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The function dtnorm() computes the density of a truncated normal distribution.

The function rtnorm() samples from a truncated normal distribution.

The function dttnorm() and rttnorm() compute the density and sample from a two-sided truncated normal distribution, respectively.

The functions with suffix _cpp perform no input checks, hence are faster.

Usage

dtnorm_cpp(x, mean, sd, point, above, log = FALSE)

dttnorm_cpp(x, mean, sd, lower, upper, log = FALSE)

rtnorm_cpp(mean, sd, point, above, log = FALSE)

rttnorm_cpp(mean, sd, lower, upper, log = FALSE)

dtnorm(x, mean, sd, point, above, log = FALSE)

dttnorm(x, mean, sd, lower, upper, log = FALSE)

rtnorm(mean, sd, point, above, log = FALSE)

rttnorm(mean, sd, lower, upper, log = FALSE)

Arguments

x

[numeric(1)]
A quantile.

mean

[numeric(1)]
The mean.

sd

[numeric(1)]
The non-negative standard deviation.

point, lower, upper

[numeric(1)]
The truncation point.

above

[logical(1)]
Truncate from above? Else, from below.

log

[logical(1)]
Return the logarithm of the density value?

Value

For dtnorm() and dttnorm(): The density value.

For rtnorm() and rttnorm(): The random draw

See also

Examples

x <- c(0, 0)
mean <- c(0, 0)
Sigma <- diag(2)

# compute density
dmvnorm(x = x, mean = mean, Sigma = Sigma)
#> [1] 0.1591549
dmvnorm(x = x, mean = mean, Sigma = Sigma, log = TRUE)
#> [1] -1.837877

# sample
rmvnorm(n = 3, mean = mean, Sigma = Sigma)
#>           [,1]      [,2]
#> [1,] 1.2039209 -2.440773
#> [2,] 0.9041923  1.425594
#> [3,] 0.1596079 -1.335400
rmvnorm(mean = mean, Sigma = Sigma, log = TRUE)
#> [1] 2.4878264 0.4307405