inline typename boost::math::tools::promote_args<T>::type
log1m_exp(const T a) {
if (a >= 0)
return std::numeric_limits<double>::quiet_NaN();
else if (a > -0.693147)
return std::log(-boost::math::expm1(a)); //0.693147 is approximatelly equal to log(2)
else
return log1m(std::exp(a));
}
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
read_corr_L(const Eigen::Array<T, Eigen::Dynamic, 1>& CPCs,
size_t K,
T& log_prob) {
Eigen::Matrix<T, Eigen::Dynamic, 1> values(CPCs.rows() - 1);
size_t pos = 0;
// no need to abs() because this Jacobian determinant
// is strictly positive (and triangular)
// see inverse of Jacobian in equation 11 of LKJ paper
for (size_t k = 1; k <= (K - 2); k++)
for (size_t i = k + 1; i <= K; i++) {
values(pos) = (K - k - 1) * log1m(square(CPCs(pos)));
pos++;
}
log_prob += 0.5 * sum(values);
return read_corr_L(CPCs, K);
}
inline fvar<T>
log1m(const fvar<T>& x) {
return fvar<T>(log1m(x.val_), -x.d_ / (1 - x.val_));
}
static inline T fun(const T& x) {
return log1m(x);
}
