Model Based Clustering Classification And Density Estimation Using Mclust In R
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Model Based Clustering Classification And Density Estimation Using Mclust In R
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Author : Luca Scrucca
language : en
Publisher: CRC Press
Release Date : 2023-04-20
Model Based Clustering Classification And Density Estimation Using Mclust In R written by Luca Scrucca and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-20 with Mathematics categories.
Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.
Finite Mixture Models
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Author : Geoffrey McLachlan
language : en
Publisher: John Wiley & Sons
Release Date : 2004-03-22
Finite Mixture Models written by Geoffrey McLachlan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-22 with Mathematics categories.
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Hands On Machine Learning With R
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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07
Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Drug Development Supported By Informatics
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Author : Hiroko Satoh
language : en
Publisher: Springer Nature
Release Date : 2024-10-18
Drug Development Supported By Informatics written by Hiroko Satoh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Science categories.
This book describes the state-of-the-art of chemoinformatics, bioinformatics, materials informatics and measurement/metrology informatics to develop drugs with desired activity or physicochemical properties and to optimize the functionality, efficacy, safety and quality of the compounds for drugs. Recently, "AI drug discovery", drug discovery research utilizing artificial intelligence technology such as machine learning, has attracted much attention. This book provides an overview of the four applied informatics fields and their applications in drug development for a wide spectrum of readers from learners to professional scientists in academia and industry. It focuses on the basic research stage of drug development with contributions from experts at the forefront of these fields. The authors hope that this book will be of assistance to explore new opportunities for collaboration between pharmaceutical science and informatics.
Copula Additive Distributional Regression Using R
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Author : Giampiero Marra
language : en
Publisher: CRC Press
Release Date : 2025-06-24
Copula Additive Distributional Regression Using R written by Giampiero Marra and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Mathematics categories.
Copula additive distributional regression enables the joint modeling of multiple outcomes, an essential aspect of many real-world research problems. This book provides an accessible overview of this modeling approach, with a particular focus on its implementation in the GJRM R package, developed by the authors. The emphasis is on bivariate responses with empirical illustrations drawn from diverse fields such as health and medicine, epidemiology, economics and social sciences. Key Features: Provides a comprehensive overview of joint regression modeling for multiple outcomes, with a focus on bivariate responses Offers a practical approach with real-world examples from various fields Demonstrates the implementation of all the discussed models using the GJRM package in R Includes supplementary resources such as data accessible through the GJRM.data package in R and additional code available on the authors' webpages This book is designed for graduate students, researchers, practitioners and analysts who are interested in using copula additive distributional regression for the joint modeling of bivariate outcomes. The methodology is accessible to readers with a basic understanding of core statistics and probability, regression, copula modeling and R.
Cluster Analysis A Primer Using R
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Author : Lior Rokach
language : en
Publisher: World Scientific
Release Date : 2024-10-08
Cluster Analysis A Primer Using R written by Lior Rokach and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Computers categories.
Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis.At its core, the book delves deeply into various clustering algorithms, covering partitioning methods, hierarchical methods, and advanced techniques such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with clear, concise explanations, supported by illustrative examples and hands-on implementations in the R programming language — a popular and powerful tool for data analysis and visualization.Recognizing the importance of cluster validation and evaluation, the book devotes a dedicated chapter to exploring a wide range of internal and external quality criteria, equipping readers with the necessary tools to assess the performance of clustering algorithms. For those eager to stay at the forefront of the field, the book also presents deep learning-based clustering methods, showcasing the remarkable capabilities of neural networks in uncovering hidden structures within complex, high-dimensional data.Whether you are a student seeking to expand your knowledge, a data analyst looking to enhance your toolbox, or a researcher exploring the frontiers of data analysis, this book will provide you with a solid foundation in cluster analysis and empower you to tackle a wide range of data-driven problems.
Spatial Analysis In Geology Using R
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Author : Pedro M. Nogueira
language : en
Publisher: CRC Press
Release Date : 2024-07-01
Spatial Analysis In Geology Using R written by Pedro M. Nogueira and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-01 with Mathematics categories.
The integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems. Spatial analysis of geological data can be used to identify patterns and trends in data, to map spatial relationships, and to model spatial processes. R is a consolidated and yet growing statistical programming language with increasing value in spatial analysis often replacing, with advantage, GIS tools. By providing a comprehensive guide for geologists to harness the power of spatial analysis in R, Spatial Analysis in Geology Using R serves as a tool in addressing real-world problems, such as natural resource management, environmental conservation, and hazard prediction and mitigation. Features: Provides a practical and accessible overview of spatial analysis in geology using R Organised in three independent and complementary parts: Introduction to R, Spatial Analysis with R, and Spatial Statistics and Modelling Applied approach with many detailed examples and case studies using real geological data Presents a collection of R packages that are useful in many geological situations Does not assume any prior knowledge of R; all code are explained in detail Supplemented by a website with all data, code, and examples Spatial Analysis in Geology Using R will be useful to any geological researcher who has acquired basic spatial analysis skills, often using GIS, and is interested in deepening those skills through the use of R. It could be used as a reference by applied researchers and analysts in public, private, or third-sector industries. It could also be used to teach a course on the topic to graduate students or for self-study.
Deep Learning And Scientific Computing With R Torch
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Author : Sigrid Keydana
language : en
Publisher: CRC Press
Release Date : 2023-04-05
Deep Learning And Scientific Computing With R Torch written by Sigrid Keydana and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-05 with Business & Economics categories.
torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++. Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold: Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classification Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with. Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
Geocomputation With R
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Author : Robin Lovelace
language : en
Publisher: CRC Press
Release Date : 2025-05-23
Geocomputation With R written by Robin Lovelace and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Mathematics categories.
Geocomputation with R is for people who want to analyze, visualize, and model geographic data with open source software. The book provides a foundation for learning how to solve a wide range of geographic data analysis problems in a reproducible, and therefore scientifically sound and scalable way. The second edition features numerous updates, including the adoption of the high-performance terra package for all raster data processing, detailed coverage of the spherical geometry engine s2, updated information on coordinate reference systems and new content on openEO, STAC, COG, and gdalcubes. The data visualization chapter has been revamped around version 4 of the tmap package, providing a fresh perspective on creating publication-quality maps from the command line. The importance of the book is also highlighted in a new foreword by Edzer Pebesma. The book equips you with the knowledge and skills necessary to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. The book is especially well-suited to: Data scientists and engineers interested in upskilling to handle spatial data. People with existing geographic data skills interested in developing powerful geosolutions via code. Anyone who needs to work with spatial data in a reproducible and scalable way. The book is divided into three parts: Foundations, Extensions, and Applications, covering progressively more advanced topics. The exercises at the end of each chapter provide the necessary skills to address various geospatial problems, with solutions and supplementary materials available at r.geocompx.org/solutions/.
Microeconometrics With R
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Author : Yves Croissant
language : en
Publisher: CRC Press
Release Date : 2025-02-14
Microeconometrics With R written by Yves Croissant and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-14 with Business & Economics categories.
This book is about doing microeconometrics, defined by Cameron and Trivedi as "the analysis of individual-level data on the economic behavior of individuals or firms using regression methods applied to cross-section and panel data" with R. Microeconometrics became increasingly popular in the last decades, thanks to the availability of many individual data sets and to the development of computer performance. R appeared in the late nineties as a clone of S. It became increasingly popular among statisticians, especially in fields where S was widely used. Twenty years ago, using R for doing econometrics analysis required a lot of programming because a lot of core methods of econometrics were not available in R. Nowadays, most of the basic methods described in the book are available in contributed packages. Moreover, the set of packages called the tidyverse developed by RStudio (now Posit) for all the basic tasks of an applied statistician (importing, tidying, transforming and visualizing data sets) makes the use of R faster and easier. The book uses extensively specialized econometrics packages and the tidyverse, and it seeks to demonstrate that the adoption of R as the primary software for an econometrician is a relevant choice. The first part of the book is devoted to the ordinary least squares estimator. Matrix algebra is progressively introduced in this part, and special attention is paid to the interpretation of the estimated coefficients. The second part goes beyond the basic OLS estimator by testing the hypothesis on which this estimator is based and providing more complex estimators relevant when some of these hypotheses are violated. Finally, the third part of the book presents specific estimators devoted to "special" responses, e.g., count, binomial or duration data. Key Features: Many applications using data sets of recent academic works are developed Testing and estimation procedures using the programming framework of R and specialized packages are presented Two companion packages (micsr and micsr.data), containing respectively functions implementing some estimation and testing procedures not available in other contributed packages and data sets used in the book, are provided