Automatic Differentiation
 
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loglogistic_log.hpp
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1#ifndef STAN_MATH_PRIM_PROB_LOGLOGISTIC_LOG_HPP
2#define STAN_MATH_PRIM_PROB_LOGLOGISTIC_LOG_HPP
3
6
7namespace stan {
8namespace math {
9
13template <bool propto, typename T_y, typename T_scale, typename T_shape,
15 T_y, T_scale, T_shape>* = nullptr>
17 const T_scale& alpha,
18 const T_shape& beta) {
19 return loglogistic_lpdf<propto, T_y, T_scale, T_shape>(y, alpha, beta);
20}
21
25template <typename T_y, typename T_scale, typename T_shape>
27 const T_y& y, const T_scale& alpha, const T_shape& beta) {
28 return loglogistic_lpdf<false>(y, alpha, beta);
29}
30
31} // namespace math
32} // namespace stan
33#endif
require_all_not_t< is_nonscalar_prim_or_rev_kernel_expression< std::decay_t< Types > >... > require_all_not_nonscalar_prim_or_rev_kernel_expression_t
Require none of the types satisfy is_nonscalar_prim_or_rev_kernel_expression.
return_type_t< T_y, T_scale, T_shape > loglogistic_log(const T_y &y, const T_scale &alpha, const T_shape &beta)
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
fvar< T > beta(const fvar< T > &x1, const fvar< T > &x2)
Return fvar with the beta function applied to the specified arguments and its gradient.
Definition beta.hpp:51
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9