regress

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.

Last 30 days

This package has been downloaded 604 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 45 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 6,130 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Nov 14, 2024 with 79 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.