Results

**1 - 2**of**2**### RDieHarder: An R interface to the DieHarder suite of Random Number Generator Tests

, 2007

"... Random number generators are critically important for computational statistics. Simulation methods are becoming ever more common for estimation; Monte Carlo Markov Chain is but one approach. Also, simulation methods such as the Bootstrap have long been used in inference and are becoming a standard p ..."

Abstract
- Add to MetaCart

Random number generators are critically important for computational statistics. Simulation methods are becoming ever more common for estimation; Monte Carlo Markov Chain is but one approach. Also, simulation methods such as the Bootstrap have long been used in inference and are becoming a standard part of a rigorous analysis. As random number

### random: An R package for true random numbers

, 2006

"... Simulation techniques are a core component of scientific computing and, more specifically, computational statistics. All simulation methods—Monte Carlo methods, bootstrapping, estimation by simulation to name but a few—rely on ‘computer-generated randomness ’ (more on this below). In practice, this ..."

Abstract
- Add to MetaCart

Simulation techniques are a core component of scientific computing and, more specifically, computational statistics. All simulation methods—Monte Carlo methods, bootstrapping, estimation by simulation to name but a few—rely on ‘computer-generated randomness ’ (more on this below). In practice, this means sequences of random numbers. Generating ‘good ’ (for a suitable metric) random