Distribution fitters (*fit) and negative-log-likelihood scoring (*like). More...
#include <memory_resource>#include <nk/value/value.hpp>#include <tuple>Go to the source code of this file.
Functions | |
| std::tuple< Value, Value, Value, Value > | nk::stats::normfit (const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr) |
| Normal MLE without censoring ([muhat, sigmahat, muci, sigmaci] = normfit(x, alpha)). | |
| std::tuple< Value, Value, Value, Value > | nk::stats::normfit (const Value &x, double alpha, const Value &cens, const Value &freq, std::pmr::memory_resource *mr=nullptr) |
| Normal MLE with optional censoring and frequencies. | |
| std::tuple< Value, Value > | nk::stats::poissfit (const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr) |
| Poisson MLE ([lhat, lci] = poissfit(x, alpha)). | |
| std::tuple< Value, Value > | nk::stats::expfit (const Value &x, double alpha, const Value &cens=Value::Empty, const Value &freq=Value::Empty, std::pmr::memory_resource *mr=nullptr) |
| Exponential MLE ([muhat, muci] = expfit(x, alpha, cens, freq)). | |
| std::tuple< Value, Value, Value, Value > | nk::stats::unifit (const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr) |
| Continuous-uniform MLE ([ahat, bhat, aci, bci] = unifit(x, alpha)). | |
| std::tuple< Value, Value > | nk::stats::lognfit (const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr) |
| Lognormal MLE without censoring ([parm, pci] = lognfit(x, alpha)). | |
| std::tuple< Value, Value > | nk::stats::lognfit (const Value &x, double alpha, const Value &cens, const Value &freq, std::pmr::memory_resource *mr=nullptr) |
| Lognormal MLE with censoring and frequencies. | |
| std::tuple< Value, Value > | nk::stats::binofit (const Value &x, const Value &n, double alpha, std::pmr::memory_resource *mr=nullptr) |
| Binomial parameter CI ([phat, pci] = binofit(x, n, alpha)). | |
| std::tuple< Value, Value > | nk::stats::raylfit (const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr) |
| Rayleigh MLE ([shat, sci] = raylfit(x, alpha)). | |
| double | nk::stats::normlike (double mu, double sigma, const Value &x, const Value &cens, const Value &freq, std::pmr::memory_resource *mr=nullptr) |
| Normal negative-log-likelihood (nLogL = normlike(mu, sigma, x, cens, freq)). | |
| double | nk::stats::explike (double mu, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Exponential nLogL (nLogL = explike(mu, x)). | |
| double | nk::stats::lognlike (double mu, double sigma, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Lognormal nLogL (nLogL = lognlike(mu, sigma, x)). | |
| double | nk::stats::gamlike (double a, double b, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Gamma nLogL (nLogL = gamlike(a, b, x)). | |
| double | nk::stats::betalike (double a, double b, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Beta nLogL (nLogL = betalike(a, b, x)). | |
| double | nk::stats::wbllike (double a, double b, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Weibull nLogL (nLogL = wbllike(a, b, x)). | |
| double | nk::stats::evlike (double mu, double sigma, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Type-I EV nLogL (nLogL = evlike(mu, sigma, x)). | |
| double | nk::stats::gevlike (double k, double sigma, double mu, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Generalised Extreme Value nLogL (nLogL = gevlike(k, sigma, mu, x)). | |
| double | nk::stats::gplike (double k, double sigma, const Value &x, std::pmr::memory_resource *mr=nullptr) |
| Generalised Pareto nLogL (nLogL = gplike(k, sigma, x), theta = 0). | |
Distribution fitters (*fit) and negative-log-likelihood scoring (*like).
Each fitter returns scalar (or per-element) parameter estimates plus 1 × 2 confidence intervals at level 1 - alpha. Default alpha = 0.05 (95% CI).
Definition in file fit.hpp.
1.17.0