R library for diffusion model analysis. DIMORA (DIffusion MOdels R Analysis) is a statistical package that allows the analysis of diffusion data using different models, among the most used and useful. The need for this package arises from the lack of a similar tool in R, the idea is therefore to provide a platform that allows the most in-depth analysis of the diffusion data. Version 0.3.6
The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition.
R (version >= 3.1.0)Dependencies (automatically installed via install.packages)
minpack.lm, numDeriv, stats, forecast, ggplot2, reshape2, deSolveFrom Rstudio or Jupyter notebook
install.packages('DIMORA')
library(DIMORA)Or cloning the Github repository to access files
$ git clone https://github.com/ZiliottoFilippoDev/DIMORA.git
$ cd DIMORA
$ cd RBass model usage example
data(DBdimora)
data <- DBdimora$iPhone[7:52]
model <- BM(data, display=T)
plot(model, type = 'all', oos = 20)
Distributed under the GPL-3.0 License. See LICENSE.md for more information.
Create a new issue in the appropriate GitHub section, so we will be able to verify and correct all the material :)
Guidolin, M. (2023). Innovation Diffusion Models: Theory and Practice, First Edition. John Wiley & Sons Ltd.