Model-Based Clustering, Classification, and Density Estimation Using mclust in R 1st Edition

Capture complex structure in your data with Model-Based Clustering, Classification, and Density Estimation Using mclust in R (1st Edition) by Luca Scrucca, Chris Fraley, T. Brendan Murphy, and Adrian E. Raftery. This definitive guide brings rigorous statistical theory together with practical R implementation, making advanced mixture modeling accessible to applied scientists, analysts, and researchers.

Dive into clear explanations of Gaussian mixture models, the EM algorithm, model selection with BIC, and robust approaches to clustering, classification, and density estimation. Rich with illustrative examples using the popular mclust package for R, the book guides you through hands-on workflows, diagnostics, and visualization techniques that turn raw data into actionable insights.

Whether you’re a statistician in academia, a data scientist in finance, a bioinformatician, or a geospatial analyst, you’ll appreciate the book’s balance of theory and practice. Real-world case studies and step-by-step code samples demonstrate how model-based methods outperform heuristic clustering in interpretability and reproducibility—valuable for teams across North America, Europe, Asia, and beyond.

Compact yet comprehensive, this edition is ideal for graduate students, researchers, and professionals seeking applied machine learning tools grounded in statistical rigor. Enhance your analytical toolkit, improve model-driven decisions, and confidently apply mixture models in production and research settings.

Order your copy today and bring principled model-based clustering and classification to your R projects—clear methodology, practical code, and global applicability in one essential resource.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.