= gsl_ran_beta_pdf (x, a, b);
function CDF.BETA (x >= 0 && x <= 1, a > 0, b > 0) = gsl_cdf_beta_P (x, a, b);
function IDF.BETA (P >= 0 && P <= 1, a > 0, b > 0)
- = gslextras_cdf_beta_Pinv (P, a, b);
+ = gsl_cdf_beta_Pinv (P, a, b);
no_opt function RV.BETA (a > 0, b > 0) = gsl_ran_beta (get_rng (), a, b);
function NCDF.BETA (x >= 0, a > 0, b > 0, lambda > 0)
= ncdf_beta (x, a, b, lambda);
// Binomial distribution.
function CDF.BINOM (k, n > 0 && n == floor (n), p >= 0 && p <= 1)
- = gslextras_cdf_binomial_P (k, p, n);
+ = gsl_cdf_binomial_P (k, p, n);
function PDF.BINOM (k >= 0 && k == floor (k) && k <= n,
n > 0 && n == floor (n),
p >= 0 && p <= 1)
// Geometric distribution.
function CDF.GEOM (k >= 1 && k == floor (k), p >= 0 && p <= 1)
- = gslextras_cdf_geometric_P (k, p);
+ = gsl_cdf_geometric_P (k, p);
function PDF.GEOM (k >= 1 && k == floor (k),
p >= 0 && p <= 1)
= gsl_ran_geometric_pdf (k, p);
a > 0 && a == floor (a),
b > 0 && b == floor (b) && b <= a,
c > 0 && c == floor (c) && c <= a)
- = gslextras_cdf_hypergeometric_P (k, c, a - c, b);
+ = gsl_cdf_hypergeometric_P (k, c, a - c, b);
function PDF.HYPER (k >= 0 && k == floor (k) && k <= c,
a > 0 && a == floor (a),
b > 0 && b == floor (b) && b <= a,
// Negative binomial distribution.
function CDF.NEGBIN (k >= 1, n == floor (n), p > 0 && p <= 1)
- = gslextras_cdf_negative_binomial_P (k, p, n);
+ = gsl_cdf_negative_binomial_P (k, p, n);
function PDF.NEGBIN (k >= 1, n == floor (n), p > 0 && p <= 1)
= gsl_ran_negative_binomial_pdf (k, p, n);
no_opt function RV.NEGBIN (n == floor (n), p > 0 && p <= 1)
// Poisson distribution.
function CDF.POISSON (k >= 0 && k == floor (k), mu > 0)
- = gslextras_cdf_poisson_P (k, mu);
+ = gsl_cdf_poisson_P (k, mu);
function PDF.POISSON (k >= 0 && k == floor (k), mu > 0)
= gsl_ran_poisson_pdf (k, mu);
no_opt function RV.POISSON (mu > 0) = gsl_ran_poisson (get_rng (), mu);