Mathematical and statistical software Books
CRC Press Sparse Graphical Modeling for High Dimensional
Book SynopsisThis book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines.Key Features: A general framework for learning sparse gTable of Contents1. Introduction to Sparse Graphical Models 2. Gaussian Graphical Models 3. Gaussian Graphical Modeling with Missing Data 4. Gaussian Graphical Modeling for Heterogeneous Data 5. Poisson Graphical Models 6. Mixed Graphical Models 7. Joint Estimation of Multiple Graphical Models 8. Nonlinear and Non-Gaussian Graphical Models 9. High-Dimensional Inference with the Aid of Sparse Graphical Modeling 10. Appendix
£999.99
Taylor & Francis Ltd Introduction to Time Series Modeling with Applications in R Chapman HallCRC Monographs on Statistics and Applied Probability
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Taylor & Francis Ltd Computer Intensive Methods in Statistics
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Taylor & Francis Ltd Computer Intensive Methods in Statistics
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Taylor & Francis Ltd R for Conservation and Development Projects
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CRC Press Bayesian Nonparametrics for Causal Inference and
Book SynopsisBayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest.The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Table of ContentsPart I. Overview of Bayesian inference in causal inference and missing data and identifiability. 1. Overview of causal inference. 2. Overview of missing data. 3. Overview of Bayesian Inference for Missing Data and Causal Inference. Part II. Bayesian nonparametrics for causal inference and missing data. 4. Identifiability and Sensitivity Analysis. 5. Bayesian Decision Trees and their Ensembles. Part III. Identification and sensitivity analysis. 6. Dirichlet Process Mixtures and extensions. 7. Gaussian process prior and Dependent Dirichlet processes. 8. Causal Inference on Quantiles using Propensity scores. 9. Causal Inference with a point treatment using an EDPM model. 10. DDP+GP for causal inference using marginal structural models. 11. DPMs for Dropout in Longitudinal Studies. 12. DPMs for Non-Monotone Missingness.
£999.99
Taylor & Francis Ltd Statistical Programming in SAS
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Taylor & Francis Ltd Statistical Programming in SAS
Book SynopsisStatistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining dataTrade Review"This book is useful for people who want to learn SAS programing, and assumes the students have knowledge of multiple linear regression and one-way ANOVA models.…The second edition has added a chapter on text processing, and reorganized the chapter order…Some topics that are relevant for the SAS Base and Certifications exams are covered, and a nice feature is the highlighting of programing tips in gray." ~Technometrics"This is a very complete book for programming SAS in statistical analyses. This second edition offers the possibility to debug some programs and provides new examples and applications, which are very useful. This book is a very useful companion tool for students or beginners in SAS, or for more experienced statisticians who already use SAS for statistical analyses."~ISCB NewsTable of ContentsContentsPreface ..............................................................................................................................................ixAcknowledgments ...................................................................................................................... xiiiAuthor .............................................................................................................................................xv1. Structuring, Implementing, and Debugging Programs to Learn about Data ...........11.1 Statistical Programming ................................................................................................11.2 Learning from Constructed, Artificial Data ...............................................................2Processing a Particular Data Set—Extracting Variable Names from aColumn of an Input Data Set.........................................................................................2Learning More about Unfamiliar Statistical Methods—Linear MixedEffects Models .................................................................................................................5Improving Your Intuition about Statistical Theory— Sampling Distributionof Means ...........................................................................................................................81.3 Good Programming Practice ...................................................................................... 11Document Your Programs! .......................................................................................... 11Use Meaningful Variable Names ................................................................................ 13Use a Variety of CaSeS in Program Statements ........................................................ 14Indent Program Statements That Naturally Go Together ....................................... 141.4 SAS Program Structure ................................................................................................ 151.5 What Is a SAS Data Set? ............................................................................................... 211.6 Internally Documenting SAS Programs ....................................................................221.7 Basic Debugging ...........................................................................................................231.8 Getting Help ..................................................................................................................27Using Help in SAS ........................................................................................................27Getting Help from a Web Browser Search .................................................................291.9 Exercises .........................................................................................................................292. Reading, Creating, and Formatting Data Sets ................................................................ 312.1 What Does a SAS DATA Step Do? .............................................................................. 312.2 Reading Data from External Files ..............................................................................33Reading Data Directly as Part of a Program—Anyone for Datalines? .................34Reading Data Sets Saved as Text—INFILE Can Be Your Friend (PROCIMPORT Too!) ................................................................................................................38Sometimes, Variables Are in Particular Columns or in Particular Formats .........402.3 Reading CSV, Excel, and TEXT Files .......................................................................... 412.4 Temporary versus Permanent Status of Data Sets ...................................................432.5 Formatting and Labeling Variables ............................................................................46Using Formats to Read and Display Variable Values ..............................................46Internal Representations and Output Displays ........................................................49Character, Numeric, Time, and Date Formats ..........................................................532.6 User-Defined Formatting .............................................................................................58Saving Formats for Later Use ......................................................................................632.7 Recoding and Transforming Variables in a DATA Step ........................................66Indicator Variables ......................................................................................................682.8 Writing Out a File or Making a Simple Report ......................................................73Simple Report Generation .........................................................................................73Exporting a File ...........................................................................................................772.9 Exercises .......................................................................................................................803. Programming a DATA Step ................................................................................................833.1 Writing Programs by Subdividing Tasks ................................................................83Estimate the Probability That a Randomly Selected 30- to 39-Year-OldMale Is Taller than a Randomly Selected Female of the Same Age .....................83Conditional Execution ...........................................................................................84Looping to Repeat a Task ......................................................................................86Returning to the Height Probability Simulation ............................................... 873.2 Ordering How Tasks Are Done ................................................................................90Missing Data in Functions .........................................................................................923.3 Indexable Lists of Variables (Also Known as Arrays) ...........................................93Defining Values in the Variable List .........................................................................93Inputting Values in the Variable List ........................................................................94Reassign Missing Value Codes for Numeric Variables “.” ...................................95Recoding Missing Values for All Numeric and Character Variables ..................953.4 Functions Associated with Statistical Distributions .............................................963.5 Generating Variables Using Random Number Generators ................................ 1023.6 Remembering Variable Values across Observations ........................................... 105Processing Multiple Observations for a Single Observation .............................. 1063.7 Case Study 1: Is the Two-Sample t-Test Robust to Violations of theHeterogeneous Variance Assumption? ................................................................. 109Case Study 1 (Revisited with DATA Step Programming) .................................. 1183.8 Efficiency Considerations—How Long Does It Take? .........................................1223.9 Case Study 2: Monte Carlo Integration to Estimate an Integral ........................ 1233.10 Case Study 3: Simple Percentile-Based Bootstrap ................................................ 1283.11 Case Study 4: Randomization Test for the Equality of Two Populations ......... 1303.12 Exercises ..................................................................................................................... 1344. Combining, Extracting, and Reshaping Data ............................................................... 1374.1 Adding Observations by SET-ing Data Sets.......................................................... 1374.2 Adding Variables by MERGE-ing Data Sets ......................................................... 1404.3 Working with Tables in PROC SQL ....................................................................... 1484.4 Converting Wide to Long Formats ......................................................................... 1614.5 Converting Long to Wide Formats ......................................................................... 1644.6 Case Study: Reshaping a World Bank Data Set .................................................... 1664.7 Building Training and Validation Data Sets ......................................................... 1754.8 Exercises ..................................................................................................................... 1794.9 Self-study Lab ............................................................................................................ 1805. Macro Programming .......................................................................................................... 1915.1 What Is a Macro and Why Would You Use It? ..................................................... 1915.2 Motivation for Macros: Numerical Integration to DetermineP(0 < Z < 1.645) ......................................................................................................... 1915.3 Processing Macros .................................................................................................... 1955.4 Macro Variables, Parameters, and Functions........................................................ 1955.5 Conditional Execution, Looping, and Macros ...................................................... 198More Complicated Macro Variable Construction ................................................203Changing Locations in a Macro during Execution ..............................................2045.6 Debugging Macro Code and Programs.................................................................206Write Out Values of Macro Variables .....................................................................206Useful SAS Options for Debugging Macros ......................................................... 2075.7 Saving Macros ........................................................................................................... 2115.8 Functions and Routines for Macros ....................................................................... 2115.9 Case Study: Macro for Constructing Training and Test Data Set for ModelComparison ............................................................................................................... 2165.10 Case Study: Processing Multiple Data Sets ...........................................................2235.11 Exercises .....................................................................................................................2276. Customizing Output and Generating Data Visualizations .......................................2296.1 Using the Output Delivery System ........................................................................229Basic Ideas ..................................................................................................................229Destinations—RTF, HTML, PDF, and More! .........................................................230What’s Produced and How to Select It ..................................................................235Another Destination That Stat Programmers Should Visit—OUTPUT ............ 2436.2 Graphics in SAS ......................................................................................................... 2496.3 ODS Statistical Graphics ..........................................................................................2506.4 Modifying Graphics Using the ODS Graphics Editor ......................................... 2576.5 Graphing with Styles and Templates .....................................................................2606.6 Statistical Graphics—Entering the Land of SG Procedures ............................... 266SGPLOT ...................................................................................................................... 266SGPANEL ................................................................................................................... 269SGSCATTER .............................................................................................................. 2716.7 Case Study: Using the SG Procedures ................................................................... 2736.8 Enhancing SG Displays—Options with SG Procedure Statements .................. 2796.9 Using Annotate Data Sets to Enhance SG Displays ............................................2846.10 Using Attribute Maps to Enhance SG Displays ................................................... 2876.11 Exercises .....................................................................................................................2907. Processing Text .................................................................................................................... 2937.1 Cleaning and Processing Text Data ....................................................................... 2937.2 Starting with Character Functions ......................................................................... 2937.3 Processing Text .......................................................................................................... 2987.4 Case Study: Sentiment in State of the Union Addresses .....................................3027.5 Case Study: Reading Text from a Web Page .........................................................3097.6 Regular Expressions ................................................................................................. 3157.7 Case Study (Revisited)—Applying Regular Expressions ................................... 3197.8 Exercises ..................................................................................................................... 3218. Programming with Matrices and Vectors ..................................................................... 3238.1 Defining a Matrix and Subscripting ...................................................................... 3238.2 Using Diagonal Matrices and Stacking Matrices ................................................. 3298.3 Using Elementwise Operations, Repeating, and Multiplying Matrices ........... 3328.4 Importing a Data Set into SAS/IML and Exporting Matrices fromSAS/IML to a Data Set .............................................................................................333Creating Matrices from SAS Data Sets and Vice Versa ........................................3338.5 Case Study 1: Monte Carlo Integration to Estimate π ..........................................3368.6 Case Study 2: Bisection Root Finder ...................................................................... 3378.7 Case Study 3: Randomization Test Using Matrices Imported from PROCPLAN ..........................................................................................................................3408.8 Case Study 4: SAS/IML Module to Implement Monte Carlo Integrationto Estimate π ..............................................................................................................3428.9 Storing and Loading SAS/IML Modules ..............................................................3448.10 SAS/IML and R .........................................................................................................3458.11 Exercises .....................................................................................................................350References ...................................................................................................................................355Index ............................................................................................................................................. 357
£999.99
Taylor & Francis Ltd Sharpening Your Advanced SAS Skills
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£999.99
Taylor & Francis Ltd Introductory Adaptive Trial Designs A Practical Guide with R Chapman HallCrc Biostatics
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£999.99
Taylor & Francis Ltd Foundations of Statistical Algorithms
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Taylor & Francis Ltd Practical Statistical Methods
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Taylor & Francis Ltd MATLAB with Applications to Engineering Physics and Finance
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Taylor & Francis Ltd Regression Modeling
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Taylor & Francis Ltd Engineering ProductionGrade Shiny Apps
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£999.99
Taylor & Francis Ltd Engineering ProductionGrade Shiny Apps
Book SynopsisFrom the Reviews[This book] contains an excellent blend of both Shiny-specific topics and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters.Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)[This] book is a gradual and pleasant invitation to the production-ready shiny apps world. It exposes a comprehensive and robust workflow powered by the {golem} package. [It] fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact.David Granjon, Sr. Expert Data Science, NovartisPresented in full color, Engineering Production-Grade ShinTrade Review"ThinkR’s book is a gradual and pleasant invitation to the production-ready shiny apps world. It focuses on the unfortunately too often forgotten general principles necessary to be successful in this quest, before exposing a comprehensive and robust workflow powered by the {golem} package. This books fills the not yet covered gap between shiny app development and deployment in such a thrilling way that it may be read in one sitting. Readers will appreciate the number of exclusive references like {shinypsum}, {gargoyle}, {crrry} and {dockerfiler} that will definitely help them to reach the production-ready graal. In the industry world, where processes robustness is a key toward productivity, this book will indubitably have a tremendous impact." – David Granjon, Sr. Expert Data Science, Novartis"[This book] contains an excellent blend of both Shiny-specific topics … and practical advice from software development that fits in nicely with Shiny apps. You will find many nuggets of wisdom sprinkled throughout these chapters…."– Eric Nantz, Host of the R-Podcast and the Shiny Developer Series (from the Foreword)Table of Contents1. About Successful Shiny Apps. 2. Planning Ahead. 3. Structuring your Project. 4. Introduction to {golem}. 5. The workflow. 6. UX Matters. 7. Don’t rush into coding. 8. Setting up for success with {golem} (#settingupsuccess). 9. Building an “ipsum-app”. 10. Building the app with {golem} 11. Build yourself a safety net. 12. Version Control. 13. Deploy your application. 14. The Need for Optimization. 15. Common Application Caveats. 16. Optimizing {shiny} Code. 17. Using JavaScript. 18. A Gentle Introduction to CSS. Appendix.
£999.99
Taylor & Francis Ltd Introduction to Time Series Modeling with Applications in R
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£999.99
Taylor & Francis Ltd Handbook of Bayesian Variable Selection
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£999.99
Taylor & Francis Ltd R Markdown Cookbook Chapman HallCRC The R Series
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£999.99
CRC Press R Markdown Cookbook
Book SynopsisThis book is designed to provide a range of examples of how to extend the functionality of your R Markdown documents. As a cookbook, this guide is recommended to new or intermediate R Markdown users.Trade Review"This book will be a popular addition on the desk of many professionals who regularly produce technical documents in the R language."~Carl Boettiger, University of California, Berkeley"Novices will benefit from reading this book because they will quickly find high quality solutions to the most common issues that users encounter. Experts will benefit because it will save them from constantly trying to rediscover ‘that thread that explains how to do that one thing.’"~ Dr. John Blischak, University of Chicago"Though I am a frequent and longtime user of RMarkdown, there were multiple points where I learned new things and capabilities that I’d never seen. Sometimes incredible features are ‘hidden"; this book eposes features and tricks that this seasoned RMarkdown user was surprised and excited by."~ Sharla Gelfand, R and Shiny Developer"This book is useful for R stats users who, while using RMarkdown, come across certain problems that can be solved. It also provides advanced users with additional options to further customize RMarkdown to their personal needs. The many subchapters address a number of issues that a large majority of users can use as a reference book for their needs. There are examples for every user, no matter what previous knowledge is available and therefore this book is useful for any user group of markdown."~Dr. Johannes Friedrich"This book is interesting for a wide level of RMarkdown users."~MSc. Yasumoto, Atsushi, HACARUS Inc.'This book is an excellent resource for promoting the applications of R software. This book can be used more as a reference book for R Markdown and should find a place on the library shelves.'- Shalabh, Journal of the Royal Statistical Society, August 2021 https://doi.org/10.1111/rssa.12743'The book covers an enormous terrain: not only straightforward use with R, but also with other languages and system like Python, SAS and Stata. Specialized output to LaTeX and custom CSS-styled HTML is also covered. Typesetting beautiful tables is described extensively. And if you want to send emails from R, you will find the instructions here. This is a good cookbook, with many delicious recipes, from simple snacks to gourmet style dishes.'- Paul Eilers, International Society for Clinical Biostatistics, 72, 2021Table of Contents Installation 2. Conceptual Overview. 3. Basics. 4. Document Elements. 5. Formatting. 6. LaTeX Output. 7. HTML Output. 8. Word. 9. Multiple Output Formats. 10. Tables. 11. Chunk Options. 12. Output Hooks (*). 13. Chunk Hooks (*). 14. Miscellaneous knitr Tricks. 15. Other Languages. 16. Managing Projects. 17. Workflow.
£999.99
Taylor & Francis Ltd Data Analytics for the Social Sciences
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Taylor & Francis Data Analytics for the Social Sciences
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CRC Press Geocomputation with R The R Series
Book SynopsisPresents a vision for an introductory and accessible textbook on geographic data analysis, modelling, and visualisation with R. The discipline of Geography captures each of these things, as well as content to get R beginners up-to-speed with R in general.Trade Review"Geocomputation with R offers several advantages. Firstly, it uses up-to-date packages, mainly the 'sf' package for vector processing which was not available at the time the previous books were written. 'sf’' is truly a game-changer in the field of working with spatial data in R. I believe this alone makes writing the new book worthwhile. Secondly, the book offers a very broad overview, trying—and in my opinion succeeding—to encompass all non-statistical themes involved in geo-computation, including subjects such as location and transport modeling in R (chapters 7-8) which were never published before. Thirdly, the book offers a lot of illustrations and clearly demonstrates key concepts in GIS and geo-computation from the R point of view. I believe these characteristics will give the book an advantage and quite possibly make it the most popular choice in the category of spatial analysis in R for several years to come…The book can be used both as reference and as a textbook…The present book will definitely become the main textbook for this course once published." ~Michael Dorman, Ben-Gurion University of the Negev"This book sets out to explain the key ideas in geocomputation – more specifically manipulating, visualising, modelling and analysing geographical data. A further aim is to achieve all of this using only open source software. Developments of recent years have made this an achievable goal, and this book provides a good starting point for readers wishing to do this… The strength of this book is therefore on the computational aspects (for example producing R-based interactive web sites using shiny) and as a comprehensive overview of the kinds of data one is likely to work with (for example taking care to ensure raster and vector data are both well represented here). The final part showcasing real-world applications is also strong. Another highlight of the book are the exercises at the end of each chapter…Finally, the book comesTable of ContentsAn introduction to R for spatial data. Loading and saving geographical data. Working with attribute data. Basic map making. Spatial classes. Coordinate systems/reprojecting. Working with vector data. Working with raster data. Raster-vector interaction. Kriging and spatial interpolation. Spatial data modelling. Advanced map making. Examples of applications.
£45.99
Taylor & Francis Ltd Applied MetaAnalysis with R and Stata
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£999.99
Springer Algorithmic Learning in a Random World
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£999.99
Springer Using SPSS for Windows Data Analysis and Graphics
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£999.99
Springer Mathematica in Action
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Springer Applied Wavelet Analysis with SPLUS
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John Wiley & Sons Inc Principles of Linear Algebra With Maple
Book SynopsisAn accessible introduction to the theoretical and computational aspects of linear algebra using MapleTM Many topics in linear algebra can be computationally intensive, and software programs often serve as important tools for understanding challenging concepts and visualizing the geometric aspects of the subject.Trade Review Table of ContentsPreface. Conventions and Notations. 1 An Introduction To Maple. 1.1 The Commands . 1.2 Programming. 2 Linear Systems of Equations and Matrices. 2.1 Linear Systems of Equations. 2.2 Augmented Matrix of a Linear System and Row Operations. 2.3 Some Matrix Arithmetic. 3 Gauss-Jordan Elimination and Reduced Row Echelon Form. 3.1 Gauss-Jordan Elimination and rref. 3.2 Elementary Matrices. 3.3 Sensitivity of Solutions to Error in the Linear System. 4 Applications of Linear Systems and Matrices. 4.1 Applications of Linear Systems to Geometry. 4.2 Applications of Linear Systems to Curve Fitting. 4.3 Applications of Linear Systems to Economics. 4.4 Applications of Matrix Multiplication to Geometry. 4.5 An Application of Matrix Multiplication to Economics. 5 Determinants, Inverses and Cramer’s Rule. 5.1 Determinants and Inverses from the Adjoint Formula. 5.2 Determinants by Expanding Along Any Row or Column . 5.3 Determinants Found by Triangularizing Matrices. 5.4 LU Factorization. 5.5 Inverses from rref. 5.6 Cramer’s Rule. 6 Basic Linear Algebra Topics. 6.1 Vectors. 6.2 Dot Product. 6.3 Cross Product. 6.4 Vector Projection. 7 A Few Advanced Linear Algebra Topics. 7.1 Rotations in Space. 7.2 ‘Rolling’ a Circle Along a Curve. 7.3 The TNB Frame. 8 Independence, Basis and Dimension for Subspaces of Rn. 8.1 Subspaces of Rn. 8.2 Independent and Dependent Sets of Vectors in Rn. 8.3 Basis and Dimension for Subspaces of Rn. 8.4 Vector Projection onto a Subspace of Rn. 8.5 The Gram-Schmidt Orthonormalization Process. 9 Linear Maps from Rn to Rm. 9.1 Basics About Linear Maps. 9.2 The Kernel and Image Subspaces of a Linear Map. 9.3 Composites of Two Linear maps and Inverses. 9.4 Change of Bases for the Matrix Representation of a Linear Map. 10 The Geometry of Linear and Affine Maps. 10.1 The Effect of a Linear Map on Area and Arclength in Two Dimensions. 10.2 The Decomposition of Linear Maps into Rotations, Reflections and Rescalings in R2. 10.3 The Effect of Linear Maps on Volume, Area and Arclength in R3. 10.4 Rotations, Reflections and Rescalings in Three Dimensions. 10.5 Affine Maps. 11 Least Squares Fits and Pseudoinverses. 11.1 Pseudoinverse to a Non-Square Matrix and Almost Solving an Overdetermined Linear System. 11.2 Fits and Pseudoinverses. 11.3 Least Squares Fits and Pseudoinverses. 12 Eigenvalues and Eigenvectors. 12.1 What Are Eigenvalues and Eigenvectors, and Why Do We Need Them? 12.2 Summary of Definitions and Methods for Computing Eigenvalues and Eigenvectors as well as the Exponential of a Matrix. 12.3 Applications of the Diagonalizability of Square Matrices. 12.4 Solving a Square First Order Linear. System of Differential Equations . . . . . . . . . . . . . . . . . . 12.5 Basic Facts About Eigenvalues and Eigenvectors, and Diagonalizability. 12.6 The Geometry of the Ellipse Using Eigenvalues and Eigenvectors. 12.7 A Maple Eigen-Procedure. Bibliography. Indices. Keyword Index. Index of Maple Commands and Packages.
£104.36
Cambridge University Press Data Analysis Using SAS Enterprise Guide
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Cambridge University Press Data Analysis Using SAS Enterprise Guide
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Cambridge University Press The MATHEMATICA Book Version 4
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Cambridge University Press Methods for Computational Gene Prediction
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Andrei Besedin Secrets of MS Excel VBAMacros for Beginners
Book Synopsis
£7.28
Society for Industrial and Applied Mathematics ARPACK Users Guide Solution of LargeScale
Book SynopsisThis book is a guide to understanding and using the software package ARPACK to solve large algebraic eigenvalue problems. The software described is based on the implicitly restarted Arnoldi method, which has been heralded as one of the three most important advances in large scale eigenanalysis in the past ten years. The book explains the acquisition, installation, capabilities, and detailed use of the software for computing a desired subset of the eigenvalues and eigenvectors of large (sparse) standard or generalized eigenproblems. It also discusses the underlying theory and algorithmic background at a level that is accessible to the general practitioner.
£65.02
Saint Philip Street Press Control Theory Tutorial Basic Concepts
Book Synopsis
£32.21
Taylor & Francis Ltd Visualizing Surveys in R
Book SynopsisFor researchers who use surveys interested in learning how to seize vast possibilities and flexibility of R in survey analysis/visualizations. Psychologists, marketeers, HR personnel, managers, other professionals who wish to standardize/automate the process for visualizing survey data. Suitable for textbook courses.Table of ContentsI Preparation. 1. Survey data. 2. Process. 3. Variables. 4. Categories. 5. Read data. 6. Parse values. 7. Validate data. 8. Pre-process data. 9. Build a dataset. 10. Basic statistics. 11. Create plots with ggplot2. 12. Save plots to files. 13. R Markdown. II Plotting. 14. Numeric plots. 15. Bar charts. 16. Percentage bars. 17. Diverging percentage bars. 18. Pie charts. 19. Lollipop plots. 20. Dot plots. 21. Heatmaps. 22. Geographic maps. 23. Missing value plots. 24. Validation plots.
£999.99
Taylor & Francis Ltd Numerical Techniques in MATLAB
Book SynopsisIn this book, various numerical methods are discussed in a comprehensive way. It delivers a mixture of theory, examples and MATLAB practicing exercises to help the students in improving their skills. To understand the MATLAB programming in a friendly style, the examples are solved. The MATLAB codes are mentioned in the end of each topic. Throughout the text, a balance between theory, examples and programming is maintained.Key Features Methods are explained with examples and codes System of equations has given full consideration Use of MATLAB is learnt for every method This book is suitable for graduate students in mathematics, computer science and engineering.Table of Contents1. Common Commands Used in Matlab. 2. System of Linear Equations. 3. Polynomial Interpolation. 4. Root Finding Methods. 5. Numerical Integration. 6. Solution of Initial Value Problems. 7. Boundary Value Problems.
£999.99
Taylor & Francis Ltd Statistical Analysis of Questionnaires
Book SynopsisStatistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing.The book covers the foundations of classical test theory (CTT), test reliability, validity, and scaling as well as item response theory (IRT) fundamentals and IRT for dichotomous and polytomous items. The authors explore the latest IRT extensions, such as IRT models with covariates, multidimensional IRT models, IRT models for hierarchical and longitudinal data, and latent class IRT models. They also describe estimation methods and diagnostics, including graphiTrade Review"This book follows a well established approach to the psychometric analysis of questionnaire data as found in educational, survey and medical research. The authors provide an in-depth discussion of the analysis of score reliability and item properties grounded in classical test theory (CTT), and of the probabilistic modeling of individual responses based on latent variable models. … Chapter 5 is a bit different and focus on the estimation of item and person parameters and the diagnostic of IRT models. The first part is rather technical but it does a good job at describing Statistical Analysis of Questionnaires the pros and cons of each technique–joint, conditional and marginal maximum likelihood–and how they could be implemented using custom software. … The authors conclude (…) by highlighting multidimensional IRT models which allow to relax the strong hypothesis of unidimensionality that is attached to all previous models, as well as the main strengths of structural equation models which can be viewed as providing the glue between factor analytic methods and IRT. Overall, the authors succeed at presenting a solid and reliable framework for psychometric analysis of questionnaire data."— Christophe Lalanne, Paris-Diderot University, in the Journal of Statistical Software, November 2017Table of ContentsPreliminaries. Classical Test Theory. Item Response Theory Models for Dichotomous Items. Item Response Theory Models for Polytomous Items. Estimation Methods and Diagnostics. Some Extensions of Traditional Item Response Theory Models.
£999.99
CRC Press Data Analytics for Business Intelligence
Book SynopsisThis book studies data, analytics, and intelligence using Boolean structure. Chapters dive into the theories, foundations, technologies, and methods of data, analytics, and intelligence.The primary aim of this book is to convey the theories and technologies of data, analytics, and intelligence with applications to readers based on systematic generalization and specialization. Sun uses the Boolean structure to deconstruct all books and papers related to data, analytics, and intelligence and to reorganize them to reshape the world of big data, data analytics, analytics intelligence, data science, and artificial intelligence. Multi-industry applications in business, management, and decision-making are provided. Cutting-edge theories, technologies, and applications of data, analytics, and intelligence and their integration are also explored. Overall, this book provides original insights on sharing computing, insight computing, platform computing, a calculus of intelligent analyti
£999.99
Taylor & Francis Ltd Forecasting and Analytics with the Augmented
Book SynopsisForecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called ADAM (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems. At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analTable of Contents1. Introduction 2. Forecasts evaluation 3. Time series components and simple forecasting methods 4. Introduction to ETS 5. Pure additive ADAM ETS 6. Pure multiplicative ADAM ETS 7. General ADAM ETS model 8. Introduction to ARIMA 9. ADAM ARIMA 10. Explanatory variables in ADAM 11. Estimation of ADAM 12. Multiple frequencies in ADAM 13. Intermittent State Space Model 14. Model diagnostics 15. Model selection and combinations in ADAM 16. Handling uncertainty in ADAM 17. Scale model for ADAM 18. Forecasting with ADAM 19. Forecasting functions of the smooth package 20. What’s next?
£999.99
Taylor & Francis Ltd Spatial Statistics for Data Science
£999.99
CRC Press Applied Statistics with Python
a huge range and FREE tracked UK delivery on ALL orders.
£116.25
SAGE Publications Inc Student Study Guide With IBM® SPSS® Workbook for Research Methods, Statistics, and Applications
The third edition of the Student Study Guide With IBM® SPSS® Workbook for Research Methods, Statistics, and Applications by Kathrynn A. Adams and Eva K. McGuire gives students even more opportunities to practice and apply their knowledge in statistics and research methods. Written by the authors of Research Methods, Statistics, and Applications, the third edition of the study guide follows the third edition of the textbook for straightforward assigning and practice. New features include practice quizzes to give students both recognition and recall activities for better retention. Learning objectives and brief chapter summaries from the main text remind students of what they′ve learned and orient students toward the exercises. In-depth exercises encourage students to build on their knowledge, requiring students to think critically and actively engage with the material. These exercises have been condensed and focus on moving students through the learning objectives at a quick pace. At the end of most chapters, "Your Research" sections encourage students to apply concepts to their own projects. Now placed at the end of book, the IBM® SPSS® workbook provides instructions for performing statistical calculations. Included in this workbook are additional exercises to practice data analysis and interpretation using the software. Answers to quizzes are listed immediately after each quiz in the book while answers to exercises are listed on the instructor resources website.
£999.99
Cambridge University Press Data Management Essentials Using SAS and JMP
Book SynopsisSAS programming is a creative and iterative process designed to empower you to make the most of your organization''s data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.Trade Review'The authors have created a very readable and gentle introduction to SAS programming and its working environment - Enterprise Guide. The text provides a valuable overview of 'navigating' in a SAS windowing environment and before moving quickly into core procedures. … a very valuable introduction to basic SAS programming for the beginning data analyst.' Glenn Gamst, University of la Verne, California'The authors of Data Management Essentials Using SAS and JMP have written a thoroughly user-friendly beginning guide to SAS programming for data exploration and management, and for designing well-constructed and informative reports. I honestly know of no better book to use for self-instruction on, or for teaching essential SAS programming skills for report design than this very well written and produced book.' Joseph M. Hilbe, Arizona State UniversityTable of Contents1. Navigation; 2. Preliminary data exploration; 3. Storing and manipulating data; 4. Advanced concepts in dataset and variable manipulation; 5. Introduction to common procedures; 6. Procedures for simple statistics; 7. More about common procedures; 8. Data visualization; 9. JMP as an alternative.
£999.99
Cambridge University Press Essentials of Programming in Mathematica
Book SynopsisEssentials of Programming in Mathematica provides an introduction suitable for readers with little or no background in the language as well as for those with some experience using programs such as C, Java, or Perl. The author, an established authority on Mathematica programming, has written an example-driven text that covers the language from first principles, as well as including material from natural language processing, bioinformatics, graphs and networks, signal analysis, geometry, computer science, and many other applied areas. The book is appropriate for self-study or as a text for a course in programming in computational science. Readers will benefit from the author''s tips, which provide insight and suggestions on small and large points. He also provides more than 350 exercises from novice through to advanced level with all of the solutions available online.Table of ContentsPreface; 1. Programming with Mathematica; 2. The Mathematica language; 3. Lists and associations; 4. Patterns and rules; 5. Functions; 6. Programs; 7. Strings; 8. Graphics and visualization; 9. Program optimization; 10. Packages; Bibliography; Index.
£999.99
