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

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Functions
Value	nk::stats::gampdf (const Value &x, double a, double b, std::pmr::memory_resource *mr=nullptr)
	Gamma pdf (y = gampdf(x, a, b)).
Value	nk::stats::gamcdf (const Value &x, double a, double b, std::pmr::memory_resource *mr=nullptr)
	Gamma cdf (F = gamcdf(x, a, b)).
Value	nk::stats::gaminv (const Value &p, double a, double b, std::pmr::memory_resource *mr=nullptr)
	Gamma inverse cdf (x = gaminv(p, a, b)).
Value	nk::stats::gamrnd (::nk::ops::RngContext &rng, double a, double b, size_t rows=1, size_t cols=1, std::pmr::memory_resource *mr=nullptr)
	Gamma random samples (r = gamrnd(a, b, rows, cols)).
std::tuple< double, double >	nk::stats::gamstat (double a, double b)
	Gamma mean and variance ([m, v] = gamstat(a, b)).
Value	nk::stats::randg (::nk::ops::RngContext &rng, double shape, size_t rows=1, size_t cols=1, std::pmr::memory_resource *mr=nullptr)
	Raw gamma(shape, 1) RNG (r = randg(shape [, rows, cols])).
Value	nk::stats::randg (::nk::ops::RngContext &rng, const Value &shapeArray, std::pmr::memory_resource *mr=nullptr)
	Per-element gamma RNG (r = randg(shapeArray)).
Value	nk::stats::gamfit (const Value &x, std::pmr::memory_resource *mr=nullptr)
	Gamma distribution MLE fit ([ahat, bhat] = gamfit(x)).
Value	nk::stats::gamfit (const Value &x, const Value &censoring, const Value &freq, std::pmr::memory_resource *mr=nullptr)
	Gamma MLE fit with right-censoring and frequency weights ([parmhat, parmci] = gamfit(x, alpha, cens, freq)).
Value	nk::stats::gamfit_ci (const Value &x, double alpha=0.05, std::pmr::memory_resource *mr=nullptr)
	95% Wald CI for gamfit (pci = gamfit_ci(x, alpha)).
Value	nk::stats::gamfit_ci (const Value &x, double alpha, const Value &censoring, const Value &freq, std::pmr::memory_resource *mr=nullptr)
	Wald CI for censored/weighted gamfit (pci = gamfit_ci(x, alpha, cens, freq)).

Functions