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).


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Reference manual

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install.packages("regress")

1.3-15 by ORPHANED, 5 months ago


http://www.csiro.au


Browse source code at https://github.com/cran/regress


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