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| change point analysis in r: Bayesian Time Series Models David Barber, A. Taylan Cemgil, Silvia Chiappa, 2011-08-11 The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science. |
| change point analysis in r: Parametric Statistical Change Point Analysis Jie Chen, A.K. Gupta, 2013-11-11 Recently there has been a keen interest in the statistical analysis of change point detec tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential. |
| change point analysis in r: Sequential Analysis Alexander Tartakovsky, Igor Nikiforov, Michele Basseville, 2014-08-27 Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently. The book reviews recent accomplishments in hypothesis testing and changepoint detecti |
| change point analysis in r: Statistical Computing with R Maria L. Rizzo, 2007-11-15 Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditiona |
| change point analysis in r: Change Point Analysis for Time Series Lajos Horváth, |
| change point analysis in r: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| change point analysis in r: Change-point Problems Edward G. Carlstein, Hans-Georg Mu ller, David Siegmund, 1994 |
| change point analysis in r: Statistical Procedures for the Medical Device Industry Wayne A. Taylor, 2017 |
| change point analysis in r: Elements of Statistical Computing R.A. Thisted, 2017-10-19 Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques. |
| change point analysis in r: Advanced R Hadley Wickham, 2015-09-15 An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does. |
| change point analysis in r: Geocomputation with R Robin Lovelace, Jakub Nowosad, Jannes Muenchow, 2019-03-22 Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), bridges to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. |
| change point analysis in r: Spatial Point Patterns Adrian Baddeley, Ege Rubak, Rolf Turner, 2015-11-11 Modern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on th |
| change point analysis in r: Climate Time Series Analysis Manfred Mudelsee, 2010-08-26 Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers. |
| change point analysis in r: Doing Meta-Analysis with R Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert, 2021-09-15 Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book |
| change point analysis in r: The BUGS Book David Lunn, Chris Jackson, Nicky Best, Andrew Thomas, David Spiegelhalter, 2012-10-02 Bayesian statistical methods have become widely used for data analysis and modelling in recent years, and the BUGS software has become the most popular software for Bayesian analysis worldwide. Authored by the team that originally developed this software, The BUGS Book provides a practical introduction to this program and its use. The text presents |
| change point analysis in r: Density Ratio Estimation in Machine Learning Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori, 2012-02-20 This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community. |
| change point analysis in r: Forecasting: principles and practice Rob J Hyndman, George Athanasopoulos, 2018-05-08 Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. |
| change point analysis in r: Long Memory in Economics Gilles Teyssière, Alan P. Kirman, 2006-09-22 Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics. |
| change point analysis in r: An Introduction to Applied Multivariate Analysis with R Brian Everitt, Torsten Hothorn, 2011-04-23 The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data. |
| change point analysis in r: Nonparametric Methods in Change Point Problems E. Brodsky, B.S. Darkhovsky, 1993-01-31 The explosive development of information science and technology puts in new problems involving statistical data analysis. These problems result from higher re quirements concerning the reliability of statistical decisions, the accuracy of math ematical models and the quality of control in complex systems. A new aspect of statistical analysis has emerged, closely connected with one of the basic questions of cynergetics: how to compress large volumes of experimental data in order to extract the most valuable information from data observed. De tection of large homogeneous segments of data enables one to identify hidden regularities in an object's behavior, to create mathematical models for each seg ment of homogeneity, to choose an appropriate control, etc. Statistical methods dealing with the detection of changes in the characteristics of random processes can be of great use in all these problems. These methods have accompanied the rapid growth in data beginning from the middle of our century. According to a tradition of more than thirty years, we call this sphere of statistical analysis the theory of change-point detection. During the last fifteen years, we have witnessed many exciting developments in the theory of change-point detection. New promising directions of research have emerged, and traditional trends have flourished anew. Despite this, most of the results are widely scattered in the literature and few monographs exist. A real need has arisen for up-to-date books which present an account of important current research trends, one of which is the theory of non parametric change--point detection. |
| change point analysis in r: Stochastic Models, Statistics and Their Applications Ansgar Steland, Ewaryst Rafajłowicz, Ostap Okhrin, 2019-10-15 This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance. |
| change point analysis in r: R Cookbook Paul Teetor, 2011-03-03 With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time.—Jeffrey Ryan, software consultant and R package author |
| change point analysis in r: Multiple Comparisons Using R Frank Bretz, Torsten Hothorn, Peter Westfall, 2016-04-19 Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes’ test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey’s all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques. Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa. See Dr. Bretz discuss the book. |
| change point analysis in r: Analyzing Compositional Data with R K. Gerald van den Boogaart, Raimon Tolosana-Delgado, 2013-06-29 This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package “compositions,” it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained. |
| change point analysis in r: Handbook of Financial Time Series Torben Gustav Andersen, Richard A. Davis, Jens-Peter Kreiß, Thomas V. Mikosch, 2009-04-21 The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle. |
| change point analysis in r: Hands-On Time Series Analysis with R Rami Krispin, 2019-05-31 Build efficient forecasting models using traditional time series models and machine learning algorithms. Key FeaturesPerform time series analysis and forecasting using R packages such as Forecast and h2oDevelop models and find patterns to create visualizations using the TSstudio and plotly packagesMaster statistics and implement time-series methods using examples mentionedBook Description Time series analysis is the art of extracting meaningful insights from, and revealing patterns in, time series data using statistical and data visualization approaches. These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models. You will learn how to preprocess raw time series data and clean and manipulate data with packages such as stats, lubridate, xts, and zoo. You will analyze data and extract meaningful information from it using both descriptive statistics and rich data visualization tools in R such as the TSstudio, plotly, and ggplot2 packages. The later section of the book delves into traditional forecasting models such as time series linear regression, exponential smoothing (Holt, Holt-Winter, and more) and Auto-Regressive Integrated Moving Average (ARIMA) models with the stats and forecast packages. You'll also cover advanced time series regression models with machine learning algorithms such as Random Forest and Gradient Boosting Machine using the h2o package. By the end of this book, you will have the skills needed to explore your data, identify patterns, and build a forecasting model using various traditional and machine learning methods. What you will learnVisualize time series data and derive better insightsExplore auto-correlation and master statistical techniquesUse time series analysis tools from the stats, TSstudio, and forecast packagesExplore and identify seasonal and correlation patternsWork with different time series formats in RExplore time series models such as ARIMA, Holt-Winters, and moreEvaluate high-performance forecasting solutionsWho this book is for Hands-On Time Series Analysis with R is ideal for data analysts, data scientists, and all R developers who are looking to perform time series analysis to predict outcomes effectively. A basic knowledge of statistics is required; some knowledge in R is expected, but not mandatory. |
| change point analysis in r: Presentation Zen Garr Reynolds, 2009-04-15 FOREWORD BY GUY KAWASAKI Presentation designer and internationally acclaimed communications expert Garr Reynolds, creator of the most popular Web site on presentation design and delivery on the Net — presentationzen.com — shares his experience in a provocative mix of illumination, inspiration, education, and guidance that will change the way you think about making presentations with PowerPoint or Keynote. Presentation Zen challenges the conventional wisdom of making slide presentations in today’s world and encourages you to think differently and more creatively about the preparation, design, and delivery of your presentations. Garr shares lessons and perspectives that draw upon practical advice from the fields of communication and business. Combining solid principles of design with the tenets of Zen simplicity, this book will help you along the path to simpler, more effective presentations. |
| change point analysis in r: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| change point analysis in r: Variational Analysis R. Tyrrell Rockafellar, Roger J.-B. Wets, 2009-06-26 From its origins in the minimization of integral functionals, the notion of variations has evolved greatly in connection with applications in optimization, equilibrium, and control. This book develops a unified framework and provides a detailed exposition of variational geometry and subdifferential calculus in their current forms beyond classical and convex analysis. Also covered are set-convergence, set-valued mappings, epi-convergence, duality, and normal integrands. |
| change point analysis in r: Statistical Methods and Modeling of Seismogenesis Nikolaos Limnios, Eleftheria Papadimitriou, George Tsaklidis, 2021-04-27 The study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. Stochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting. |
| change point analysis in r: Drawdown Paul Hawken, 2017-04-18 • New York Times bestseller • The 100 most substantive solutions to reverse global warming, based on meticulous research by leading scientists and policymakers around the world “At this point in time, the Drawdown book is exactly what is needed; a credible, conservative solution-by-solution narrative that we can do it. Reading it is an effective inoculation against the widespread perception of doom that humanity cannot and will not solve the climate crisis. Reported by-effects include increased determination and a sense of grounded hope.” —Per Espen Stoknes, Author, What We Think About When We Try Not To Think About Global Warming “There’s been no real way for ordinary people to get an understanding of what they can do and what impact it can have. There remains no single, comprehensive, reliable compendium of carbon-reduction solutions across sectors. At least until now. . . . The public is hungry for this kind of practical wisdom.” —David Roberts, Vox “This is the ideal environmental sciences textbook—only it is too interesting and inspiring to be called a textbook.” —Peter Kareiva, Director of the Institute of the Environment and Sustainability, UCLA In the face of widespread fear and apathy, an international coalition of researchers, professionals, and scientists have come together to offer a set of realistic and bold solutions to climate change. One hundred techniques and practices are described here—some are well known; some you may have never heard of. They range from clean energy to educating girls in lower-income countries to land use practices that pull carbon out of the air. The solutions exist, are economically viable, and communities throughout the world are currently enacting them with skill and determination. If deployed collectively on a global scale over the next thirty years, they represent a credible path forward, not just to slow the earth’s warming but to reach drawdown, that point in time when greenhouse gases in the atmosphere peak and begin to decline. These measures promise cascading benefits to human health, security, prosperity, and well-being—giving us every reason to see this planetary crisis as an opportunity to create a just and livable world. |
| change point analysis in r: Computational Genomics with R Altuna Akalin, 2020-12-16 Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015. |
| change point analysis in r: Using R for Introductory Statistics John Verzani, 2018-10-03 The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package=UsingR)), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing. |
| change point analysis in r: Limit Theorems in Change-Point Analysis Miklós Csörgö, Lajos Horváth, 1997-12-29 Change-point problems arise in a variety of experimental and mathematical sciences, as well as in engineering and health sciences. This rigorously researched text provides a comprehensive review of recent probabilistic methods for detecting various types of possible changes in the distribution of chronologically ordered observations. Further developing the already well-established theory of weighted approximations and weak convergence, the authors provide a thorough survey of parametric and non-parametric methods, regression and time series models together with sequential methods. All but the most basic models are carefully developed with detailed proofs, and illustrated by using a number of data sets. Contains a thorough survey of: The Likelihood Approach Non-Parametric Methods Linear Models Dependent Observations This book is undoubtedly of interest to all probabilists and statisticians, experimental and health scientists, engineers, and essential for those working on quality control and surveillance problems. Foreword by David Kendall |
| change point analysis in r: Numerical Bayesian Methods Applied to Signal Processing Joseph J.K. O Ruanaidh, William J. Fitzgerald, 2012-12-06 This book is concerned with the processing of signals that have been sam pled and digitized. The fundamental theory behind Digital Signal Process ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87]. The term Digital Signal Processing, in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing would usually contain detailed de scriptions of the standard mathematical machinery often used to describe signals. It would also motivate an approach to real world problems based on concepts and results developed in linear systems theory, that make use of some rather interesting properties of the time and frequency domain representations of signals. While this book assumes some familiarity with traditional methods the emphasis is altogether quite different. The aim is to describe general methods for carrying out optimal signal processing. |
| change point analysis in r: R in Action Robert Kabacoff, 2015-03-03 R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. R in Action, Second Edition is a language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. Purchase of the print book comes with an offer of a free PDF eBook from Manning. Also available is all code from the book. |
| change point analysis in r: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| change point analysis in r: Change-Point Analysis in Nonstationary Stochastic Models Boris Brodsky, 2016-12-12 This book covers the development of methods for detection and estimation of changes in complex systems. These systems are generally described by nonstationary stochastic models, which comprise both static and dynamic regimes, linear and nonlinear dynamics, and constant and time-variant structures of such systems. It covers both retrospective and sequential problems, particularly theoretical methods of optimal detection. Such methods are constructed and their characteristics are analyzed both theoretically and experimentally. Suitable for researchers working in change-point analysis and stochastic modelling, the book includes theoretical details combined with computer simulations and practical applications. Its rigorous approach will be appreciated by those looking to delve into the details of the methods, as well as those looking to apply them. |
| change point analysis in r: Machine Learning Kevin P. Murphy, 2012-08-24 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. |
| change point analysis in r: Regression Modeling Strategies Frank E. Harrell, 2013-03-09 Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with too many variables to analyze and not enough observations, and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve safe data mining. |
changepoint: Methods for Changepoint Detection - The Comprehensive R ...
The p-value of the identified changepoint. If method is PELT then a vector is returned containing the changepoint locations for the penalty supplied. This always ends with n. If the penalty is … See more
changepoint: An R Package for Changepoint Analysis
In its simplest form, change-point detection is the name given to the problem of estimating the point at which the statistical properties of a sequence of observations change. Detecting such changes …
Change-Point Analysis: A Powerful New Tool For Detecting …
Change-point analysis is a powerful new tool for determining whether a change has taken place. It is capable of detecting subtle changes missed by control charts. Further, it better characterizes the …
Behavioral Change Point Analysis in R: The bcpa package - UMD
The bcpa package is designed to streamline the implementation of the \behavioral change point analysis" (BCPA, Gurarie et al. 2009) for any time-stamped movement data, i.e. in which there …
decp: Complete Change Point Analysis - cloud.r-project.org
An object of class ’decp_result’ containing the ordered change points, the summary of the jump sizes for each pair of segments, the Confidence Interval (C.I.) of each detected change point, the …
mcp: An R Package for Regression With Multiple Change Points
Part 1 of this paper introduces the regression model(s) underlying mcp. Part 2 discusses priors for change point models. Part 3 demonstrates the usage of mcp and it’s features. Part 4 discusses …
CPAT: Change Point Analysis Tests - RStudio
This implements Andrews’ test for end-of-sample change, as described by Andrews (2003). This test was derived for detecting a change in multivarate data, aso originally described.
ecp: An R Package for Nonparametric Multiple Change Point …
designed to perform Bayesian single change point analysis of univariate time series. It returns the posterior probability of a change point occurring at each time index in the series.
ecp: An R Package for Nonparametric Multiple Change Point …
We introduce the R (R Core Team 2014) package ecp for multiple change point analysis of multivariate time series (Matteson and James 2014). The ecp package provides methods for …
The R Package Management System: Bayesian Change Point …
R is the lingua franca of statistics: The syntax is simple and well-suited for data exploration and analysis. It has excellent graphical capabilities. It is extensible, with over 2500 packages available …
changepoint: An R Package for Changepoint Analysis
One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence.
changepoints: A Collection of Change-Point Detection Methods
Perform the adversarially robust change point detection method with automatic selection of the contamination proportion epsilon when treating the inliner distributions as Gaussian. Usage
SOME ASPECTS OF CHANGE-POINT ANALYSIS
We shall present here a brief account of some of the areas of change-point analysis. The most basic problems are those of testing the hypothesis of "no change," and of estimating a change-point …
bcp: An R Package for Performing a Bayesian Analysis of …
Barry and Hartigan(1993) propose a Bayesian analysis for change point problems. We provide a brief summary of selected work on change point problems, both preceding and following Barry …
Non-Parametric Trend Tests and Change-Point Detection
This test computes both the slope (i.e. linear rate of change) and intercept according to Sen’s method. First, a set of linear slopes is calculated as follows:
ecp: An R Package for Nonparametric Multiple Change Point …
We introduce the ecp R package for multiple change point analysis of multivariate time series (Matteson and James, 2013). The ecp package provides methods for change point analysis that …
segmented: Regression Models with Break-Points / Change …
Estimation and inference of regression models with piecewise linear relationships, also known as segmented regression models, with a number of break-points fixed or to be ‘selected’.
CHANGE POINT ANALYSIS AND REGIME - KIT
common terms were \change point" for the point in time where the change takes place and \regime switch" for the occurrence of a di erent regime after the change point.
ggchangepoint: Combines Changepoint Analysis with 'ggplot2'
This tool, however, combines 'changepoint', 'changepoint.np' and 'ecp' together, and uses 'ggplot2' to visualize changepoints.
Rbeast: Bayesian Change-Point Detection and Time Series …
BEAST seeks to im-prove time series decomposition by forgoing the single-best-model'' concept and embrac- ing all competing models into the inference via a Bayesian model averaging scheme.
changepoint: Methods for Changepoint Detection - The …
Implements various mainstream and specialised changepoint methods for finding single and mul-tiple changepoints within data. Many popular non-parametric and frequentist methods are in …
changepoint: An R Package for Changepoint Analysis
In its simplest form, change-point detection is the name given to the problem of estimating the point at which the statistical properties of a sequence of observations change. Detecting such …
Change-Point Analysis: A Powerful New Tool For Detecting …
Change-point analysis is a powerful new tool for determining whether a change has taken place. It is capable of detecting subtle changes missed by control charts. Further, it better …
Behavioral Change Point Analysis in R: The bcpa package
The bcpa package is designed to streamline the implementation of the \behavioral change point analysis" (BCPA, Gurarie et al. 2009) for any time-stamped movement data, i.e. in which there …
decp: Complete Change Point Analysis - cloud.r-project.org
An object of class ’decp_result’ containing the ordered change points, the summary of the jump sizes for each pair of segments, the Confidence Interval (C.I.) of each detected change point, …
mcp: An R Package for Regression With Multiple Change Points
Part 1 of this paper introduces the regression model(s) underlying mcp. Part 2 discusses priors for change point models. Part 3 demonstrates the usage of mcp and it’s features. Part 4 …
CPAT: Change Point Analysis Tests - RStudio
This implements Andrews’ test for end-of-sample change, as described by Andrews (2003). This test was derived for detecting a change in multivarate data, aso originally described.
ecp: An R Package for Nonparametric Multiple Change Point …
designed to perform Bayesian single change point analysis of univariate time series. It returns the posterior probability of a change point occurring at each time index in the series.
ecp: An R Package for Nonparametric Multiple Change Point …
We introduce the R (R Core Team 2014) package ecp for multiple change point analysis of multivariate time series (Matteson and James 2014). The ecp package provides methods for …
The R Package Management System: Bayesian Change …
R is the lingua franca of statistics: The syntax is simple and well-suited for data exploration and analysis. It has excellent graphical capabilities. It is extensible, with over 2500 packages …
changepoint: An R Package for Changepoint Analysis
One of the key challenges in changepoint analysis is the ability to detect multiple changes within a given time series or sequence.
changepoints: A Collection of Change-Point Detection Methods
Perform the adversarially robust change point detection method with automatic selection of the contamination proportion epsilon when treating the inliner distributions as Gaussian. Usage
SOME ASPECTS OF CHANGE-POINT ANALYSIS
We shall present here a brief account of some of the areas of change-point analysis. The most basic problems are those of testing the hypothesis of "no change," and of estimating a change …
bcp: An R Package for Performing a Bayesian Analysis of …
Barry and Hartigan(1993) propose a Bayesian analysis for change point problems. We provide a brief summary of selected work on change point problems, both preceding and following Barry …
Non-Parametric Trend Tests and Change-Point Detection
This test computes both the slope (i.e. linear rate of change) and intercept according to Sen’s method. First, a set of linear slopes is calculated as follows:
ecp: An R Package for Nonparametric Multiple Change Point …
We introduce the ecp R package for multiple change point analysis of multivariate time series (Matteson and James, 2013). The ecp package provides methods for change point analysis …
segmented: Regression Models with Break-Points / Change …
Estimation and inference of regression models with piecewise linear relationships, also known as segmented regression models, with a number of break-points fixed or to be ‘selected’.
CHANGE POINT ANALYSIS AND REGIME - KIT
common terms were \change point" for the point in time where the change takes place and \regime switch" for the occurrence of a di erent regime after the change point.
ggchangepoint: Combines Changepoint Analysis with 'ggplot2'
This tool, however, combines 'changepoint', 'changepoint.np' and 'ecp' together, and uses 'ggplot2' to visualize changepoints.
Rbeast: Bayesian Change-Point Detection and Time Series …
BEAST seeks to im-prove time series decomposition by forgoing the single-best-model'' concept and embrac- ing all competing models into the inference via a Bayesian model averaging …
