What this book covers

Chapter 1, Programming with R, presents an overview of how data is stored and accessed in R. Then, we will go over how to load data into R using built-in functions and useful packages for easy import from Excel worksheets. We will also cover how to use flow control statements and functions to reduce complexity and help you program more efficiently.

Chapter 2, Statistical Methods with R, presents an overview of how to summarize your data and get useful statistical information for downstream analysis. We will show you how to plot and get statistical information from probability distributions and how to test the fit of your sample distribution to well-defined probability distributions.

Chapter 3, Linear Models, covers linear models, which are probably the most commonly used statistical methods to study the relationships between variables. The Generalized linear model section will delve into a bit more detail than typical R books, discussing the nature of link functions and canonical link functions.

Chapter 4, Nonlinear Methods, reviews applications of nonlinear methods in R using both parametric and nonparametric methods for both theory-driven and exploratory analysis.

Chapter 5, Linear Algebra, covers algebra techniques in R. We will also learn linear algebra operations including transposition, inversion, matrix multiplication, and a number of matrix transformations.

Chapter 6, Principal Component Analysis and the Common Factor Model, helps you understand the application of linear algebra to covariance and correlation matrices. We will cover how to use PCA to account for total variance in a set of variables and how to use EFA to model common variance among these variables in R.

Chapter 7, Structural Equation Modeling and Confirmatory Factor Analysis, covers the fundamental ideas underlying structural equation modeling, which are often overlooked in other books discussing SEM in R, and then delve into how SEM is done in R.

Chapter 8, Simulations, explains how to perform basic sample simulations and how to use simulations to answer statistical problems. We will also learn how to use R to generate random numbers, and how to simulate random variables from several common probability distributions.

Chapter 9, Optimization, explores a variety of methods and techniques to optimize a variety of functions. We will also cover how to use a wide range of R packages and functions to set up, solve, and visualize different optimization problems.

Chapter 10, Advanced Data Management, walks you through the basic techniques for data handling and some basic memory management considerations.