regress: Gaussian linear models with linear covariance structure

Share:

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).

Author
David Clifford and Peter McCullagh. Additional contributions by HJ Auinger.
Date of publication
2014-07-14 07:48:25
Maintainer
David Clifford <david.clifford@csiro.au>
License
GPL
Version
1.3-14
URLs

View on CRAN

Man pages

regress
Fit a Gaussian Linear Model with Linear Covariance Structure

Files in this package

regress
regress/inst
regress/inst/CITATION
regress/tests
regress/tests/predictionVariance.r
regress/tests/regressPaper.R
regress/tests/OLS.r
regress/tests/BLUP.tests.R
regress/tests/regress.tests.R
regress/NAMESPACE
regress/R
regress/R/printSummary.R
regress/R/ginv.R
regress/R/BLUP.R
regress/R/regress.R
regress/R/reml.R
regress/R/MatrixInversions.R
regress/MD5
regress/DESCRIPTION
regress/man
regress/man/regress.Rd