ggm                   package:ggm                   R Documentation

_T_h_e _p_a_c_k_a_g_e '_g_g_m': _s_u_m_m_a_r_y _i_n_f_o_r_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This package provides functions for defining and manipulating
     graphs and for fitting graphical Gaussian models.

_F_u_n_c_t_i_o_n_s:

     The main functions can be classified as follows.

        *  Functions for defining graphs (undirected, directed acyclic,
            ancestral graphs): 'UG', 'DAG', 'makeAG';

        *  Functions for doing graph operations (parents, boundary, 
           cliques, connected components, fundamental cycles,
           d-separation): 'pa', 'bd', 'cliques', 'conComp',
           'fundCycles', 'dSep';

        *  Function for finding covariance and concentration graphs
           induced by marginalization and conditioning:
           'inducedCovGraph', 'inducedConGraph';

        *  Functions for finding multivariate regression graphs and
           chain graphs induced by marginalization and conditioning:
           'inducedRegGraph',  'inducedChainGraph', 'inducedDAG';

        *  Functions for fitting by ML Gaussian DAGs, concentration
           graphs,  covariance graphs and ancestral graphs: 'fitDag',
           'fitConGraph', 'fitCovGraph', 'fitAncestralGraph';

        *  Functions for testing several conditional independences:
           'shipley.test';

        *  Functions for checking global identification  of DAG
           Gaussian  models with one  latent variable
           (Stanghellini-Vicard's condition for concentration graphs, 
           new sufficient conditions for DAGs): 'isGident',
           'checkIdent';

        *  Functions for fitting Gaussian DAG models with one latent
           variable: 'fitDagLatent'.

     The package is intended as a contribution to the gR-project 
     derscribed by Lauritzen (2002).

_A_u_t_h_o_r_s:

     Giovanni M. Marchetti, Dipartimento di Statistica ``G. Parenti''.
     Universit\`a di Firenze, Italy; 

     Mathias Drton,  Department of Statistics, University of Chicago,
     USA.

_A_c_k_n_o_w_l_e_d_g_e_m_e_n_t_s:

     Many thanks to Fulvia Pennoni for testing some of  the functions,
     to Elena Stanghellini for discussion and examples and to Claus
     Dethlefsen and Jens Henrik Badsberg for suggestions and
     corrections. Helpful discussions with Nanny Wermuth, are
     gratefully acknowledged. Thanks also to Michael Perlman and Thomas
     Richardson.

     Giovanni Marchetti has been supported by MIUR, Italy, under grant 
     scheme PRIN 2002, and Mathias Drton has been supported by NSF
     grant  DMS-9972008 and University of Washington RRF grant 65-3010.

_R_e_f_e_r_e_n_c_e_s:

     Lauritzen, S. L. (2002). gRaphical Models in R. _R News_, 3(2)39.

