monomvn-package           package:monomvn           R Documentation

_E_s_t_i_m_a_t_i_o_n _f_o_r _M_u_l_t_i_v_a_r_i_a_t_e _N_o_r_m_a_l _a_n_d _S_t_u_d_e_n_t-_t _D_a_t_a _w_i_t_h _M_o_n_o_t_o_n_e _M_i_s_s_i_n_g_n_e_s_s

_D_e_s_c_r_i_p_t_i_o_n:

     Estimation of multivariate normal and student-t data of arbitrary
     dimension where the pattern of missing data is monotone. Through
     the use of parsimonious/shrinkage regressions (plsr, pcr, lasso,
     ridge,  etc.), where standard regressions fail,  the package can
     handle a nearly arbitrary amount of missing data. The current
     version supports maximum likelihood inference and a full Bayesian
     approach employing scale-mixtures for the lasso
     (double-exponential) and Normal-Gamma priors, and Student-t
     errors.  Monotone  data augmentation extends this Bayesian
     approach to arbitrary missingness patterns.  A fully functional
     standalone interface to the  Bayesian lasso (from Park & Casella),
     the Normal-Gamma (from Griffin & Brown), and ridge regression with
     model  selection via Reversible Jump, and student-t errors (from
     Geweke) is  also provided

_D_e_t_a_i_l_s:

     For a fuller overview including a complete list of functions,
     demos and vignettes, please use 'help(package="monomvn")'.

_A_u_t_h_o_r(_s):

     Robert B. Gramacy bobby@statslab.cam.ac.uk

     Maintainer:  Robert B. Gramacy bobby@statslab.cam.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Robert B. Gramacy, Joo Hee Lee and Ricardo Silva (2008). _On
     estimating covariances between many assets with histories  of
     highly variable length_. 
      Preprint available on arXiv:0710.5837: <URL:
     http://arxiv.org/abs/0710.5837>

     <URL: http://www.statslab.cam.ac.uk/~bobby/monomvn.html>

_S_e_e _A_l_s_o:

     'monomvn', the now defunct 'norm' package, 'mvnmle'

