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My project requires generating a substantial amount (in the order of 10^7, for example) of random numbers that is normally distributed, and this step takes a significant amount of computational resources.
Is there a more efficient method than rnorm for generating such data?
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Random numbers from a normal distribution can be generated using runif() function. We need to specify how many numbers we want to generate. Additionally we can specify the range of the uniform distribution using max and min argument. If not provided, the default range is between 0 and 1 .
The dqrng package provides fast random number generators (RNG) with good statistical properties for usage with R. It combines these RNGs with fast distribution functions to sample from uniform, normal or exponential distributions.
randint() function feels quite slow, in comparison to other randomness-generating functions.
If we want to generate standard normal random numbers then rnorm function of R can be used but need to pass the mean = 0 and standard deviation = 1 inside this function.
There are in-built functions in R to generate a set of random numbers from standard distributions like normal, uniform, binomial distributions, etc. In the next section we will see different functions like runif (), rnorm (), rbinom () and rexp () to generate random numbers. Popular Course in this category
Here is one example below to generate and print 50 values between 1 and 99 using runif () function. A random number generator helps to generate a sequence of digits that can be saved as a function to be used later in operations. Random number generator doesn’t actually produce random values as it requires an initial value called SEED.
Random Number Generator Functions There are in-built functions in R to generate a set of random numbers from standard distributions like normal, uniform, binomial distributions, etc. In the next section we will see different functions like runif (), rnorm (), rbinom () and rexp () to generate random numbers. Popular Course in this category
Here, n refers to how many random numbers to generate. a and b are the lower and upper limits of the distribution respectively. The default values for min and max are 0 and 1.
Yes, you can use the Ziggurat method via RcppZiggurat. This is one of the faster/fastest algorithm for standard normally distributed random numbers.
The pdf vignette has timing comparison charts; there is also one on package page on my server.
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