weibull.hpp File Reference

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

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Functions
Value	nk::stats::wblpdf (const Value &x, double a, double b, std::pmr::memory_resource *mr=nullptr)
	Weibull pdf (y = wblpdf(x, a, b)).
Value	nk::stats::wblcdf (const Value &x, double a, double b, std::pmr::memory_resource *mr=nullptr)
	Weibull cdf (p = wblcdf(x, a, b)).
Value	nk::stats::wblinv (const Value &p, double a, double b, std::pmr::memory_resource *mr=nullptr)
	Weibull inverse cdf (x = wblinv(p, a, b)).
Value	nk::stats::wblrnd (::nk::ops::RngContext &rng, double a, double b, size_t rows=1, size_t cols=1, std::pmr::memory_resource *mr=nullptr)
	Weibull random samples (r = wblrnd(a, b, rows, cols)).
std::tuple< double, double >	nk::stats::wblstat (double a, double b)
	Weibull mean and variance ([m, v] = wblstat(a, b)).
Value	nk::stats::wblfit (const Value &x, std::pmr::memory_resource *mr=nullptr)
	Weibull MLE fit ([ahat, bhat] = wblfit(x)).
Value	nk::stats::wblfit (const Value &x, const Value &censoring, const Value &freq, std::pmr::memory_resource *mr=nullptr)
	Weibull MLE fit with right-censoring and frequency weights ([parmhat, parmci] = wblfit(x, alpha, cens, freq)).
Value	nk::stats::wblfit_ci (const Value &x, double alpha=0.05, std::pmr::memory_resource *mr=nullptr)
	95% Wald CI for wblfit (pci = wblfit_ci(x, alpha)).
Value	nk::stats::wblfit_ci (const Value &x, double alpha, const Value &censoring, const Value &freq, std::pmr::memory_resource *mr=nullptr)
	Wald CI for censored/weighted wblfit (pci = wblfit_ci(x, alpha, cens, freq)).