See stats::Normal.
Arguments
- mean
Scalar mean parameter, or
NULL
as a placeholder.- sd
Scalar standard deviation parameter, or
NULL
as a placeholder.
See also
Other Distributions:
Distribution
,
dist_bdegp()
,
dist_beta()
,
dist_binomial()
,
dist_blended()
,
dist_dirac()
,
dist_discrete()
,
dist_empirical()
,
dist_erlangmix()
,
dist_exponential()
,
dist_gamma()
,
dist_genpareto()
,
dist_lognormal()
,
dist_mixture()
,
dist_negbinomial()
,
dist_pareto()
,
dist_poisson()
,
dist_translate()
,
dist_trunc()
,
dist_uniform()
,
dist_weibull()
Examples
mu <- 0
sigma <- 1
d_norm <- dist_normal(mean = mu, sd = sigma)
x <- d_norm$sample(20)
d_emp <- dist_empirical(x)
plot_distributions(
empirical = d_emp,
theoretical = d_norm,
estimated = d_norm,
with_params = list(
estimated = list(mean = mean(x), sd = sd(x))
),
.x = seq(-3, 3, length.out = 100)
)