# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1
dval_1 = pystats.dnorm(1.0, 0.0, 1.0)
# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1, and return the log value
dval_2 = pystats.dnorm(1.0, 0.0, 1.0, True)
# Same as above, but using default values and named parameters
dval_3 = pystats.dnorm(1.0, log=True)
# Evaluate the normal CDF at x = 1, mu = 0, sigma = 1
pval = pystats.pnorm(1.0, 0.0, 1.0)
# Evaluate the Laplacian quantile at q = 0.1, mu = 0, sigma = 1
qval = pystats.qlaplace(0.1, 0.0, 1.0)
# Draw from a t-distribution with dof = 30
rval = pystats.rt(dof=30)
# List output
beta_rvs = pystats.rbeta(100, 3.0, 2.0)
# List input
beta_cdf_vals = pystats.pbeta(beta_rvs, 3.0, 2.0)