Automatic Differentiation
 
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◆ cov_matrix_constrain_lkj() [2/5]

template<typename T , require_eigen_vector_t< T > * = nullptr>
Eigen::Matrix< value_type_t< T >, Eigen::Dynamic, Eigen::Dynamic > stan::math::cov_matrix_constrain_lkj ( const T &  x,
size_t  k,
return_type_t< T > &  lp 
)
inline

Return the covariance matrix of the specified dimensionality derived from constraining the specified vector of unconstrained values and increment the specified log probability reference with the log absolute Jacobian determinant.

Template Parameters
Ttype of the vector (must be derived from Eigen::MatrixBase and have one compile-time dimension equal to 1)
Parameters
xInput vector of unconstrained partial correlations and standard deviations.
kDimensionality of returned covariance matrix.
lpLog probability reference to increment.
Returns
Covariance matrix derived from the unconstrained partial correlations and deviations.

Definition at line 59 of file cov_matrix_constrain_lkj.hpp.