regress

Package: regress
Version: 1.3-14
Date: 2014-07-14
Title: Gaussian linear models with linear covariance structure
Author: David Clifford and Peter McCullagh. Additional contributions by
HJ Auinger.
Maintainer: David Clifford <david.clifford@csiro.au>
Description: Functions to fit Gaussian linear model by maximising the
residual log likelihood where the covariance structure can be
written as a linear combination of known matrices. Can be used
for multivariate models and random effects models. Easy
straight forward manner to specify random effects models,
including random interactions. Code now optimised to use
Sherman Morrison Woodbury identities for matrix inversion in
random effects models. We've added the ability to fit models
using any kernel as well as a function to return the mean and
covariance of random effects conditional on the data (BLUPs).
License: GPL
URL: http://www.csiro.au
Suggests: nlme, MASS
SystemRequirements:
Packaged: 2014-07-14 00:51:44 UTC; cli065
Repository: CRAN
Date/Publication: 2014-07-14 07:48:25
NeedsCompilation: no

Install log

* installing to library '/home/ddbj/local/lib64/R/library'
* installing *source* package 'regress' ...
** package 'regress' successfully unpacked and MD5 sums checked
** R
** inst
** preparing package for lazy loading
** help
*** installing help indices
  converting help for package 'regress'
    finding HTML links ... done
    regress                                 html  
** building package indices
** testing if installed package can be loaded
* DONE (regress)
Making 'packages.html' ... done