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).


Reference manual

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1.3-15 by ORPHANED, 2 years ago

Browse source code at

Authors: David Clifford and Peter McCullagh. Additional contributions by HJ Auinger.

Documentation:   PDF Manual  

Task views: Analysis of Spatial Data

GPL license

Suggests nlme, MASS

Imported by cape, synbreed.

Suggested by gap.

See at CRAN