Description: Implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering
finite gaussian mixture models for both multivariate and univariate datasets. The initialization is done by randomly
selecting the samples from the dataset as the mean (meu) of the Gaussian(s). This version implements the faster alternative
EM* that avoids revisiting data by leveraging the heap structure. The algorithm returns a set of Gaussian
parameters-posterior probabilities, mean (meu), co-variance matrices (multivariate)/standard-deviation
(univariate) and priors. Reference: Hasan Kurban, Mark Jenne, Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

Title: Clustering Big Data using Expectation Maximization Star (EM*)
big data (gaussian mixture models for both multivariate and univariate datasets). This version
implements the faster alternative EM* that avoids revisiting data by leveraging the heap structure.
The implementation supports both random and K-means++ based initialization. Reference: Hasan Kurban, Mark Jenne,
Mehmet M. Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>. This work is partially supported by

implements the faster alternative-EM* that expedites convergence via structure based data segregation.
The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma,
Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban,

Title: DCE Data Reshaping and Processing
Authors@R: person("Daniel", "Perez-Troncoso", email = "danielperez@ugr.es",
Description: Prepare the results of a DCE to be analysed through choice models.'DCEmgmt' reshapes DCE data from wide to long format considering the special characteristics of a DCE. 'DCEmgmt' includes the function 'DCEestm' which estimates choice models once the database has been reshaped with 'DCEmgmt'.
Author: Daniel Perez-Troncoso [aut, cre]

Title: Create, Survey and Analyse Discrete Choice Experiments
Description: This app is intended to be a complete package for creating, surveying, and analysing discrete choice experiments. Although all these functionalities are available, the app can also be used only to obtain the design of a discrete choice experiment. Reference: Perez-Troncoso, D. (2021) <https://github.com/danielpereztr/DCEtool>.
Imports: DT, shiny, shinythemes, survival, idefix, writexl, shinyjs,
Author: Daniel Perez Troncoso [aut, cre]
Maintainer: Daniel Perez Troncoso <danielperez@ugr.es>