2nd Ed. — Apress, 2019. — 712 p. in color. — ISBN: 9781484242148, EISBN 9781484242155. Code files only! Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming...
Packt Publishing, 2018. — 376 p. — ISBN: 978-1788624145. !Only code files Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two...
Packt Publishing, 2017. — 366 p. !Code files only Key Features Use R's popular packages—such as ggplot2, ggvis, ggforce, and more—to create custom, interactive visualization solutions. Create, design, and build interactive dashboards using Shiny A highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them...
Packt Publishing, 2016. — 1783 p. Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining...
Packt Publishing, 2017. — 992 p. — ASIN B072XN54R9. +True PDF +Sample files Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques...
Packt Publishing, 2017. — 413 p. — ISBN: 978-1787127524. The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how...
Packt Publishing, 2017. — 992 p. — ASIN B072XN54R9. +Sample files Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to...
Packt Publishing, 2017. — 992 p. — ASIN B072XN54R9. +Sample files Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to...
Практические наборы данных (datasets) к книге Lansley G., Cheshire J. An Introduction to Spatial Data Analysis and Visualisation in R Архив содержит следующие файлы: camdenhousesales.zip (shape-файл) - к главам 7, 8, 11 camdenoa11.zip (shape-файл) - к главам 5-12 practicaldata.csv (CSV-файл) - к главам 2-7, 9, 10, 11 camdenhousesales15.csv (CSV-файл) - к главе 6
Packt Publishing, 2017. — 413 p. — ISBN: 978-1787127524. The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how...
Packt Publishing, 2016. — 398 p. — ISBN: 978-1785881169. +Code files Harness actionable insights from your data with computational statistics and simulations using R About This Book Learn five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling) in-depth using real-world case studies A unique book that...
Amazon Digital Service, 2017. — 562 p. — ASIN B071DTSCPS. +Code Files. This book introduces the reader to the use of R and RStudio as a platform for processing and analyzing financial data. The book covers all necessary knowledge for using R, from its installation in your computer to the organization and development of scripts. For every chapter, the book presents practical and...
2nd ed. — Packt Publishing, 2017. — 420 p. — ASIN B01N5XJ3O0. +Sample files Key Features Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning...
Код и наборы данных (datasets) к книге: Berlinger E., Illés F., Vadász T. et al., Mastering R for Quantitative Finance В архив включены: Исходный код на R к примерам для глав 1-13. Данные к примерам для глав 2, 6, 7, 11, 12, 13
Код и учебные материалы к книге: Kumar A., Paul A., Mastering Text Mining with R. В архив включены: Код для примеров на R к главам 4, 6, 7. Данные к примерам и тестовые материалы для глав 2, 5, 6.
Packt Publishing, 2016. — 582 p. — ISBN: 978-1-78588-977-6. + Sample files. Key Features Explore the R language from basic types and data structures to advanced topics Learn how to tackle programming problems and explore both functional and object-oriented programming techniques Learn how to address the core problems of programming in R and leverage the most popular packages...
Packt Publishing, 2016. — 582 p. — ISBN: 978-1-78588-977-6. + Sample files. Key Features Explore the R language from basic types and data structures to advanced topics Learn how to tackle programming problems and explore both functional and object-oriented programming techniques Learn how to address the core problems of programming in R and leverage the most popular packages...
Packt Publishing, 2016. — 246 p. — ISBN: 978-1-78439-103-4. Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature. The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the...
Packt Publishing, 2016. — 250 p. — ISBN10: 1784392057. — ISBN13: 978-1784392055 We'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian...
Packt Publishing, 2017. - 284p. - ASIN: B06XTHMMWH +Sample files Key Features Understand the basics of R and how they can be applied in various Quantitative Finance scenarios Learn various algorithmic trading techniques and ways to optimize them using the tools available in R. Contain different methods to manage risk and explore trading using Machine Learning. Book Description...
Packt Publishing, 2016. — 1919 p. — ASIN B01N7AE091. Proficiently analyze data and apply machine learning techniques Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R Construct a predictive model by using a variety of machine learning packages Who This Book Is For This Learning Path is ideal for...
New York: Packt Publishing , 2016. — 446 p.
Key Features:
Load, manipulate and analyze data from different sources,
Gain a deeper understanding of fundamentals of applied statistics,
A practical guide to performing data analysis in practice.
Book Description:
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the...
2nd Edition. — Manning Publications, 2015. — 608 p. — ISBN: 978-1617291388. Bonus chapter 23 and source code from this book placed here. Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any...
Meys J., de Vries A. R for Dummies
Wiley – 2012, 406 pages
ISBN: 1119962846, 9781119962847
Still trying to wrap your head around R?
With more than two million users, R is the open-source programming language standard for data analysis and statistical modeling. R is packed with powerful programming capabilities, but learning to use R in the real world can be overwhelming...
Journal of Statistical Software. August 2009, Volume 31, Issue
1. + 5 appendices.
R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing.
This paper...
Mokken scale analysis (MSA) is a scaling procedure for both dichotomous and polytomous items. It consists of an item selection algorithm to partition a set of items into Mokken scales and several methods to check the assumptions of two nonparametric item response theory models: the monotone homogeneity model and the double monotonicity model. First, we present an R package mokken...
Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other...
Tem response theory models (IRT) are increasingly becoming established in social science research, particularly in the analysis of performance or attitudinal data in psychology, education, medicine, marketing and other fields where testing is relevant. We propose the R package eRm (extended Rasch modeling) for computing Rasch models and several extensions. A main characteristic of...
The behavioral, educational, and social sciences are undergoing a paradigmatic shift in methodology, from disciplines that focus on the dichotomous outcome of null hypothesis significance tests to disciplines that report and interpret effect sizes and their corresponding confidence intervals. Due to the arbitrariness of many measurement instruments used in the behavioral,...
In computerized testing, the test takers' responses as well as their response times on the items are recorded. The relationship between response times and response accuracies is complex and varies over levels of observation. For example, it takes the form of a tradeoff between speed and accuracy at the level of a fixed person but may become a positive correlation for a population...
The Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of log-linear-by-linear association (LLLA) models, which are derived from a Rasch family...
Variance component models are generally accepted for the analysis of hierarchical structured data. A shortcoming is that outcome variables are still treated as measured without an error. Unreliable variables produce biases in the estimates of the other model parameters. The variability of the relationships across groups and the group-effects on individuals' outcomes differ...
The Rasch sampler is an efficient algorithm to sample binary matrices with given marginal sums. It is a Markov chain Monte Carlo (MCMC) algorithm. The program can handle matrices of up to 1024 rows and 64 columns. A special option allows to sample square matrices with given marginals and fixed main diagonal, a problem prominent in social network analysis. In all cases the...
We describe an implementation of simple, multiple and joint correspondence analysis in R. The resulting package comprises two parts, one for simple correspondence analysis and one for multiple and joint correspondence analysis. Within each part, functions for computation, summaries and visualization in two and three dimensions are provided, including options to display...
Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These assumptions are often violated, particularly in educational testing situations, in...
The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in...
This paper describes the implementation of Heckman-type sample selection models in R. We discuss the sample selection problem as well as the Heckman solution to it, and argue that although modern econometrics has non- and semiparametric estimation methods in its toolbox, Heckman models are an integral part of the modern applied analysis and econometrics syllabus. We describe the...
Quantile regression for censored survival (duration) data offers a more flexible alternative to the Cox proportional hazard model for some applications. We describe three estimation methods for such applications that have been recently incorporated into the R package quantreg: the Powell (1986) estimator for fixed censoring, and two methods for random censoring, one introduced by...
We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression...
The structure of the package vars and its implementation of vector autoregressive, structural vector autoregressive and structural vector error correction models are explained in this paper. In addition to the three cornerstone functions VAR(), SVAR() and SVEC() for estimating such models, functions for diagnostic testing, estimation of a restricted models, prediction, causality...
Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA...
Panel data econometrics is obviously one of the main fields in the profession, but most of the models used are difficult to estimate with R. plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference.
Palaeoecology is an important branch of ecology that uses the subfossil remains of organisms preserved in lake, ocean and bog sediments to inform on changes in ecosystems and the environment through time. The analogue package contains functions to perform modern analogue technique (MAT) transfer functions, which can be used to predict past changes in the environment, such as...
This paper provides a brief introduction to the R package bio.infer, a set of scripts that facilitates the use of maximum likelihood (ML) methods for predicting environmental conditions from assemblage composition. Environmental conditions can often be inferred from only biological data, and these inferences are useful when other sources of data are unavailable. ML prediction...
Multivariate analyses are well known and widely used to identify and understand structures of ecological communities. The ade4 package for the R statistical environment proposes a great number of multivariate methods. Its implementation follows the tradition of the French school of "Analyse des Donnees" and is based on the use of the duality diagram. We present the theory of the...
Ade4 is a multivariate data analysis package for the R statistical environment, and ade4TkGUI is a Tcl/Tk graphical user interface for the most essential methods of ade
4. Both packages are available on CRAN. An overview of ade4TkGUI is presented, and the pros and cons of this approach are discussed. We conclude that command line interfaces (CLI) and graphical user interfaces...
Knowledge of the environmental features affecting habitat selection by animals is important for designing wildlife management and conservation policies. The package adehabitat for the R software is designed to provide a computing environment for the analysis and modelling of such relationships. This paper focuses on the preliminary steps of data exploration and analysis, performed...
Results of ecological models differ, to some extent, more from measured data than from empirical knowledge. Existing techniques for validation based on quantitative assessments sometimes cause an underestimation of the performance of models due to time shifts, accelerations and delays or systematic differences between measurement and simulation. However, for the application of...
The analysis of matrix population models has become a fundamental tool in ecology, conservation biology, and life history theory. In this paper, I present demogR, a package for analyzing age-structured population models in R. The package includes tools for the construction and analysis of matrix population models. In addition to the standard analyses commonly used in evolutionary...
A complete assessment of population growth and viability from field census data often requires complex data manipulations, statistical routines, mathematical tools, programming environments, and graphical capabilities. We therefore designed an R package called popbio to facilitate both the construction and analysis of projection matrix models. The package consists primarily of...
Год выпуска: 2005 | Издательство: Wiley | ISBN: 0470022973 |Язык: Английский | Количество страниц: 333
Computer software is an essential tool for many statistical modelling and data analysis techniques, aiding in the implementation of large data sets in order to obtain useful results. R is one of the most powerful and flexible statistical software packages available, and enables...
The package dcemriS4 provides a complete set of data analysis tools for quantitative assessment of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Image processing is provided for the ANALYZE and NIfTI data formats as input with all parameter estimates being output in NIfTI format. Estimation of T1 relaxation from multiple flip-angle acquisitions, using either...
Two packages, oro.dicom and oro.nifti, are provided for the interaction with and manipulation of medical imaging data that conform to the DICOM standard or ANALYZE/NIfTI formats. DICOM data, from a single file or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is used to...
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