9#include <memory_resource>
10#include <nk/value/value.hpp>
40 std::pmr::memory_resource *mr =
nullptr);
52 std::pmr::memory_resource *mr =
nullptr);
64 std::pmr::memory_resource *mr =
nullptr);
77 size_t rows = 1,
size_t cols = 1,
78 std::pmr::memory_resource *mr =
nullptr);
90std::tuple<double, double>
91gevstat(
double k,
double sigma,
double mu);
124 std::pmr::memory_resource *mr =
nullptr);
Core MATLAB-compatible multidimensional array and scalar value type.
std::tuple< double, double > gevstat(double k, double sigma, double mu)
GEV mean and variance ([m, v] = gevstat(k, sigma, mu)).
Value gevpdf(const Value &x, double k, double sigma, double mu, std::pmr::memory_resource *mr=nullptr)
GEV pdf (y = gevpdf(x, k, sigma, mu)).
Value gevinv(const Value &p, double k, double sigma, double mu, std::pmr::memory_resource *mr=nullptr)
GEV inverse cdf (x = gevinv(p, k, sigma, mu)).
Value gevcdf(const Value &x, double k, double sigma, double mu, std::pmr::memory_resource *mr=nullptr)
GEV cdf (p = gevcdf(x, k, sigma, mu)).
Value gevfit_ci(const Value &x, double alpha=0.05, std::pmr::memory_resource *mr=nullptr)
95% Wald CI for gevfit (pci = gevfit_ci(x, alpha)).
Value gevrnd(::nk::ops::RngContext &rng, double k, double sigma, double mu, size_t rows=1, size_t cols=1, std::pmr::memory_resource *mr=nullptr)
GEV random samples (r = gevrnd(k, sigma, mu, rows, cols)).
Value gevfit(const Value &x, std::pmr::memory_resource *mr=nullptr)
GEV MLE fit ([parmhat, parmci] = gevfit(x, alpha)).