.

Documentation

The following is a preliminary Manual and Reference Sheet.

• CUBmods’s documentation on ReadTheDocs

References

• Piccolo D. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5(1):85–104
• D'Elia A. and Piccolo D. (2005). A mixture model for preferences data analysis. Computational Statistics & Data Analysis, 49(3):917–934
• Capecchi S. and Piccolo D. (2017). Dealing with heterogeneity in ordinal responses, Quality and Quantity, 51(5), 2375--2393
• Iannario M. and Piccolo D. (2016a). A comprehensive framework for regression models of ordinal data. Metron, 74(2), 233--252
• Iannario M. and Piccolo D. (2016b). A generalized framework for modelling ordinal data. Statistical Methods and Applications, 25, 163--189
• Manisera M, Zuccolotto P (2014a). Modeling “don’t know” responses in rating scales. Pattern Recognit Lett, 45:226–234
• Piccolo D., Simone R. and Iannario M. (2019). Cumulative and CUB models for rating data: a comparative analysis. International Statistical Review, 87(2), 207-236
• Piccolo D. and Simone R. (2019). The class of CUB models: statistical foundations, inferential issues and empirical evidence. Statistical Methods & Applications, 28, 389-435
• Pierini M. (2024). Modelli della classe CUB in python. Bachelor's thesis L-41. Universitas Mercatorum, Rome, IT, 1–-79

Credits

@Author: Massimo Pierini

@Date: 2023-24

@ThanksTo: Domenico Piccolo, Rosaria Simone

@Contacts: cub@maxpierini.it