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 (best linear unbiased predictors, BLUPs). Clifford and McCullagh (2006) <>.


Reference manual

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1.3-21 by Karl W Broman, 2 years ago

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Authors: David Clifford [aut] , Peter McCullagh [aut] , HJ Auinger [ctb] , Karl W Broman [ctb, cre]

Documentation:   PDF Manual  

Task views: Analysis of Spatial Data

GPL-2 license

Suggests nlme, MASS

Imported by cape.

See at CRAN