Installation

Installing R

R is open-source, and available for all major platforms. See this documentation for detailed instructions, or simply download and install it.

Optionally, but highly recommended, you should also install a good script editor, such as Rstudio, or emacs (with ESS mode).

Installing the cda package and its dependencies

The easiest way to install packages is from within R itself; it will download the files from the central repository and install the packages in a local library.

Quick way

install.packages("cda", dependencies = TRUE)

Alternative: step-by-step way: pre-installing required and suggested dependencies manually

required <- c("Rcpp", # R/C++ interface
              "RcppArmadillo", # Rcpp interface with the Armadillo linear algebra C++ library
              "statmod", # provides Gauss-Legendre quadrature points for orientation-averaging
              "randtoolbox", # helping functions for Quasi-Monte Carlo integration
              "reshape2", # data reshaping
              "plyr", # convenience split-and-apply functions
              "dielectric", # permittivity of common materials
              )
suggested <- c(
               "rgl", # R interface to OpenGL for 3D visualisation
               "ggplot2", # plotting and data reshaping
               "knitr", # literate programming (run code embedded in the wiki files)
               "testthat", # testing code correctness
               "gridExtra" # misc. plotting facilities
               )
dependencies <- c(required, suggested)
install.packages(dependencies)
install.packages("cda")

Development version

The code under development is available on github; the most recent package can be installed from R,

library(devtools)
install_github("cda", "baptiste")

On Windows, some tools will be required to compile the C++ code; for convenience here is a link to the currrent Windows binary.

Loading the package

library(cda)
?"cda-package" # access to help

First steps

## Introductory document
vignette("CD-theory", package = "cda")

## A few examples
demo(package = "cda")
# then run 
# demo(dimer_cd) # etc.

Basic example

Simulating extinction and optical activity from a dimer of gold nanorods

Demos and tutorials

clusters generate 3D views of some predefined cluster shapes using RGL rendering or povray

dimer_linear extinction spectrum of a dimer of gold nanorods

dimer_cd circular dichroism for a chiral dimer of gold nanorods

helix_cd circular dichroism for a helix of gold nanoparticles

diffractive_chain extinction spectrum of a linear chain of gold nanorods

diffractive_array extinction spectrum of a square array of gold nanorods

lattice_sum lattice sum for a 2D square array of dipoles

multiple_incidence dispersion plot with varying angles of incidence

Technical aspects and tests

averaging_method compares the performance of GL quadrature, QMC, and basic grid