This package provides random matrices with a defined covariance structure. The package began as an exercise to show performance improvements of using vectorized R code rather than explicit `for`

loops. Later, it became a vehicle for working with the `Rcpp`

and `RcppArmadillo`

packages and learning how to incorporate them into packages of my own. Future work may include `FORTRAN`

code to further explore the capabilities of using compiled code in R scripts and packages.

You can see an exploration the thought process just described in the package vignette. Different methods for function definitions are used and performance is assessed.

library(mvrt) S <- convert_R2S(make_cor_mat(.9),2:3) # Create a covariance matrix x <- mvrt( 30, 4:5, S, 29) # Generate random data with n = 30 y <- mvrt2(30, 4:5, S, 29, .01) # Random data with maximum abs deviation # from input parameters specified # Correlation matrix of x cor(x) ## [,1] [,2] ## [1,] 1.0000000 0.8884765 ## [2,] 0.8884765 1.0000000 # Correlation matrix of y cor(y) ## [,1] [,2] ## [1,] 1.000000 0.905187 ## [2,] 0.905187 1.000000

I recommend that you have `devtools`

installed in order to download and install this package. To do so, type the following into your R console:

if(!require(devtools)) install.packages("devtools") devtools::install_github("pegeler/mvrt")