9#include <memory_resource>
10#include <nk/value/value.hpp>
37 std::pmr::memory_resource *mr =
nullptr);
49 std::pmr::memory_resource *mr =
nullptr);
61 std::pmr::memory_resource *mr =
nullptr);
74 size_t rows = 1,
size_t cols = 1,
75 std::pmr::memory_resource *mr =
nullptr);
87std::tuple<double, double>
88gpstat(
double k,
double sigma,
double theta);
120 std::pmr::memory_resource *mr =
nullptr);
Core MATLAB-compatible multidimensional array and scalar value type.
Value gpinv(const Value &p, double k, double sigma, double theta, std::pmr::memory_resource *mr=nullptr)
Generalised Pareto inverse cdf (x = gpinv(p, k, sigma, theta)).
Value gpfit(const Value &x, std::pmr::memory_resource *mr=nullptr)
Generalised Pareto MLE fit ([khat, sigmahat] = gpfit(x)).
Value gppdf(const Value &x, double k, double sigma, double theta, std::pmr::memory_resource *mr=nullptr)
Generalised Pareto pdf (y = gppdf(x, k, sigma, theta)).
std::tuple< double, double > gpstat(double k, double sigma, double theta)
Generalised Pareto mean and variance ([m, v] = gpstat(k, sigma, theta)).
Value gpcdf(const Value &x, double k, double sigma, double theta, std::pmr::memory_resource *mr=nullptr)
Generalised Pareto cdf (p = gpcdf(x, k, sigma, theta)).
Value gpfit_ci(const Value &x, double alpha=0.05, std::pmr::memory_resource *mr=nullptr)
95% Wald CI for gpfit (pci = gpfit_ci(x, alpha)).
Value gprnd(::nk::ops::RngContext &rng, double k, double sigma, double theta, size_t rows=1, size_t cols=1, std::pmr::memory_resource *mr=nullptr)
Generalised Pareto random samples (r = gprnd(k, sigma, theta, rows, cols)).