Linear Mixed-Effects Models Using R

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This bo

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

Introductory statistics texts do not consider non-linear regression, and textbooks that do are advanced treatments for mathematicians. So pharmacological researchers Motulsky (U. of California-San Die

Build incredible LEGO® models unlike any you’ve ever seen before, with the amazing transparent pieces included in this fun set. Shine lights through the LEGO elements to create colourful reflections, crystal-like effects and fun new ways to play. Use the pieces to build animals, monsters, magical creatures and loads more, before shining lights through them to see the full effect.Create an enchanting underwater world, funny faces, spooky ghosts, frozen treats, fairy-tale friends and so much more. Included in the book inside are more than 100 amazing ideas for LEGO builds, activities and challenges. Each of the play ideas featured in the book can be accomplished using the 46 LEGO pieces included (not all models can be built at the same time).LEGO, the LEGO logo and the Brick and Knob configurations are trademarks of the LEGO Group. ©2025 The LEGO Group.

'The most important person you've never heard of' - The New York Times'Partha Dasgupta provides the compass we urgently need by bringing economics and ecology together, we can help save the natural world at what may be the last minute and in doing so, save ourselves. - David Attenborough'This is the best book in economics for a general reader since Adam Smith s The Wealth of Nations.' - Paul R EhrlichImagine a football team which measures its success only on the basis of the goals it scores and doesn t count the goals it concedes. That football team could be losing right through without recognising itFor as long as they have existed, our economic models have served us an incomplete picture.The models and metrics tells us that our economies are healthy because they are growing.However, this doesn't account for the fact that our growth is driven by a resource that we take for free and treat as infinite: nature. For centuries we have been using it as if it were both, but we know now, more

This 5th edition of this popular graduate textbook, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. It includes numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The R package 'astsa' has had major updates and the text will reflect those updates. In general, the graphics have been improved. New topics include random number generation, modeling and fitting predator-prey interactions, more emphasis on structural models, testing for linearity, discussion of EM algorithm is more extensive, Bayesian analysis of state space models and MCMC is more extensive (including new scripts in astsa), particle methods are introduced, stochastic volatility coverage is expanded, changepoint detection is introduced (new topic).The

Gain hands-on experience creating, manipulating, and optimizing motion graphics for film, video, the web, and mobile devices. Learn how to animate text and images, customize a wide range of effects, track and sync content, rotoscope, manipulate timing, correct color, and remove unwanted objects. Customize cameras and lighting to create compelling 3D content. Design Motion Graphics templates for editing in Adobe Premiere Pro. Deform and animate objects and video using robust Puppet tools. The fastest, easiest, most comprehensive way to learn Adobe After Effects Classroom in a Book(R), the best-selling series of hands-on software training workbooks, offers what no other book or training program does--an official training series from Adobe, developed with the support of Adobe product experts. Adobe After Effects Classroom in a Book contains 15 lessons that cover the basics and beyond, providing countless tips and techniques to help you become more productive with the program. You can foll

Chunyan Li is a course instructor with many years of experience in teaching about time series analysis. His book is essential for students and researchers in oceanography and other subjects in the Earth sciences, looking for a complete coverage of the theory and practice of time series data analysis using MATLAB. This textbook covers the topic's core theory in depth, and provides numerous instructional examples, many drawn directly from the author's own teaching experience, using data files, examples, and exercises. The book explores many concepts, including time; distance on Earth; wind, current, and wave data formats; finding a subset of ship-based data along planned or random transects; error propagation; Taylor series expansion for error estimates; the least squares method; base functions and linear independence of base functions; tidal harmonic analysis; Fourier series and the generalized Fourier transform; filtering techniques: sampling theorems: finite sampling effects; wavelet

How do we make sense of complex evidence? What are the cognitive principles that allow detectives to solve crimes, and lay people to puzzle out everyday problems? To address these questions, David Lagnado presents a novel perspective on human reasoning. At heart, we are causal thinkers driven to explain the myriad ways in which people behave and interact. We build mental models of the world, enabling us to infer patterns of cause and effect, linking words to deeds, actions to effects, and crimes to evidence. But building models is not enough; we need to evaluate these models against evidence, and we often struggle with this task. We have a knack for explaining, but less skill at evaluating. Fortunately, we can improve our reasoning by reflecting on inferential practices and using formal tools. This book presents a system of rational inference that helps us evaluate our models and make sounder judgments.

How do we make sense of complex evidence? What are the cognitive principles that allow detectives to solve crimes, and lay people to puzzle out everyday problems? To address these questions, David Lagnado presents a novel perspective on human reasoning. At heart, we are causal thinkers driven to explain the myriad ways in which people behave and interact. We build mental models of the world, enabling us to infer patterns of cause and effect, linking words to deeds, actions to effects, and crimes to evidence. But building models is not enough; we need to evaluate these models against evidence, and we often struggle with this task. We have a knack for explaining, but less skill at evaluating. Fortunately, we can improve our reasoning by reflecting on inferential practices and using formal tools. This book presents a system of rational inference that helps us evaluate our models and make sounder judgments.

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern

Based on the author's forty years of teaching experience, this unique textbook covers both basic and advanced concepts of optimization theory and methods for process systems engineers. Topics covered include continuous, discrete and logic optimization (linear, nonlinear, mixed-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and decomposition techniques (Lagrangean and Benders decomposition). Assuming only a basic background in calculus and linear algebra, it enables easy understanding of mathematical reasoning, and numerous examples throughout illustrate key concepts and algorithms. End-of-chapter exercises involving theoretical derivations and small numerical problems, as well as in modeling systems like GAMS, enhance understanding and help put knowledge into practice. Accompanied by two appendices containing web links to modeling systems and models related to applications in PSE, this is an essential

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models

Understand common scheduling as well as other advanced operational problems with this valuable reference from a recognized leader in the field. Beginning with basic principles and an overview of linear and mixed-integer programming, this unified treatment introduces the fundamental ideas underpinning most modeling approaches, and will allow you to easily develop your own models. With more than 150 figures, the basic concepts and ideas behind the development of different approaches are clearly illustrated. Addresses a wide range of problems arising in diverse industrial sectors, from oil and gas to fine chemicals, and from commodity chemicals to food manufacturing. A perfect resource for engineering and computer science students, researchers working in the area, and industrial practitioners.

Many students find it daunting to move from studying environmental science, to designing and implementing their own research proposals. This book provides a practical introduction to help develop scientific thinking, aimed at undergraduate and new graduate students in the earth and environmental sciences. Students are guided through the steps of scientific thinking using published scientific literature and real environmental data. The book starts with advice on how to effectively read scientific papers, before outlining how to articulate testable questions and answer them using basic data analysis. The Mauna Loa CO2 dataset is used to demonstrate how to read metadata, prepare data, generate effective graphs and identify dominant cycles on various timescales. Practical, question-driven examples are explored to explain running averages, anomalies, correlations and simple linear models. The final chapter provides a framework for writing persuasive research proposals, making this an essent

Many students find it daunting to move from studying environmental science, to designing and implementing their own research proposals. This book provides a practical introduction to help develop scientific thinking, aimed at undergraduate and new graduate students in the earth and environmental sciences. Students are guided through the steps of scientific thinking using published scientific literature and real environmental data. The book starts with advice on how to effectively read scientific papers, before outlining how to articulate testable questions and answer them using basic data analysis. The Mauna Loa CO2 dataset is used to demonstrate how to read metadata, prepare data, generate effective graphs and identify dominant cycles on various timescales. Practical, question-driven examples are explored to explain running averages, anomalies, correlations and simple linear models. The final chapter provides a framework for writing persuasive research proposals, making this an essent

China-Africa economic tie has experienced lasting rapid growth since the 2000s, attracting lots of discussion on its nature and effects. A key question is whether Chinese engagements provide an alternative paradigm to existing mainstream models, like Washington Consensus, for developing countries. However, theories on state-market dichotomy can hardly explain the strong momentum of bilateral cooperation. By examining a broad range of practices with solid field research, including trade, infrastructure, agriculture, manufacturing, industrial zones, labor and socio-environmental preservation, this book proposes a new angle of non-linear circular causality to understand Chinese approaches to work with Africa. Guided by the pursuit for sustainable growth rather than by specific models, Chinese actors are able to experiment diverse methods to foster structural transformation in Africa. In particular, the author carefully records mutual influences between Chinese and African stakeholders at

This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends these

Using mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a syste

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their

Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t-tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets, step-by-step R code demonstrating analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience, from students, researchers or professionals looking to improve their

Scholars typically emphasize the importance of organized networks and long-term relationships for sustaining electoral clientelism. Yet electoral clientelism remains widespread in many countries despite the weakening of organized parties. This book offers a new account of how clientelism and campaigning work in weak party systems and in the absence of stable party-broker relationships. Drawing on an in-depth study of Peru using a mixed methods approach and cross-national comparisons, Muñoz reveals the informational and indirect effects of investments made at the campaign stage. By distributing gifts, politicians buy the participation of poor voters at campaign events. This helps politicians improvise political organizations, persuade poor voters of candidates' desirability, and signal electoral viability to strategic donors and voters, with campaign dynamics ultimately shaping electoral outcomes. Among other contributions, the book sheds new light on role of donations and business acto

Do you want to deepen your understanding of complex systems and design integrated circuits more quickly? Learn how with this step-by-step guide that shows, from first principles, how to employ estimation techniques to analyze and solve complex problems in IC design using a simplified modeling approach. Applications are richly illustrated using real-world examples from across IC design, from simple circuit theory, to the electromagnetic effects and high frequency design, and systems such as data converters and phase-locked loops. Basic concepts like inductance and capacitance are related to one other and other RF phenomena inside a modern chip, enhancing understanding without the need for simulators. Use the easy-to-follow models presented to start designing your own products, from inductors and amplifiers to more complex systems. Whether you are an early-career professional or researcher, graduate student, or established IC engineer looking to reduce your reliance on commercial softwar

約翰・凱奇的《日記》是20世紀美國前衛藝術中最獨特且深具思想性的作品之一，橫跨1965至1981年，融合隨機操作、禪意故事與深刻社會觀察。這本擴充版平裝書完整收錄全部八部內容，並新增凱奇第九部手稿影印頁與真菌學家大衛・羅斯的專文。書中文字經由機率方式決定排版、字體與顏色，視覺效果如音樂般律動，呈現語言的聲響與空間感。從日常生活到全球議題，凱奇用機緣與幽默回應戰爭、藝術與人性，是當代思想與藝術實驗的經典之作。"Over 16 years, beginning in 1965, John Cage compiled anecdotes, observations and koanlike tales, originally typing everything on an IBM Selectric and using chance methods to determine the formatting of texts that twist down each page. The Siglio edition preserves the graphic effects, but, more important, it gives a sense of the company he kept during these years--Marcel Duchamp, R. Buckminster Fuller, D.T. Suzuki--and of his passionate feeling about a world locked in a state of perpetual warfare. Cage has a reputation for being a Zen-inspired wit. He was also much more, an intensely engaged moral thinker." -Holland Cotter, New York TimesNow available in an expanded paperback edition, Diary registers Cage's assessment of the times in which he lived as well as his often uncanny portent

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are

Scholars typically emphasize the importance of organized networks and long-term relationships for sustaining electoral clientelism. Yet electoral clientelism remains widespread in many countries despite the weakening of organized parties. This book offers a new account of how clientelism and campaigning work in weak party systems and in the absence of stable party-broker relationships. Drawing on an in-depth study of Peru using a mixed methods approach and cross-national comparisons, Muñoz reveals the informational and indirect effects of investments made at the campaign stage. By distributing gifts, politicians buy the participation of poor voters at campaign events. This helps politicians improvise political organizations, persuade poor voters of candidates' desirability, and signal electoral viability to strategic donors and voters, with campaign dynamics ultimately shaping electoral outcomes. Among other contributions, the book sheds new light on role of donations and business acto

Solitons emerge in various non-linear systems as stable localized configurations, behaving in many ways like particles, from non-linear optics and condensed matter to nuclear physics, cosmology and supersymmetric theories. This book provides an introduction to integrable and non-integrable scalar field models with topological and non-topological soliton solutions. Focusing on both topological and non-topological solitons, it brings together debates around solitary waves and construction of soliton solutions in various models and provides a discussion of solitons using simple model examples. These include the Kortenweg-de-Vries system, sine-Gordon model, kinks and oscillons, and skyrmions and hopfions. The classical field theory of scalar field in various spatial dimensions is used throughout the book in presentation of related concepts, both at the technical and conceptual level. Providing a comprehensive introduction to the description and construction of solitons, this book is ideal

Actuaries have access to a wealth of individual data in pension and insurance portfolios, but rarely use its full potential. This book will pave the way, from methods using aggregate counts to modern developments in survival analysis. Based on the fundamental concept of the hazard rate, Part I shows how and why to build statistical models, based on data at the level of the individual persons in a pension scheme or life insurance portfolio. Extensive use is made of the R statistics package. Smooth models, including regression and spline models in one and two dimensions, are covered in depth in Part II. Finally, Part III uses multiple-state models to extend survival models beyond the simple life/death setting, and includes a brief introduction to the modern counting process approach. Practising actuaries will find this book indispensable, and students will find it helpful when preparing for their professional examinations.

Presenting a rigorous treatment of the physical and mechanical basis for the modelling of sedimentary basins, this book supplies geoscientists with practical tools for creating their own models. It begins with a thorough grounding in properties of porous media, linear elasticity, continuum mechanics and rock compressibility. Chapters on heat flow, subsidence, rheology, flexure and gravity consider sedimentary basins in the context of the Earth's lithosphere, and the book concludes with coverage of pore space cementation, compaction and fluid flow. The volume introduces basic, state-of-the-art models and demonstrates how to reproduce results using tools like MATLAB® and Octave. Main equations are derived from first principles, and their basic solutions are obtained and then applied. Separate notes sections supply more technical details, and the text is illustrated throughout with real-world examples, applications and test exercises. This is an accessible introduction to quantitative mod

Anomalous diffusion has been detected in a wide variety of scenarios, from fractal media, systems with memory, transport processes in porous media, to fluctuations of financial markets, tumour growth, and complex fluids. Providing a contemporary treatment of this process, this book examines the recent literature on anomalous diffusion and covers a rich class of problems in which surface effects are important, offering detailed mathematical tools of usual and fractional calculus for a wide audience of scientists and graduate students in physics, mathematics, chemistry and engineering. Including the basic mathematical tools needed to understand the rules for operating with the fractional derivatives and fractional differential equations, this self-contained text presents the possibility of using fractional diffusion equations with anomalous diffusion phenomena to propose powerful mathematical models for a large variety of fundamental and practical problems in a fast-growing field of rese

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques. This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses. The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations. All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options. Common pitfalls, data issues, and interpretation of model results are also addressed. Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.