The mclust package is a powerful R package for model-based clustering, classification, and density estimation using Gaussian finite mixture models. Version 5 of the package includes new covariance structures, dimension reduction capabilities for visualization, model selection criteria, initialization strategies for the EM algorithm, and bootstrap-based inference, making it a comprehensive tool for data analysis via finite mixture modeling. The package is widely used in various fields, including geochemistry, chemometrics, DNA sequence analysis, gene expression data, hydrology, wind energy, industrial engineering, epidemiology, food science, clinical psychology, political science, and anthropology. The popularity of mclust is reflected in its high download numbers and its position as one of the top packages on CRAN. The package provides a variety of functions for clustering, classification, and density estimation, including tools for model selection, visualization, and inference. The package also supports supervised classification, where the goal is to build a classifier that can assign an observation to one of K known classes. The package includes functions for density estimation, which allows for the approximation of any given probability distribution using finite mixture models. The package also provides functions for bootstrap-based inference, which allows for the estimation of standard errors and confidence intervals for model parameters. The package is widely used in both applied and theoretical research for its flexibility and robustness in handling a wide range of data types and modeling tasks.The mclust package is a powerful R package for model-based clustering, classification, and density estimation using Gaussian finite mixture models. Version 5 of the package includes new covariance structures, dimension reduction capabilities for visualization, model selection criteria, initialization strategies for the EM algorithm, and bootstrap-based inference, making it a comprehensive tool for data analysis via finite mixture modeling. The package is widely used in various fields, including geochemistry, chemometrics, DNA sequence analysis, gene expression data, hydrology, wind energy, industrial engineering, epidemiology, food science, clinical psychology, political science, and anthropology. The popularity of mclust is reflected in its high download numbers and its position as one of the top packages on CRAN. The package provides a variety of functions for clustering, classification, and density estimation, including tools for model selection, visualization, and inference. The package also supports supervised classification, where the goal is to build a classifier that can assign an observation to one of K known classes. The package includes functions for density estimation, which allows for the approximation of any given probability distribution using finite mixture models. The package also provides functions for bootstrap-based inference, which allows for the estimation of standard errors and confidence intervals for model parameters. The package is widely used in both applied and theoretical research for its flexibility and robustness in handling a wide range of data types and modeling tasks.