_S_i_n_g_u_l_a_r _V_a_l_u_e _D_e_c_o_m_p_o_s_i_t_i_o_n _o_f _a _M_a_t_r_i_x
	
	     svd(x, nu=min(n,p), nv=min(n,p))
	
	_A_r_g_u_m_e_n_t_s:
	
	           x : a matrix whose SVD decomposition is to be
	               computed.
	
	          nu : the number of left eigenvectors to be com-
	               puted.  This must be one of 0, nrow(x) and
	               ncol(x).
	
	          nv : the number of right eigenvectors to be com-
	               puted.  This must be one of 0, and ncol(x).
	
	_D_e_s_c_r_i_p_t_i_o_n:
	
	     svd provides an interface to the LINPACK routine DSVDC.
	     The singular value decompostion plays an important role
	     in many statistical techniques.
	
	_V_a_l_u_e_s:
	
	     The SVD decomposition of the matrix as computed by LIN-
	     PACK.  The components in the returned value correspond
	     directly to the values returned by DSVDC.
	
	      d : a vector containing the singular values of x.
	
	      u : a matrix whose columns contain the left eigenvec-
	          tors of x.
	
	      v : a matrix whose columns contain the right eigenvec-
	          tors of x.
	
	_R_e_f_e_r_e_n_c_e_s:
	
	     Dongarra, J. J., J. R. Bunch, C. B. Moler and G. W.
	     Stewart (1978).  LINPACK Users Guide, SIAM Publica-
	     tions, Philadelphia.
	
	_S_e_e _A_l_s_o:
	
	     qr.