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:David Clifford [aut], Peter McCullagh [aut], HJ Auinger [ctb], Karl W Broman [ctb, cre]

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'))

Peer review:

Bug tracker:https://github.com/kbroman/regress/issues

On CRAN:

5.97 score 4 stars 1 packages 154 scripts 511 downloads 15 mentions 2 exports 0 dependencies

Last updated 2 years agofrom:e4096b8b21. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 18 2024
R-4.5-winOKOct 18 2024
R-4.5-linuxOKOct 18 2024
R-4.4-winOKOct 18 2024
R-4.4-macOKOct 18 2024
R-4.3-winOKOct 18 2024
R-4.3-macOKOct 18 2024

Exports:BLUPregress

Dependencies: