Automatic Differentiation
 
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log1m_exp.hpp
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1#ifndef STAN_MATH_REV_FUN_LOG1M_EXP_HPP
2#define STAN_MATH_REV_FUN_LOG1M_EXP_HPP
3
8
9namespace stan {
10namespace math {
11
24template <typename T, require_stan_scalar_or_eigen_t<T>* = nullptr>
25inline auto log1m_exp(const var_value<T>& x) {
26 return make_callback_var(log1m_exp(x.val()), [x](auto& vi) mutable {
27 as_array_or_scalar(x.adj())
28 -= as_array_or_scalar(vi.adj()) / as_array_or_scalar(expm1(-x.val()));
29 });
30}
31
32} // namespace math
33} // namespace stan
34#endif
fvar< T > log1m_exp(const fvar< T > &x)
Return the natural logarithm of one minus the exponentiation of the specified argument.
Definition log1m_exp.hpp:22
var_value< plain_type_t< T > > make_callback_var(T &&value, F &&functor)
Creates a new var initialized with a callback_vari with a given value and reverse-pass callback funct...
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...