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

Version: 1.3-21
Suggests: nlme, MASS
Published: 2020-06-18
DOI: 10.32614/CRAN.package.regress
Author: David Clifford [aut], Peter McCullagh [aut], HJ Auinger [ctb], Karl W Broman ORCID iD [ctb, cre]
Maintainer: Karl W Broman <broman at>
License: GPL-2
NeedsCompilation: no
Citation: regress citation info
Materials: README
In views: MixedModels, Spatial
CRAN checks: regress results


Reference manual: regress.pdf


Package source: regress_1.3-21.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): regress_1.3-21.tgz, r-oldrel (arm64): regress_1.3-21.tgz, r-release (x86_64): regress_1.3-21.tgz, r-oldrel (x86_64): regress_1.3-21.tgz
Old sources: regress archive

Reverse dependencies:

Reverse imports: cape


Please use the canonical form to link to this page.