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 teaches
you the essential and fundamental concepts in statistical modeling
and simulation Who This Book Is For This book is for users who are
familiar with computational methods. If you want to learn about the
advanced features of R, including the computer-intense Monte-Carlo
methods as well as computational tools for statistical simulation,
then this book is for you. Good knowledge of R programming is
assumed/required. What You Will Learn * The book aims to explore
advanced R features to simulate data to extract insights from your
data. * Get to know the advanced features of R including
high-performance computing and advanced data manipulation * See
random number simulation used to simulate distributions, data sets,
and populations * Simulate close-to-reality populations as the
basis for agent-based micro-, model- and design-based simulations *
Applications to design statistical solutions with R for solving
scientific and real world problems * Comprehensive coverage of
several R statistical packages like boot, simPop, VIM, data.table,
dplyr, parallel, StatDA, simecol, simecolModels, deSolve and many
more. In Detail Data Science with R aims to teach you how to begin
performing data science tasks by taking advantage of Rs powerful
ecosystem of packages. R being the most widely used programming
language when used with data science can be a powerful combination
to solve complexities involved with varied data sets in the real
world. The book will provide a computational and methodological
framework for statistical simulation to the users. Through this
book, you will get in grips with the software environment R. After
getting to know the background of popular methods in the area of
computational statistics, you will see some applications in R to
better understand the methods as well as gaining experience of
working with real-world data and real-world problems. This book
helps uncover the large-scale patterns in complex systems where
interdependencies and variation are critical. An effective
simulation is driven by data generating processes that accurately
reflect real physical populations. You will learn how to plan and
structure a simulation project to aid in the decision-making
process as well as the presentation of results. By the end of this
book, you reader will get in touch with the software environment R.
After getting background on popular methods in the area, you will
see applications in R to better understand the methods as well as
to gain experience when working on real-world data and real-world
problems. Style and approach This book takes a practical, hands-on
approach to explain the statistical computing methods, gives advice
on the usage of these methods, and provides computational tools to
help you solve common problems in statistical simulation and
computer-intense methods.
General
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