Automatic Differentiation
 
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owens_t.hpp
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1#ifndef STAN_MATH_PRIM_FUN_OWENS_T_HPP
2#define STAN_MATH_PRIM_FUN_OWENS_T_HPP
3
6#include <boost/math/special_functions/owens_t.hpp>
7
8namespace stan {
9namespace math {
10
58inline double owens_t(double h, double a) {
59 if (unlikely(std::isnan(h) || std::isnan(a))) {
60 return std::numeric_limits<double>::quiet_NaN();
61 }
62 return boost::math::owens_t(h, a);
63}
64
75template <typename T1, typename T2, require_any_container_t<T1, T2>* = nullptr,
76 require_all_not_var_and_matrix_types<T1, T2>* = nullptr>
77inline auto owens_t(T1&& a, T2&& b) {
79 [](auto&& c, auto&& d) {
80 return owens_t(std::forward<decltype(c)>(c),
81 std::forward<decltype(d)>(d));
82 },
83 std::forward<T1>(a), std::forward<T2>(b));
84}
85
86} // namespace math
87} // namespace stan
88
89#endif
#define unlikely(x)
auto apply_scalar_binary(F &&f, T1 &&x, T2 &&y)
Base template function for vectorization of binary scalar functions defined by applying a functor to ...
fvar< T > owens_t(const fvar< T > &x1, const fvar< T > &x2)
Return Owen's T function applied to the specified arguments.
Definition owens_t.hpp:26
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
bool isnan(const stan::math::var &a)
Checks if the given number is NaN.
Definition std_isnan.hpp:18