# Mastering Scientific Computing with R

 Learn Master data management in R Perform hypothesis tests using both parametric and nonparametric methods Understand how to perform statistical modeling using linear methods Model nonlinear relationships in data with kernel density methods Use matrix operations to improve coding productivity Utilize the observed data to model unobserved variables Deal with missing data using multiple imputations Simplify high-dimensional data using principal components, singular value decomposition, and factor analysis With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions. Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method. Perform publication-quality science using R Use some of Râ€™s most powerful and least known features to solve complex scientific computing problems Learn how to create visual illustrations of scientific results 432 12 hours 57 minutes 9781783555253 31 Jan 2015
 Data structures in R Loading data into R Basic plots and the ggplot2 package Flow control Functions General programming and debugging tools Summary
 Descriptive statistics Probability distributions Fitting distributions Hypothesis testing Summary
 An overview of statistical modeling Linear regression Analysis of variance Generalized linear models Generalized additive models Linear discriminant analysis Principal component analysis Clustering Summary
 Matrices and linear algebra The physical functioning dataset Basic matrix operations Triangular matrices Matrix decomposition Applications Summary
 Datasets The basic ideas of SEM Matrix representation of SEM SEM model fitting and estimation methods Comparing OpenMx to lavaan Summary
 Basic sample simulations in R Pseudorandom numbers Monte Carlo simulations Monte Carlo integration Rejection sampling Importance sampling Simulating physical systems Summary
 One-dimensional optimization Linear programming Quadratic programming General non-linear optimization Other optimization packages Summary
 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