7#include <memory_resource>
8#include <nk/value/value.hpp>
38std::tuple<Value, Value, Value, Value>
40 std::pmr::memory_resource *mr =
nullptr);
55std::tuple<Value, Value, Value, Value>
57 std::pmr::memory_resource *mr =
nullptr);
68std::tuple<Value, Value>
70 std::pmr::memory_resource *mr =
nullptr);
85std::tuple<Value, Value>
88 std::pmr::memory_resource *mr =
nullptr);
99std::tuple<Value, Value, Value, Value>
101 std::pmr::memory_resource *mr =
nullptr);
115std::tuple<Value, Value>
117 std::pmr::memory_resource *mr =
nullptr);
132std::tuple<Value, Value>
134 std::pmr::memory_resource *mr =
nullptr);
146std::tuple<Value, Value>
148 std::pmr::memory_resource *mr =
nullptr);
159std::tuple<Value, Value>
161 std::pmr::memory_resource *mr =
nullptr);
184 std::pmr::memory_resource *mr =
nullptr);
194 std::pmr::memory_resource *mr =
nullptr);
205 std::pmr::memory_resource *mr =
nullptr);
215 std::pmr::memory_resource *mr =
nullptr);
225 std::pmr::memory_resource *mr =
nullptr);
235 std::pmr::memory_resource *mr =
nullptr);
245 std::pmr::memory_resource *mr =
nullptr);
256 std::pmr::memory_resource *mr =
nullptr);
266 std::pmr::memory_resource *mr =
nullptr);
Core MATLAB-compatible multidimensional array and scalar value type.
static const Value Empty
MATLAB-style empty matrix sentinel — a 0×0 DOUBLE value.
double 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)).
std::tuple< Value, Value > 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 > 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 > poissfit(const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr)
Poisson MLE ([lhat, lci] = poissfit(x, alpha)).
double gplike(double k, double sigma, const Value &x, std::pmr::memory_resource *mr=nullptr)
Generalised Pareto nLogL (nLogL = gplike(k, sigma, x), theta = 0).
std::tuple< Value, Value > 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)).
double betalike(double a, double b, const Value &x, std::pmr::memory_resource *mr=nullptr)
Beta nLogL (nLogL = betalike(a, b, x)).
double lognlike(double mu, double sigma, const Value &x, std::pmr::memory_resource *mr=nullptr)
Lognormal nLogL (nLogL = lognlike(mu, sigma, x)).
double gamlike(double a, double b, const Value &x, std::pmr::memory_resource *mr=nullptr)
Gamma nLogL (nLogL = gamlike(a, b, x)).
std::tuple< Value, Value, Value, Value > normfit(const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr)
Normal MLE without censoring ([muhat, sigmahat, muci, sigmaci] = normfit(x, alpha)).
double wbllike(double a, double b, const Value &x, std::pmr::memory_resource *mr=nullptr)
Weibull nLogL (nLogL = wbllike(a, b, x)).
std::tuple< Value, Value > raylfit(const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr)
Rayleigh MLE ([shat, sci] = raylfit(x, alpha)).
std::tuple< Value, Value, Value, Value > unifit(const Value &x, double alpha, std::pmr::memory_resource *mr=nullptr)
Continuous-uniform MLE ([ahat, bhat, aci, bci] = unifit(x, alpha)).
double explike(double mu, const Value &x, std::pmr::memory_resource *mr=nullptr)
Exponential nLogL (nLogL = explike(mu, x)).
double 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 evlike(double mu, double sigma, const Value &x, std::pmr::memory_resource *mr=nullptr)
Type-I EV nLogL (nLogL = evlike(mu, sigma, x)).