Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.
| Version: | 1.5.2 |
| Imports: | Matrix (≥ 1.2-4), matrixcalc (≥ 1.0-3), maxLik (≥ 1.3-4), stats (≥ 3.2.5), parallel (≥ 3.3.0), graphics (≥ 3.3.0), grDevices (≥ 3.3.0) |
| Suggests: | testthat, knitr, rmarkdown, R.rsp, MASS, plotly (≥ 4.7.1) |
| Published: | 2019-05-16 |
| Author: | Posthuma Partners |
| Maintainer: | Marco Nijmeijer <nijmeijer at posthuma-partners.nl> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | lmvar results |
| Reference manual: | lmvar.pdf |
| Vignettes: |
Introduction to the package Math details |
| Package source: | lmvar_1.5.2.tar.gz |
| Windows binaries: | r-devel: lmvar_1.5.2.zip, r-release: lmvar_1.5.2.zip, r-oldrel: lmvar_1.5.2.zip |
| macOS binaries: | r-release (arm64): lmvar_1.5.2.tgz, r-oldrel (arm64): lmvar_1.5.2.tgz, r-release (x86_64): lmvar_1.5.2.tgz, r-oldrel (x86_64): lmvar_1.5.2.tgz |
| Old sources: | lmvar archive |
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