Here is an example of an R program that generates a random number from the standard normal distribution (also known as a Gaussian or normal distribution) using the `rnorm()`

function from the `stats`

library:

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# Generate a random number from the standard normal distribution x <- rnorm(n = 1) # Print the result print(paste("Random number:", x)) |

This program uses the `rnorm()`

function to generate a single random number from the standard normal distribution with a mean of 0 and a standard deviation of 1. The `n`

argument specifies the number of random numbers to generate, which in this case is 1. The result is stored in the variable `x`

You can generate random numbers from other distributions, like uniform, exponential and etc. For example, the `runif()`

function to generate random numbers from a uniform distribution with a specified range, the `rexp()`

function to generate random numbers from an exponential distribution.

Here’s an example for the uniform distribution:

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# Generate a random number from the uniform distribution x <- runif(n = 1, min = 0, max = 10) # Print the result print(paste("Random number:", x)) |

And an example for the exponential distribution

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# Generate a random number from the exponential distribution x <- rexp(n = 1, rate = 1) # Print the result print(paste("Random number:", x)) |

In R, the `rnorm()`

function generates random numbers from a normal distribution with a specified mean and standard deviation. By default, `rnorm()`

generates random numbers from the standard normal distribution, which has a mean of 0 and a standard deviation of 1.

The `runif()`

function generates random numbers from a uniform distribution, it take 3 arguments `n`

, `min`

, and `max`

where `n`

is the number of random numbers you want to generate, `min`

and `max`

specifies the range of the uniform distribution.

The `rexp()`

function generates random numbers from an exponential distribution, it takes 2 arguments `n`

and `rate`

where `n`

is the number of random numbers you want to generate and `rate`

is the inverse of the mean of the distribution.

The `rnorm()`

, `runif()`

and `rexp()`

are all part of the R base package and are used to generate random numbers from specific probability distributions. These functions are often used in simulations and statistical modeling. When you call these functions, R uses a built-in random number generator to produce a sequence of random numbers that follow the specified probability distribution.

You can also specify a seed for the random number generator using the `set.seed()`

function which allows you to reproduce the same sequence of random numbers for a given seed value. This can be useful for debugging and for comparing results between different runs of a simulation or statistical model.