Paul Gerrard, Radia M. Johnson - Mastering Scientific Computing with R [2015, PDF, ENG]

1: Programming with R
Data structures in R
Loading data into R
Basic plots and the ggplot2 package
Flow control
Functions
General programming and debugging tools
Summary

2: Statistical Methods with R
Descriptive statistics
Probability distributions
Fitting distributions
Hypothesis testing
Summary

3: Linear Models
An overview of statistical modeling
Linear regression
Analysis of variance
Generalized linear models
Generalized additive models
Linear discriminant analysis
Principal component analysis
Clustering
Summary

4: Nonlinear Methods
Nonparametric and parametric models
The adsorption and body measures datasets
Theory-driven nonlinear regression
Visually exploring nonlinear relationships
Extending the linear framework
Nonparametric nonlinear methods
Nonparametric methods with the np package
Summary

5: Linear Algebra
Matrices and linear algebra
The physical functioning dataset
Basic matrix operations
Triangular matrices
Matrix decomposition
Applications
Summary

6: Principal Component Analysis and the Common Factor Model
A primer on correlation and covariance structures
Datasets used in this chapter
Principal component analysis and total variance
Formative constructs using PCA
Exploratory factor analysis and reflective constructs
Summary

7: Structural Equation Modeling and Confirmatory Factor Analysis
Datasets
The basic ideas of SEM
Matrix representation of SEM
SEM model fitting and estimation methods
Comparing OpenMx to lavaan
Summary

8: Simulations
Basic sample simulations in R
Pseudorandom numbers
Monte Carlo simulations
Monte Carlo integration
Rejection sampling
Importance sampling
Simulating physical systems
Summary

9: Optimization
One-dimensional optimization
Linear programming
Quadratic programming
General non-linear optimization
Other optimization packages
Summary

10: Advanced Data Management
Cleaning datasets in R
String processing and pattern matching
Floating point operations and numerical data types
Memory management in R
Missing data
The Amelia package
The mice package
Summary