{"title":"Taylor Arnold","description":null,"products":[{"product_id":"distant-viewing-book-taylor-arnold-9780262546133","title":"Distant Viewing","description":null,"brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":49752881889553,"sku":"NGR9780262546133","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0262546132.jpg?v=1751324235"},{"product_id":"humanities-data-in-r-book-taylor-arnold-9783319207018","title":"Humanities Data in R","description":"​This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.    ​","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50008202051857,"sku":"CIN3319207016G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/3319207016.jpg?v=1751415369"},{"product_id":"corporations-and-other-business-organizations-book-james-d-cox-9781647080631","title":"Corporations and Other Business Organizations","description":"Receive complimentary lifetime digital access to the eBook with new print purchase.  This compilation contains statutes, rules, materials, and forms affecting conventional business corporations, benefit corporations, flexible purpose corporations, general partnerships, limited liability partnerships, limited partnerships, limited liability limited partnerships, and limited liability companies. In addition to the Delaware statutes for corporations and LLCs as well as the 2016 updated Model Business Corporations Act, select provisions from other states are included to provide a rich array of comparative approaches. The most recent ISS Guidelines that pertain to a wide range of corporate governance issues are included as well as disclosure and governance guidelines of the New York Stock Exchange. The materials provide recent interpretive guidance on important corporate issues from the SEC. The materials also include Illustrative documents such as an LLC operating agreement, a forum selection bylaw, a proxy form, the Johnson \u0026amp; Johnson no-action materials regarding arbitration bylaws, a governance agreement entered into with an activist investor, and a poison pill rights plan. The materials are expertly arranged by leading scholars in the field and are intended for law school study. The 2020 supplement is several hundred pages shorter than previous editions as the authors have eliminated the Rules and Regulations of the Securities Act of 1933. They made this change as the now omitted provisions are not the regular fare of the business organization class; they have, however, carried forward the federal regulatory material that is central to areas studied in all business organization courses.","brand":"WoB","offers":[{"title":"US \/ WELL_READ \/ SBYB","offer_id":50094033109265,"sku":"CIN1647080630A","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":50098438308113,"sku":"CIN1647080630G","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":51880114520337,"sku":"CIN1647080630VG","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/1647080630.jpg?v=1750863763"},{"product_id":"computational-approach-to-statistical-learning-book-taylor-arnold-9780367570613","title":"A Computational Approach to Statistical Learning","description":"A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.    The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.    Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015.    Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010.    Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51001594118417,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51001596969233,"sku":"NIN9780367570613","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52151460921617,"sku":"NLS9780367570613","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0367570610.jpg?v=1751357460"},{"product_id":"computational-approach-to-statistical-learning-book-taylor-arnold-9781138046375","title":"A Computational Approach to Statistical Learning","description":"A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.  The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51020588515601,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51020589400337,"sku":"NIN9781138046375","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":51681825423633,"sku":"GOR014348066","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":51845114233105,"sku":"CIN113804637XG","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":52486534267153,"sku":"NLS9781138046375","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/113804637X.jpg?v=1751423218"},{"product_id":"humanities-data-in-r-book-taylor-arnold-9783031625657","title":"Humanities Data in R","description":"This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts.    This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets.    Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix.    The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51061209170193,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51061211824401,"sku":"NIN9783031625657","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52595185877265,"sku":"NLS9783031625657","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/303162565X.jpg?v=1770287386"},{"product_id":"humanities-data-in-r-book-taylor-arnold-9783031625688","title":"Humanities Data in R","description":"This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The book is designed to be a bridge between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts.    This 2nd edition of Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis, with one chapter dedicated to each stage of the data-science pipeline (data collection, visualization, manipulation, and relational joins). Following this, text analysis, networks, temporal data, geospatial data, and image analysis each have a dedicated chapter. These are grounded in examples to move readers beyond the intimidation of adding new tools to their research. The final section of the book extends the core material with additional computer science techniques for processing large datasets.    Everything is hands-on: image analysis is explained using digitized photographs from the 1930s, and networks are applied to page links on Wikipedia. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix.    The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":52500825080081,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":52500825702673,"sku":"NGR9783031625688","price":0.0,"currency_code":"GBP","in_stock":false}]},{"product_id":"humanities-data-in-r-book-taylor-arnold-9783319366715","title":"Humanities Data in R","description":"This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility).","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":52596910850321,"sku":"NLS9783319366715","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9783319366715.jpg?v=1761073407"}],"url":"https:\/\/www.worldofbooks.com\/collections\/author-books-by-taylor-arnold.oembed","provider":"World of Books ","version":"1.0","type":"link"}