Course overview

The notes contain a substantial amount of snippets of code that are fully self-contained within the chapter in which they are included. This allows understanding of how the methods and theory translate neatly to the practice. The software employed in the course is the statistical language R and its most common IDE (Integrated Development Environment) nowadays, RStudio. Prior basic knowledge of both is assumed.1

The Shiny interactive apps on the notes can be downloaded and run locally, which in particular allows inspection of their codes. Check out this GitHub repository for the sources.

We will employ several packages that are not included within R by default. These can be installed as:

# Installation of required packages
packages <- c("ks", "mvtnorm", "nor1mix", "rgl", "misc3d", "viridis",
"manipulate", "geometry", "numDeriv", "OceanView", "ISLR",
"emstreeR", "circular", "maps", "MASS", "microbenchmark",
"np", "locfit", "latex2exp", "dgof", "goftest", "nortest",
"boot", "energy")
install.packages(packages)

The notes make explicit mention of the package to which a function belongs by using the operator ::, unless when the use of the functions of a package is very repetitive and that package is loaded. You can load all the packages by running:

# Load packages
lapply(packages, library, character.only = TRUE)

1. Among others: basic programming in R, ability to work with objects and data structures, ability to produce graphics, knowledge of the main statistical functions, and ability to run scripts in RStudio.↩︎