regress: Gaussian linear models with linear covariance structure

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).

AuthorDavid Clifford and Peter McCullagh. Additional contributions by HJ Auinger.
Date of publication2014-07-14 07:48:25
MaintainerDavid Clifford <david.clifford@csiro.au>
LicenseGPL
Version1.3-14
http://www.csiro.au

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Files

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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