mybassm 0.1.1

Overview

The Bass Model Package provides various functions for analyzing and predicting the diffusion of innovative products using the Bass Model. This package is based on the paper “Forecasting the Diffusion of Innovative Products Using the Bass Model at the Takeoff Stage” by Suddhachit Mitra and Professor Hovhanissyan lectures.

Features

• Calculate diffusion using the Bass Model.

• Estimate parameters using the bass_f function.

• Generate cumulative adoption curve using the bass_F function.

• Predict future adoption using the predict_bass_model function.

• Plot the predicted adoption curve.

Installation

You can install the Bass Model Package from PyPI using pip:

$ pip install mybassm

Usage

The package provides the following main functions:

• diffusion: Calculate the diffusion of the product based on the Bass Model.

• bass_f: Calculates the fraction of the total market that has adopters at time t using the Bass diffusion model.

• bass_F: Calculates the fraction of the total market that has adopted up to and including time t using the Bass diffusion model.

• predict_bass_model: Predicts future adoption rates based on a given set of time periods and the estimated Bass model parameters.

Examples

Here’s a simple example to demonstrate the usage of the Bass Model Package:

from mybassm.mybassm import diffusion, bass_f, bass_F, predict_bass_model

# Calculate diffusion
diffusion_rate = diffusion(sales, t)

# Estimate Bass Model parameters
p, q = bass_f(t, p, q)

# Generate cumulative adoption curve
t_values, cumulative_adoption = bass_F(t, p, q)

# Predict future adoption
params= p,q,m
predicted_adoption = predict_bass_model(params, t)

#Plots the bass model
plot_bass_model(params, y_pred)
plot_bass(p, q, title)

License

The Bass Model Package is released under the MIT License. For more information, see the LICENSE

References

• Suddhachit Mitra, “Forecasting the Diffusion of Innovative Products Using the Bass Model at the Takeoff Stage.”

• Professor Hovhanissyan lectures.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request on the Github

Authors

Anahit Zakaryan

0.1.1 (2023-05-17)

0.1.0 (2023-05-17)

• First release on PyPI.