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R Markdown The Definitive Guide

R Markdown The Definitive Guide

R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown you can easily create reproducible data analysis reports presentations dashboards interactive applications books dissertations websites and journal articles while enjoying the simplicity of Markdown and the great power of R and other languages. In this book you will learn Basics: Syntax of Markdown and R code chunks how to generate figures and tables and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards Tufte handouts xaringan/reveal. js presentations websites books journal articles and interactive tutorials Advanced topics: Parameterized reports HTML widgets document templates custom output formats and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages including knitr rmarkdown bookdown blogdown shiny xaringan and animation. He has published three other books Dynamic Documents with R and knitr bookdown: Authoring Books and Technical Documents with R Markdown and blogdown: Creating Websites with R Markdown. J. J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown flexdashboard learnr and radix. Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse. | R Markdown The Definitive Guide

GBP 31.99
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R For College Mathematics and Statistics

R For College Mathematics and Statistics

R for College Mathematics and Statistics encourages the use of R in mathematics and statistics courses. Instructors are no longer limited to ``nice'' functions in calculus classes. They can require reports and homework with graphs. They can do simulations and experiments. R can be useful for student projects for creating graphics for teaching as well as for scholarly work. This book presents ways R which is freely available can enhance the teaching of mathematics and statistics. R has the potential to help students learn mathematics due to the need for precision understanding of symbols and functions and the logical nature of code. Moreover the text provides students the opportunity for experimenting with concepts in any mathematics course. Features:Does not require previous experience with RPromotes the use of R in typical mathematics and statistics course workOrganized by mathematics topicsUtilizes an example-based approach Chapters are largely independent of each other

GBP 42.99
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R Markdown Cookbook

R Markdown Cookbook

This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software. Those new to R Markdown will appreciate the short practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose ‘hidden’ features support customization and demonstrate the many new and varied applications of the software. After reading this book users will learn how to: Enhance your R Markdown content with diagrams citations and dynamically generated text Streamline your workflow with child documents code chunk references and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python D3 and more into your analysis | R Markdown Cookbook

GBP 28.99
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Graphical Data Analysis with R

Graphical Data Analysis with R

See How Graphics Reveal InformationGraphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda. org/GDA. Graphical data analysis is useful for data cleaning exploring data structure detecting outliers and unusual groups identifying trends and clusters spotting local patterns evaluating modelling output and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

GBP 42.99
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R Companion for Sampling Design and Analysis Third Edition

R Companion for Sampling Design and Analysis Third Edition

The R Companion for Sampling: Design and Analysis designed to be read alongside Sampling: Design and Analysis Third Edition by Sharon L. Lohr (SDA; 2022 CRC Press) shows how to use functions in base R and contributed packages to perform calculations for the examples in SDA. No prior experience with R is needed. Chapter 1 tells you how to obtain R and RStudio introduces basic features of the R statistical software environment and helps you get started with analyzing data. Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA with code output and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors. R features and functions are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book you will know how to use R to select and analyze almost any type of probability sample. All R code and data sets used in this book are available online to help you develop your skills analyzing survey data from social and public opinion research public health crime education business agriculture and ecology. | R Companion for Sampling Design and Analysis Third Edition

GBP 28.99
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Nonparametric Statistical Methods Using R

Nonparametric Statistical Methods Using R

A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R including the packages Rfit and npsm. The book first gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and two-sample problems procedures for regression models computation for general fixed-effects ANOVA and ANCOVA models and time-to-event analyses. The last two chapters cover more advanced material including high breakdown fits for general regression models and rank-based inference for cluster correlated data. The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice. Through numerous examples it shows readers how to apply these methods using R.

GBP 39.99
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Robust Statistical Methods with R Second Edition

Robust Statistical Methods with R Second Edition

The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment and the updated chapter on the multivariate model contains additional material on visualization of multivariate data in R. A new chapter on robust procedures in measurement error models concentrates mainly on the rank procedures less sensitive to errors than other procedures. This book will be an invaluable resource for researchers and postgraduate students in statistics and mathematics. Features• Provides a systematic practical treatment of robust statistical methods• Offers a rigorous treatment of the whole range of robust methods including the sequential versions of estimators their moment convergence and compares their asymptotic and finite-sample behavior• The extended account of multivariate models includes the admissibility shrinkage effects and unbiasedness of two-sample tests• Illustrates the small sensitivity of the rank procedures in the measurement error model• Emphasizes the computational aspects supplies many examples and illustrations and provides the own procedures of the authors in the R software on the book’s website | Robust Statistical Methods with R Second Edition

GBP 39.99
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Computational Genomics with R

Computational Genomics with R

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.

GBP 42.99
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Introduction to Probability with R

Introduction to Probability with R

Based on a popular course taught by the late Gian-Carlo Rota of MIT with many new topics covered as well Introduction to Probability with R presents R programs and animations to provide an intuitive yet rigorous understanding of how to model natural phenomena from a probabilistic point of view. Although the R programs are small in length they are just as sophisticated and powerful as longer programs in other languages. This brevity makes it easy for students to become proficient in R. This calculus-based introduction organizes the material around key themes. One of the most important themes centers on viewing probability as a way to look at the world helping students think and reason probabilistically. The text also shows how to combine and link stochastic processes to form more complex processes that are better models of natural phenomena. In addition it presents a unified treatment of transforms such as Laplace Fourier and z; the foundations of fundamental stochastic processes using entropy and information; and an introduction to Markov chains from various viewpoints. Each chapter includes a short biographical note about a contributor to probability theory exercises and selected answers. The book has an accompanying website with more information.

GBP 42.99
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Using R for Item Response Theory Model Applications

Using R for Item Response Theory Model Applications

Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas medical research and business as well. Using R for Item Response Theory Model Applications is a practical guide for students instructors practitioners and applied researchers who want to learn how to properly use R IRT packages to perform IRT model calibrations with their own data. This book provides practical line-by-line descriptions of how to use R IRT packages for various IRT models. The scope and coverage of the modeling in the book covers almost all models used in practice and in popular research including: dichotomous response modeling polytomous response modeling mixed format data modeling concurrent multiple group modeling fixed item parameter calibration modelling with latent regression to include person-level covariate(s) simple structure or between-item multidimensional modeling cross-loading or within-item multidimensional modeling high-dimensional modeling bifactor modeling testlet modeling two-tier modeling For beginners this book provides a straightforward guide to learn how to use R for IRT applications. For more intermediate learners of IRT or users of R this book will serve as a great time-saving tool for learning how to create the proper syntax fit the various models evaluate the models and interpret the output using popular R IRT packages.

GBP 39.99
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Joint Models for Longitudinal and Time-to-Event Data With Applications in R

Joint Models for Longitudinal and Time-to-Event Data With Applications in R

In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest e. g. prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory focusing on applications of joint modeling but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at:http://jmr. r-forge. r-project. org/ | Joint Models for Longitudinal and Time-to-Event Data With Applications in R

GBP 42.99
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Statistics for Linguists: An Introduction Using R

Statistics for Linguists: An Introduction Using R

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields including Psychology Cognitive Science and Data Science.

GBP 42.99
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Foundational and Applied Statistics for Biologists Using R

Foundational and Applied Statistics for Biologists Using R

Full of biological applications exercises and interactive graphical examples Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complicated procedures step by step and thus better understand the process of obtaining analysis results. The graphical capabilities of R are used to provide interactive demonstrations of simple to complex statistical concepts. Assuming only familiarity with algebra and general calculus the text offers a flexible structure for both introductory and graduate-level biostatistics courses. The first seven chapters address fundamental topics in statistics such as the philosophy of science probability estimation hypothesis testing sampling and experimental design. The remaining four chapters focus on applications involving correlation regression ANOVA and tabular analyses. Unlike classic biometric texts this book provides students with an understanding of the underlying statistics involved in the analysis of biological applications. In particular it shows how a solid statistical foundation leads to the correct application of procedures a clear understanding of analyses and valid inferences concerning biological phenomena. Web ResourceAn R package (asbio) developed by the author is available from CRAN. Accessible to those without prior command-line interface experience this companion library contains hundreds of functions for statistical pedagogy and biological research. The author’s website also includes an overview of R for novices.

GBP 42.99
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Environmental and Ecological Statistics with R

Environmental and Ecological Statistics with R

Emphasizing the inductive nature of statistical thinking Environmental and Ecological Statistics with R Second Edition connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature the book explains the approach to solving a statistical problem covering model specification parameter estimation and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment and using several core examples throughout the book the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models including linear and nonlinear models classification and regression trees generalized linear models and multilevel models. It also discusses the use of simulation for model checking and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development it eases the transition from scientific hypothesis to statistical model.

GBP 39.99
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Reliability Centered Maintenance – Reengineered Practical Optimization of the RCM Process with RCM-R

Reliability Centered Maintenance – Reengineered Practical Optimization of the RCM Process with RCM-R

Reliability Centered Maintenance – Reengineered: Practical Optimization of the RCM Process with RCM-R® provides an optimized approach to a well-established and highly successful method used for determining failure management policies for physical assets. It makes the original method that was developed to enhance flight safety far more useful in a broad range of industries where asset criticality ranges from high to low. RCM-R® is focused on the science of failures and what must be done to enable long-term sustainably reliable operations. If used correctly RCM-R® is the first step in delivering fewer breakdowns more productive capacity lower costs safer operations and improved environmental performance. Maintenance has a huge impact on most businesses whether its presence is felt or not. RCM-R® ensures that the right work is done to guarantee there are as few nasty surprises as possible that can harm the business in any way. RCM-R® was developed to leverage on RCM’s original success at delivering that effectiveness while addressing the concerns of the industrial market. RCM-R® addresses the RCM method and shortfalls in its application - It modifies the method to consider asset and even failure mode criticality so that rigor is applied only where it is truly needed. It removes (within reason) the sources of concern about RCM being overly rigorous and too labor intensive without compromising on its ability to deliver a tailored failure management program for physical assets sensitive to their operational context and application. RCM-R® also provides its practitioners with standard based guidance for determining meaningful failure modes and causes facilitating their analysis for optimum outcome. Includes extensive review of the well proven RCM method and what is needed to make it successful in the industrial environment Links important elements of the RCM method with relevant International Standards for risk management and failure management Enhances RCM with increased emphasis on statistical analysis bringing it squarely into the realm of Evidence Based Asset Management Includes extensive experience based advice on implementing and sustaining RCM based failure management programs | Reliability Centered Maintenance – Reengineered Practical Optimization of the RCM Process with RCM-R®

GBP 42.99
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Biomarker Analysis in Clinical Trials with R

Biomarker Analysis in Clinical Trials with R

The world is awash in data. This volume of data will continue to increase. In the pharmaceutical industry much of this data explosion has happened around biomarker data. Great statisticians are needed to derive understanding from these data. This book will guide you as you begin the journey into communicating understanding and synthesizing biomarker data. From the Foreword Jared Christensen Vice President Biostatistics Early Clinical Development Pfizer Inc. Biomarker Analysis in Clinical Trials with R offers practical guidance to statisticians in the pharmaceutical industry on how to incorporate biomarker data analysis in clinical trial studies. The book discusses the appropriate statistical methods for evaluating pharmacodynamic predictive and surrogate biomarkers for delivering increased value in the drug development process. The topic of combining multiple biomarkers to predict drug response using machine learning is covered. Featuring copious reproducible code and examples in R the book helps students researchers and biostatisticians get started in tackling the hard problems of designing and analyzing trials with biomarkers. Features:Analysis of pharmacodynamic biomarkers for lending evidence target modulation. Design and analysis of trials with a predictive biomarker. Framework for analyzing surrogate biomarkers. Methods for combining multiple biomarkers to predict treatment response. Offers a biomarker statistical analysis plan. R code data and models are given for each part: including regression models for survival and longitudinal data as well as statistical learning models such as graphical models and penalized regression models.

GBP 39.99
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W. R. Bion’s Theories of Mind A Contemporary Introduction

W. R. Bion’s Theories of Mind A Contemporary Introduction

Annie Reiner’s introduction to Wilfred Bion’s theories of mind presents Bion’s intricate ideas in an accessible original way without compromising their complexity. Reiner uses comparisons to painting literature and philosophy and detailed clinical examples to provide an experience of Bion’s work that can be felt as well as thought. The book explores many of Bion’s theoretical and clinical innovations and examines the controversy surrounding his concept of O. Reiner provides evidence of a continuity between Bion’s early ideas and his later more esoteric work. W. R. Bion’s Theories of Mind will be essential reading for psychoanalysts and psychoanalytic candidates as well as students of psychoanalytic and psychological history and anyone looking for a readable introduction to Bion’s work. | W. R. Bion’s Theories of Mind A Contemporary Introduction

GBP 19.99
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The Clinical Thinking of W. R. Bion in Brazil Supervisions and Commentaries

The Clinical Thinking of W. R. Bion in Brazil Supervisions and Commentaries

The Clinical Thinking of W. R. Bion in Brazil is comprised of thirteen transcriptions of supervisions Wilfred Bion conducted during his three teaching and speaking tours of Brazil. During these tours Bion conducted over 130 public supervisions of analytic cases in English in which he explained his theories and illustrated their clinical application. Following on from the first volume Bion in Brazil: Supervisions and Commentaries (2017) this book presents each supervision in full with an accompanying commentary written by a senior Brazilian psychoanalyst and Bionian scholar. Arguably no psychoanalyst has had as much impact on psychoanalytic development in Brazil than Bion and this collection of his seminars presented here for the first time acts as a historical document and testament to his legacy in contemporary analysis. The Clinical Thinking of W. R. Bion in Brazil provides a unique opportunity for contemporary psychoanalysts candidates and students to hear the distinctive ‘voice’ of Bion observe how he listens in conversation and learn how he would intervene in and interpret a clinical situation. | The Clinical Thinking of W. R. Bion in Brazil Supervisions and Commentaries

GBP 29.99
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A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio

A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio

This is a concise easy to use step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open source software R. In this book Dr. Watkins systematically reviews each decision step in EFA with screen shots of R and RStudio code and recommends evidence-based best practice procedures. This is an eminently applied practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face while applying EFA along with how to implement interpret and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral medical and social sciences. | A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio

GBP 42.99
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An Introduction to Statistical Inference and Its Applications with R

An Introduction to Statistical Inference and Its Applications with R

Emphasizing concepts rather than recipes An Introduction to Statistical Inference and Its Applications with R provides a clear exposition of the methods of statistical inference for students who are comfortable with mathematical notation. Numerous examples case studies and exercises are included. R is used to simplify computation create figures and draw pseudorandom samples—not to perform entire analyses. After discussing the importance of chance in experimentation the text develops basic tools of probability. The plug-in principle then provides a transition from populations to samples motivating a variety of summary statistics and diagnostic techniques. The heart of the text is a careful exposition of point estimation hypothesis testing and confidence intervals. The author then explains procedures for 1- and 2-sample location problems analysis of variance goodness-of-fit and correlation and regression. He concludes by discussing the role of simulation in modern statistical inference. Focusing on the assumptions that underlie popular statistical methods this textbook explains how and why these methods are used to analyze experimental data.

GBP 42.99
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Psychoanalysis with Wilfred R. Bion Contemporary Approaches Actuality and The Future of Psychoanalytic Practice

Psychoanalysis with Wilfred R. Bion Contemporary Approaches Actuality and The Future of Psychoanalytic Practice

Psychoanalysis with Wilfred R. Bion is the product of François Lévy’s efforts over a period of twenty years to represent clearly the classical elements and the innovatory propositions of the thought and work of Bion who offers both new and modified ways of practising and thinking about the psychoanalytic experience. Bion’s thought methodical and intuitive gave rise to profound modifications in the approach to the psychology of groups clinical work with psychoses and the conception of the genesis of thought. Some of his original notions – psychic growth processes of thinking transformations alpha function maternal reverie – constitute valuable tools for rethinking psychoanalytic practice. This book places Bion’s thought within a filiation that is faithful to those of Sigmund Freud and Melanie Klein. It shows the parallels that exist between Bion’s formalisations and those of Lacan. It also lays emphasis on the mechanisms of thought arising from the negative (André Green) from logic (Lewis Carroll) from causalist philosophy (David Hume) from literature (Milton Blanchot) and from the physical sciences (Stephen Hawking). Finally Lévy underlines the importance of placing individuals within the collective from which they have originated. Psychoanalysis with Wilfred R. Bion will appeal to psychoanalysts and psychoanalytic psychotherapists looking to draw on the ideas of one of the most important and influential figures in the history of psychoanalysis. | Psychoanalysis with Wilfred R. Bion Contemporary Approaches Actuality and The Future of Psychoanalytic Practice

GBP 36.99
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The Second 'R' Writing Development in the Junior School

The Second 'R' Writing Development in the Junior School

Originally published in 1976. How do children learn to write? What stages to they pass through in mastering this skill? What part can teachers play in aiding their development? These are some of the questions that this book sets out to answer. This book offers a perspective on writing which places children's language resources and their development at its centre. It discusses the purpose of writing ways of classifying its variety providing contexts for writing its treatment in schools and methods for helping children to overcome difficulties. A section explores the arguments for a writing policy or programme in schools and offering guidance on considerations that shape policy making. | The Second 'R' Writing Development in the Junior School

GBP 31.99
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Deepening In-Class and Online Learning 60 Step-by-Step Strategies to Encourage Interaction Foster Inclusion and Spark Imagination

Deepening In-Class and Online Learning 60 Step-by-Step Strategies to Encourage Interaction Foster Inclusion and Spark Imagination

Deepening In-Class and Online Learning shows teachers how to make learning joyful as they translate successful classroom strategies to virtual learning. The book includes more than 60 step-by-step strategies that encourage interaction foster inclusion and spark imagination. Each activity is presented in a consistent format ready-to-use in-class and for online learning. Whether teaching virtually or adding digital activities to in-class instruction this book explores effective ways for students to present communicate and collaborate. Innovative activities range from discussing hot topics and sharing personal stories to visual boards and digital storytelling. Also included is an up-to-date glossary of digital tools to help make sense of the shifting landscape in today’s classrooms. | Deepening In-Class and Online Learning 60 Step-by-Step Strategies to Encourage Interaction Foster Inclusion and Spark Imagination

GBP 29.99
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Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources the Bayesian approach provides a flexible framework for drug development. Despite its advantages the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners Bayesian Analysis with R for Drug Development: Concepts Algorithms and Case Studies describes a wide range of Bayesian applications to problems throughout pre-clinical clinical and Chemistry Manufacturing and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical clinical and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang Ph. D. is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books 15 book chapters and over 90 peer-reviewed papers on diverse scientific and statistical subjects including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick Ph. D. is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences having developed and taught courses in several areas including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences. | Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

GBP 38.99
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Technology Business and the Market From R&D to Desirable Products

Technology Business and the Market From R&D to Desirable Products

John Sheldrake’s long experience of teaching business and management to engineers has highlighted a gap in the knowledge of students and practitioners alike between their grasp of developments in science and technology and how these developments lead to the creation of successful products. Using case studies Technology Business and the Market explores the impact of new materials techniques and technologies and looks at the links between innovation entrepreneurship business (including finance) design manufacturing branding and marketing. The author examines the ways in which scientific endeavour is conditioned and even distorted by contextual issues such as finance and fashion. This demonstration of the synthesis of technology business and the market has relevance for students practitioners and policy makers in established and emerging markets. | Technology Business and the Market From R&D to Desirable Products

GBP 31.99
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