ldmvnorm

sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a G

ldmvnorm               package:sbgcop               R Documentation

_L_o_g _M_u_l_t_i_v_a_r_i_a_t_e _N_o_r_m_a_l _D_e_n_s_i_t_y

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the log of the multivariate normal density

_U_s_a_g_e:

     ldmvnorm(Y, S)

_A_r_g_u_m_e_n_t_s:

       Y: an n x p matrix  

       S: a p x p positive definite matrix  

_D_e_t_a_i_l_s:

     This function computes the log density of the data matrix 'Y'
     under the model that the rows are independent samples from a 
     mean-zero multivariate normal distribution with covariance matrix 
     'S'.

_V_a_l_u_e:

     A real number.

_A_u_t_h_o_r(_s):

     Peter Hoff

_E_x_a_m_p_l_e_s:

     Y<-matrix(rnorm(9*7),9,7) 
     ldmvnorm(Y,diag(7))