#include <memory_resource>
#include <nk/value/value.hpp>
#include <tuple>

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Functions
Value	nk::stats::evpdf (const Value &x, double mu, double sigma, std::pmr::memory_resource *mr=nullptr)
	Type-I EV (Gumbel-for-minima) pdf (y = evpdf(x, mu, sigma)).
Value	nk::stats::evcdf (const Value &x, double mu, double sigma, std::pmr::memory_resource *mr=nullptr)
	Type-I EV cdf (p = evcdf(x, mu, sigma)).
Value	nk::stats::evinv (const Value &p, double mu, double sigma, std::pmr::memory_resource *mr=nullptr)
	Type-I EV inverse cdf (x = evinv(p, mu, sigma)).
Value	nk::stats::evrnd (::nk::ops::RngContext &rng, double mu, double sigma, size_t rows=1, size_t cols=1, std::pmr::memory_resource *mr=nullptr)
	Type-I EV random samples (r = evrnd(mu, sigma, rows, cols)).
std::tuple< double, double >	nk::stats::evstat (double mu, double sigma)
	Type-I EV mean and variance ([m, v] = evstat(mu, sigma)).
Value	nk::stats::evfit (const Value &x, std::pmr::memory_resource *mr=nullptr)
	Type-I EV (Gumbel-min) MLE fit ([muhat, sigmahat] = evfit(x)).
Value	nk::stats::evfit (const Value &x, const Value &censoring, const Value &freq, std::pmr::memory_resource *mr=nullptr)
	Type-I EV (Gumbel-min) MLE fit with right-censoring and frequency weights ([parmhat, parmci] = evfit(x, alpha, cens, freq)).
Value	nk::stats::evfit_ci (const Value &x, double alpha=0.05, std::pmr::memory_resource *mr=nullptr)
	Wald CI for evfit (pci = evfit_ci(x, alpha)).
Value	nk::stats::evfit_ci (const Value &x, double alpha, const Value &censoring, const Value &freq, std::pmr::memory_resource *mr=nullptr)
	Wald CI for censored/weighted evfit (pci = evfit_ci(x, alpha, cens, freq)).

Functions