regress v1.3-15


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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).

Functions in regress

Name Description
regress Fit a Gaussian Linear Model with Linear Covariance Structure
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Date 2014-07-14
License GPL
Packaged 2017-04-21 14:16:04 UTC; ripley
Repository CRAN
Date/Publication 2017-04-21 14:55:38 UTC
NeedsCompilation no
X-CRAN-Original-Maintainer David Clifford
X-CRAN-Comment Orphaned and corrected on 2017-04-21 as check errors were not corrected despite reminders.

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