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).

Author | David Clifford and Peter McCullagh. Additional contributions by HJ Auinger. |

Date of publication | 2014-07-14 07:48:25 |

Maintainer | David Clifford <david.clifford@csiro.au> |

License | GPL |

Version | 1.3-14 |

http://www.csiro.au |

regress

regress/inst

regress/inst/CITATION

regress/tests

regress/tests/predictionVariance.r

regress/tests/regressPaper.R

regress/tests/OLS.r

regress/tests/BLUP.tests.R

regress/tests/regress.tests.R

regress/NAMESPACE

regress/R

regress/R/printSummary.R
regress/R/ginv.R
regress/R/BLUP.R
regress/R/regress.R
regress/R/reml.R
regress/R/MatrixInversions.R
regress/MD5

regress/DESCRIPTION

regress/man

regress/man/regress.Rd
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.