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")
```

- Browse source code at

http://github.com/pegeler/mvrt - Report a bug at

http://github.com/pegeler/mvrt/issues

- Paul Egeler

Author, maintainer