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Simulation for Data Science with R

You're reading from Simulation for Data Science with R Effective Data-driven Decision Making

Product type Paperback

Published in Jun 2016

Publisher Packt

ISBN-13 9781785881169

Length 398 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Matthias Templ

View More author details

Table of Contents (13) Chapters

Preface

1. Introduction

2. R and High-Performance Computing FREE CHAPTER

3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions

4. Simulation of Random Numbers

5. Monte Carlo Methods for Optimization Problems

6. Probability Theory Shown by Simulation

7. Resampling Methods

8. Applications of Resampling Methods and Monte Carlo Tests

9. The EM Algorithm

10. Simulation with Complex Data

11. System Dynamics and Agent-Based Models

Index

What you need for this book

This book heavily depends on the software environment R, version 3.2 or newer (https://cran.r-project.org/). In most chapters, independent and standalone code is written to show methods and execute examples, and no additional packages of R are needed. For a few chapters, additional R packages such as deSolve, cvTools, laeken, VIM, and few others must be installed within R. The packages dplyr and ggplot2 are used throughout the book.

Optionally, the use of a script editor for R, such as RStudio (https://www.rstudio.com/) or Architect + Eclipse (https://www.openanalytics.eu/architect), is recommended.

The rest of the chapter is locked

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Authors (1)

Matthias Templ

Matthias Templ

Matthias Templ is associated professor at the Institute of Statistics and Mathematical Methods in Economics, Vienna University of Technology (Austria). He is additionally employed as a scientist at the methods unit at Statistics Austria, and together with two colleagues, he owns the company called data-analysis OG. His main research interests are in the areas of imputation, statistical disclosure control, visualization, compositional data analysis, computational statistics, robustness teaching in statistics, and multivariate methods. In the last few years, Matthias has published more than 45 papers in well-known indexed scientific journals. He is the author and maintainer of several R packages for official statistics, such as the R package sdcMicro for statistical disclosure control, the VIM package for visualization and imputation of missing values, the simPop package for synthetic population simulation, and the robCompositions package for robust analysis of compositional data. In addition, he is the editor of the Austrian Journal of Statistics that is free of charge and open-access. The probability is high to find him at the top of a mountain in his leisure time.

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