Package: regress 1.3-22
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) <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.
Authors:
regress_1.3-22.tar.gz
regress_1.3-22.zip(r-4.5)regress_1.3-22.zip(r-4.4)regress_1.3-22.zip(r-4.3)
regress_1.3-22.tgz(r-4.4-any)regress_1.3-22.tgz(r-4.3-any)
regress_1.3-22.tar.gz(r-4.5-noble)regress_1.3-22.tar.gz(r-4.4-noble)
regress_1.3-22.tgz(r-4.4-emscripten)regress_1.3-22.tgz(r-4.3-emscripten)
regress.pdf |regress.html✨
regress/json (API)
# Install 'regress' in R: |
install.packages('regress', repos = c('https://kbroman.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kbroman/regress/issues
Last updated 2 years agofrom:e4096b8b21. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 17 2024 |
R-4.5-win | OK | Nov 17 2024 |
R-4.5-linux | OK | Nov 17 2024 |
R-4.4-win | OK | Nov 17 2024 |
R-4.4-mac | OK | Nov 17 2024 |
R-4.3-win | OK | Nov 17 2024 |
R-4.3-mac | OK | Nov 17 2024 |
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
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Fit a Gaussian Linear Model with Linear Covariance Structure | BLUP print.regress regress summary.regress |